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==== Front BMJ OpenBMJ OpenbmjopenbmjopenBMJ Open2044-6055BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bmjopen-2016-01321510.1136/bmjopen-2016-013215Radiology and ImagingResearch150617261713Incidental findings on brain MRI of cognitively normal first-degree descendants of patients with Alzheimer's disease: a cross-sectional analysis from the ALFA (Alzheimer and Families) project Brugulat-Serrat Anna 1Rojas Santiago 12Bargalló Nuria 34Conesa Gerardo 5Minguillón Carolina 1Fauria Karine 1Gramunt Nina 1Molinuevo José Luis 1Gispert Juan Domingo 16 1 Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, Barcelona, Spain 2 Unit of Human Anatomy and Embryology, Faculty of Medicine, Department of Morphological Sciences, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, Spain 3 Magnetic Resonance Imaging Core Facility, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain 4 Centre Mèdic Diagnòstic Alomar, Barcelona, Spain 5 Servicio de Neurocirugía, Hospital del Mar, Barcelona, Spain 6 Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, SpainCorrespondence to Dr Juan Domingo Gispert; jdgispert@fpmaragall.orgAB-S and SR contributed equally. 2017 24 3 2017 7 3 e01321528 6 2016 9 2 2017 13 2 2017 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Objectives To describe the prevalence of brain MRI incidental findings (IF) in a cohort of cognitively normal first-degree descendants of patients with Alzheimer's disease (AD). Design Cross-sectional observational study. Setting All scans were obtained with a 3.0 T scanner. Scans were evaluated by a single neuroradiologist and IF recorded and categorised. The presence of white matter hyperintensities (WMH) was determined with the Fazekas scale and reported as relevant if ≥2. Participants 575 participants (45–75 years) underwent high-resolution structural brain MRI. Participants were cognitively normal and scored over the respective cut-off values in all the following neuropsychological tests: Mini-Mental State Examination (≥26), Memory Impairment Screen (≥6), Time Orientation Subtest of the Barcelona Test II (≥68), verbal semantic fluency (naming animals ≥12). Clinical Dementia Rating (CDR) had to be 0. Results 155 participants (27.0%) presented with at least one IF. Relevant WMH were present in 7.8% of the participants, and vascular abnormalities, cyst and brain volume loss in 10.7%, 3.1% and 6.9% of the study volunteers, respectively. Neoplastic brain findings were found in 2.4% of participants and within these, meningiomas were the most common (1.7%) and more frequently found in women. A positive correlation between increasing age and the presence of IF was found. Additionally, brain atrophy greater than that expected by age was significantly more prevalent in participants without a parental history of AD. Conclusions Brain MRIs of healthy middle-aged participants show a relatively high prevalence of IF even when study participants have been screened for subtle cognitive alterations. Most of our participants are first-degree descendants of patients with AD, and therefore these results are of special relevance for novel imaging studies in the context of AD prevention in cognitively healthy middle-aged participants. Trial registration number NCT02198586. Alzheimer's diseasepreventioncerebral MRIincidental findinghealthylate middle-aged ==== Body Strengths and limitations of this study Estimating the chance of discovering incidental findings (IF) helps clinicians and researchers to adequately inform and manage these situations. One hundred and fifty-five participants (27.0%), most of them cognitively normal first-degree descendants of patients with Alzheimer's disease (AD), presented with at least one IF. All images were reviewed by the same radiologist, thus maximising the homogeneity of the readings and reports. Our results are relevant for studies aimed at preventing AD in cognitively healthy middle-aged participants with increased risk of developing the disease. The generalisability of the results to the general population may be limited. Introduction MRI provides excellent spatial resolution and tissue characterisation without making use of ionising radiation. These advantages have spurred its use to image the brains of healthy individuals in clinical and research settings.1 2 In these scans, it is not unusual to detect incidental findings (IF): unexpected abnormalities of potential clinical significance and unrelated to the purpose of the study. Estimating the chance of discovering IF is important to help clinicians and researchers to adequately inform individuals and grant adequate access to standard medical care in order to manage these situations.3 Therefore, in experimental protocols of human brain imaging research, it is important to anticipate the detection of IF and establish proper pathways for their management according to clinical and ethical considerations.4 5 The prevalence of IF reported in the literature shows a great variability as a function of several factors: the specific cohort characteristics, the image sequence in the MRI protocol (including whether contrast is used or not), the experience and number of image readers and the use of predefined analysis protocols and the postprocessing methodology of the images.6 7 In a recent meta-analysis that included 19 559 participants aged between 11 and 63 years, an IF prevalence of 2.7% was found.3 In particular, markers of cerebrovascular disease were excluded from this analysis. The authors concluded that IF prevalence increased with age and with higher resolution of the scans. In agreement, other studies in older populations have found significantly higher occurrences of IF. In observational studies, 32% (from a total of 700 participants, mean age 72.5 years),7 9.5% (from a total of 5800 participants with a mean age of 64.9 years)8 and 77.9% (from a total of 503 participants with a mean age of 75.3 years)9 of asymptomatic participants presented with IF. Therefore, IF are commonly revealed in neuroimaging research, but their occurrence greatly differs between study populations. In addition to these factors, the discrepancy in the reported IF prevalence can also be accounted for by the definition of what constitutes a ‘finding’. For example, white matter hyperintensities (WMH) are often reported as ‘normal’ findings in elderly individuals, since more than half of the healthy elderly population (>65 years old) has some degree of white matter lesions10 and this proportion is even higher in individuals with vascular risk factors such as hypertension and diabetes.11–13 In this manuscript, we describe the prevalence of brain MRI IF in a cohort of 575 cognitively normal participants of the ALFA (for Alzheimer and Families) study (Molinuevo et al. The ALFA project: a research platform to identify early pathophysiological features of Alzheimer’s disease. Submitted). Current research supports that Alzheimer's disease (AD) pathology develops for several years before the onset of clinical symptoms.14 The main goal of the ALFA study is to characterise the preclinical stage of AD and the most salient characteristic of this cohort is the elevated percentage of first-degree descendants of patients with AD. We compared the prevalence of IF in first-degree relatives of patients with AD versus non-relatives. Since familiar history is a common enrichment strategy for AD prevention trials (eg, PREVENT-Alzheimer programme;15 the Adult children Study16 and the Wisconsin Registry for Alzheimer's Prevention Program17), this might be of interest in the scope of novel studies aimed at preventing AD in cognitively healthy participants with increased risk of developing the disease (Molinuevo et al, Submitted).1 18 Methods Participants The ALFA parent cohort, established by the Barcelonaβeta Brain Research Center (BBRC), is composed of 2743 cognitively healthy participants, mostly adult children of patients with AD, aged between 45 and 75 years, and was formed as a research platform from which to establish studies for the detection of factors indicative of AD in asymptomatic individuals (for a full description of the ALFA population, please refer to the Clinicaltrials.gov Identifier: NCT01835717 and ref. 19). ALFA participants were cognitively normal and scored over the respective cut-off values in all the following neuropsychological tests: Mini-Mental State Examination (≥26),20 Memory Impairment Screen (≥6),21 Time Orientation Subtest of the Barcelona Test II (≥68),22 verbal semantic fluency (naming animals ≥12).23 Clinical Dementia Rating (CDR) had to be 0.24 All ALFA cohort participants were asked about their parental history of AD at baseline and categorised as family history positive (FH+) if they had at least one of their parents who had been diagnosed with AD before the age of 75. FH+ and FH− matched by sex and age groups were invited to participate in the present study (NCT02198586) which resulted in the inclusion of 608 individuals of the ALFA parent cohort that had no contraindications to brain MRI. Recruitment was initiated in April 2014 and finished in June 2015. Ethical considerations The MRI study protocol registered at Clinicaltrials.gov (Identifier: NCT02198586). It has been conducted in accordance with the directives of the Spanish Law 14/2007, of 3rd of July, on Biomedical Research (Ley 14/2007 de Investigación Biomédica). All participants accepted the study procedures by signing an informed consent form. Brain MRI acquisition characteristics Scans were obtained with a 3.0 T scanner (GE Discovery MR750 W 3T). The MRI protocol was identical for all participants and included high-resolution three-dimensional structural images weighted in T1 with an isotropic voxel size of 1 mm3. The acquisition parameters were TR/TE/TI=8.0/3.7/450 ms, NSA=1, flip angle=8° and a matrix size of 256×256×160. In addition, three T2-weighted sequences (256×256, 1×1×3 mm matrix) were acquired: fluid attenuation inversion recovery (FLAIR: TR/TE/TI=11 000/90/2600 ms, flip angle=160°), fast spin echo (TR/TE=5000/85 ms, flip angle=110°) and gradient-recalled echo (GRE: TR/TE=1300/23 ms, flip angle=15°). Radiological reporting Scans were evaluated by the same trained neuroradiologist within the following week from MRI acquisition. All participants received the neuroradiological report of the MRI. An independent clinical consultant reviewed those that contained IF and clinically relevant IF (eg, tumours, vascular abnormalities, WMH with comorbidities, cysts, chiari malformations, syringomyelia, ventriculomegaly suspicious of normal pressure hydrocephalus and encephalomalacia) were personally informed and participants referred for follow-up to the appropriate specialist (n=90/155). All individuals were offered a telephonic helpline should they present with additional questions or need further clarifications on the findings. WMH were evaluated using the Fazekas scale,25 a well-validated and established qualitative visual rating method, which separately categorises the severity of deep and periventricular lesions, on a scale from 0 to 3 (0: none or a single punctate WHM lesion, (1) multiple punctate lesions, (2) beginning confluency of lesions (bridging) and (3) large confluent lesions). WMH of Fazekas score ≥2 were reported as IF because, despite appearing in some normally functioning participants, these values are considered as relevant.9 26 27 Brain volume loss was considered as IF by the radiologist when it was greater than that expected by age. Statistical analyses IF were categorised as WMH, vascular abnormalities (including lacunar infarcts, microhaemorrhages, aneurysms, cavernous malformations and malformations of venous development), cysts, neoplasias and others, including brain volume loss, and their prevalence calculated. The CIs were computed by Bayesian calculation. The effect of ageing in the most prevalent IF was assessed by means of a Pearson product-moment correlation coefficient (r). We also stratified participants into three different groups according to their age (between 45 and 54, between 55 and 64 and between 65 and 75). IF's prevalence per sex and age group was also quantified. The χ2 test was used to assess for statistically significant differences in each most prevalent IF category between sexes and in brain atrophy and WMH between participants with or without a family history of AD. SPSS V.15.0 for Windows was used for all the statistical analyses. Differences were considered to be significant at p<0.05. Results Six hundred and eight ALFA parent cohort participants were invited to take part in the present brain MRI study. Of these, 595 volunteers agreed to undergo MRI and 575 provided valid MRIs. Reasons that prevented MRI acquisition were claustrophobia (n=16), physical size or shape that precluded from lying in the scanner (n=3), and an imaging artefact caused by irremovable MRI-compatible metallic earrings (n=1). The main sociodemographic characteristics of the study participants and the results of the neuropsychological screening tests are shown in table 1. Out of the 575 individuals included in the study, 227 (39.5%) were men and 348 (60.5%) women, with a mean age of 58.2 and 57.5 years, respectively. Table 1 Characteristics of the study population (N=575) Neuropsychological screening Age, years (SD) Women (%) Education, years (SD) MMSE MIS TO SF 45–54 years 49.8 60.53 14.2 29.2 7.9 70 23.4 (n=211) (2.3) (3.3) (0.9) (0.4) (0.0) (4.9) 55–64 years 59.6 60.8 13.6 29.0 7.8 70 22.4 (n=245) (2.8) (3.6) (1.1) (0.5) (0.0) (5.3) 65–75 years 68.3 56.7 12.9 28.8 7.6 70 21.2 (n=119) (2.9) (3.6) (1.2) (0.6) (0.0) (5.0) Total 58.6 57.8 13.6 29 7.7 70 22.5 (N=575) (7.3) (3.5) (1.1) (0.5) (0.0) (5.2) MIS, Memory Impairment Screen; MMSE, Mini-Mental State Examination; SF, verbal Semantic Fluency (naming animals); TO, Time Orientation Subtest of the Barcelona Test II. Prevalence of IF One hundred and fifty-five (27.0% (95% CI 23.5% to 30.7%)) participants presented with at least one IF: 64 were men (mean age 57.7 years) and 91 women (mean age 57.8 years). Table 2 shows the prevalence of each IF. Table 2 Prevalence of incidental findings Finding n (%) 95% CI White matter hyperintensities* 45 (7.83) (5.9 to 10.3) Vascular abnormalities  Lacunar infarcts 17 (2.96) (1.8 to 4.7)  Microhaemorrhages (n=9)   Single (cortical/deep) 2 (0.35)/2 (0.35) (0.10 to 1.24)/(0.10 to 1.24)   Various (cortical/deep) 4 (0.70)/1 (0.17) (0.28 to 1.77)/(0.04 to 0.96)  Structural vascular abnormalities (n=36)   Aneurysm 1 (0.17) (0.04 to 0.96)   Cavernous malformation (single/various) 14 (2.43)/1 (0.17) (1.466 to 4.04)/(0.04 to 0.96)   Malformation of venous development 20 (3.48) (2.27 to 5.31) Cysts  Arachnoid (supratentorial/infratentorial) 3 (0.52)/5 (0.87) (0.19 to 1.51)/(0.38 to 2.01)  Pineal† 2 (0.35) (0.10 to 1.24)  Neuroepithelial 1 (0.17) (0.04 to 0.96)  Choroidal fissure cyst 2 (0.35) (0.10 to 1.24)  Posterior fossa cyst‡ 4 (0.70) (0.28 to 1.77)  Right hippocampus cyst 1 (0.17) (0.04 to 0.96) Neoplasias  Meningioma 10 (1.74) (0.95 to 3.16)  Pituitary mass 2 (0.35) (0.10 to 1.24)  Small intraventricular mass§ 1 (0.17) (0.04 to 0.96)  Cerebellar hemispheric mass 1 (0.17) (0.04 to 1.96) Other abnormalities  Brain volume loss¶ (n=40)   Frontal 8 (1.39) (0.71 to 2.71)   Temporal 8 (1.39) (0.71 to 2.71)   Parietal 7 (1.22) (0.60 to 2.48)   Cerebellum and brain stem 4 (0.69) (0.28 to 1.77)   Diffuse loss of brain volume 13 (2.26) (1.33 to 3.82)  Chiari malformation type I 6 (1.04) (0.49 to 2.25)  Syringomyelia 1 (0.17) (0.04 to 0.96)  Ventriculomegaly suspicious of NPH** 2 (0.35) (0.10 to 1.24)  Non-specific focus of altered signal†† 4 (0.52) (0.28 to 1.77)  Extracerebral findings (n=9)   Left frontal hyperostosis 1 (0.17) (0.04 to 0.96)   Signal alterations of clivus 1 (0.17) (0.04 to 0.96)   Other otorhinolaryngological processes‡‡ 6 (1.04) (0.49 to 2.25)   Right eye diameter increased 1 (0.17) (0.04 to 0.96)  Encephalomalacia after traumatic brain injury 1 (0.17) (0.04 to 0.96) *Fazekas scale score ≥2. †>1 cm in diameter. ‡Arachnoid cyst versus mega cisterna magna. §Possible subependymoma. ¶Enlargement of the subarachnoid spaces and sulcus (bigger than that expected by age). **Normal pressure hydrocephalus. ††Excluding white matter hyperintensities related to small vessel disease. ‡‡Excluding mild inflammatory disease (mucosal thickening or small retention cysts). With regard to WMH, 43 (7.4% (95% CI 5.6% to 9.9%)) individuals presented with a Fazekas 2 and 2 (0.3% (95% CI 0.1% to 1.2%)) with a Fazekas 3. Vascular abnormalities were present in 10.7% (95% CI 8.6% to 13.6%) of the study participants, the most prevalent being malformations of venous development (3.4% (95% CI 2.2% to 5.3%)) and lacunar infarcts (2.9% (95% CI 1.8% to 4.7%)) followed by single cavernous malformations (2.4% (95% CI 1.4% to 4.0%)) and microhaemorrhages (1.5% (95% CI 0.8% to 2.9%)). Cysts, including arachnoid and neuroepithelial ones, were found in 3.1% (95% CI 1.9% to 4.8%) of the cases. The prevalence of neoplasias was of 2.4% (95% CI 1.5% to 4.0%), whereas 10 participants (1.7% (95% CI 0.9% to 3.2%)) presented with a meningioma. Concerning other abnormalities, 7.0% (95% CI 5.1% to 9.3%) of the participants showed a brain volume loss greater than that expected by age and 1.0% (95% CI 0.5% to 2.2%) of them had a Chiari type I malformation. Finally, nine participants presented with extracerebral findings. Representative images of specific IF can be found in figure 1. Figure 1 Incidental findings on brain MRI. (A) White matter hyperintensities. (B) Lacunar infarct. (C) Cavernous malformation. (D) Malformation of venous development. (E) Arachnoid cyst. (F) Meningioma. (G) Non-specific focus of altered signal. (H) Brain volume loss. (I) Chiari malformation type I. (J) Otorhinolaryngology process. (K) Ventriculomegaly suspicious of NPH. (L) Brain stem atrophy. Age-specific distribution of IF As a whole, a positive correlation between the prevalence of IF and increasing age was found (r=0.254, p<0.001). IF were more frequent in the 65–75 years old group (n=54, 45.4% (95% CI 36.7% to 54.3%)) than in the 55–64 years old (n=68, 27.8% (95% CI 22.5% to 33.7%)) and the 45–54 years old (n=33, 15.6% (95% CI 11.4% to 21.2%)) ones. Table 3 shows the age-specific distribution of the most frequent IF. Table 3 Age distribution of the most prevalent incidental findings Finding 45–54 years 95% CI 55–64 years 95% CI 65–75 years 95% CI (n=211) (n=245) (n=119) White matter hyperintensities, n (%)* 7 (3.32) (1.64 to 6.68) 21 (8.57) (5.68 to 12.75) 17 (14.29) (9.13 to 21.71) Vascular abnormalities*  Lacunar infarct, n (%)* 2 (0.95) (0.29 to 3.36) 6 (2.45) (1.15 to 5.23) 8 (6.72) (3.48 to 12.71)  Microhaemorrhage, n (%)* 0 – 5 (2.04) (0.90 to 4.67) 4 (3.36) (1.36 to 8.31)  Single cavernous malformation, n (%) 2 (0.95) (0.29 to 3.36) 8 (3.26) (1.6 to 6.30) 4 (3.36) (1.36 to 8.31)  Malformation of venous development, n (%) 9 (4.26) (2.28 to 7.90) 10 (4.08) (2.25 to 7.37) 1 (0.84) (0.20 to 4.55) Neoplasias  Meningioma, n (%) 3 (1.42) (0.51 to 4.07) 5 (2.04) (0.90 to 4.67) 2 (1.68) (0.52 to 5.89)  Pituitary mass, n (%) 0 – 2 (0.82) (0.25 to 2.90) 0 –  Small intraventricular mass,† n (%) 1 (0.47) (0.11 to 2.60) 0 – 0 –  Cerebellar hemispheric mass, n (%) 0 – 1 (0.41) (0.09 to 2.24) 0 – Brain volume loss*  Frontal, n (%)* 0 – 1 (0.41) (0.90 to 2.24) 7 (5.88) (2.92 to 11.64)  Temporal, n (%)* 2 (0.95) (0.29 to 3.36) 2 (0.82) (0.25 to 2.90) 4 (3.36) (1.36 to 8.31)  Parietal, n (%)* 2 (0.95) (0.29 to 3.36) 2 (0.82) (0.25 to 2.90) 4 (3.36) (1.36 to 8.31)  Cerebellum and brain stem, n (%) 1 (0.47) (0.11 to 2.60) 1 (0.41) (0.90 to 2.24) 2 (1.68) (0.52 to 5.89)  Diffuse loss of brain volume, n (%)* 1 (0.47) (0.11 to 2.60) 4 (1.63) (0.66 to 4.11) 8 (6.72) (3.48 to 12.71) *Statistically significant positive correlation with increasing age (r, p<0.005). †Possible subependymoma. With regard to specific categories, a positive correlation was found between the incidence of relevant WMH (r=0.165, p<0.001), vascular abnormalities (r=0.125, p=0.003) and brain volume loss (r=0.358, p<0.001) with increasing age. Concerning vascular abnormalities, a statistically significant higher prevalence of both lacunar infarcts (r=0.116, p≤0.005) and microhaemorrhages (r=0.136, p=0.001) with increasing age was also found. With respect to brain volume loss, cortical atrophy showed a positive correlation (r=0.240, p<0.001), whereas cerebellar and brain stem atrophies did not. Sex-specific distribution of IF We found no statistically significant differences between genders in the general prevalence of IF (p=0.589). Unexpected findings were found in 28.2% of the men and 26.1% of the women. When the most prevalent categories of IF were analysed, statistically significant differences between sexes were found in brain volume loss (p=0.039) that was more frequent in men (9.7% (95% CI 6.5% to 14.2%)) than women (5.2% (95% CI 3.3% to 8.0%)) and quasi-significant differences in neoplasias (p=0.051) that were more prevalent in women (4.3% (95% CI 2.6% to 6.9%)) than in men (1.3% (95% CI 0.5% to 3.8%)). Within neoplasias, meningiomas were more frequent in women (n=9 women, n=1 man). None of the other IF categories showed statistically significant differences between genders. Family history of AD and prevalence of IF As a whole, we found no statistically significant differences in the prevalence of IF between participants who had a family history of AD and those who did not (p=0.149). IF were found in the 24.9% (95% CI 20.8% to 29.6%) of participants with a positive family history of AD and the 30.5% (95% CI 24.6% to 37.0%) of individuals with no family history of AD. The prevalence of WMH (p=0.408) was not significantly different between volunteers with or without a family history. Unexpectedly, brain volume loss showed significant differences (p=0.005) between groups being more prevalent in the FH− group (table 4). Table 4 Distribution of the most prevalent incidental findings according to family history of AD FH− (n=210) FH+ (n=365) Finding n (%) 95% CI n (%) 95% CI White matter hyperintensities 19 (9.0) (5.8 to 13.7) 26 (7.1) (4.9 to 10.2) Vascular abnormalities 19 (9.0) (5.8 to 13.7) 41 (5.2) (3.4 to 8.0)  Lacunar infarcts 7 (3.3) (1.6 to 6.7) 10 (2.7) (1.5 to 4.8)  Microhaemorrhage 5 (2.4) (1.0 to 5.4) 4 (1.1) (0.4 to 2.8)  Single cavernous malformation 3 (1.4) (0.5 to 4.0) 11 (3.0) (1.7 to 5.3)  Malformation of venous development 4 (1.9) (0.7 to 4.8) 16 (4.4) (2.7 to 7.0) Neoplasia 6 (2.8) (1.3 to 6.0) 8 (2.2) (1.1 to 4.3)  Meningioma 3 (1.4) (0.5 to 4.0) 7 (1.9) (0.9 to 3.9)  Pituitary mass* 1 (0.5) (0.1 to 2.6) 1 (0.3) (0.0 to 1.5)  Small intraventricular mass*† 1 (0.5) (0.1 to 2.6) 0 –  Cerebellar hemispheric mass* 1 (0.5) (0.1 to 2.6) 0 – Brain volume loss‡ 22 (10.5) (7.0 to 15.4) 18 (4.9) (3.1 to 7.7)  Frontal* 6 (2.8) (1.3 to 6.0) 2 (0.5) (0.2 to 1.9)  Temporal 4 (1.9) (0.7 to 4.8) 4 (1.1) (0.4 to 2.8)  Parietal 4 (1.9) (0.7 to 4.8) 3 (0.8) (0.3 to 2.4)  Cerebellum and brain stem 2 (0.9) (0.3 to 3.4) 2 (0.5) (0.2 to 1.9)  Diffuse loss of brain volume 6 (2.8) (1.3 to 6.0) 7 (1.9) (0.9 to 3.9) *Statistical analysis excluded due to small n. †Possible subependymoma. ‡Finding a correlation with increasing age (r, p<0.05). AD, Alzheimer's disease; FH+, mother and/or father who had been diagnosed with AD before the age of 75; FH−, mother and/or father who had not been diagnosed with AD before the age of 75. Discussion In this study, we aimed at describing the prevalence of IF from brain MRI in healthy participants aged between 45 and 75 of a population-based study, most of them first-degree descendants of patients with AD. The IF found were classified on the basis of their MRI characteristics alone and were not confirmed by further studies. IF were found in 27.0% of the participants, which is similar to studies involving older participants7 and higher than the prevalence typically reported in most previous studies with comparable populations.8 Nevertheless, some other papers report much higher prevalence rates.9 These discrepancies can be mostly accounted for by the criteria for defining what constitutes an IF, technical features (type and quality of MRI sequences and the training of the scan reader)13 28–30 and the characteristics of participants included (presence of comorbidities, screening selection and ethnicity).28 29 For instance, in a retrospective study that included 1000 asymptomatic volunteers, only 18% of them presented with IF.7 In comparison to our study, the age range of their population (3–83 years old) was wider including very young participants who are less prone to the present parenchyma atrophy or vascular pathology. Overall, we found a positive correlation between the prevalence of IF and increasing age, while no sex-specific differences appeared significant. In addition to participant's age, the resolution of the MRIs used in previous studies was generally worse, thus reducing their capability of detecting microbleeds or small cavernomas. In general, a higher prevalence of IF is reported in studies using at least one high-resolution sequence.8 9 13 30–33 A T2-weighted GRE facilitates the detection of haemorrhage, cerebral microbleeds and calcifications.34 A T2-weighted sequence is especially sensitive in detecting infratentorial brain pathology; meanwhile, FLAIR is dedicated to identifying small vessel disease.34 On the other hand, we did not use contrast-enhanced MRI. The absence of contrast is thought to leave some small lesions unnoticed,6 and underestimate the prevalence of IF.13 Differences in the definition of IF also contribute to the variation of the reported IF prevalence among previous studies in the literature. In most of them,8 13 28–30 the classification of IF was based on previous guidelines,28 consisting of three categories as a function of their clinical relevance. In our case, we chose to categorise any structural finding discovered as an IF regardless of its clinical relevance. In this regard, other studies did not include WMH as an IF,13 28 29 which were reported as age-related changes. However, we considered WMH with a Fazekas score ≥2 as IF, because they have been regarded as secondary to small vessel pathology by other authors.25–27 35 WMH have important clinical and risk factor associations, underlining that they should not be ignored as inevitable ‘silent’ consequences of the physiological ageing of the brain.36 In our study, 7.8% of the participants presented with relevant WMH (Fazekas score ≥2) and their prevalence significantly increased with advancing age. These results confirm previous findings where a 10-fold increase in the prevalence of WMH was found in participants older than 55, especially in those with risk factors for small vessel disease such as hypertension and diabetes.3 11 13 Nevertheless, the prevalence of WMH in our study is lower than in other works evaluating IF in healthy individuals, most likely because those included older participants.7 9 10 37 Asymptomatic lacunar infarcts are frequently reported on imaging studies on elderly asymptomatic individuals.34 Our results are in agreement with previous studies reporting that lacunes are common IF in the brains of individuals in their 60s, and their prevalence as well as size increased with age.9 38 39 As far as brain volume loss is concerned, 7.0% of our study's participants presented with brain volume loss greater than that expected by age and its prevalence significantly increased with increasing age. One study involving older participants (73 years old) revealed a slightly higher brain volume loss prevalence (18%) than ours.7 In this regard, it has to be noted that the inclusion criteria for our study were very strict in the definition of normal cognition. Therefore, participants with subclinical cognitive impairment may have been excluded from the study, thus resulting in a lower prevalence of cortical atrophies. Generally, brain volume loss is not considered an IF since it is relatively normal in the elderly.3 28 30 However, we considered those with brain volume loss greater than that expected by age as an IF because their manifestation may reflect the presence of subclinical pathology. Indeed, it is known that the rate of progression of global and regional brain atrophy is associated with future cognitive deterioration and conversion to dementia.40–42 Unexpectedly, individuals without a family history of AD showed a greater prevalence of abnormal brain atrophies for their age. However, this difference was driven by atrophies in the frontal lobe, and therefore it cannot be attributable to early AD pathology. In regions known to be affected by AD, such as the temporal and parietal cortices, no differences in atrophy prevalence were found between participants with and without a familiar history of AD. With regard to gender-specific distribution of IF, statistically significant differences between genders were found in the prevalence of brain volume loss that was more frequent in men, and neoplasias that were more prevalent in women. Within the latter, and similarly to previous works,3 7–9 13 28 meningiomas were the most common neoplastic brain finding (1.7%). The incidence of meningiomas has been reported to be about threefold higher in women, with the greatest difference observed between the ages of 30 and 59.43 In our study, the higher prevalence of meningiomas found than in a previously reported study (0.9% in ref. 13) may be attributed to the use of MRIs of higher spatial resolution. Asymptomatic meningiomas require close clinical and radiological follow-up to rule out quickly enlarging tumours.43 Our sample was selected through a very accurate screening process to ensure that participants included were clinically and cognitively normal. Nevertheless, although Chiari malformations constituted an exclusion criterion, we found six participants who were unaware of harbouring them. Another strength of our study, which may lead to a higher reported prevalence, is that the MRI protocol was uniform for all participants and high-resolution MRI sequences were used. In addition, all images were reviewed by the same neuroradiologist, thus maximising the homogeneity of the readings and reports. Indeed, the experience of the reader is another factor that has an influence on the detection of IF.13 29–34 44–46 The strict recruitment criteria in the ALFA study may underlie the main limitation of this study in that the results reported here may not reflect the prevalence of IF in the general population. A greater percentage of our volunteers were first-degree descendants of patients with AD than what would be expected from the general population. Therefore, our prevalence estimates should not be regarded from an epidemiological perspective, but are of interest for design of AD prevention trials. Another limitation is the operationalisation of family history status as enrichment criteria for these trials. Ideally, family history should be supported by clinical records that might be difficult to access. In our cohort, 53% of the cases with a positive family history were backed up by confirmed medical records. On top of this, there is a certain arbitrariness in establishing a cut-off value in the age of AD onset in the index case to determine a positive family history status and selecting different threshold values may impact the observed prevalence estimates. In the ALFA cohort, this threshold is fixed at <75 years based on previous literature supporting that the age of AD onset in the index case needs to be limited as dementia occurring at a very old age is less likely to have a strong genetic component.47 48 This 75-year-old limit has been used by us and other studies that combine multiple susceptibility loci into a global genetic risk score to improve the prediction of individuals at risk of suffering AD.49 There is still an open debate regarding the disclosure of IF to participants participating in imaging studies, since there is still a lack of evidence on which to base practice on the balance of harm versus benefit in telling research participants about findings.5 The existing literature has evaluated the will of participants in medical and non-medical settings to be informed. In this respect, among study participants surveyed in the USA in 2005, 90% of 105 respondents said that they would to be informed of any IF, of whom 60% preferred this to be done by a physician in the research team.50 In any case, further research to better understand the clinical and ethical implications of IF and their disclosure is needed for developing evidence-based policies for their management. In our study, volunteers were informed about our policy to disclose non-clinically relevant findings and agreed so by signing the study's informed consent form. All participants received a radiological report of their MRI (not just those presenting a finding (it being clinically relevant or not), but also those presenting no findings at all). A trained physician explained the findings to participants in order to provide clear information about their clinical relevance or lack of it. Clinically relevant findings were referred for specialist follow-up. Non-clinically relevant findings were also reported and volunteers were facilitated by a helpline should they have further questions or needed additional clarifications. Even though we did not measure the psychological impact of disclosing non-clinically relevant findings, it is worth mentioning that out of the 65 events, none of them ever made use of this helpline. In general, we did not perceive any case in which disclosure caused any inconvenience: participants acknowledged the information and felt the feedback positively. Nevertheless, it would be interesting to investigate the psychological impact of knowing these findings on the quality of life of these participants. In conclusion, we describe here that brain MRIs of healthy middle-aged participants show a relatively high prevalence of IF (27.0%) even after excluding individuals with subtle cognitive alterations. As a whole, a positive correlation between the prevalence of IF and increasing age was found and, within specific IF categories, relevant WMH, lacunes and brain volume loss prevalence significantly increased with age. Jointly, no significant differences between genders in the general prevalence of IF were found. However, brain volume loss was more frequent in men and neoplasias were more prevalent in women. The main limitation of this study is the particular recruitment criteria in the ALFA project which argues against the generalisation of our data in the general population. In addition, the difficulty in establishing a cut-off value in the age of AD onset in the index case may have an impact on whether IF are more prevalent in first-degree relatives of patients with AD. Nevertheless, it is worth mentioning that most of our participants are first-degree descendants of patients with AD, and therefore the results presented here are of special relevance for novel imaging studies in the context of AD prevention in cognitively healthy middle-aged participants. This publication is part of the ALFA study (ALzheimer and FAmilies). The authors would like to express their most sincere gratitude to the ALFA project volunteers, without whom this research would have not been possible. In memory of Maria Thos i Negre, the authors would like to express their gratitude for her donation to the Pasqual Maragall Foundation for research on Alzheimer's prevention. Contributors: JLM, KF and JDG made substantial contributions to the conception and design of the work. AB, NB, GC, CM, KF, JLM and JDG contributed to the acquisition and analysis of data. AB, SR, NB, CM, KF, NG, JLM and JDG were involved in the interpretation of data for the work. AB, SR, CM, NG, JLM and JDG were the main contributors to drafting the manuscript that was then critically revised for important intellectual content by all its co-authors. All co-authors approved the final version of the manuscript to be submitted and are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Funding: The research leading to these results has received funding from ‘la Caixa’ Foundation. Additional funding was obtained from Fondo de Investigación Sanitaria (FIS), Instituto de Salud Carlos III (ISC-III) under grant PI12/00326 and Barcelona city council under agreement #0724/13 and 0940/16. JDG holds a ‘Ramón y Cajal’ fellowship (RYC-2013-13054). Competing interests: JLM has provided scientific advice or has been an investigator or data monitoring board member receiving consultancy fees from: Novartis, Pfizer, Eisai, Janssen-Cilag, Lundbeck, Roche, Bayer, Bristol-Myers Squibb, GE Health Care, Merz, MSD, GlaxoSmithKline, Astra-Zeneca, Avid, Lilly, Boehringer-Inghelmein, Biokit, Piramal, IBL and Fujireibio-Europe. Ethics approval: Clinical Research Ethical Committee, Parc de Salut Mar, Barcelona. Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: No additional data are available. ==== Refs References 1 Carrillo MC , Brashear HR , Logovinsky V Can we prevent Alzheimer's disease? Secondary “prevention” trials in Alzheimer's disease . Alzheimers Dement 2013 ;9 :123 –31.e1 . 10.1016/j.jalz.2012.12.004 23411394 2 Marcus DS , Wang TH , Parker J Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults . 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==== Front BMJ OpenBMJ OpenbmjopenbmjopenBMJ Open2044-6055BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bmjopen-2016-01454410.1136/bmjopen-2016-014544Public HealthResearch150617241692169417031724Determinants of second pregnancy among pregnant women: a hospital-based cross-sectional survey in China Xu Xianglong 123Zuo Hanxiao 123http://orcid.org/0000-0002-3099-3299Shi Zumin 4Rao Yunshuang 5Wang LianLian 678Zeng Huan 123Zhang Lei 9101112Sharma Manoj 1314Reis Cesar 15Zhao Yong 123 1 School of Public Health and Management, Chongqing Medical University, Chongqing, China 2 Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, China 3 The Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, China 4 Faculty of Health Sciences, School of Medicine, The University of Adelaide, North Terrace Adelaide, South Australia, Australia 5 School of Nursing, Chongqing Medical University, Chongqing, China 6 The Department of Obstetrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China 7 Department of Reproduction Health and Infertility, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China 8 Canada-China-New Zealand Joint Laboratory of Maternal and Fetal Medicine, Chongqing Medical University, Chongqing, China 9 Research Center for Public Health, School of Medicine, Tsinghua University, Beijing, China 10 Faculty of Medicine, Central Clinical School, Nursing and Health Sciences, Monash University, Melbourne, Australia 11 Faculty of Medicine, School of Public Health and Preventive Medicine, Nursing and Health Sciences, Monash University, Melbourne, Australia 12 Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia 13 Department of Behavioral and Environmental Health, Jackson State University, Jackson, UK 14 Walden University, USA 15 Department of Preventive Medicine, Loma Linda University Medical Center, Loma Linda, California, USACorrespondence to Dr Yong Zhao; zhaoyongzb@qq.com2017 27 3 2017 7 3 e0145443 10 2016 30 1 2017 20 2 2017 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Objectives This study aimed to explore determinants of second pregnancy and underlying reasons among pregnant Chinese women. Design The study was a population-based cross-sectional survey. Setting 16 hospitals in 5 provinces of Mainland China were included. Participants A total of 2345 pregnant women aged 18 years or above were surveyed face to face by investigators between June and August 2015. Main outcome measures The pregnancy statuses (first or second pregnancy) and reasons for entering second pregnancy. Results A total of 1755 (74.8%) and 590 (25.2%) women in their respective first and second pregnancies were enrolled in this study. The most common self-reported reasons for entering second pregnancy among participants included the benefits to the first child (26.1%), love of children (25.8%), adoption of the 2-child policy (11.5%), concerns about losing the first child (7.5%) and suggestions from parents (7.5%). Pregnant women with low (prevalence ratio (PR) 1.96; 95% CI 1.62 to 2.36) and moderate education level (PR 1.97; 95% CI 1.65 to 2.36) were more likely to have a second pregnancy than their higher educated counterparts. Income was inversely associated with second pregnancy. However, unemployed participants (PR 0.79; 95% CI 0.66 to 0.95) were less likely to enter a second pregnancy than those employed. Women with moderate education were 3 times more likely to have a second child following the ‘2-child policy’ than the low education level subgroup. Conclusions 1 in every 4 pregnant women is undergoing a second pregnancy. The benefits of the firstborn or the love of children were the key drivers of a second pregnancy. Low socioeconomic status was positively associated with a second pregnancy as well. The new 2-child policy will have an influence on China's demographics. EPIDEMIOLOGYPREVENTIVE MEDICINEPUBLIC HEALTHSOCIAL MEDICINEREPRODUCTIVE MEDICINE ==== Body Strengths and limitations of this study The study includes a large number of participants living in five provinces in China and a face-to-face interview. This study has implications in the implementation and enforcement of China's new universal two-child policy. The main limitation is the relatively small number of participants from a rural area, which requires cautious interpretations of the study results, especially among rural women. Introduction Owing to the one-child policy, China has experienced low birth rates for three decades. Recent data indicated that the overall birth rate of the Chinese population declined from 2.106% in 1990 to 1.237% in 2014,1 which has led to long-term social and economic consequences.2 The one-child rule applies mainly to urban residents and government employees; thus, in rural areas, where ∼70% of the population resides, a second child is allowed after 5 years, especially if the firstborn is a girl.3 Along with the birth rate decline, rapid population ageing is becoming a public health problem in China. The percentage of people over 65 years of age increased from 5.6% in 1990 to 10.1% in 2014.4 Urbanisation and rapid ageing resulted in various problems such as urban resources relocation, poor air quality and social insecurity.5 The increasing number of ‘empty nest’ elderly (who live alone unaccompanied by any family member) has created huge demands for healthcare and other related services.6 Furthermore, the labour force supply has been declining. Fertility desire or intention is an important factor influencing fertility trends.7 Fertility intention is associated with the economic, social and cultural environment within which the fertility awareness is cultivated, including family needs, motivation, intention and preferences.8 Procreation conception is often influenced by family social status, educational factors and fertility intention, which changes in adulthood to manifest as decreased or increased intended family size.9 Fertility rate and reproductive number are low in developed countries, and these are affected by the conflict between individual will and family decisions.10 The fertility intention of having a second child largely depends on the number of sons in South Asia.11 Son preference is prevalent among the Chinese, especially in rural areas.12 However, this reality has been changing with improvements in the social security and educational systems, as well as the infrastructure. Under the influence of the family planning policy beginning in the 1970s, the fertility preference of son preference is not prevalent anymore.13 ‘Selective two-child policy’ was introduced at the end of 2013, and it allowed couples nationwide to have a second child if either parent is an only child. On 29 October 2015, the Chinese government announced a new universal two-child policy, allowing all couples to have a second child, and cancelled the rewards for having only one child and the extended maternity leave. In the short term, the two-child policy may significantly boost the service sector development, which in turn may guide investments towards more efficient and profitable areas. Furthermore, the policy may raise the percentage of newborns, which in the long run will significantly increase housing demand and consumption.14 However, whether people in China will respond to the new two-child policy and have a second child remains unknown. Understanding the determinants and the reasons for having a second child is important in several aspects including the implementation of the new universal two-child policy and health service planning. Nevertheless, no studies on this focus have been conducted in China. Therefore, the purpose of this study was to explore second pregnancy determinants and main reasons for the second pregnancy by addressing three research questions as follows: (1) What is your main reason for the second pregnancy?; (2) What are the second pregnancy determinants among pregnant Chinese women?; (3)What are the factors that affect the main reason for the second pregnancy? Methods Research methods The study design and methods have been reported previously.15–17 All pregnant women visiting 16 hospitals in Chongqing, Chengdu, Zunyi, Liaocheng and Tianjin between June and August 2015 were invited to participate. We excluded women with serious complications or cognitive disorders. Chongqing, Chengdu and Zunyi are in South China, whereas Liaocheng and Tianjin are in North China. A total of 2400 women participated in the study with a response rate of 97.76%. All participants provided written consent. Measurements Outcome variable: Pregnancy status (first or second pregnancy). The definition of second pregnancy is dependent on the outcome of the first pregnancy (ie, live birth). Sociodemographic variables Age was recoded into three categories: 18–25, 26–35 and 36–45 years. Participants were asked about their residence (urban/rural), family income per capita (<¥4500, ¥4500–¥9000 and >¥9000) and employment status (rural migrant workers/urban and rural unemployed, unemployed/industrial workers of non-agricultural registered permanent residence/individual business/business services staff/civil servants/senior manager and middle-level manager in large and medium enterprises/private entrepreneur/professionals/clerks/students/others). On the basis of the Chinese hospital ranking system, hospital capacity/quality rank was recorded as high, medium and low. Women were also asked about their ethnicity (Han or minority) and whether she or her husband was an only child. Marital status was categorised as unmarried, first marriage, remarried and divorced/widowed. Pregnancy was divided into three trimesters. Education level was categorised as low (junior middle school or below), medium (senior high school, vocational or technical secondary school) and high (university). In the multivariable analysis for second pregnancy determinants, employment status was categorised as manual (rural migrant workers/industrial workers of non-agricultural registered permanent residence/business services staff), non-manual (individual business/civil servants/senior manager and middle-level manager in large and medium enterprises/private entrepreneur/professionals/clerk/and students), unemployed and others. The study indicated the following second pregnancy reasons: Benefits for the first child: benefit for the growth and the future of the first child. Love of children: love children. Adoption of the ‘two-child policy’: ‘two-child policy’ is the most second pregnancy reason. Concerns about losing the first child: parents who have lost their only child are known as Shidu parents in China. They are worried about losing the first child. Suggestions from parents: parents strongly recommend having a second child. Gender: son preference or girl preference was included. Disability of the first child: the first child is disabled. They would like to have a healthy child. Others: other reasons are those that are not listed in the above seven reasons. Statistical analyses Participant characteristics were summarised using frequencies and percentages and presented with descriptive analyses (means, SDs and percentages). The χ2 tests were used to compare the categorical variables. A Poisson model was applied in the multivariate analysis to assess the factors associated with the second pregnancy. In the Poisson regression analysis, prevalence ratios (PRs) and 95% CIs were calculated. The choice of the method of Poisson model is justified because of the high prevalence of the outcome (second pregnancy).18 Seven multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the common reasons for entering a second pregnancy: (1) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘benefits for the first child’ for entering a second pregnancy; (2) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘love of children’ for entering a second pregnancy; (3) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘adoption of the two-child policy’ for entering a second pregnancy; (4) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘concerns of losing the first child’ for entering a second pregnancy; (5) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘suggestions from parents’ for entering a second pregnancy; (6) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘gender’ for entering a second pregnancy; and (7) multivariable logistic regression analyses were used to evaluate the sociodemographic factors related to the key reason for ‘disability of the first child’ as a reason for entering a second pregnancy, respectively. We included sociodemographic variables (nationality, single child, husband was a single child, marital status, education level, residence, income, job, age and hospital capacity level) with ‘key reasons for entering second pregnancy’ as the dependent variable in the regression model with backward elimination to retain those factors that were still significant. All statistics were performed using two-sided tests, and statistical significance was considered at p<0.05. All data analyses were performed using statistical software (SAS V.9.1; SAS Institute, Cary, North Carolina, USA). Results A total of 1755 (74.8%) and 590 (25.2%) women in their respective first and second pregnancies were enrolled, 19.8% of whom lived in urban areas. Among the women aged 26–35 years, 67.2% and 70.3% of them were in their first and second pregnancies, respectively. For high education level, we had 72.8% and 52.9% of the women in their first and second pregnancies, respectively. About half of the pregnant women or their husbands were an only child themselves. Among the women in their second pregnancy, 438 (74.2%) were visiting high capacity hospitals and 26.4% had a low education level (table 1). Table 1 Characteristics of the study participants by number of pregnancy (n, %) Variable First pregnancy Second pregnancy Number 1755 (74.8) 590 (25.2) Hospital capacity level  High 1386 (79.0) 438 (74.2)  Medium 202 (11.5) 109 (18.5)  Low 167 (9.5) 43 (7.3) Age (years)  18–25 546 (31.1) 78 (13.2)  26–35 1180 (67.2) 415 (70.3)  36–45 29 (1.7) 97 (16.5) Nationality  Han nationality 1690 (96.3) 562 (95.3)  Minority 65 (3.7) 28 (4.8) Single child  No 960 (54.7) 339 (57.5)  Yes 795 (45.3) 251 (42.5) Husband was a single child  No 847 (48.3) 325 (55.1)  Yes 908 (51.7) 265 (44.9) Marital status  First marriage 1678 (95.6) 527 (89.3)  Unmarried 36 (2.1) 13 (2.2)  Remarried 25 (1.4) 45 (7.6)  Divorced or widowed 16 (0.9) 5 (0.9) Education level  Low 246 (14.0) 156 (26.4)  Medium 232 (13.2) 122 (20.7)  High 1277 (72.8) 312 (52.9) Residence  Rural 314 (17.9) 151 (25.6)  Urban 1441 (82.1) 439 (74.4) Income  Low 428 (24.4) 183 (31.0)  Medium 759 (43.3) 230 (39.0)  High 568 (32.4) 177 (30.0) Employment  Rural migrant workers 59 (3.4) 59 (10.0)  Urban and rural unemployed, half of the unemployed 423 (24.1) 130 (22.0)  Industrial workers of a non-agricultural registered permanent residence 38 (2.2) 12 (2.0)  Individual business 117 (6.7) 82 (13.9)  Business services staff 122 (7.0) 33 (5.6)  Civil servants 326 (18.6) 72 (12.2)  Senior manager and middle-level manager in large and medium enterprises 70 (4.0) 26 (4.4)  Private entrepreneur 56 (3.2) 31 (5.3)  Professionals 194 (11.1) 50 (8.5)  Clerk 112 (6.4) 27 (4.6)  Students 14 (0.8) 1 (0.2)  Others 224 (12.8) 67 (11.4) Education level was categorised as ≤primary school, junior middle school (basic education), ≥a senior high school (including vocational/technical secondary school and junior college), (secondary education) and ≥senior college and university (higher education). The multivariable logistic regression model showed that marital status, education, employment status, income, age and hospital capacity level were associated with a second pregnancy. Compared with first marriage women, remarried women were about three times more likely to enter a second pregnancy (PR 1.63; 95% CI 1.29 to 2.07). Pregnant women with low (PR 1.96; 95% CI 1.62 to 2.36) and medium education levels (PR 1.97; 95% CI 1.65 to 2.36) were more likely to enter a second pregnancy than their higher educated counterparts. Moreover, pregnant women with medium income (PR 0.83; 95% CI 0.71 to 0.97) were less likely to enter a second pregnancy than their low-income counterparts. Similarly, age was associated with increased likelihood of entering a second pregnancy: the PR for a second pregnancy was 2.51, 3.41 (95% CI 2.01 to 3.14) and 6.03 (95% CI 4.70 to 7.73) for those aged 18–25, 26–35 and 36–45 years, respectively. Compared with women with non-manual jobs, the unemployed women (PR 0.79; 95% CI 0.66 to 0.95) were less likely to have second pregnancies. Compared with those being registered in a low ranking hospital, pregnant women who were admitted to a medium ranking hospital were more likely to enter a second pregnancy (PR 1.43, 95% CI 1.07 to 1.91). Furthermore, rural residence does not contribute to a higher second pregnancy rate (table 2). Table 2 Adjusted prevalence ratios for second pregnancy according to sociodemographic factors among pregnant women Parameter PR 95% CI p Value Nationality  Han nationality 1  Minority 1.08 0.81 to 1.43 0.597 Single child  No 1  Yes 1.12 0.97 to 1.29 0.122 Husband was a single child  No 1  Yes 0.99 0.86 to 1.14 0.848 Marital status  First marriage 1  Unmarried 1.18 0.74 to 1.89 0.495  Remarried 1.63 1.29 to 2.07 <0.0001*  Divorced or widowed 0.65 0.30 to 1.43 0.284 Education level  High 1  Low 1.96 1.62 to 2.36 <0.0001*  Medium 1.97 1.65 to 2.36 <0.0001* Residence  Rural 1  Urban 0.89 0.75 to 1.046 0.154 Income  Low 1  Medium 0.83 0.71 to 0.97 0.022*  High 0.88 0.73 to 1.05 0.16 Job  Non-manual 1  Manual 0.9 0.75 to 1.09 0.294  Unemployed 0.79 0.66 to 0.95 0.011*  Others 0.99 0.79 to 1.24 0.939 Age  18–25 years old 1  26–35 years old 2.51 2.01 to 3.14 <0.0001*  36–45 years old 6.03 4.70 to 7.73 <0.0001* Hospital capacity level  Low 1  High 1.12 0.86 to 1.456 0.418  Medium 1.43 1.07 to 1.91 0.016* *Statistically significant (p<0.05). PR, prevalence ratios. Among the pregnant women who were having a second pregnancy, the common reasons for entering a second pregnancy included: benefits for the first child (26.1%), love of children (25.8%), adoption of the two-child policy (11.5%), concerns about losing the first child (7.5%), suggestions from parents (7.5%), gender (2.5%) and disability of the first child (1.4%; figure 1). Figure 1 The common reasons for entering a second pregnancy. The multivariable logistic regression analysis indicated that women with high education were less likely to be influenced by parents than those with low education (OR 0.16; 95% CI 0.07 to 0.39). Mothers who were an only child themselves (OR 0.36; 95% CI 0.16 to 0.80) or those living in urban areas (OR 0.52; 95% CI 0.27 to 0.99) tend to be less concerned about losing the first child as a reason for having a second child than their counterparts. Compared with Han Chinese women, women with minority backgrounds were 2.67 times more likely to have a second child because of their love for children. Compared with the low education level group, women with medium education levels were three times more likely to have a second child in adoption of the ‘two-child policy’. Parents who were an only child themselves were less likely to report ‘love of children’ as a reason for having a second child than their counterparts (table 3). Table 3 Logistic regression model for main reasons of the second pregnancy according to sociodemographic factors Parameter OR 95% CI p value Suggestions from parents Education level  Low education 1.00  Medium education 1.00 0.49 to 2.04 0.993  High education 0.16 0.07 to 0.39 <0.000* Concerns about losing the first child Single child  No 1.00  Yes 0.36 0.16 to 0.80 0.012* Residence  Rural 1.00  Urban 0.52 0.27 to 0.99 0.047* Love children Nationality  Han nationality 1.00  Minority 2.67 1.18 to 6.04 0.018* Single child  No 1.00  Yes 0.54 0.35 to 0.83 0.005* Husband was a single child  No 1.00  Yes 0.56 0.37 to 0.85 0.006* Disability of the first child Nationality  Han nationality 1.00  Minority 6.82 1.31 to 35.57 0.023* Adopting the ‘two-child policy’ Education level  Low education 1.00  Medium education vs low education 3.08 1.41 to 6.70 0.005*  High education vs low education 1.56 0.76 to 3.22 0.225 Others Nationality  Han nationality 1.00  Minority 5.68 1.94 to 16.61 0.002* *Statistically significant (p<0.05). Discussion In this cross-sectional study involving participants from five provinces in China, we found that one in every four pregnant women is entering a second pregnancy 1 year after the new second-child policy. Furthermore, low education and low income are positively associated with a second pregnancy among the respondents. Either parent being an only child was not associated with the likelihood of having a second child. The reasons for having a second pregnancy differ in sociodemographic factors. The finding that only one in every four pregnant women is in their second pregnancy is beyond our expectation, given the large number of eligible women in China. The reasons for this result require further investigation. The rationale may be attributed to the ‘Demographic-Economic Paradox’, which states that economic development is the best contraceptive.19 Moreover, we hypothesise that changes in family size and high costs of education, housing and medical service are among the main motives for fewer women opting for a second pregnancy. Previous studies also show concern about ‘the costs of providing for children’ as a reason for not having a second child,20 given that parents traditionally pay for their children's education and housing. In some of the study areas, the cost of an apartment is almost 6.6 times in Sichuan province, 6.3 times in Chongqing, 5.1 times in Guizhou, 5.8 times in Shandong province and 10.0 times in Tianjin more than the average annual salary in 2015.21 One of the key findings of this study is the increased tendency of low socioeconomic status groups to have second pregnancies. Pregnant women with low and medium education levels were more likely to have second pregnancies than their higher educated counterparts. A previous study showed that women who graduated from college tend to have fewer children than those with high school degrees or lower education levels.22 Another study showed that although women with higher education were often expected to have more children than their less educated counterparts, the number of pregnancies was often less than in the low education level group.7 According to the literature,23 24 highly educated women were more frequently revising their birth intentions downwards of having more children than less educated women, especially when they were nearing the end of their fertile years. Given that the percentage of highly educated women has been increasing over time as their fertility birth rate has been declining in many European countries7 and in China, gaining more knowledge about the effect of education on fertility decision-making is of particular importance. Future research is required to determine the factors that affect fertility decision-making among highly educated women in China. Compared with women with low income, those with medium income were less likely to have a second pregnancy. Low socioeconomic status groups were more likely to have a second pregnancy even if they are intensifying the poverty of life, with more pressure on feeding, healthcare, housing, education and pension after the second childbirth. A survey realised among women in two highly developed rural counties of China showed a negative association between income and number of children.25 This study found that poorer women tend to be more willing to have a second pregnancy, while higher socioeconomic status groups are less likely to give birth for a second time. This finding is averse to the quality promotion (especially in the aspect of education) of the entire population. In the past, the one-child policy implementation was difficult to implement in rural areas; however, the two-child policy may be challenging today and in the future in some developed areas and among high socioeconomic status groups. Future studies are necessary to address these potential problems. In China, the typical family structure is 4–2–1: four grandparents and two parents, with a single child who is expected to support both parents and grandparents.26 Owing to the typical family structure, husbands and wives need to support parents from both sides as they are raising their second child. In this study, we found that living in a rural area does not contribute to a higher rate of second pregnancy. Previous studies showed that people in rural areas might be strongly influenced by traditional ideas; thus, women would be more affected by first child gender preference,12 in addition to the higher child mortality rate in rural areas compared with urban areas.2 Gender bias in family formation, such as sex-selective abortion, sex ratio imbalance and other phenomena, are well documented in China.27 However, under the influence of the family planning policy beginning in the 1970s, the fertility concept began to change with less focus or expectations of son preference.13 This study found that only 2.5% of respondents had a second child because of the firstborn's gender, which suggests that the fertility concept has changed in rural areas; as such, the difference in the ratio of the second child between rural and urban areas will be minimal. This study found that age was associated with a greater likelihood of entering the second pregnancy. A high proportion of second pregnancy was seen among those aged 26–35 (70.3%) or 36−45 (16.5%) years. Possible reasons for the age of having a first child in China being on the rise may include economic development, the popularisation of superior education and higher employment pressures. Also, education and career were reported as important factors in the women's decisions to delay marriage and motherhood.28 Second, since the 1970s, when the family planning policy was implemented, most women who intended to have a second child were not allowed to do so. These women may have waited until the new two-child policy was implemented; that could be an explanation for so many older pregnant women. This study was conducted only 1 year after the implementation of the new two-child policy ‘selective two-child policy’, which is a relatively short period for analysis. Many young women have no child or only one. Given the work and life pressures, child education concerns and so on, these women may be less willing to plan for a second pregnancy. The age at which women have their firstborn bears implications for schooling, labour force participation and overall family size. Compared with other western countries, the age of first pregnancy in China is lesser. Along with the USA, many other developed nations (eg, Italy, the Netherlands and Switzerland) have observed increases in average age at first pregnancy, with some countries averaging near 30 years of age.29 One concern about this phenomenon is the increased pregnancy risks associated with mother's older age. Furthermore, a previous study showed that many parents (35 years and older) may find child-rearing challenging (taking care of their infant, dealing with the issues of helping their adolescent children and taking care of their elderly parents) after the second childbirth.2 The only-child mother's desire to have a second baby is stronger than that of a non-only-child mother.13 Previous studies showed that fondness of the child, released pension pressure and family inheritance events are possible factors influencing an only-child mother to conceive the second child.17 Interestingly, an only-child parent was less likely to report love for children as a reason for having a second child than their counterparts. An only-child mother enjoyed love from parents alone; as such, they may also have the concept of taking comprehensive care of children ingrained deep in their minds, as well as the lack of suffering consciousness about losing a child. An editorial concluded that in modern, economically developed China, the women's decisions tend to influence the size of Chinese families over the next generation more than the policymakers in Beijing.26 This study determined that only about 10% of the participants adopted the two-child policy. It also found that pregnant women with medium education levels were more likely to have a second pregnancy in accordance with the ‘two-child policy’ than pregnant women with low education levels. Further research studies are necessary to investigate how to enhance the acceptance and execution of the policy in the high socioeconomic status group. This study includes limitations that should be further addressed. First, the reason behind the low proportion of women having a second pregnancy remains undetermined. Second, there may be a selection bias and the sample was not nationally representative. Not all pregnant women in the five cities attended the 16 selected hospitals. The sample consisted of pregnant women in five regions, namely Chongqing, Chengdu and Zunyi in South China, and Liaocheng and Tianjin in North China. In this study, city-fixed effects were not controlled in the regression models. City-fixed effects may exist, but we are not sure. Third, more than 90% of the respondents were Han Chinese; as such, the conclusion of this study may not apply to minorities; more than half of the respondents have high education levels. Thus, this study may be not applicable to low education level groups. Fourth, although we adjusted for several socioeconomic status-related variables (ie, residence, educational level and income), residual confounding can still affect the second pregnancy variable. Furthermore, we did not collect the information on the gender of the first child. Finally, only a small number of rural women were included in the study, and such an outcome may affect the representativeness of this population, requiring cautious interpretations of the study results, especially among rural women. Conclusions One in every four pregnant women is undergoing a second pregnancy. Women with low education and low income were more likely to have second pregnancies. Either parent being an only child was not associated with the likelihood of having a second child. Rural residence does not contribute to a higher second pregnancy rate. The new two-child policy will significantly influence the demographics in China. The findings have implications for the implementation and enforcement of China's new universal two-child policy. The authors thank the project sponsors, the Medjaden Academy and Research Foundation for Young Scientists and Summer Social Practice Project of School of Public Health and Management, Chongqing Medical University. They also thank team members for their support and contributions to this study. Twitter: Follow Cesar Reis @drcesarreis Contributors: All authors contributed to the overall conception and design of the study. XX contributed to the study design, data analysis, data interpretation and drafting of the manuscript. HaZ, YR, LW and YZ participated in the design of the study and helped draft the manuscript. MS, HuZ, LZ and CR contributed to the interpretation of study results and helped draft the manuscript. All authors read and approved the final manuscript. Funding: This project was supported by the Medjaden Academy and Research Foundation for Young Scientists (grant number MJR20150047). This study was also funded by the Summer Social Practice Project of School of Public Health and Management, Chongqing Medical University. Competing interests: None declared. Ethics approval: The study protocol was approved by the Ethics Committee of Chongqing Medical University (no. 2015008). Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: No additional data are available. ==== Refs References 1 National Health and Family Planning Commission of the People's Republic of China . China health and family planning statistical yearbook 2015 . Beijing, China : Pecking Union Medical College Press , 2015 . 2 Gong W , Xu DR , Caine ED Challenges arising from China's two-child policy . Lancet 2016 ;387 :1274 10.1016/S0140-6736(16)30020-4 3 Li J Gender inequality, family planning, and maternal and child care in a rural Chinese county . Soc Sci Med 2004 ;59 :695 –708 . 10.1016/j.socscimed.2003.11.041 15177828 4 Ding QJ , Hesketh T Family size, fertility preferences, and sex ratio in China in the era of the one child family policy: results from national family planning and reproductive health survey . 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Demogr Res 2008 ;19 :557 –98 . 11 Jayaraman A , Mishra V , Arnold F The relationship of family size and composition to fertility desires, contraceptive adoption and method choice in South Asia . Int Perspect Sex Reprod Health 2009 ;35 :29 –38 . 10.1363/ifpp.35.029.09 19465346 12 Ding W , Zhang Y When a son is born. The impact of fertility patterns on family finance in rural China . China Econ Rev 2014 ;30 :192 –208 . 10.1016/j.chieco.2014.06.008 13 Jia Z Fertility desire changes of Chinese after 1950s . Popul Econ 2009 ;04 :24 –28+33 . 14 Xiaobo Q Two-child policy to balance demographics. China Daily. http://www.chinadaily.com.cn/opinion/2015-11/05/content_22374656.htm. (accessed 5 Nov 2015 ). 15 Wang L , Xu X , Baker P Patterns and associated factors of caesarean delivery intention among expectant mothers in China: implications from the implementation of China's new National Two-Child Policy . 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PMC005xxxxxx/PMC5372154.txt

==== Front BMJ OpenBMJ OpenbmjopenbmjopenBMJ Open2044-6055BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bmjopen-2016-01375310.1136/bmjopen-2016-013753Health Services ResearchResearch1506170416831701171016941703Differences between determinants of in-hospital mortality and hospitalisation costs for patients with acute heart failure: a nationwide observational study from Japan Sasaki Noriko Kunisawa Susumu Ikai Hiroshi Imanaka Yuichi Department of Healthcare Economics and Quality Management, Kyoto University Graduate School of Medicine, Kyoto, JapanCorrespondence to Dr Yuichi Imanaka; imanaka-y@umin.net2017 22 3 2017 7 3 e0137534 8 2016 17 11 2016 17 1 2017 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Objectives Although current case-mix classifications in prospective payment systems were developed to estimate patient resource usage, whether these classifications reflect clinical outcomes remains unknown. The efficient management of acute heart failure (AHF) with high mortality is becoming more important in many countries as its prevalence and associated costs are rapidly increasing. Here, we investigate the determinants of in-hospital mortality and hospitalisation costs to clarify the impact of severity factors on these outcomes in patients with AHF, and examine the level of agreement between the predicted values of mortality and costs. Design Cross-sectional observational study. Setting and participants A total of 19 926 patients with AHF from 261 acute care hospitals in Japan were analysed using administrative claims data. Main outcome measures Multivariable logistic regression analysis and linear regression analysis were performed to examine the determinants of in-hospital mortality and hospitalisation costs, respectively. The independent variables were grouped into patient condition on admission, postadmission procedures indicating disease severity (eg, intra-aortic balloon pumping) and other high-cost procedures (eg, single-photon emission CT). These groups of independent variables were cumulatively added to the models, and their effects on the models' abilities to predict the respective outcomes were examined. The level of agreement between the quartiles of predicted mortality and predicted costs was analysed using Cohen's κ coefficient. Results In-hospital mortality was associated with patient's condition on admission and severity-indicating procedures (C-statistics 0.870), whereas hospitalisation costs were associated with severity-indicating procedures and high-cost procedures (R2 0.32). There were substantial differences in determinants between the outcomes. In addition, there was no consistent relationship observed (κ=0.016, p<0.0001) between the quartiles of in-hospital mortality and hospitalisation costs. Conclusions The determinants of mortality and costs for hospitalised patients with AHF were generally different. Our results indicate that the same case-mix classifications should not be used to predict both these outcomes. Case-mix classificationin-hospital mortalityhospitalization costsresource utilization ==== Body Strengths and limitations of this study The study provides a novel understanding that determinants of in-hospital mortality and hospitalisation costs differ in acute heart failure in Japan. Our findings indicate that the same case-mix classifications should not be used to predict both inpatient mortality and costs, which would support the development and implementation of future case-mix classifications. External validity of the study could not be determined, and further research is required to investigate the determinants of resources and clinical outcomes in other acute diseases as well as in other settings including other countries. Introduction Prospective payment systems such as the Diagnosis-Related Group (DRG) system in the USA and the Diagnosis Procedure Combination Per-Diem Payment System (DPC/PDPS) in Japan use case-mix classifications that were developed to estimate patient resource usage according to their diseases and underlying conditions.1–6 Each DRG or DPC classification includes patients who have similar patterns of resource usage and are comparable from a clinical perspective. The general aim of these systems is to adequately control the inherent variations in patient's disease severity and to ensure fair reimbursements to providers that treat different types of patients under limited resources and rising medical costs, especially in the context of increasingly ageing societies.2 Despite implicit assumptions that severely ill patients consume more resources, the detailed relationship between severity and outcomes such as mortality and hospitalisation costs remains unknown.7 For example, in the All Patients Refined Diagnosis Related Groups (APR-DRG; 3M HIS),1–4 8 a patient can be classified into one of the following four levels of severity within each disease category: no/minor complications or comorbidities (CC), moderate CC, major CC, and extreme CC.3 These classifications are determined according to each patient's severity of illness and risk of mortality, where the former is used to adjust payment and the latter to adjust mortality rates.1–4 8 Similarly, numerous risk-adjustment tools known as severity measures were created and applied in the USA to predict hospital resource consumption and inpatient death in the 1980s and 1990s using multiple payment system data samples.2 7–10 However, these tools are frequently proprietary, and their underlying logic is unavailable for scrutiny.2 4 9 The appropriate adjustment of inpatient mortality and hospitalisation costs continues to be an important consideration in different prospective payment systems throughout the world, despite the increasing availability of big data and advances in information technology. Acute heart failure (AHF) is associated with high hospitalisation and mortality rates, and the rising number of patients with AHF is placing a growing economic burden on healthcare systems; as a result, the efficient management of this disease is being required in various countries.11–13 There is an increasing need for the effective and efficient distribution of limited healthcare resources for general healthcare, and also for the treatment of heart failure. In this study, we aimed to investigate the determinants of in-hospital mortality and hospitalisation costs in patients with AHF to elucidate the impact of patient severity factors, and to examine the level of agreement between the predicted values of mortality and costs. Methods Data source The Quality Indicator/Improvement Project (QIP) is a long-term benchmarking research project involving the voluntary participation of Japanese hospitals that are reimbursed under the DPC/PDPS.5 6 The DPC/PDPS is a point-based system for hospital reimbursements used by the majority of acute care hospitals in Japan. DPC/PDPS data are characterised by the inclusion of numerous factors, such as age, sex, admission route, severity levels at admission for specific diseases (eg, New York Heart Association functional class, or NYHA, for AHF; Killip class for acute myocardial infarction; and staging of various cancers), presence of comorbidities, complications, examinations, procedures, medications and costs incurred during hospitalisation. For this study, data were obtained from 265 acute care hospitals enrolled in the QIP. We analysed patients who had been discharged from these hospitals between April 2010 and March 2011. In order to identify patients with AHF, we first identified patients with a diagnosis of heart failure (International Classification of Diseases, 10th revision code: I50.x) that required the highest usage of healthcare resources for that hospitalisation episode; we then identified patients whose records had an ‘acute exacerbation of heart failure’ code, which is available in the DPC/PDPS data. Patients were included in the analysis if they were aged 20 years or older on admission and had a length of stay (LOS) duration shorter than 60 days. A total of 25 043 patients fulfilled these inclusion criteria. The patient selection process is presented in figure 1, and the final sample for analysis comprised 19 926 patients from 261 hospitals. Figure 1 Diagram of patient selection. DPC/PDPS, Diagnosis Procedure Combination Per-Diem Payment System; ICD, International Classification of Diseases; NYHA, New York Heart Association. Statistical analysis The data were analysed using a multivariable logistic regression analysis and linear regression analysis with the dependent variables of in-hospital mortality (dichotomous variable) and hospitalisation costs (continuous variable), respectively. Using these regression models, we examined the factors associated with the target outcome measures. Hospitalisation costs were estimated for each hospitalisation episode using DPC/PDPS points for reimbursements, where each point represents 10 yen.5 6 The predictive abilities of the logistic regression models were assessed using C-statistics, and the predictive abilities of the linear regression models were assessed using R2 values. The independent variables were categorised into three groups: group 1 involved patient characteristics and condition on admission, and included patient age, sex, route of admission (emergency admission with ambulance use, emergency admission without ambulance use or scheduled admission), NYHA class (II, III or IV) and major comorbidities (hypertension, ischaemic heart disease, atrial fibrillation, fatal arrhythmia and shock). These variables were based on those used in a model that we had previously developed.14 Group 2 involved postadmission treatments that may indicate disease severity (hereafter referred to as ‘severity-indicating procedures’), and comprised six disease severity classes based on hierarchical and mutually exclusive combinations of the following: percutaneous cardiopulmonary support (PCPS), intra-aortic balloon pumping (IABP), tracheal intubation and catecholamine use. The six classes were PCPS use (regardless of IABP, intubation or catecholamine use), IABP use (regardless of intubation or catecholamine use), intubation or catecholamine use, intubation (with no catecholamine use), catecholamine use only, and none of the aforementioned treatments. In addition, this group included dialysis-related procedures and blood transfusions as independent variables. Group 3 involved other postadmission high-cost examinations and treatments performed after admission (hereafter referred to as ‘high-cost procedures’), and included percutaneous coronary intervention (PCI), scintigraphy and single-photon emission CT (SPECT). The three groups of independent variables were sequentially incorporated using the forced entry method into model 1 (group 1 variables only), model 2 (groups 1 and 2 variables only) and model 3 (groups 1–3 variables). The impact of each group of independent variables on the models' predictive abilities was examined. When determining the disease severity class of each patient, we preferentially selected the treatment that indicated the highest level of severity and did not allow for duplicates in cases. As high-cost procedures would have little direct relevance to in-hospital mortality, we did not include these factors as independent variables in the logistic regression models. Therefore, we analysed models 1 and 2 for in-hospital mortality, and models 1–3 for hospitalisation costs. Next, predicted in-hospital mortality and hospitalisation costs were divided into quartile classes using the estimates from model 2 of the logistic regression model for in-hospital mortality (which included the most clinically relevant independent variables) and model 3 of the linear regression model for hospitalisation costs (which had the highest predictive ability). Cohen's κ coefficient was used to evaluate the level of agreement between the two sets of quartile classes to avoid random concordance. All statistical analyses were conducted using SPSS software V.19.0J (SPSS, Chicago, Illinois, USA). P values (two-tailed) below 0.05 were considered statistically significant. Final cost estimates were converted to US dollars using the 2010 purchasing power parity rate from the Organisation for Economic Co-operation and Development (OECD) data (US$1.00=111.63 Japanese yen).15 Results Hospital and patient characteristics The 261 acute care hospitals in the study sample had a mean of 353 general care beds (range 30–1106). Among these hospitals, 170 (65%) were accredited as training facilities by the Japanese Circulation Society, and there was a median of 3 cardiologists per facility (range 0–20). The proportions of hospitals according to establishing entity were approximately 18%, 35% and 46% for public, national and private hospitals, respectively. The patient characteristics are summarised in table 1. There was a fairly even distribution between men and women among the 19 926 patients. The mean age was 79 years, indicating that the study sample tended towards an older population. Approximately 82% of the patients were admitted to the emergency department, and 32% had used ambulances. In addition, 71% of patients were admitted with moderate-to-high levels of disease severity (NYHA classes III or IV). The unadjusted in-hospital mortality rate was 8.7%, the mean LOS duration was 19.9 days, and the mean hospitalisation cost was US$8284 per patient. Table 1 Patient characteristics, postadmission procedures and patient outcomes n=19 926 Patient characteristics  Women, n (%) 9884 (49.6)  Age in years, mean±SD 79.0±11.9  Route of admission, n (%)   Emergency with ambulance use 6393 (32.1)   Emergency without ambulance use 9903 (49.7)   Scheduled 3630 (18.2)  New York Heart Association functional class, n (%)   Class II 5796 (29.1)   Class III 7318 (36.7)   Class IV 6812 (34.2)  Comorbidities, n (%)   Hypertension 10 588 (53.1)   Ischaemic heart disease 6120 (30.7)   Atrial fibrillation/flutter 5369 (26.9)   Fatal arrhythmia 385 (1.9)   Shock 321 (1.6) Postadmission treatments indicating disease severity, n (%)  Disease severity classes*   No target treatments provided 16 079 (80.7)   Catecholamine use only 3090 (15.5)   Intubation with no catecholamine use 179 (0.9)   Intubation with catecholamine use 364 (1.8)   Intra-aortic balloon pumping† 194 (1.0)   Percutaneous cardiopulmonary support‡ 20 (0.1)  Dialysis-related procedures 790 (4.0)  Blood transfusion 1340 (6.7) Other postadmission high-cost examinations and treatments, n (%)  Percutaneous coronary intervention 840 (4.2)  Scintigraphy 226 (1.1)  Single-photon emission CT 1840 (9.2) Patient outcomes  In-hospital mortality, n (%) 1725 (8.7)  Length of stay, days (mean; SD; median) 19.9; 12.1; 17.0  Hospitalisation costs (US$) (mean; SD; median) 8284; 7448; 6473 *These six disease-severity classes are based on hierarchical and mutually exclusive combinations. †Regardless of intubation or catecholamine use. ‡Regardless of intra-aortic balloon pumping, intubation or catecholamine use. Moreover, 81% of the patients did not undergo any severity-indicating procedures, and only 4% and 7% of the patients had dialysis-related procedures and blood transfusions, respectively. For other high-cost procedures, 4% of patients underwent PCI and 9% underwent SPECT. Difference in determinants of in-hospital mortality and hospitalisation costs The results of the regression analyses to examine the effects of the independent variables on in-hospital mortality and hospitalisation costs are presented in table 2. In-hospital mortality was analysed using model 1 (group 1 variables only) and model 2 (groups 1 and 2 variables only), whereas hospitalisation costs were also analysed using model 3 (groups 1–3 variables). Table 2 Determinants of in-hospital mortality and hospitalisation costs Dependent variables In-hospital mortality Hospitalisation costs Model 1 Model 2 Model 1 Model 2 Model 3 Independent variables OR Unstandardised coefficients (US$) Patient characteristics  Women (reference category: men) 0.923 1.017 −205 −109 36  Age (reference category: 20–59 years)   60–69 years 1.334 1.307 313 222 250   70–79 years 2.257*** 2.661*** −189 −60 18   80–89 years 4.146*** 6.009*** −1181*** −827*** −454*   ≥90 years 7.086*** 12.751*** −2027*** −1400*** −678**  Route of admission (reference category: scheduled)   Emergency with ambulance use 1.185* 0.891 1201*** 772*** 521***   Emergency without ambulance use 0.916 0.807* 530*** 422** 276*  NYHA functional class (reference category: class II)   Class III 2.121*** 2.011*** 608*** 467*** 494***   Class IV 6.812*** 5.929*** 1121*** 727*** 635***  Comorbidities   Hypertension 0.307*** 0.32*** −200 61 −120   Ischaemic heart disease 0.573*** 0.522*** 1520*** 1484*** 409***   Atrial fibrillation/flutter 0.561*** 0.604*** −145 333** 457***   Fatal arrhythmia 1.637** 1.081 5635*** 4597*** 4547***   Shock 3.058*** 1.189 3335*** −584 −412 Postadmission treatments indicating disease severity  Disease severity classes† (reference category: no target treatments provided)   Catecholamine use only – 5.543*** – 1740*** 1565***   Intubation with no catecholamine use – 12.699*** – 1253* 1162*   Intubation or catecholamine use – 20.966*** – 1681*** 1610***   Intra-aortic balloon pumping‡ – 8.062*** – 22 628*** 13 854***   Percutaneous cardiopulmonary support§ – 42.048*** – 15 421*** 8360***  Dialysis-related procedures – 1.729*** – 2739*** 2117***  Blood transfusion – 1.383** 4186*** 3884*** Other postadmission high-cost examinations and treatments  Percutaneous coronary intervention – – – – 13 842***  Scintigraphy – – – – 3683***  Single-photon emission CT – – – – 3324***  C-statistics (95% CIs) 0.805 0.870 – – – (0.794–0.815) (0.862–0.879)  R2 – – 0.044 0.178 0.320 Model 1: independent variables included only patient characteristics and condition on admission; model 2: independent variables included postadmission treatments that may indicate disease severity in addition to those of model 1; model 3: independent variables included other high-cost examinations and treatments in addition to those of model 2. ***p<0.001; **p<0.01; *p<0.05. †These six disease-severity classes are based on hierarchical and mutually exclusive combinations. ‡Regardless of intubation or catecholamine use. §Regardless of intra-aortic balloon pumping, intubation or catecholamine use. NYHA, New York Heart Association. In the logistic regression analysis for in-hospital mortality, the C-statistics (95% CI) for models 1 and 2 were 0.805 (0.794 to 0.815) and 0.870 (0.862 to 0.879), respectively. Although the inclusion of patient characteristics and condition on admission imparted a fairly high level of predictive ability on in-hospital mortality, the addition of severity-indicating procedures further improved predictive ability. Model 2 identified the following postadmission treatments to be significantly associated with increased in-hospital mortality: catecholamine use only (OR 5.54), intubation (OR 12.70), intubation or catecholamine use (OR 20.97), and PCPS use (OR 42.05). The OR for IABP was relatively lower at 8.06. In addition, dialysis-related procedures (OR 1.73) and blood transfusions (OR 1.38) were also associated with higher in-hospital mortality, although the OR was noticeably lower than that of the disease severity classes. In the linear regression analysis for hospitalisation costs, the R2 values for models 1–3 were 4%, 18% and 32%, respectively. Model 1, which included only patient characteristics and condition on admission, had a low coefficient of determination. However, the inclusion of severity-indicating procedures in model 2 resulted in an approximately fourfold increase in predictive ability, and the further inclusion of high-cost procedures in model 3 increased predictive ability almost eightfold. Model 3 identified the following determinants of hospitalisation costs: emergency admission with ambulance use (increase of approximately US$520), NYHA class III or IV on admission (US$490 and US$640, respectively), fatal arrhythmia (US$4550), ischaemic heart disease (US$410) and atrial fibrillation (US$460). All factors in the severity-indicating procedures and high-cost procedures were also significantly associated with higher hospitalisation costs. In particular, the following had a substantial impact on increasing costs: IABP and PCI use (both associated with an increase of US$13 850); PCPS use (US$8360); blood transfusion, scintigraphy or SPECT (US$3600); and dialysis-related procedures (US$2120). NYHA class and severity-indicating procedures were associated with both increased in-hospital mortality and hospitalisation costs. In contrast, several patient characteristics (age, route of admission and comorbidities) demonstrated conflicting effects on the outcomes. For example, age appeared to be strongly associated with increased mortality but lower costs, whereas ischaemic heart disease was associated with lower mortality but higher costs. Route of admission and fatal arrhythmia were significantly associated with higher hospitalisation costs, but had no effect on in-hospital mortality. Figure 2 shows the association between the unstandardised coefficients of in-hospital mortality risk (model 2) and hospitalisation costs (model 3). Although increased age was generally associated with higher mortality but lower costs, extremely ill patients who required PCPS were associated with higher mortality and lower costs when compared with similar patients who required only IABP. Figure 2 Relationship between the in-hospital mortality (model 2) and the hospitalisation costs (model 3). AF/AFL, atrial fibrillation/atrial flutter; CA, catecholamine; HT, hypertension; IABP, intra-aortic balloon pumping; IHD, ischaemic heart disease; NYHA class, New York Heart Association functional class; PCPS, percutaneous cardiopulmonary support. Poor agreement between the quartile classes of predicted in-hospital mortality and hospitalisation costs Table 3 shows the 4×4 contingency table of the quartile classes of predicted in-hospital mortality (model 2) and predicted hospitalisation costs (model 3). Agreement between the quartile classes of these model 2 outcomes was extremely poor (κ=0.016, p<0.0001), indicating that predicted mortality had no association with predicted hospitalisation costs. Table 3 Poor agreement between the quartile classes of predicted in-hospital mortality and hospitalisation costs   Predicted hospitalisation costs*   Low high n (%) Class 1 Class 2 Class 3 Class 4 Total Predicted in-hospital mortality* Low High Class 1 1243 (6.2) 1415 (7.1) 1209 (6.1) 1014 (5.1) 4881 (24.5) Class 2 1672 (8.4) 1250 (6.3) 1054 (5.3) 1104 (5.5) 5080 (25.5) Class 3 1380 (6.9) 1394 (7.0) 1036 (5.2) 1174 (5.9) 4984 (25.0) Class 4 692 (3.5) 908 (4.6) 1689 (8.5) 1692 (8.5) 4981 (25.0) Total 4987 (25.0) 4967 (24.9) 4988 (25.0) 4984 (25.0) 19 926 (100.0) Numbers in parentheses indicate percentages of the total; Cohen's κ=0.016, p<0.0001. Class1: below the lower quartile; class 2: between the lower quartile and median; class 3: between median and upper quartile; class 4: above upper quartile. *Model 2 of in-hospital mortality and model 3 of hospitalisation costs were used for prediction. Discussion In the present study, we conducted a detailed examination of the determinants of in-hospital mortality and hospitalisation costs in a variety of AHF practice settings. In particular, our analysis found exceedingly poor agreement between the quartile classes of in-hospital mortality and hospitalisation costs, which indicated a lack of a consistent relationship between the outcomes. In table 1, the majority of patients with AHF (approximately 80%) did not undergo any of the target severity-indicating procedures, which is consistent with analyses of global registries in several countries.12 In consideration of this severely ill portion of the population, there is a need to focus on accurately identifying the cost drivers and accounting for the appropriate predictors of hospitalisation costs that are distinct from the predictors of mortality. Therefore, we discriminated severity-indicating procedures which have a greater impact on mortality than on hospitalisation costs and other high-cost procedures which are more important predictors of hospitalisation costs in this study. The combination of patient condition on admission, severity-indicating procedures and high-cost procedures had differing levels of influence on predicting mortality and costs. Specifically, model 2 (involving patient condition on admission and severity-indicating procedures) had a high level of predictive ability for mortality (C-statistic 0.870). For hospitalisation costs, patient condition on admission alone provided a negligible degree of predictive ability (R2 0.044). However, the determinant of coefficient rose considerably with the inclusion of severity-indicating procedures in model 2 (R2 0.178), and rose further with the inclusion of high-cost procedures in model 3 (R2 0.320; table 2). The extremely poor agreement level between the quartile classes of mortality and costs clarified the lack of a consistent relationship between the outcomes, which was beyond our expectations (table 3). Interestingly, our study further revealed that the implicit assumption that severely ill patients will consume more resources is not necessarily true, especially when a patient is extremely ill and close to death; this was similar to the findings of a previous study.7 16 Figure 2 demonstrates that an association between older age and lower hospitalisation costs, which is consistent with an earlier report.13 Moreover, IABP had a relatively lower risk for mortality (OR 8.06) than intubation (OR 12.70), intubation or catecholamine use (OR 20.97), and PCPS use (OR 42.05); however, IABP was associated with the highest additional costs but lowest mortality among them. In contrast, patients who required PCPS demonstrated the highest risk of mortality but lower costs than IABP patients. Continuing life-prolonging care provided in IABP patients was considered to be a major contributing factor of increasing costs compared with PCPS patients who generally die soon. We could learn the differential impact of IABP and PCPS on the two outcomes as a representative example which seemed to be clinically plausible. The implications of our findings are that the same case-mix classifications should not be used to predict both costs and mortality in patients with AHF. Disease-specific case-mix classifications in consolidated DRG or DPC groups would not accurately predict mortality. A previous study has reported that mortality rates for individual DRGs are a poor measure of quality, even under an optimistic assumption of perfect case-mix adjustment.17 In addition, our study demonstrated that the segregation of specific high-cost procedures that indicate disease severity and other high-cost procedures such as PCI, scintigraphy and SPECT may help to explain inpatient mortality and costs more accurately. To the best of our knowledge, this is the first study to investigate both the determinants and inter-relationship between in-hospital mortality and hospitalisation costs in AHF participants across Japan. Previous studies from other countries have reported conflicting results in the relationship between costs and mortality,16 18–20 and some have reported that decreased resource usage leads to worse outcomes.20 This study found that the determinants of mortality and costs for hospitalised patients with AHF were generally different. Accordingly, these findings would support the accurate assessment of resources and quality, which is an important step in the future development and implementation of case-mix classifications. This study had the following limitations. First, the sample hospitals were limited to those that have adopted the DPC/PDPS. Moreover, the inherent differences in healthcare systems among countries may limit the generalisability of our findings. However, this study focused on patients with AHF, and our sample included a wide variety of acute care hospitals. As a result, our analysis may be fairly robust to variations in patient and hospital characteristics. Second, it is difficult to directly compare mortality (a dichotomous variable) and hospitalisation costs (a continuous variable). However, we were able to clarify the differential impact of various disease severity classes and other independent variables available in the clinical setting to each of the outcome measures, and used the κ coefficient to demonstrate the poor agreement between the outcomes. Third, the individual expertise and experience of each physician may induce variations in the provision of high-cost procedures,6 but we were unable to investigate these factors using administrative data. Fourth, the absence of ventricular function data may decrease our ability to assess severity of illness at admission. Finally, the external validity of the study could not be determined, and further research is required to investigate the determinants of outcomes in other acute diseases as well as in other settings including other countries. Conclusions The determinants of in-hospital mortality and hospitalisation costs were generally different in patients with AHF. Our results indicate that the same case-mix classifications should not be used to predict both costs and mortality. Contributors: NS and YI had full access to all the data, were involved in the study design and wrote the manuscript. NS, SK, HI and YI acquired, analysed and interpreted the data, as well as performed the statistical analyses. YI acquired funding for the study. All authors were involved in critical revisions and approval of the final manuscript. Funding: This research was financially supported in part by a Health Sciences Research Grant from the Ministry of Health, Labour and Welfare of Japan (H27-iryo-ippan-001), a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science ([A]16H02634), and a Health, Labour and Welfare Policy Research Grant (H28-seisaku-shitei-009). Competing interests: None declared. Ethics approval: This study was approved by the Ethics Committee of Kyoto University Graduate School and Faculty of Medicine, Japan. Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: No additional data are available. ==== Refs References 1 Cretin S , Worthman LG Alternative systems for case-mix classification in health care financing . R-3457-HCFA. Prepared for the Health Care Financing Administration, U.S Department of Health and Human Services. The RAND Publication Series. ISBN 0-8330-0763-7 http://www.acove.com/content/dam/rand/pubs/reports/2008/R3457.pdf 2 Iezzoni L , ed. Risk adjustment for measuring health care outcomes . 4th edn. Chicago : Health Administration Press , 2003 . 3 Rutledge R , Osler T The ICD-9-based illness severity score: a new model that outperforms both DRG and APR-DRG as predictors of survival and resource utilization . J Trauma 1998 ;45 :791 –9 . 10.1097/00005373-199810000-00032 9783623 4 Kongstvedt PR , Goldfield NI , Plocher DW Using data and provider profiling in medical management . In: Kongstvedt PR , ed. 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==== Front BMJ OpenBMJ OpenbmjopenbmjopenBMJ Open2044-6055BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bmjopen-2016-01397210.1136/bmjopen-2016-013972Global HealthProtocol150616991704170617301701Innovative public–private partnership to target subsidised antimalarials: a study protocol for a cluster randomised controlled trial to evaluate a community intervention in Western Kenya Laktabai Jeremiah 1Lesser Adriane 2Platt Alyssa 23Maffioli Elisa 24Mohanan Manoj 245Menya Diana 6Prudhomme O'Meara Wendy 267Turner Elizabeth L 23 1 Moi School of Medicine, Eldoret, Kenya 2 Duke Global Health Institute, Duke University, Durham, North Carolina, USA 3 Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA 4 Department of Economics, Duke University, Durham, North Carolina, USA 5 Sanford School of Public Policy, Duke University, Durham, North Carolina, USA 6 Moi University School of Public Health, College of Health Sciences, Eldoret, Kenya 7 Division of Infectious Diseases and International Health, Duke University Medical Center, Durham, North Carolina, USACorrespondence to Dr Elizabeth L Turner; liz.turner@duke.edu2017 20 3 2017 7 3 e01397223 8 2016 23 12 2016 12 1 2017 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Introduction There are concerns of inappropriate use of subsidised antimalarials due to the large number of fevers treated in the informal sector with minimal access to diagnostic testing. Targeting antimalarial subsidies to confirmed malaria cases can lead to appropriate, effective therapy. There is evidence that community health volunteers (CHVs) can be trained to safely and correctly use rapid diagnostic tests (RDTs). This study seeks to evaluate the public health impact of targeted antimalarial subsidies delivered through a partnership between CHVs and the private retail sector. Methods and analysis We are conducting a stratified cluster-randomised controlled trial in Western Kenya where 32 community units were randomly assigned to the intervention or control (usual care) arm. In the intervention arm, CHVs offer free RDT testing to febrile individuals and, conditional on a positive test result, a voucher to purchase a WHO-qualified artemisinin combination therapy (ACT) at a reduced fixed price in the retail sector. Study outcomes in individuals with a febrile illness in the previous 4 weeks will be ascertained through population-based cross-sectional household surveys at four time points: baseline, 6, 12 and 18 months postbaseline. The primary outcome is the proportion of fevers that receives a malaria test from any source (CHV or health facility). The main secondary outcome is the proportion of ACTs used by people with a malaria-positive test. Other secondary outcomes include: the proportion of ACTs used by people without a test and adherence to test results. Ethics and dissemination The protocol has been approved by the National Institutes of Health, the Moi University School of Medicine Institutional Research and Ethics Committee and the Duke University Medical Center Institutional Review Board. Findings will be reported on clinicalstrials.gov, in peer-reviewed publications and through stakeholder meetings including those with the Kenyan Ministry of Health. Trial registration number Pre-results, NCT02461628. community health volunteersmalariaantimalarial subsidiesrapid diagnostic testNational Institute of Allergy and Infectious Diseaseshttp://dx.doi.org/10.13039/100000060R01AI110478 ==== Body Strengths and limitations of this study This protocol describes a robust study design: stratified randomised allocation contributes to good internal validity and outcome measures are collected independently of the intervention implementation. The study is conducted in a rural region of Western Kenya and is representative of other malaria-endemic regions in East Africa, which promotes external validity. The public–private partnership intervention evaluated in this study was developed using outcomes from our related pilot study and has cross-sectoral support. The intervention draws on both an existing community health volunteer system and a robust medicine retail sector. In the absence of either element, implementation could be challenging in other settings. Introduction In most malaria-endemic countries, including Kenya, a large fraction of fevers are treated in the informal health sector at retail medicine outlets such as chemists, pharmacists and small, unregulated medicine shops.1–5 These retail outlets are often found in more accessible locations than formal health services, particularly for rural households, but diagnostic testing is uncommon and first-line artemisinin combination therapy (ACT) can be expensive. In the absence of publicly-funded ACT subsidies, fewer than 15% of fevers treated for malaria receive appropriate, effective therapy in the retail sector.6 Starting in 2010, phase 1 of the Global Fund Affordable Medicines Facility—malaria (AMFm) piloted private-sector subsidies for ACTs in eight countries: Cambodia, Ghana, Kenya, Madagascar, Niger, Nigeria, Tanzania (mainland and Zanzibar) and Uganda.7 The pilot led to a drop in the retail prices of ACTs in most pilot countries below that of cheaper, ineffective drugs and substantial cost savings were seen by the end consumer. The retail market share of ACTs in Kenya jumped from 12% to 61% in the first 18 months of the programme.8 Yet the sharp reduction in ACT cost may have encouraged overuse of the drug. Malaria is not the cause of illness in a large proportion (36–77%) of febrile patients seeking care within the retail sector.9 10 In Tanzania, one study determined that the majority (80%) of clients obtaining ACTs from retail shops were not in fact parasitemic, while parasitemic clients purchased ACTs only 69% of the time.11 This points to a clear need to improve targeting of antimalarials purchased in the retail sector. In 2012, the Global Fund revised the AMFm strategy and ended the stand-alone ACT subsidy. Instead, countries could incorporate wholesale drug subsidies into their malaria control portfolios and provide subsidised RDTs to the private sector using Global Fund grants. The Kenyan Ministry of Health continued the private sector subsidy, but not at the same level provided by the AMFm programme. As a result, while ACTs are still sold over the counter and are widely available in the private retail sector, the retail price of ACTs increased approximately threefold.12 13 Appropriate targeting of subsidised ACTs could be improved by adding confirmatory testing to the programme. Using conditional subsidies for ACTs, dependent on a positive diagnostic test, could further enhance targeting and sustainability of the subsidy. The hypothesis is that such a strategy would reduce ACT overuse. In theory, this could be achieved using point-of-care malaria rapid diagnostic tests (RDTs). Evidence from several settings in Africa shows that, with appropriate training and supervision, laypersons can safely and correctly use RDTs for community diagnosis.10 11 14 15 Yet the potential impact of incorporating diagnostic testing into an ACT subsidy programme operating within the retail sector remains largely unknown. To address this evidence gap, we designed a public–private partnership (PPP) intervention to target antimalarials at those with confirmed malaria infection. This protocol paper describes the rationale and design of a cluster randomised controlled trial (c-RCT) in an area of Western Kenya with high malaria transmission. The goal of the c-RCT is to implement the PPP and to evaluate its impact on uptake of malaria testing and drug purchasing behaviour of febrile community members. Aims and objectives The overall objective of this study is to evaluate the public health impact of targeted antimalarial subsidies delivered through a partnership between CHVs and the private retail sector in Kenya. As part of the study, public-health sector CHVs offer and administer free RDTs to children over 1 year and adults with a fever in intervention communities. Conditional on the results of the test, those with a positive result are offered a voucher to purchase a subsidised WHO-qualified ACT at a participating local medicine retailer. The primary hypothesis is that this public–private partnership intervention will increase uptake of malaria testing before treatment and will lead to appropriate drug purchasing decisions after testing. The primary outcome is the proportion of people with fever that receive a malaria test (either RDT or microscopy) from any source (either CHVs or health facilities) at 12 months, which will be compared between the intervention and control arms. Methods and analysis The SPIRIT (Standard Protocol Items for Randomised Trials) recommendations were referenced in developing this protocol. Overall study design The study is being carried out in two subcounties in Western Kenya (Bungoma East and Kiminini) with similar malaria burden but different access to health services (figure 1). We used a stratified c-RCT design to assign all 32 eligible community units (CUs) in the area to either the intervention or control arm. A community unit is an existing administrative unit that averages about 1000 households (∼5000 people). In order to be eligible for the study, a CU had to have an existing system of trained community health volunteers (CHVs) in place. In practice, each eligible CU has ∼20 CHVs and one community health extension worker (CHEW) who supervises the work of the CHVs, although the number of households and active CHVs per CU varies from place to place. Since the structure of community health activities by CU entails CHVs serving only their home CU, CUs were the natural choice for clusters in the c-RCT design: contamination between clusters is expected to be minimal. In the existing structure, CHVs are a volunteer workforce involved in short-term health campaigns and health promotion with no salary from the Ministry of Health. They occasionally receive remuneration for periodic donor-funded activities. Intervention-arm CHVs are reimbursed for study-related travel for supervision and the community-based organisations that they have established receive a small bonus twice per year. Figure 1 Study area and health facility access in selected community units. In the intervention arm, CHVs offer household members free RDT testing and a voucher allowing the purchase of a qualified ACT at a reduced fixed price in the retail sector conditional on a positive test result. Individuals in the control arm receive usual care; they have access to standard CHV visits as per the Kenya Community Strategy Implementation guidelines.16 In practice, usual care means that control arm individuals decide whether they seek treatment in the formal sector (ie, testing and treatment via a health facility) or in the informal sector (ie, private retailers) in which no testing is currently available but where government-subsidised antimalarials are available. Study outcomes and additional data are being collected via population-based cross-sectional household surveys of febrile individuals at four time points: baseline (from June 2015), 6, 12 and 18 months postbaseline. The households are selected randomly and independently of whether they received service from a CHV. Data obtained from these population-based surveys will be used to evaluate the impact of the community intervention. The intervention strategy is summarised in figure 2. Figure 2 Diagram of intervention strategy. CHWs, community health workers. We chose a repeated cross-sectional survey c-RCT design rather than a cohort c-RCT design (in which a cohort of individuals is tracked over time) because we are interested in the population-level effects of the intervention on febrile individuals and the repeated cross-sectional design is a natural choice for such research questions.17 If we had chosen a cohort design, we would have had to recruit both febrile and non-febrile individuals at baseline to be followed over time in order to identify sufficient febrile individuals at each follow-up time point. This would have been prohibitively expensive due to the large required sample size. Interventions The PPP intervention to target antimalarials at those with confirmed malaria infection was developed based on results from an earlier study we conducted in the study region.18 The study was a 2×2 factorial individually randomised trial whose goal was to test the use of a voucher system with a conditional ACT subsidy to improve uptake of testing and to determine the preferred subsidy levels of both the ACT and the RDT.18 We evaluated two levels of RDT prices (free vs US$0.5 cost to the client) and two levels of the conditional ACT subsidy (no subsidy vs US$0.6 subsidy per dose). Findings from that trial indicate that (1) uptake of testing across all sources is improved when offered through CHVs compared to health facilities alone, (2) fewer clients chose to be tested when asked to pay for testing and (3) drug shops sometimes increased the base price of drugs for clients presenting a voucher, effectively reducing or negating the intended discount. On the basis of this evidence, we decided to offer free RDTs and, conditional on a positive result, a voucher for a fixed reduced price ACT rather than a fixed discount for an ACT. Intervention enrolment of the current c-RCT started in late July 2015. As in our earlier study, the RDTs are offered by study-trained CHVs and the ACTs are available for purchase at local retailers. The CareStart Malaria HRP-2 Pf RDT is used for all community-based testing, since it has been shown to have an average sensitivity and specificity of 94.8% and 95.2%, respectively, for Plasmodium falciparum infection when compared to light microscopy.19 In the public component of the PPP intervention, all CHVs in the intervention CUs were invited for training on how to test for malaria using RDTs. The training was based on a validated 3-day Kenya Ministry of Health curriculum in conjunction with practical, skills oriented sessions. They were also taught how to take an axillary temperature using a digital thermometer and to recognise danger signs according to the WHO training module on malaria control case management guidelines.20 Only CHVs who demonstrated competence during the theoretical and practical assessment were invited to participate in the intervention. The study team is monitoring CHVs through supervisory visits, initially every 2 weeks for the first 3 months of implementation, then monthly thereafter. CHVs requiring support are provided additional on-the-job training when necessary. Clients who meet all of the following inclusion criteria are eligible to participate in the intervention (ie, given the opportunity to receive a CHV-administered RDT and conditional voucher): (1) age ≥1 year, (2) self-reported fever or history of fever or feeling unwell with a malaria-like illness within the past 2 days or axillary temperature above 37.5° and, (3) consent from the client or their parent/legal guardian (if under 18 years). See English version of the consent forms in online supplementary material. The exclusion criteria include signs of severe disease or other problems requiring immediate referral to a health facility, or if the client has already visited a health facility or has already taken or purchased antimalarials for the current illness. 10.1136/bmjopen-2016-013972.supp1supplementary material In the intervention arm, the CHV administers an RDT to each eligible participant who presents with a fever or malaria-like symptoms at any point during the 18-month implementation period. All participants receive a paper form with the results of their RDT clearly stated. Those with a negative RDT are advised to visit a health facility with documented test results. The negative RDT cassettes are collected by the study team during supervisory visits. Any individual with danger signs is referred to a facility regardless of RDT results. All of the government health facilities serving the intervention CUs have been sensitised about the study and asked to consider the results of the RDT in clinical management of the patient (ie, treating with ACT if positive, repeating test only when microscopy is available). In the private component of the PPP intervention, a participant who has a positive RDT is given a serialised voucher for a WHO-approved quality-assured ACT to be purchased at a participating drug shop at a reduced fixed price according to the age-specific dosage required (table 1). The voucher is valid for 3 days from the date of issue. The holder may redeem the voucher by providing both the voucher and the positive RDT cassette to the participating medicine outlet to enable verification. Both are collected by the study team in return for payment of the difference between the normal retail price and the voucher price plus 5 Kenya shillings (KES). A woman with a positive RDT who the CHV determines may be pregnant (based on client self-report of last menstrual period) would not be offered a voucher, but would instead receive a referral to a health facility. Table 1 Pricing scheme for voucher holders compared to standard retail prices Age group Average unsubsidised price (KES*) Study-subsidised price for voucher holders (KES*) Adult dose (>15 years) 100–120 40 9–15 years 80 20 3–8 years 50 15 1–2 years 40 10 <1 year – Not eligible *KES: Kenya Shilling; US$1=KES100.8 (Central Bank of Kenya, May 2016). Setting Kiminini subcounty covers an area of 395.3 km² with an estimated population of 231 191, of which 40% is covered by the study. About 18% of the population does not have formal education, and 39.9% lives below the poverty line (County Government of Trans Nzoia, 2013). Bungoma East subcounty covers an area of 404.4 km² with an estimated population of 260 150, of which 35% is covered by the study. About 14% of the population does not have a formal education, and 53% lives below the poverty line (County Government of Bungoma, 2013). Both subcounties have a similar malaria burden, predominantly P. falciparum with perennial transmission. The study population consists of all individuals resident in the 18 eligible community units in Bungoma East subcounty and the 14 eligible community units in Kiminini subcounty. Randomisation The study area is naturally divided into three distinct geographic areas: Bokoli, Ndivisi (both in Bungoma East subcounty) and Kiminini with 8, 10 and 14 CUs, respectively (figure 1). Since four of the CUs in each of Ndivisi and Kiminini have health facilities with laboratories that perform malaria testing, the three areas are then naturally divided into five strata, each of which has an even number of CUs. Stratified randomisation of the 32 CUs within these 5 strata was used in order to reduce the probability of baseline imbalances in the outcomes due to differential access to healthcare. Randomisation was performed by the lead statistician (ELT) using Stata SE V.14.O Software (College Station: Texas: Statcorp LP). Half of the community units within each strata were randomly allocated to the intervention arm so that half of the CUs in each subcounty were in the intervention arm (9 in Bungoma East subcounty and 7 in Kiminini subcounty). Sensitisation and recruitment The CHVs, CHEWs, local health management teams, village elders and chiefs facilitated sensitisation about the intervention. Most households are already familiar with their local CHV and anyone feeling ill with a malaria-like illness is advised to contact the CHV. All retail medicine shops serving the intervention clusters were identified through a comprehensive census and invited to participate. A total of 36 shops have been enrolled across both subcounties. The shop owners and attendants have been trained on current Government of Kenya malaria treatment guidelines,21 the role of RDTs in case management and the study procedures. They are encouraged to stock quality-assured ACTs at the normal government-subsidised price. Prior to participation, the shop owners sign an informed consent and a terms of reference document. Outcomes All study outcomes will be measured in individuals who have experienced a febrile illness in the previous 4 weeks. These outcomes will be ascertained through four cross-sectional household surveys (baseline, 6, 12 and 18 months). While outcome assessors cannot be blinded to the study arm due to the unblinded nature of the trial, data analysts will be blinded to the study arm. The primary outcome is the proportion of fevers that receives a malaria test (either RDT or microscopy) from any source (either CHVs or health facilities). The main secondary outcome is the proportion of all ACTs taken by people with a malaria positive test. Additional secondary outcomes are: the proportion of all ACTs taken by people without a test, the proportion of those with a positive test who get an ACT and the proportion of those with a negative test who get an ACT. We will also compare drug adherence among those who redeem a voucher for their ACT and those who pay the retail price. ‘Appropriate ACT use’ is defined as “taking ACT if positive or not taking ACT if negative among those who took a malaria test” and ‘targeted ACT use’ is defined as “taking ACT if positive or not taking ACT if negative among all participants”.18 Additional data are collected for intervention participants by the CHV who record temperature, participant age and RDT results, as well as client follow-up information (health condition and actions taken) for each participant 4 days after testing. Figure 3 shows the expected behaviour of febrile study participants in the intervention and control arms. We assume that this is the same at each of the three follow-up time points. Figure 3 Expected behaviour and test results of febrile study participants in the intervention and control arms. ACT, artemisinin combination therapy. Survey procedures Study outcomes and additional data will be collected via the four household surveys. Each survey round will take ∼2 months to complete across all 32 CUs. To meet eligibility criteria, respondents must (1) reside in a study CU, (2) be older than 1 year and (3) report a history of a malaria-like illness among at least one household member within the past 4 weeks. Information about children <18 years is obtained by interviewing the parent/guardian. For only one reported fever per household for the previous 4-week period, the survey team records the type and source of any drug(s) taken, and self-reported test results of any diagnostic test for malaria (RDT or microscopy) performed prior to treatment. If there is more than one individual with a history of fever, an adult is selected over a child, or if both are adults or children, selection is based on the alphabetical order of the given names. When made available, relevant drug packaging and test records are reviewed to reduce recall bias. Survey teams also have examples of antimalarial drug packaging to help with identification. Sample size calculation There are two distinct target sample sizes in this study. First, the number of eligible respondents (ie, who report on action taken for fever either by themselves or by a household member); second, the number of individuals (households) surveyed in order to meet the target number of eligible respondents. The target sample size of eligible respondents is 640/arm at each time point (40 in each of the 32 CU, 1280 in total). Power is based on a cluster randomised two-sample, two-tailed t-test for the comparison of two proportions at a single time point using standard formulae, which use the coefficient of variation (CV) as the measure of between-cluster variability.23 To ensure that our overall two-tailed type I error (α) was 5%, we fixed the α level at 1.667% (ie, 5%/3) for each of the three follow-up time points (6, 12 and 18 months postbaseline), using the conservative Bonferroni correction.22 To further protect against other possible losses in power, we conservatively based the power on a matched-cluster design which includes a larger design penalty than the stratified-design sample size calculator for the same assumed level of clustering.23 Assumptions on effect sizes, CV and ICC for the primary and secondary outcomes are shown in table 2. Each CV was estimated using a published strategy:23 CU-specific proportions were assumed to be normally distributed, centred on the control arm proportion with SD derived from an assumed range for 95% of the CU-specific proportions (ie, for a width of ∼4SD). Conservative estimates for the range were used as follows: 50% for the primary outcome and for the proportion of ACTs taken by those with no test; 25% for the proportion of ACTs taken by those with a positive test; and 10% for the proportion that takes ACT after a positive test and for the proportion that takes ACT after a negative test. Using the assumed CVs, the ICC values were then estimated using the following formula: ICC=CV2×π/(1-π), where π is the assumed control-arm proportion for the outcome.23 Table 2 Summary of assumed intervention effects, clustering and power for primary and secondary outcomes Outcome Intervention vs Control CV* ICC* Assumed n per cluster† Power‡ Primary  Proportion of fevers with test§ 70% vs 31% 0.40 0.073 40 98% Secondary  Proportion of ACT taken by those who test positive§ 72.5% vs 36.5% 0.17** 0.017** 10 >99%  Proportion of ACT taken by those with no test§ 16.9% vs 56.6% 0.22 0.064 10 >99%  Proportion who take ACT after a positive test§ 90% vs 70% 0.04** 0.003** 5 NA††  Proportion who take ACT after a negative test§ 10% vs 10% 0.25 0.007 7 NA†† Values in each arm are calculated using the assumed probabilities from pilot data (shown on the branches of figure 3) where 37.4% of intervention and 25.6% of control arm participants are estimated to take ACTs. *Clustering estimated using methods suggested by Hayes and Moulton (2009).23 Details for CV presented in the text with ICC estimated using ICC=CV2×π/(1-π), where π is the assumed control-arm proportion for the outcome. †Set at the minimum of n per intervention arm cluster and n per control arm cluster, derived using assumed probabilities in figure 3 and based on 40 fevers per cluster. ‡Power at each of 3 follow-up time points for a cluster-randomised trial of 16 control CUs vs 16 intervention CUs at an overall 5% type-1 error rate for each outcome with Bonferroni correction for 3 time points. §Assumptions based on pilot data as listed on the branches of figure 3. **Values differ from the IRB protocol because a plausible range of values for the control arm used updated pilot data. ††Not applicable (NA) since assumed to be equal in both arms. ACT, artemisinin combination therapy; CU, community units; CV, coefficient of variation; ICC, intraclass correlation coefficient; IRB, Institutional Review Board. On the basis of pilot data from Bungoma, we hypothesised an increase from 31% in control to 70% in intervention in our primary outcome of uptake of testing (table 2 and figure 3). With a target sample size of 640 eligible respondents per arm (40 in each of the 32 CU, 1280 in total) at each time point and assuming a conservative ICC estimate of 0.073 (corresponding to a CV of 0.40), we will have more than 95% power to detect this hypothesised effect size. On the basis of our earlier work, we assume that the PPP intervention increases both uptake of testing (from 31% to 70%) and the proportion that takes ACT after a positive test (from 70% to 90%), but that there is no effect on the proportion of positive test results (43%) or on ACT purchasing behaviour conditional on a negative test (10% in both arms) or for those with no test (21% in both arms, table 2 and figure 3). On the basis of these assumptions, the assumed effect sizes for the secondary outcomes are derived together with the sample sizes. In particular, it is estimated that the intervention will increase the proportion of ACT taken that is taken by those with a positive test from 36.5% to 72.5% and will decrease the proportion of ACT taken by those with no test from 56.6% to 16.9% with excellent power for both outcomes (table 2). On the basis of our a priori assumptions, the intervention is not expected to have an effect on the two secondary outcomes of ACT purchasing behaviour after a negative test or no test. Overall, 37.4% of intervention participants and 25.6% of control participants are expected to take ACT (figure 3). For example, in the intervention arm, this is based on the 27.1% of all participants who are positive and take ACT, the 4% who are negative and take ACT, and the 6.3% without a test who take ACT. For the second target sample size, in order to obtain the required sample size of 1280 febrile individuals, we assume that we will need to sample a total of 5766 individuals (2883 per arm) at each of the 3 follow-up surveys. This is based on an assumed population 4-week fever prevalence of 22.2%, which we derived assuming: under 5 years 4-week fever prevalence of 35.5% (our preliminary work showed: 33% in Bungoma East and 38% in Kiminini); fever incidence in older children and adults is roughly half of that in under 5 years; and a population mix of 1:3 for under 5 years versus older children and adults. Data management Data about intervention participants and their RDT results are being collected by CHVs using customised carbonless-copy client registers designed to be read and digitised by Captricity for automated data entry. Data are routinely scanned and digitised by field supervisors. The household survey data are collected on encrypted, password protected Android netbooks running the OpenDataKit platform. Internal consistency checks and data quality checks are programmed into the forms as well as evaluated by data managers. When in the field, netbooks are locked in a secure cabinet nightly and data are removed from the tablets several times per week. Only deidentified data are shared with data analysts. On completion of the trial and publication of trial findings, the final trial data set may be requested from the authors. Data analysis All study outcomes are individual-level binary outcome measures. Estimated intervention effects will be reported with 95% CIs. We will compare each of the study outcomes between study arms at each time point. Analyses will be based on the intention-to-treat principle. We will use a mixed effects modelling approach with a random intercept for each CU (to account for clustering) and fixed effects for strata (to account for the stratified design). Models with adjusted and unadjusted estimates will be presented for all analyses. Adjustment covariates will include: age, sex, household size and household socioeconomic status. Sensitivity analyses will be conducted if other relevant covariates are identified. Analyses may be adjusted for the baseline level of the outcome variable in order to improve the precision of estimation of the intervention effect. Owing to the repeated cross-sectional c-RCT design, we will not need to account for missing data due to attrition of study participants. Instead, survey non-response may lead to non-representativeness of the cross-sectional samples. We will describe the non-response rates and compare them between arms. Given our prior experience in the region, non-response is anticipated to be minimal and to be comparable between arms. Autonomy of testing and treatment decisions depends on the age of the febrile individual and potentially other subgroups. We will evaluate each subgroup effect by testing for an interaction effect between the covariate (eg, age) and treatment arm. If there is evidence of different intervention effects by subgroup, we will report results separately for each subgroup. Subanalyses will be performed to determine the sensitivity of results to the precise definition of the outcome. The first set of subanalyses will look at the set of main outcomes using only malaria tests for which documentation was provided to the data collectors. Our primary aim is to determine whether there is a significant difference between the two study arms in the proportion of clients with fever who are tested prior to any treatment after adjusting for relevant covariates at each of the follow-up periods. We will use the Benjamini Hochberg procedure for determining significance of the three tests of the difference between groups at each follow-up time point.24 We will also compare secondary measures using the same modelling and adjustment approach. Process evaluation and monitoring The study team visits participating retail outlets several times each month to monitor availability of ACTs and prices of antimalarials. The team cross-checks redeemed vouchers with the used positive RDTs. To facilitate implementation monitoring, CHVs and retail outlets in the intervention arm routinely provide records, the study team rereads used RDTs, and supervisors conduct unannounced retail outlet visits and observations of CHV testing. A process evaluation plan has also been developed to provide a holistic strategy for monitoring and describing the context of intervention implementation, as well as to provide insight into opportunities for improvement, replication and potential for a further scale up. We have explicitly identified the mechanisms for systematically tracking and summarising the key inputs, activities and outputs of the intervention in order to assess inherent assumptions, penetration, unintended spillover and fidelity of project implementation to the protocol. We have also identified and will develop tools to address information gaps, in particular with regard to gathering information on participating CHVs' and drug shop attendants' perspectives on feasibility, acceptability and motivation related to their intervention roles. Informed consent CHVs obtain written consent from eligible clients before testing. For minors (under 18 years of age), a parent or legal guardian is asked to provide consent, along with verbal assent from minors over the age of 8 years. Household survey respondents provide verbal consent. Shop owners of participating retail medicine outlets are asked to provide written informed consent. For shops that choose not to participate in the voucher scheme, we seek verbal consent to collect only study-related survey data such as stocking and sales of antimalarials. Both the Duke University Medical Center Institutional Review Board and Institutional Research and Ethics Committee ethical approval bodies granted waivers for documentation of informed consent for both the household survey and retail outlet survey participants; verbal consent was sought from potential participants in these activities. Waivers were granted on the grounds that for both activities, the consent document would be the only information linking the respondent to his or her study ID and the surveying presents no more than minimal risk to the client and does not include any procedure for which consent would normally be required outside of the research setting. Trial oversight The trial management committee (TMC) includes all coauthors of this protocol paper, with leadership provided by the study principal investigator, Dr O'Meara. Other specific roles are CHV oversight (Dr Menya), retail sector liaison and oversight (Dr Laktabai), data analysis oversight (Dr Turner), data management and data analysis (Ms Platt), study coordination (Ms Lesser) and advisory roles on economics (Dr Mohanan and Ms Maffioli). The TMC meets weekly to monitor field activities. No data safety and monitoring committee was created since the rapid diagnostic tests administered by CHVs are routinely used in a range of different settings and are considered safe when used by those carefully trained to use them, such as the CHVs in our study. Since no interim analyses are planned and data quality is monitored by the TMC through (blinded) periodic reports, no data monitoring committee was created. Dissemination Findings from this study will be shared through stakeholder meetings including with the Kenyan Ministry of Health, peer-reviewed publications and conference presentations. Results will also be reported through clinicaltrials.gov. The authors thank Dr Indrani Saran for her valuable comments on a draft of this manuscript and Ms Paige Meier for her editorial assistance with references, figures and formatting. Contributors: JL, AL, AP and ELT drafted the manuscript. All authors contributed to the study design. All authors read and approved of the final version of the manuscript. Funding: This work is supported by the National Institutes of Health-National Institute of Allergy and Infectious Diseases (NIH-NIAID) grant number R01AI110478. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Allergy and Infectious Diseases or the National Institutes of Health. The study sponsor had no influence on the study design; data collection, analysis or interpretation; content of the manuscript, nor the authors' decision to submit this manuscript. The researchers operated independently from the funder in these matters. All authors had full access to all data and take responsibility for the integrity and accurate analysis of the data. Competing interests: All authors have completed the ICMJE uniform disclosure form at http://www.icmje.org/coi_disclosure.pdf and declare: all authors had financial support from the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (US) for the submitted work. Ethics approval: Moi University School of Medicine Institutional Research and Ethics Committee (approval number 0001403) and the Duke University Medical Center Institutional Review Board (Pro00063384). The study has also been registered on clinicaltrials.gov (NCT02461628). 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Community Strategy Implementation Guidelines for Managers of the Kenya Essential Package for Health at the Community Level . 2007 https://www.slideshare.net/chskenya/community-health-strategyimplementationguide2007 17 Murray DM Design and analysis of group-randomized trials . Oxford, UK : Oxford University Press , 1998 . 18 Prudhomme O'Meara W , Mohanan M , Laktabai J Assessing the independent and combined effects of subsidies for antimalarials and rapid diagnostic testing on fever management decisions in the retail sector: results from a factorial randomised trial in western Kenya . BMJ Global Health 2016 ;1 :e000101 10.1136/bmjgh-2016-000101 19 Abba K , Deeks JJ , Olliaro P Rapid diagnostic tests for diagnosing uncomplicated P. falciparum malaria in endemic countries . Cochrane Database Syst Rev 2011 ;(7) :Cd008122 10.1002/14651858.CD008122.pub2 21735422 20 World Health Organization . Training module on malaria control: case management . Malta : World Health Organization , 2012 (c). 21 Republic of Kenya Ministry of Health . National guidelines for the diagnosis, treatment and prevention of malaria in Kenya . 4th edn , 2014 . 22 Aickin M , Gensler H Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods . Am J Public Health 1996 ;86 :726 –8 . 10.2105/AJPH.86.5.726 8629727 23 Hayes RJ , Moulton LH Cluster randomised trials . Boca Raton, FL : CRC Press , 2009 . 24 Benjamini Y , Yekutieli D The control of the false discovery rate in multiple testing under dependency . The Ann Stat 2001 ;29 :1165 –88 . 10.1214/aos/1013699998

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==== Front RMD OpenRMD OpenrmdopenrmdopenRMD Open2056-5933BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR 28405473rmdopen-2016-00041010.1136/rmdopen-2016-000410Rheumatoid Arthritis1506Original articlePatient-reported outcomes from a phase III study of baricitinib in patients with conventional synthetic DMARD-refractory rheumatoid arthritis Emery Paul 1Blanco Ricardo 2Maldonado Cocco Jose 3Chen Ying-Chou 4Gaich Carol L 5DeLozier Amy M 5de Bono Stephanie 5Liu Jiajun 5Rooney Terence 5Chang Cecile Hsiao-Chun 5Dougados Maxime 6 1 Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK 2 Department of Rheumatology, Hospital Universitario Marqués de Valdecilla, IDIVAL, Santander, Cantabria, Spain 3 Buenos Aires University School of Medicine, University of Buenos Aires, Buenos Aires, Argentina 4 Division of Rheumatology, Department of Internal Medicine, Chang Gung Memorial Hospital—Kaohsiung Medical Center, Chang Gung University College of Medicine, Kaohsiung, Taiwan 5 Eli Lilly and Company, Indianapolis, Indiana, USA 6 Department of Rheumatology, Hôpital Cochin, Assistance Publique, Hôpitaux de Paris, INSERM (U1151), Clinical Epidemiology and Biostatistics, PRES Sorbonne Paris-Cité, Paris Descartes University, Paris, FranceCorrespondence to Professor Paul Emery; p.emery@leeds.ac.uk2017 21 3 2017 3 1 e00041018 11 2016 24 1 2017 1 3 2017 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Objectives To evaluate the effect of baricitinib on patient-reported outcomes (PROs) in patients with active rheumatoid arthritis (RA) and an inadequate response or intolerance to conventional synthetic disease-modifying antirheumatic drugs. Methods In this phase III study, patients were randomised 1:1:1 to placebo (N=228), baricitinib 2 mg once daily (QD, N=229) or baricitinib 4 mg QD (N=227). PROs included the Health Assessment Questionnaire-Disability Index (HAQ-DI), Patient's Global Assessment of Disease Activity (PtGA), patient's assessment of pain, measures from patient electronic daily diaries (duration and severity of morning joint stiffness (MJS), Worst Tiredness, Worst Joint Pain), Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), SF-36, EuroQol 5-D index scores and visual analogue scales (VAS) and the Work Productivity and Activity Impairment Questionnaire-RA. The primary time point for the study was week 12. Treatment comparisons were assessed with logistic regression for categorical measures and analysis of covariance for continuous variables. Results Statistically significant improvements were observed for both baricitinib groups versus placebo in HAQ-DI, PtGA, pain, daily diary measures, EuroQoL index scores and SF-36 physical component score at week 12 and for those measures when assessed at week 24. Baricitinib 2 mg and baricitinib 4 mg were statistically significantly improved versus placebo for the EuroQoL VAS and FACIT-F, respectively, at week 24. Conclusions Baricitinib 2 or 4 mg provided significant improvement versus placebo in PROs across different domains of RA, including physical function, MJS, fatigue, pain and quality of life. Trial registration number NCT01721057; Results. DMARDs (synthetic)Patient perspectiveRheumatoid Arthritis ==== Body Key messages What is already known about this subject? Patient-reported outcomes are obtained directly from patients, reflecting how they feel and function in relation to rheumatoid arthritis (RA) and to RA therapy; this information may improve the quality of patient care and health-related quality of life, which is an important treatment goal. In the RA-BUILD study, baricitinib (2 or 4 mg once daily) was associated with clinical improvement and inhibition of progressive radiographic joint damage in patients with RA and an inadequate response or intolerance to conventional synthetic disease-modifying antirheumatic drugs. What does this study add? This study describes the patient-reported outcome (PRO) data collected in RA-BUILD and assesses whether the overall efficacy of baricitinib is reflected in clinically meaningful changes in PROs. Once-daily baricitinib 2 or 4 mg produced significant improvements compared with placebo in most of the prespecified PROs, including physical function, fatigue, duration and severity of morning joint stiffness, pain and health-related quality of life; for some PROs, including selected daily diary measures, baricitinib treatment with the 4 mg dose resulted in more rapid improvement compared with the 2 mg dose. How might this impact on clinical practice? The efficacy of baricitinib is not limited to physician-based or laboratory-based assessments, but translates to a positive benefit with respect to patients' health-related quality of life and overall function; the study results suggest that baricitinib is a potentially valuable addition to the rheumatoid arthritis treatment arsenal for patients struggling with this common and disabling condition. Introduction Rheumatoid arthritis (RA) is a chronic, systemic disease associated with inflammatory activity and joint damage that result in disability, pain and other impairments.1–4 Subsequently, RA adversely affects patients' daily activities and health-related quality of life (HRQOL),5–8 and the patient-reported burden of RA is considered an important factor in the management of RA.9 10 Selected patient-reported outcomes (PROs) are incorporated in the American College of Rheumatology (ACR) improvement criteria and composite measures of disease activity, such as the Disease Activity Score (DAS), Clinical Disease Activity Index (CDAI) and Simplified Disease Activity Index (SDAI).11 The information obtained with these measures is useful to assess efficacy, yet is limited in assessing the range of health domains and treatment effectiveness important to patients with RA, such as the HRQOL, fatigue, and well-being in daily life and in work. A number of PRO instruments have therefore been developed to measure the physical, emotional and social burden of RA. Since these PRO measures are obtained directly from the patients and are not influenced by other individuals, they may more accurately reflect how the patient feels and functions in relation to RA and to therapy.12 13 The PRO measures also may facilitate doctor–patient communication and shared decision-making to improve the quality of patient care,14 15 which is an overarching principle recommended by EULAR.16 Baricitinib is a selective inhibitor of Janus kinase (JAK)1/JAK2 that interrupts signalling in pathways for several cytokines considered important in RA pathogenesis.17 In the Phase III RA-BUILD clinical trial, baricitinib (2 or 4 mg once daily (QD)) was associated with clinical improvement and inhibition of progression of radiographic joint damage in patients with RA and an inadequate response (IR) or intolerance to conventional synthetic disease-modifying antirheumatic drugs (csDMARDs).18 The current paper describes the PRO data collected in RA-BUILD and assesses whether the overall efficacy of baricitinib is reflected in clinically meaningful changes in PROs. Patients and methods Patients RA-BUILD (NCT01721057) was a randomised, 24-week, double-blind, placebo-controlled, parallel-group, international phase III study. The primary end point of the study was the proportion of patients achieving an ACR 20% response at week 12 (baricitinib 4 mg vs placebo). Full details of the study have been reported previously.18 Briefly, patients were randomly assigned (1:1:1) to placebo, baricitinib 2 mg or baricitinib 4 mg QD in addition to any stable background therapies. Patients were ≥18 years with active RA (≥6/68 tender and ≥6/66 swollen joints; serum high-sensitivity C-reactive protein ≥3.6 mg/L (upper limit of normal 3.0 mg/L)) and an IR (despite prior therapy) or intolerance to ≥1 csDMARDs. The use of up to two concomitant csDMARDs was permitted, but not required, at study entry; these must have been used for at least the preceding 12 weeks with stable doses for at least the preceding 8 weeks. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and Good Clinical Practice guidelines and was approved by the institutional review board or ethics committee for each centre involved. All patients provided written informed consent. Methods The PROs were prespecified secondary outcomes of the study. Physical function was measured using the Health Assessment Questionnaire-Disability Index (HAQ-DI).19 20 Scores range from 0 to 3, with lower scores reflecting better physical function and, thus, less disability. Changes in the HAQ-DI score were assessed in the context of a minimum clinically important difference (MCID) of 0.22.21 Disease activity and arthritis pain were measured using the Patient's Global Assessment of Disease Activity (PtGA) and patient's assessment of pain visual analogue scales (VAS, 0–100 mm); higher scores indicate more disease activity or pain. Duration of morning joint stiffness (MJS), MJS severity, Worst Tiredness and Worst Joint Pain (referred to as diary PROs) were collected using a daily electronic diary from day 1 through week 12 and were key secondary endpoints. The latter three measures were assessed with numeric rating scales, ranging from 0 to 10, with 10 being the worst level. Fatigue was assessed by the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale (range 0 to 52), with higher scores representing less fatigue.22 For the FACIT-F, a 3–4-point change has been considered an MCID,22–24 and in this study, a value of 3.5623 was used to assess the clinical relevance of changes in FACIT-F scores. Health-related quality of life (HRQOL) was evaluated using the Medical Outcomes Study (MOS) Short-Form-36 (SF-36; V.2, Acute),25 26 in which eight domains are normalised (scored from 0 to 100) and are z-transformed to calculate the physical (PCS) and mental (MCS) component scores. An MCID of 5 was used to assess the clinical relevance of changes in SF-36 component scores.27 28 The EuroQol 5-Dimensions (EQ-5D) Health State Profile was also used to assess HRQOL. The EQ-5D consisted of two components: a descriptive system of the respondent's health and a rating of his/her current health state (0–100 mm VAS), in which the end points are labelled ‘best imaginable health state (100)’ and ‘worst imaginable health state (0)’.29 The UK and US scoring algorithms provide an index score using the UK or US population weighting to normalise it to that population;30–32 index score ranged from 0 (death) to 1 (perfect health). The Work Productivity and Activity Impairment Questionnaire-Rheumatoid Arthritis (WPAI-RA) scale was used to measure the health and symptoms of overall work productivity and impairment of regular activities during the past 7 days. Scores are calculated as impairment percentages with higher percentages indicating greater impairment and less productivity.33 Non-diary PROs were assessed at baseline and at weeks 1, 2, 4 and every 4 weeks thereafter to week 24, with the exceptions of the WPAI-RA (not collected at week 1) and SF-36, EQ-5D and FACIT-F data, which were collected at baseline, week 4 and then every 4 weeks until week 24. Statistical analyses Randomised patients treated with ≥1 dose of the study drug were included in the efficacy analyses under a modified intention-to-treat principle (mITT analysis set). Treatment comparisons for categorical and continuous efficacy measures were performed using logistic regression and analysis of covariance (ANCOVA), respectively, with baseline value (for continuous measures), treatment, geographical region and centrally-confirmed presence of baseline joint erosions in the model. For diary PRO data, the ANCOVA models were based on the average of scores collected in the 7 days prior to the week 12 visit date without baseline adjustment for the duration of MJS and were analysed by the non-parametric method, Wilcoxon rank-sum test. For the ANCOVA model, the least squares means (also considered as estimated marginal means) in each treatment group were derived to estimate the treatment difference. For the non-parametric method, the medians in each treatment group were described. In post hoc analyses evaluating the kinetics of initial effect, daily scores from the day of randomisation (day 1) up to day 28 were also assessed, without weekly averaging. For the daily score analysis, mixed models for repeated measures with treatment, geographical region, centrally confirmed presence of baseline joint erosions (yes/no), study day and the interaction of treatment-by-day as covariates were applied, with duration of MJS, analysed by non-parametric methods using the Wilcoxon rank-sum test. All statistical analyses were conducted using SAS V.9.2 (SAS Institute, Cary, North Carolina, USA). Patients who were rescued or discontinued were defined thereafter as non-responders (non-responder imputation) for all categorical efficacy measures. For continuous measures, the last observations before rescue or discontinuation were carried forward (modified last observation carried forward method). The WPAI-RA measures were censored after rescue or discontinuation without imputation applied. For the ANCOVA analysis for continuous variables, t-tests were performed to test the treatment differences. For the logistic regression analyses for categorical variables, Wald tests were performed to assess treatment comparisons. All analyses were based on a significance level of 0.05 (two-sided). p-Values were not adjusted for multiple comparisons. Results Patients A total of 684 patients were randomised: 228 received placebo, 229 received baricitinib 2 mg and 227 received baricitinib 4 mg. Patient disposition has been described in Dougados et al.18 In summary, baseline patient characteristics and disease activity were similar among the groups (table 1). Overall, 298 (44%), 210 (31%) and 171 (25%) patients had previously received 1, 2 or ≥3 csDMARDs, respectively. Baseline PROs indicated a significant disease burden, which was consistent with the baseline clinical disease activity (table 1). Table 1  Patient characteristics, disease activity and patient-reported outcomes at baseline Placebo (N=228) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=227) Patient characteristics  Age, years 51.4 (13) 52.2 (12) 51.8 (12)  Female patients, n (%) 189 (83) 184 (80) 187 (82)  Duration of RA (time from symptom onset), years 7.2 (8) 7.6 (8) 7.7 (8)  Concomitant corticosteroid use, n (%) 114 (50) 117 (51) 115 (51)  Concomitant MTX use, n (%) 167 (73) 170 (74) 171 (75)  Mean (SD) MTX dose, mg/week 16 (5) 16 (5) 16 (5) Number of prior csDMARDS, n (%)  1 96 (42) 104 (45) 98 (43)  2 81 (36) 61 (27) 68 (30)  ≥3 50 (22) 61 (27) 60 (26) Number of concomitant csDMARDs, n (%)  0 17 (8) 18 (8) 13 (6)  1 150 (66) 145 (63) 151 (67)  MTX 109 (48) 111 (49) 114 (50)  Non-MTX 41 (18) 34 (15) 37 (16)  2 55 (24) 58 (25) 57 (25)  MTX + non-MTX 52 (23) 51 (22) 51 (23)  2 non-MTX 3 (1) 7 (3) 6 (3)  ≥3 6 (3) 8 (4) 6 (3) Disease activity  Swollen joint count, of 66 13 (7) 14 (9) 14 (7)  Tender joint count, of 68 24 (15) 24 (14) 24 (14)  hsCRP, mg/L 18 (20) 18 (22) 14 (15)  ESR, mm/hour 44 (25) 44 (23) 41 (24)  DAS28-hsCRP 5.5 (0.9) 5.6 (1.0) 5.6 (0.9)  SDAI 37 (12) 38 (13) 38 (12)  CDAI 35 (12) 37 (13) 36 (12) Patient-reported outcome measures  Health Assessment Questionnaire-Disability Index (HAQ-DI, 0–3) 1.50 (0.60) 1.51 (0.62) 1.55 (0.60)  Patient's Global Assessment of Disease Activity (0–100 VAS) 60 (21) 62 (20) 60 (22)  Patient's Assessment of Pain (0–100 VAS) 57 (23) 60 (21) 57 (22)  Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F, 0–52) 26.6 (11.1) 26.6 (11.5) 27.3 (11.1) Short Form 36 (SF-36)  Physical component score (PCS) 32.2 (8.5) 32.5 (8.4) 32.2 (8.1)  Mental component score (MCS) 45.7 (11.5) 45.0 (11.5) 46.3 (12.3) European Quality of Life-5 Dimensions-5 Level (EQ-5D)  Health State Index Score, UK algorithm 0.543 (0.214) 0.507 (0.249) 0.516 (0.236)  VAS (0–100) 51.6 (19.7) 53.1 (20.5) 52.8 (20.0) Data displayed are mean (SD), unless otherwise stated. CDAI, Clinical Disease Activity Index; csDMARDs, conventional synthetic disease-modifying antirheumatic drugs; DAS, Disease Activity Score modified to include the 28 diarthrodial joint count; ESR, erythrocyte sedimentation rate; hsCRP, high-sensitivity C reactive protein; MTX, methotrexate; SDAI, Simplified Disease Activity Index; VAS, visual analogue scale. Patient-reported outcomes HAQ-DI, PtGA and pain As reported in Dougados et al,18 statistically significant improvements compared with placebo were observed at week 12 for HAQ-DI, PtGA and pain. Furthermore, for baricitinib 4 mg versus placebo, statistically significant improvements were evident as early as week 1 for HAQ-DI (p≤0.05 for all time points), PtGA (p≤0.01 for all time points) and patient's assessment of pain (p≤0.05 for all time points). For baricitinib 2 mg, statistically significant improvements versus placebo were observed as early as week 8 for HAQ-DI (p=0.001 for time points after week 8), week 2 for PtGA (p≤0.05 for time points after week 2) and week 4 for patient's assessment of pain (p≤0.01 for time points after week 4). Significant improvements in physical function and reductions in PtGA and pain were maintained at weeks 12 and 24 (table 3). The proportion of patients who met or exceeded the MCID for HAQ-DI was 54%, 69% and 64% for placebo, baricitinib 2 mg and baricitinib 4 mg, respectively (p=0.001 for baricitinib 2 mg vs placebo; p=0.027 for 4 mg vs placebo) at week 12 and was 42%, 64% and 60% at week 24 (p=0.001 for both baricitinib groups vs placebo). Dougados et al18 also assessed treatment effect based on background csDMARD use. A subgroup analysis with the outcome measure, change from baseline in HAQ-DI, suggested no heterogeneity of treatment effect based on background csDMARD therapy (see online supplementary table S1). 10.1136/rmdopen-2016-000410.supp1supplementary data Diary PROs: duration of MJS, MJS severity, Worst Tiredness and Worst Joint Pain At week 12, reductions were observed in duration and severity of MJS (table 2; for duration and severity: p=0.002 for baricitinib 2 mg vs placebo, p=0.001 for baricitinib 4 mg vs placebo), Worst Tiredness (p=0.049 for baricitinib 2 mg vs placebo, p=0.027 for baricitinib 4 mg vs placebo) and Worst Joint Pain (p=0.001 for both baricitinib groups vs placebo). Table 2 Day 1, week 1 and week 12 data from patient daily diaries Diary PRO measures Day 1 Median (IQR) Week 1 Median (IQR) Week 12 Median (IQR) Placebo (N=228) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=227) Placebo (N=228) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=227) Placebo (N=228) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=227) Duration of morning joint stiffness, min 60.0 (30.0, 180.0) 80.0 (30.0, 180.0) 75.0 (30.0, 180.0) 84.0 (36.8, 195.0)  80.0 (36.7, 157.5) 80.0 (32.9, 188.6) 60.0 (22.9, 162.9) 44.4** (9.1, 120.0) 34.6*** (7.4, 96.4) Day 1 Mean (SD) Week 1 LSM (95% CI) Week 12 LSM (95% CI) Severity of morning joint stiffness 5.5 (2.1) 5.5 (2.1) 5.3 (2.1) 5.1 (5.0 to 5.3) 5.0 (4.8 to 5.2) 4.9* (4.7 to 5.1) 4.1 (3.8 to 4.4) 3.5** (3.2 to 3.8) 3.4*** (3.1 to 3.7) Worst Tiredness 5.8 (2.0) 5.7 (2.3) 5.7 (2.2) 5.2 (5.0 to 5.4) 5.2 (5.0 to 5.4) 5.0 (4.9 to 5.2) 4.5 (4.2 to 4.8) 4.1* (3.8 to 4.4) 4.0* (3.7 to 4.4) Worst Joint Pain 5.8 (2.0) 5.9 (2.2) 5.7 (2.0) 5.6 (5.4 to 5.8) 5.3 (5.2 to 5.5) 5.2** (5.0 to 5.4) 4.7 (4.4 to 5.0) 3.8*** (3.5 to 4.1) 3.8*** (3.5 to 4.2) *p≤0.05; **p≤0.01; ***p≤0.001. LSM, least-squares mean. With baricitinib 4 mg versus placebo, a statistically significant reduction in the severity of MJS (p≤0.05) and in Worst Joint Pain (p≤0.01) was first seen at week 1, in Worst Tiredness at week 2 (p≤0.05) and in duration of MJS at week 4 (p≤0.05).18 With baricitinib 2 mg, the statistically significant (p≤0.05) reductions were first observed at week 8 for the duration and severity of MJS, at week 12 for Worst Tiredness and at week 2 for Worst Joint Pain.18 Consistent with the weekly averaged data, the daily diary scores showed significant improvement in patients receiving baricitinib 2 or 4 mg compared with placebo. The greatest rapidity and magnitude of benefit was observed with baricitinib 4 mg, with improvements observed as early as day 3 for Worst Tiredness, day 4 for the severity of MJS and Worst Joint Pain and day 10 for the duration of MJS (see online supplementary figure S1). Functional Assessment of Chronic Illness Therapy-Fatigue Treatment with baricitinib 4 mg was associated with significant improvement in FACIT-F at week 24 (figure 1). The improvements in the FACIT-F score were observed at week 4, the first assessment of the measure (figure 1). Numeric improvements in FACIT-F were observed for baricitinib 2 mg versus placebo, but a statistically significant improvement was only observed at week 20. Figure 1 Change from baseline for Functional Assessment of Chronic Illness Therapy-Fatigue. For FACIT-F, the proportion of patients who met or exceeded the MCID was 59%, 63% and 65% for placebo, baricitinib 2 mg and baricitinib 4 mg, respectively, at week 12 (p=0.323 for baricitinib 2 mg vs placebo and p=0.209 for baricitinib 4 mg vs placebo), and was 43%, 59% and 60% at week 24 (p=0.001 for both baricitinib groups vs placebo). Health-related quality of life Short Form 36 Patients treated with baricitinib 2 or 4 mg reported statistically significant improvements compared with placebo in most of the 8 SF-36 domains at weeks 12 and 24. The values for the social functioning, role emotional and the mental health domains improved for the baricitinib treatment groups, but differences from placebo were not statistically significant (see online supplementary table S2). Compared with placebo-treated patients, patients in both baricitinib treatment groups reported statistically significantly improved values for the SF-36 PCS (figure 2A), from the first postbaseline assessment at week 4 and maintained through week 24. At week 12, the proportion of patients who met or exceeded the MCID for placebo, baricitinib 2 mg and baricitinib 4 mg, respectively, was 40%, 57% and 53% (p=0.001 for baricitinib 2 mg vs placebo; p=0.006 for baricitinib 4 mg vs placebo) and was 34%, 56% and 56% at week 24 (p=0.001 for both baricitinib groups vs placebo). For the SF-36 MCS measure, numeric, but not statistically significant, differences in the change from baseline were found between the baricitinib-treated groups versus placebo. The proportion of patients who met or exceeded the MCID was not statistically significantly different from placebo for either baricitinib treatment group (figure 2B). Figure 2 Change from baseline for the physical and mental component score for the SF-36. (A) Physical component score. (B) Mental component score. EuroQol 5-Dimensions At weeks 12 and 24, statistically significant improvement in the EQ-5D UK index score was observed for both baricitinib treatment groups versus placebo (table 3). A statistically significant improvement in the EQ-5D UK index score was observed at the first postbaseline assessment, week 4, for baricitinib 4 mg versus placebo, but not for baricitinib 2 mg versus placebo (data not shown). Table 3 Least-squares mean change from baseline at 12 and 24 weeks for patient-reported outcomes PRO measures Week 12 LSM (95% CI) Week 24 LSM (95% CI) Placebo (N=228) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=227) Placebo (N=228) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=227) Physical function (HAQ-DI) −0.36 (−0.43 to −0.29) −0.57*** (−0.64 to −0.50) −0.56*** (−0.63 to −0.48) −0.38 (−0.46 to −0.30) −0.62*** (−0.70 to −0.54) −0.62*** (−0.70 to −0.54) Patient's Global Assessment of Disease Activity (PtGA) −16.8 (−20.0 to −13.6) −25.3*** (−28.5 to −22.2) −25.8*** (−29.1 to −22.6) −18.8 (−22.0 to −15.6) −27.6*** (−30.8 to −24.5) −29.1*** (−32.4 to −25.9) Patient's Assessment of Pain −15.6 (−18.9 to −12.3) −25.4*** (−28.6 to −22.2) −23.4*** (−26.7 to −20.1) −19.6 (−22.9 to −16.3) −27.4*** (−30.6 to −24.2) −27.9*** (−31.2 to −24.6) EuroQol-5-Dimensions (EQ-5D-5L)  Health State Index Score  UK algorithm 0.092 (0.066 to 0.119) 0.165*** (0.139 to 0.191) 0.162*** (0.135 to 0.189) 0.091 (0.063 to 0.119) 0.157*** (0.130 to 0.184) 0.186*** (0.158 to 0.215)  US algorithm 0.066 (0.048 to 0.085) 0.117*** (0.099 to 0.135) 0.112*** (0.093 to 0.131) 0.062 (0.042 to 0.082) 0.111*** (0.092 to 0.130) 0.131*** (0.111 to 0.151) VAS 4.5 (1.7 to 7.4) 13.5*** (10.7 to 16.2) 11.3*** (8.4 to 14.1) 7.9 (4.7 to 11.1) 13.9** (10.8 to 17.0) 11.0 (7.8 to 14.2) **p≤0.01, ***p≤0.001 vs placebo. LSM, least-squares mean; VAS, visual analogue scale. For the EQ-5D VAS, at week 12, statistically significant improvement in the EQ-5D VAS was observed for both baricitinib treatment groups versus placebo (p=0.001 for both baricitinib groups vs placebo); this was maintained through week 24 for the 2 mg group (p=0.005 vs placebo) but not for the 4 mg group (p=0.159 vs placebo) (table 3). Baricitinib-treated patients were not significantly different from placebo-treated patients at week 4, the first data assessment (data not shown). Similar results were observed with the US algorithm for the EQ-5D (table 3). Work productivity and activity impairment At baseline, only 34–40% of the patients were employed (table 4). Patients treated with baricitinib 2 or 4 mg reported statistically significantly improved regular activity compared with placebo-treated patients at week 12 (p=0.004 for baricitinib 2 mg vs placebo and p=0.003 for baricitinib 4 mg vs placebo) but not at week 24 for either group (p=0.156 for baricitinib 2 mg vs placebo and p=0.179 for baricitinib 4 mg vs placebo; table 4). Among those patients who were employed at baseline and those who maintained employment at weeks 12 or 24, statistically significant improvements with respect to presenteeism for baricitinib 2 mg and work productivity loss for baricitinib 2 mg and 4 mg were seen compared with placebo at week 12 but not at week 24 (table 4). No statistically significant differences between groups were seen for absenteeism. Table 4 Work Productivity and Activity Impairment Questionnaire-Rheumatoid Arthritis (WPAI-RA) at baseline and at 12 and 24 weeks WPAI-RA Baseline, mean (SD) Week 12, LSM (95% CI) change from baseline Week 24, LSM (95% CI) change from baseline Question administered to all patients Placebo (N=225) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=226) Placebo (N=206) Baricitinib 2 mg (N=222) Baricitinib 4 mg (N=213) Placebo (N=141) Baricitinib 2 mg (N=187) Baricitinib 4 mg (N=187) Mean (SD) daily activity impairment due to RA administered to all patients at baseline and LSM (95% CI) change from baseline at week 12 or 24  Percent activity impairment due to RA 57 (23) 58 (25) 56 (23) −13 (−16 to −10) −19** (−23 to −16) −20** (−23 to −16) −20 (−23 to −16) −23 (−27 to −20) −23 (−26 to −19) Number (%) of patients who were employed at baseline and at baseline and week 12 or 24 Questions administered to patients who were employed Placebo (N=225) Baricitinib 2 mg (N=229) Baricitinib 4 mg (N=226) Placebo (N=83) Baricitinib 2 mg (N=85) Baricitinib 4 mg (N=73) Placebo (N=55) Baricitinib 2 mg (N=73) Baricitinib 4 mg (N=66)  Employed at time point and baseline, n (%) of patients 90 (40) 88 (38) 76 (34) 78 (94) 80 (94) 70 (96) 48 (87) 67 (92) 61 (92) Mean (SD) presenteeism, work productivity loss, and absenteeism at baseline and LSM (95% CI) change from baseline at week 12 or 24  Percentage impairment while working due to RA (presenteeism) 43 (24) 46 (26) 42 (24) −8 (−13 to −2) −14 (−20 to −8) −16* (−22 to −11) −18 (−25 to −12) −20 (−26 to −15) −22 (−27 to −16)  Percentage of overall work impairment due to RA (work productivity loss) 45 (25) 51 (28) 44 (25) −3 (−9 to 3) −13* (−20 to −7) −11* (−18 to −5) −18 (−25 to −11) −18 (−24 to −12) −19 (−26 to −13)  Percentage of work time missed due to RA (absenteeism)  8 (20) 19 (30)  9 (23) 4 (−1 to 9) −1 (−6 to 4) 2 (−3 to 7) −4 (−10 to 3) 1 (−4 to 7) 1 (−5 to 6) *p≤0.05, **p≤0.01 vs placebo. Discussion In this analysis of the Phase III RA-BUILD trial, baseline PROs revealed long duration (∼90 min) and severity of MJS, severe impairment of physical function and high levels of pain and fatigue (including tiredness) among patients with RA and an IR to csDMARDs, who had not previously been treated with a biological DMARD. Once-daily baricitinib 2 or 4 mg produced significant improvements compared with placebo in most of the prespecified PROs, including physical function, fatigue, duration and severity of MJS, pain and HRQOL. For some PROs, including selected daily diary measures, treatment with the 4 mg dose resulted in more rapid improvement compared with the 2 mg dose. The PROs demonstrated a rapid onset of action for baricitinib; statistically significant improvements were seen as early as week 1 in the HAQ-DI, PtGA and the patient assessment of pain, and were maintained until the end of the trial at week 24. Similar results were seen with severity of MJS, Worst Tiredness, Worst Joint Pain as assessed using the patient diaries,18 and the PROs continued to improve to week 12. Consistent with these results, patients treated with baricitinib 2 or 4 mg reported greater improvement in HRQOL, as measured by the EQ-5D and SF-36 PCS, compared with placebo-treated patients. For the SF-36 component scores, a change from baseline of either 2.5 or 5 is typically considered an MCID.27 28 This analysis applied the higher threshold for the MCID and found that approximately half of the baricitinib-treated patients reported scores that met or exceeded the MCID for the SF-36 PCS (figure 2A). In contrast with the SF-36 PCS, no statistically significant differences were observed between baricitinib-treated and placebo-treated patients with the SF-36 MCS. Across treatment groups at baseline, the SF-36 MCS values ranged from 45 to 46, which are close to 50, the population norm.27 This suggests limited impairment for the SF-36 MCS at baseline; consequently, a marked improvement with therapy would not have been expected. The SF-36 MCS result aligns with previous results from other trials of baricitinib.34–36 The WPAI-RA data are difficult to interpret, as the number of patients who were employed at baseline and employed throughout the trial was small, and the 24-week duration is most likely a relevant limitation for this measure. A study with a longer duration and a focus on work productivity may provide more definitive evidence regarding the potential treatment effects of baricitinib on work productivity. The results from this analysis are similar to those observed in three other phase III randomised clinical trials of baricitinib in different patient populations.34–36 Additionally, the improvements in the PROs with baricitinib in the current analysis were directionally consistent with the results reported with approved biological or targeted synthetic DMARD therapies in analogous patient populations.37–40 The limitations of this analysis include the use of carrying forward the last observations before rescue or discontinuation as a method of imputation for some continuous measures. Although an appropriate method of handling such data, this method assumes that the PRO values would not change over time if these events had not occurred, an unverifiable assumption. Most patients (89%), however, completed the trial;18 sensitivity analyses assessing missing data (data not shown) suggest that the study results would not be influenced by this data handling method. Additionally, this is a short-term study of 24 weeks, which does not allow for an assessment of the long-term treatment effects of baricitinib. This may particularly affect work productivity assessments. The employment status of patients in this study did not improve significantly, most likely because of the short duration of the trial; patients with disease-associated unemployment are unlikely to recover, then search for and find new work within 6 months. Furthermore, patients from 22 countries participated in the trial; the different rates of unemployment and policies related to workplace accommodations for RA may have influenced a patient's employment status. This study has used a number of established PRO measures that assess outcomes of interest for patients. Some measures are incorporated in the ACR core set measures, and others, such as the EQ-5D and SF-36, are commonly used HRQOL instruments that may more broadly assess the effects of RA and its treatment on patients. Information from PROs may help the healthcare provider and patient determine the optimal treatment plan for the patient by (1) identifying the onset of action for when the patient will feel an improvement in symptoms, (2) evaluating the length of time the patient should try the treatment before determining that it is ineffective and (3) assessing if the treatment is still efficacious after initial improvement. Observing positive changes in PROs may help the patient adhere to therapy, an important element of treatment effectiveness. Additionally, the inclusion of PRO measures in clinical trials also helps to compare results across studies and patient populations and to assess the broader societal impact of RA and treatment for it. In conclusion, the RA-BUILD study demonstrated that treatment with baricitinib 2 or 4 mg provided a significant improvement compared with placebo in most PROs across different domains of RA, including physical function, MJS, fatigue, pain and HRQOL. Improvements tended to occur rapidly, most notably for the 4 mg dose, and were maintained throughout the 24-week trial. These results confirm that the efficacy of baricitinib is not limited to physician-based or laboratory-based assessments, but that efficacy translates to a positive benefit on patients' quality of life and overall function. Consistent with prior observations, these data support a conclusion that baricitinib is a potentially valuable addition to the RA treatment arsenal for patients struggling with this common and disabling condition. This study and manuscript were sponsored by Eli Lilly and Incyte Corporation. The authors would like to thank Molly Tomlin, MS, of Eli Lilly and Company for her assistance with manuscript preparation and process support and Scott Beattie, PhD, for statistical support. Contributors: All authors have made substantial contributions to the intellectual content of the manuscript. Specifically, these authors contributed to the acquisition of the study data: PE, RB, JMC, Y-CC, SdB, TR and MD. These authors participated in the conception of the study: PE, MD, SdB, TR, CLG and AMD. These authors analysed the data for the study: JL. All authors assisted in the interpretation of the data for the paper, provided critical revision of the paper and gave final approval for the paper's submission. Funding: Eli Lilly and Company and Incyte Corporation. Competing interests: PE has received grant/research support or consulting support from Abbott, AbbVie, Bristol Myers Squibb, Eli Lilly and Company, MSD, Novartis, Pfizer, Roche, Samsung, Takeda and UCB. RB has received research grants and/or participated in advisory boards from Abbvie, BMS, Janssen, Novartis, Pfizer, Lilly, MSD and Roche. JMC has received research grants and speaker fees from Abbott (AbbVie), Bristol Myers Squibb, Boehringer Ingelheim, Eli-Lilly, Merck Sharp Dohme, Novartis, Pfizer, Roche, Sanofi-Aventis, Schering-Plough and UCB. Y-CC has received speaker's bureau fees and/or grant research support from AbbVie, Bristol Myers Squibb, Eli Lilly and Company and Pfizer. CLG, AMD, SdB, JL, TR and CH-CC are full-time employees of Eli Lilly and Company and may own stock or stock options in Eli Lilly and Company. MD has received grant/research support or consulting support from AbbVie, Bristol Myers Squibb, Eli Lilly and Company, Novartis, Pfizer, Roche, Sanofi and UCB. Ethics approval: The Ethics Committee from each participating centre approved the study. Provenance and peer review: Not commissioned; externally peer reviewed. Data sharing statement: Additional data may be available on request from the study sponsors. ==== Refs References 1 Carr A , Hewlett S , Hughes R Rheumatology outcomes: the patient's perspective . J Rheumatol 2003 ;30 :880 –3 .12672221 2 Klarenbeek NB , Güler-Yüksel M , van der Kooij SM The impact of four dynamic, goal-steered treatment strategies on the 5-year outcomes of rheumatoid arthritis patients in the BeSt study . Ann Rheum Dis 2011 ;70 :1039 –46 . 10.1136/ard.2010.141234 21415052 3 Strand V , Cohen S , Crawford B Patient-reported outcomes better discriminate active treatment from placebo in randomized controlled trials in rheumatoid arthritis . Rheumatology (Oxford) 2004 ;43 :640 –7 . 10.1093/rheumatology/keh140 15039493 4 Taylor PC , Moore A , Vasilescu R A structured literature review of the burden of illness and unmet needs in patients with rheumatoid arthritis: a current perspective . 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Arthritis Rheum 2004 ;50 :1400 –11 . 10.1002/art.20217 15146409 38 Kremer JM , Genant HK , Moreland LW Effects of abatacept in patients with methotrexate-resistant active rheumatoid arthritis: a randomized trial . Ann Intern Med 2006 ;144 : 865 –76 . 10.7326/0003-4819-144-12-200606200-00003 16785475 39 Emery P , Deodhar A , Rigby WF Efficacy and safety of different doses and retreatment of rituximab: a randomised, placebo-controlled trial in patients who are biological naïve with active rheumatoid arthritis and an inadequate response to methotrexate (Study Evaluating Rituximab's Efficacy in MTX iNadequate rEsponders (SERENE)) . Ann Rheum Dis 2010 ;69 :1629 –35 . 10.1136/ard.2009.119933 20488885 40 Strand V , Kremer JM , Gruben D Tofacitinib in combination with conventional DMARDs in patients with active rheumatoid arthritis: PROs from a phase 3 randomized controlled trial . Arthritis Care Res (Hoboken) 2016 . doi:10.1002/acr. 23004 10.1002/acr.23004

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==== Front BMJ OpenBMJ OpenbmjopenbmjopenBMJ Open2044-6055BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bmjopen-2016-01362010.1136/bmjopen-2016-013620Public HealthResearch15061724170317241699Disparities between research attention and burden in liver diseases: implications on uneven advances in pharmacological therapies in Europe and the USA Ndugga Nambi 1Lightbourne Teisha G 2Javaherian Kavon 2http://orcid.org/0000-0003-0012-484XCabezas Joaquin 1Verma Neha 2Barritt A Sidney IV1Bataller Ramon 1 1 Divisions of Gastroenterology and Hepatology, Chapel Hill, North Carolina, USA 2 Biochemistry, Departments of Medicine and Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USACorrespondence to Ramon Bataller; bataller@med.unc.edu2017 22 3 2017 7 3 e01362029 7 2016 5 1 2017 2 2 2017 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Objectives Effective oral therapies for hepatitis B and C have recently been developed, while there are no approved pharmacological therapies for alcoholic and non-alcoholic fatty liver diseases (ALD and NAFLD). We hypothesise that fewer advances in fatty liver diseases could be related to disparities in research attention. Methods We developed the Attention-to-Burden Index (ABI) that compares the research activities during 2010–2014, and an estimate of disease burden of these 4 major liver diseases. The resulting ratio reflects either overattention (positive value) or inadequate attention (negative value) compared with disease burden. The mean research attention and disease burden were calculated from 5 and 6 different parameters, respectively. The efficacy rate of current pharmacological therapies was assessed from published clinical trials. Findings The mean research attention for hepatitis B and C was 31% and 47%, respectively, while NAFLD and ALD received 17% and 5%. The overall burden was 5% and 28% for hepatitis B and C, and 17% and 50% for NAFLD and ALD. The calculated ABI for hepatitis B and C revealed a +6.7-fold and +1.7-fold overattention, respectively. NAFLD received an appropriate attention compared with its burden, while ALD received marked inadequate attention of −9.7-fold. The efficacy rate of current pharmacological agents was 72% for hepatitis B, 89% for hepatitis C, 25% for non-alcoholic steatohepatitis and 13% for alcoholic hepatitis. Importantly, we found a positive correlation between the mean attention and the efficacy rate of current therapies in these 4 major liver diseases. Interpretation There are important disparities between research attention and disease burden among the major liver diseases. While viral hepatitis has received considerable attention, there is a marked inadequate attention to ALD. There is a critical need to increase awareness of ALD in the liver research community. alcoholic liver diseaseHepatitis C virusHepatitis B virusNon-alcoholic fatty liver diseasePUBLIC HEALTH ==== Body Strengths and limitations of this study This is the first comprehensive systematic study assessing the relative attention to the major liver diseases. This study develops a novel tool to estimate the ratio between the research attention and burden of major liver disease (ie, the Attention-to-Burden Index or ABI). We found a strong correlation between the efficacy of current pharmacological therapies and the degree of research attention devoted to each major liver disease, which strongly suggests the need to reallocate more research resources to alcoholic liver disease. The global burden of the major liver disease is largely unknown and we used an array of different variables. The efficacy of pharmacological therapies for viral hepatitis can be easily calculated based on viral parameters, while the parameters to evaluate the resolution of fatty liver diseases are not well established. Introduction Liver diseases are a major cause of morbidity and mortality.1 The main causes include chronic viral hepatitis B (HBV) and C (HCV), as well as alcoholic and non-alcoholic fatty liver diseases (ALD and NAFLD, respectively). The role of these causes on the burden of liver disease depends on disease severity and on geographical factors. In global north countries such as France, fatty liver diseases (NAFLD, ALD or its combination) account for most cases of liver fibrosis while HBV is highly prevalent in Asian countries.2 3 Regarding advanced liver disease, ALD is the main cause of cirrhosis worldwide, accounting for 50% of the cases.4 In Europe and the USA, stark increases in morbidity due to alcohol-associated liver disease have been observed in the past 15 years.5 6 Importantly, the role of NAFLD as a cause of advanced liver disease and hepatocellular carcinoma has markedly increased in many countries due to the epidemics of diabetes and obesity.7 Major advances have been made in the management of viral hepatitis, such as vaccines and oral therapies for HBV, and oral regimes for HCV.8 9 In the past decade, there has been an increased focus on NAFLD, with studies identifying targeted therapies and subsequent clinical trials.10 11 In comparison to the advances, ALD has been afforded scant attention, with few advances in its management.12 Patients with ALD are mostly identified at late stages of the disease, and programmes for early detection are scarce. Moreover, the genetic and environmental factors are largely unknown. We hypothesised that differences in advancement between viral hepatitis and fatty liver diseases, in particular ALD, are related to varying allocations of research resources. There are no systematic studies assessing the relative research on different aetiologies of liver disease, or how commensurate research attention is with disease burden. The current study was undertaken to fill this gap. Methods General design For this systematic analysis, data on the research attention were obtained at multiple levels for the four main liver disease aetiologies, carried out during the years 2010–2014, and analysed during 2015. The aetiologies categorised were HCV, HBV, NAFLD and ALD, while data on less prevalent liver diseases were considered but not included in the final analysis. A quantitative scoring system was used to define and subsequently measure research attention to liver disease aetiology. When a single cause of liver disease was identified in a presentation, grant, clinical trial or scientific publication, full points (1.0 point) were allocated to the disease. When two causes of liver disease were identified, points were equally shared (0.5 point each). When the focus of a study could be attributed to more than two causes of liver disease or the study investigated a common mechanism (eg, hepatocellular apoptosis) and/or consequence of liver diseases (eg, ascites), the finding was excluded. Basic science studies that investigated common mechanisms of liver disease were also excluded. Quantification of research attention The relative level of attention to each liver disease was calculated at five different levels: primary research presented at major scientific liver meetings, drugs in development, research opportunities, clinical trials and publications. The mean research attention was calculated by averaging the relative level of attention devoted to each liver disease in each of the five categories. Major scientific liver meetings The scoring system for the two major global liver scientific meetings (American Association Study of Liver Diseases (AASLD) and European Association for the Study of the Liver (EASL)) was designed to reflect the quantity of presentations as well as the level of presentation. The scores were allocated as follows: 5 points, title of a large symposium (eg, postgraduate course); 4 points, title of small courses or joint workshops; 3 points, scientific presentations at an oral general meeting or plenary session, presidential lectures or state-of-the-art lectures and titles of parallel session; 2 points, scientific presentations at a parallel session, early morning workshops, meet-the-professor luncheon or grand rounds and titles from sections of the poster presentations; and 1 point, scientific presentation as a poster. The number of abstracts in each category was multiplied by the allocated score to provide an overall weighted research attention. Only clinical and translational studies including human samples were included. Scores for attention are reported as a percentage or total N. Drugs in development A systematic online search of the 38 major pharmaceutical companies was made to determine the amount of pharmacological therapies in development for the four major liver diseases. Drugs in development that were specifically indicated for one of the four liver diseases were included in the analysis. Research opportunities The National Institutes of Health (NIH) Research Portfolio Reporting Tools (RePORTER) was accessed to identify public funding opportunities in the USA. The following Boolean searches were performed using these terms: ‘alcoholic liver disease’ excluding ‘non-alcoholic fatty liver disease’, ‘hepatitis C virus’, ‘hepatitis B virus’, ‘nonalcoholic fatty liver disease’ excluding ‘alcoholic liver disease’. To identify public grant opportunities for the European Union (EU), the Community Research and Development Information Service (CORDIS) advanced search database was used, and the ‘Only Projects’ tab was selected and the same search items were entered. The number of corresponding grants was recorded by disease category. Clinical trials and scientific publications The same search terms used for the research opportunities section were entered into Clinicaltrials.gov and PubMed to identify ongoing clinical trials and publications, respectively. Observational and interventional ongoing studies for the four major liver diseases were identified. Trials and/or publications on unspecific liver diseases (ie, cirrhosis) were excluded. Estimation of disease burden We estimated the burden of each of the four main liver diseases by combining parameters indicative of early and advanced liver disease as well as hospitalisation costs. The following parameters were used to estimate the relative burden of each of the major four liver diseases: (1) liver-related mortality in the USA;13 (2) causes of liver fibrosis estimated by non-invasive tests;14 (3) causes of liver cirrhosis;15 (4) cause of liver transplantation in the USA and the EU. The causes of liver transplantation in Europe were identified from the European Liver Transplantation Registry. To account for patients with concurrent aetiologies (eg, HCV/ALD), we divided the number of patients with HCV/ALD and assigned half of them to HCV and half of them to ALD.16 17 (5) Inpatient hospitalisation costs of patients with liver disease in the USA.18 (6) Hospital admissions and discharges: the data were obtained from the total number of patient discharges for each of the four main liver diseases.18 Estimation of rate of efficacy of current pharmacological therapies We conducted a systematic review of the main controlled clinical trials testing targeted therapies for viral hepatitis and fatty liver diseases. Studies published during the period 2010–2014 in the most cited journals were included and reviewed. For HBV, the cure rate was determined by DNA clearance or persistent reduction of Hepatitis B Surface Antigen (HBsAg). For HCV, we considered the sustained viral response (SVR) at 12 weeks to help determine drug efficacies. In patients with biopsy-proven non-alcoholic steatohepatitis (NASH), we defined drug efficacy as histological improvement of patients in the NAFLD Activity Score (NAS) score and/or fibrosis. Regarding ALD, in the absence of consistent studies in patients with early disease, rates of efficacy were based on 28-day survival in patients with alcoholic hepatitis (AH). Development of the Attention-to-Burden Index The Attention-to-Burden Index (ABI) was developed to compare the research activities during 2010–2014, and an estimate of disease burden of the four major liver diseases. The ABI for each liver disease was calculated as the mean research attention divided by the mean disease burden for each liver disease. A resulting ratio close to 1 reflects an appropriate attention compared with the burden. In contrast, ABI reflects either overattention (positive value) or inadequate attention (negative value) compared with the burden. Statistical analysis Descriptive data are reported for liver meeting attention, drug development attention, published article attention, clinical trial attention, research grant attention and burden of disease as either N or per cent where appropriate. Ratios were created comparing the proportion of disease attention and proportion of disease burden. SPSS software (Chapel Hill, North Carolina, USA) was used to determine the Pearson's correlation between drug efficacy and research attention to the four main liver diseases. An analysis of variance test was also carried out to determine the level of significance of the correlation between research attention and pharmaceutical drug efficacy. Results Analysis of research attention to major liver diseases (2010–2014) We first performed a systematic analysis of the research attention paid to the four main liver diseases during the period 2010–2014 at five different levels. EASL meetings had a research attention of 55% and 25% for HCV and HBV, respectively, while NAFLD and ALD received 13% and 7%, respectively. AASLD meetings provided similar research focus to HCV and HBV (attentions of 49% and 32%, respectively) while NAFLD and ALD received 14% and 4% (figure 1A, table 1 and online supplementary table S1). Table 1 Weighted research attention to main liver diseases in the two most attended international liver conferences, 2010–2014 Weighted values 2010–2014 HCV HBV NAFLD ALD International Liver Conference EASL AASLD EASL AASLD EASL AASLD EASL AASLD Symposia* 277.5 47.5 92.5 49.5 35 10 25 15 Titles of sessions† 144 278 100 164 50 110 42 44 Oral—general presentations‡ 78 43.5 21 13.5 27 6 3 3 Oral—parallel presentations§ 33 54 26 39 13 20 11 16 Poster presentation¶ 945.5 1708 423.5 1135 231 475.5 98.5 135 Total weighted points 1478 2131 663 1401 356 621.5 179.5 213 *Incidence multiplied by 5 research points. It includes courses, joint workshops and industry-supported satellite symposia. †Incidence multiplied by 4 research points. It includes titles of entire oral parallel session and poster categories. ‡Incidence multiplied by 3 research points. It includes plenary sessions, presidential lectures, state-of-the-art lectures and European Liver Patients Association (ELPA) workshops. §Incidence multiplied by 2 research points. It includes early morning workshops, meet-the-professor luncheons and grand rounds. ¶Incidence multiplied by 1 research point. AASLD, American Association for the Study of the Liver; ALD, alcoholic liver disease; EASL, European Association for the Study of Liver Diseases; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease. Figure 1 Parameters of research attention to the four major liver diseases. The relative level of attention devoted to each liver disease from different parameters: (A) detailed analysis of all presentations at the two major annual scientific liver meetings (AASLD and EASL); (B) research opportunities offered by public agencies in the USA and in the EU; (C) ongoing registered clinical trials (ClinicalTrials.gov); (D) scientific publications (PubMed). AASLD, American Association Study of Liver Diseases; AH, alcoholic hepatitis; ALD, alcoholic liver disease; EASL, European Association for the Study of the Liver; EU, European Union; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease; NASH, non-alcoholic steatohepatitis; NIH, National Institutes of Health. 10.1136/bmjopen-2016-013620.supp1supplementary tables In the analysis of drugs in the pipeline from the main 38 drug companies, we found 74 drugs specifically indicated to treat these four major liver diseases. Eighty-two per cent of such drugs were for HCV, mainly oral-acting interferon-free regimes. A smaller proportion of drugs were devoted to HBV (12%), while only 6% of the drugs in development were focused on treating either NAFLD (3%) or ALD (3%; see online supplementary table S2). Next, research opportunities were assessed in public agencies in the USA and the EU (see online supplementary table S3). In the USA and the EU, it was found that the majority of funding opportunities were devoted to HCV (48% and 61%, respectively), followed by HBV (21% and 30%; figure 1B). In contrast, NAFLD received less attention (20% in the USA and 7% in the EU) while ALD received minimal funding opportunities (3% in the USA and 0% in the EU). Next, registered clinical trials were analysed to determine the proportion of focus devoted to the four main liver diseases. A total of 1273 clinical trials were found, and most studies were devoted to test anti-HCV and HBV drugs (32% and 36%, respectively; figure 1C). The proportion of ongoing trials for NAFLD was 5%, while 18% of ongoing trials were specifically devoted to ALD (table 2). These differences were similar when the clinical trials were divided between interventional and observational trials (data not shown). Finally, the relative number of scientific publications devoted to different types of liver diseases was analysed for the period 2010–2014. A total of 39 093 publications were found on the four main liver diseases included in our study. Published studies for HCV and HBV accounted for 46% and 32%, respectively (figure 1D). The publications for NAFLD were lower at 17%, and for ALD, even more so at 5%. Overall, the mean research attention afforded to the four main liver diseases was greater for HCV and HBV at 47% and 31%, respectively, than it was for NAFLD and ALD at 17% and 5%, respectively (figure 2). Table 2 Research attention from clinical trials, public agencies and PubMed HCV (n, %) HBV NAFLD ALD Drugs in development 61 (82) 9 (12) 2 (3) 2 (3) EASL 1478 (55) 663 (25) 356 (13) 179.5 (7) AASLD 2131 (49) 1401 (32) 621.5 (14) 135 (5) PubMed 15 438 (39) 10 724 (27) 5518 (14) 1728 (4) NIH grants 738 (48) 328 (21) 300 (20) 47 (3) EU grants 52.5 (61) 25.5 (30) 6 (7) 0 (0) Clinical trials 407 (32) 461 (36) 66 (5) 235 (18) Mean research attention 47% 31% 17% 5% Percentages in parentheses calculated from a combination of HCV, HBV, NAFLD and ALD. AASLD, American Association for the Study of the Liver; ALD, alcoholic liver disease; EASL, European Association for the Study of Liver Diseases; EU, European Union; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease; NIH, National Institutes of Health. Figure 2 Calculation of mean research attention to the four major liver diseases. The mean research attention to the four main liver diseases (HBV, HCV, NAFLD and ALD) was calculated from five parameters: scientific publications (PubMed); research opportunities offered by public agencies in the USA and the EU; ongoing registered clinical trials (ClinicalTrials.gov); detailed analysis of all presentations at the two major annual scientific liver meetings (AASLD and EASL); and number of drugs in development in the pipeline of 38 major pharmaceutical companies. AASLD, American Association Study of Liver Diseases; ALD, alcoholic liver disease; EASL, European Association for the Study of the Liver; EU, European Union; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease. Estimation of disease burden for the major liver diseases In the absence of systematic studies assessing the burden of the major liver disease worldwide, we next estimated the overall burden of the four major liver diseases by combining data from six different parameters (see methods). The relative numbers of mortality caused by liver disease in the USA can be attributed to 23% HCV, 0.54% to HBV, 3% to NAFLD and 50% to ALD.13 The indications of liver fibrosis in the USA were 10% to HCV, 7% to HBV, 48% to NAFLD and 35% to ALD.14 The causes of liver cirrhosis in the USA were 47% to HCV, 3% to HBV, 27% to NAFLD and 25% to ALD.15 The main indications of liver transplantation in the EU and the USA were attributed as 28% and 40% to HCV, 11% HBV (EU), in the USA there were no data on HBV due to its low indication rate for transplantation, 10% and 16% to NAFLD, and 39% and 23% to ALD.16 17 HCV and HBV contributed to $167 785 million (15%) and $52 062 (5%) of hospitalisation costs, while NAFLD and ALD contributed to $45 755 (4%) and $848 189 (76%), respectively, in the USA.18 The relative number of hospital admissions and discharges were obtained from Peery et al's18 calculation of the burden of gastrointestinal disease in the USA in 2012, and were attributed to the four aetiologies of liver disease: at 14 749 (17%) and 4568 (5%) to HCV and HBV, and at 2858 (3%) and 64 752 (74%) to NAFLD and ALD (see online supplementary table S4). Taking into account all these parameters, the mean disease burden was calculated at 28% and 5% for HCV and HBV, respectively, and 17% and 50% for NAFLD and ALD (figure 3). Figure 3 Estimation of mean disease burden to four main liver diseases. The mean disease burden to the four main liver diseases (HBV, HCV, NAFLD and ALD) was calculated from seven parameters: total number of US hospitals discharged, US hospitalisation costs, OLTY-EU, OLTY-US, cirrhosis-US, fibrosis and US mortality. ALD, alcoholic liver disease; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease. Relative research attention compared with disease burden (ABI) In order to critically analyse if there are disparities between the attention that the liver research community pays to the major liver disease and their burden, we generated ABI (if the relative attention equals the relative burden of a given liver disease, the ratio should be 1). The calculated ABI for hepatitis B and C revealed a +6.7-fold and +1.7-fold overattention, respectively. NAFLD received an ABI of −1.2-fold, reflecting its recent rise in attention within the field of hepatology, while ALD received a marked inadequate attention of −9.7-fold (figure 4 and table 3). The ABI indicates that while viral hepatitis has been extensively studied during the past years, ALD is being markedly overlooked. Table 3 Attention to Burden Index HBV HCV NAFLD ALD Mean research attention 29% 51% 15% 5% Mean disease burden 5% 28% 17% 50% Ratio 6.71 1.67 0.93 0.10 Fold-over ratio 6.71 1.67 −1.08 −9.68 Attention-to-Burden Score 6.71 1.7 −1.1 −9.7 The Attention-to-Burden score is bolded to highlight it. It is the final score that was calculated that informed the Attention Burden Index. ALD, alcoholic liver disease; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease. Figure 4 ABI for the four major liver diseases. ABI was calculated using the ratio of mean research attention (comprising different parameters shown in figure 1) to mean disease burden (comprising the parameters shown in figure 2) of the four main liver diseases. A value >1 reflects overattention compared with the disease burden, while a value <1 reflects inadequate attention. ABI, Attention-to-Burden Index ALD, alcoholic liver disease; HBV, hepatitis B virus; HCV, hepatitis C virus; NAFLD, non-alcoholic fatty liver disease. Efficacy rate of current therapies: correlation with the degree of research attention Finally, we hypothesised that there is a relationship between the degree of research attention to each major liver disease and the therapeutic advances. In order to address this question, we conducted a systematic review of the main controlled clinical trials testing targeted therapies for viral hepatitis and fatty liver diseases. For HBV and HCV, we determined the efficacy in terms of sustained viral response. In patients with biopsy-proven NASH, we defined drug efficacy as histological improvement of patients in the NAS and/or fibrosis. Regarding ALD, there are no consistent clinical trials to test targeted therapies in patients with early/compensated forms. Therefore, we focus on the efficacy of drugs in improving short-term survival in patients with AH. Following these criteria, the efficacy rate of current pharmacological agents was 72% for HBV, 89% for HCV, 25% for NASH and 13% for AH (figure 5A and see online supplementary table S5). Importantly, there was a positive relationship between the mean attention to each major liver disease and the success rate of pharmacological therapies (figure 5B). This figure strongly suggests that the greater the concentration of research on one liver disease, the more likely to develop efficacious drugs.18–46 Figure 5 Correlation between the efficacy of current drug therapies for hepatitis and the mean research attention. (A) Current mean rate of efficacy for therapeutic drugs; (B) correlation between the efficacy of current drug therapies for hepatitis and the mean research attention. The efficacy of current drug therapies to treat chronic hepatitis (HCV), chronic hepatitis B (HBV), NASH and AH was calculated based on large published clinical trials (see Methods section). Definition criteria of drug efficacy for each of the four type of hepatitis were: HCV: sustained viral response at 12 weeks; HBV: achievement of end points suppressing viral replication; NASH: reduction in NAS or fibrosis score; AH: effect on short-term mortality rate. AH, alcoholic hepatitis; HBV, hepatitis B virus; HCV, hepatitis C virus; NAS, NAFLD Activity Score; NASH, non-alcoholic steatohepatitis. Discussion In the past decade, major advances have been achieved in the field of viral hepatitis, with the development of highly active oral antiviral therapies. In clear contrast, there are no approved therapies for NAFLD and ALD and interventions for AH are largely unchanged over the past 40+ years. We hypothesised that these discrepant advances are related to unequal allocation of research resources. Our systematic study found marked disparities between research attention and disease burden among the aetiologies of liver disease. We compared multiple modalities of hepatology research attention (eg, scientific meeting presentations, research funding, drugs under development, funding opportunities and publications) with measures of liver disease burden (prevalence, fibrosis and healthcare costs). We found a lack of correlation between research attention and disease burden. Overall, viral hepatitis received a 5–7-fold overattention, while alcoholic liver disease received a 10-fold underattention. There are a few similar studies in other fields of medicine that have investigated research attention and disease burden.47 Using a similar methodology to ours, these studies used clinicaltrials.gov, while others compared NIH funding to disease burden. Unfortunately, the discrepancy between funding and burden has become a common theme. The finding of attention–burden discordance is most likely multifactorial. HBV and HCV are causes of cirrhosis and hepatocellular carcinoma, and are important public health issues in their own right.1 2 Therapeutic targets for viral hepatitis are better defined than those for fatty liver diseases (eg, proteins regulating viral replication), and there is a significant interest to develop antiviral agents. It is important to recognise that the nature of these diseases also plays an important role in the development of drug therapies. The pathways of viral hepatitis (HBV and HCV) diseases have a significant positive effect on the advancement of effective drug therapies compared with that of fatty liver diseases. HBV and HCV have clear aetiologies, with transmission of the virus occurring in determined pathways which provide scientific researchers with clear targets for the development of pharmacotherapies. In contrast, fatty liver diseases (NAFLD and ALD) have multiple causes and are linked to other health conditions, their complex aetiologies, which necessitate the development of therapies that address multiple targets, and may contribute to the lack of drugs targeted to NAFLD or ALD. It is observed that increases in attention become self-perpetuating, with scientific advances (ie, drug development efforts, research allotment, etc) influencing greater research attention (more presentations, drugs, funding opportunities and publications) and vice versa. The inadequate attention especially to ALD is complex and includes the social stigma surrounding alcoholism, poorly defined targets for therapy, a lack of good animal models, few non-invasive markers of severity and poor general awareness of the diseases. Additionally, there is a lack of large-scale early detection efforts, and thus patients are often identified at very late stages of the disease. These challenges alongside patient identification, research and therapy result in a high burden of disease. Our findings should be viewed as a comparative assessment of relative attention and burden among the major liver diseases. While we completed an extensive search, which expanded over a 5-year period and involved multiple areas within both the public and private sectors, our estimates were derived from pre-existing databases with their own respective inclusion and exclusion criteria. Thus, limitations of this study include the possibility of incomplete estimates. Other limitations include the inability to complete a systematic review of the global burden of the major liver diseases due to the limited number of articles that focus on fatty liver disease burdens. The limited and heterogeneous resources used for burden estimates reflect the need for further research. Consequences of this large discrepancy among hepatitis and fatty liver diseases include extraordinary advances in the therapy of HCV and HBV prevention, while little advancements in early detection and treatment of ALD and NAFLD have been made. It is unknown if these differences impact mortality. The next decade could benefit from greater research attention being allocated to NAFLD and ALD. Translational studies identifying targets for therapy for fatty liver disease will certainly encourage pharmaceutical companies to invest in clinical trials. Ultimately, efforts by public funding agencies and the scientific community to address the diseases with the greatest public health burden will result in improved health outcomes for the greatest number of people. A striking finding of our study is the minimal attention that both public and private sources pay to ALD, which is the main cause of cirrhosis globally.4 In recent years, a concurrent increase in addiction behaviour and the prevalence of resulting illnesses such as ALD has been observed in the USA and Europe.5 6 Accompanying the sharp rise in ALD cases is the increasing burden of patients with liver disease on national health systems, identifying them as the main group expenditures in patient hospitalisation costs.18 In spite of its significant health and socioeconomic burden, few major advances have been made in the management of patients with ALD. The pharmacological therapies used today have not changed since 1971 (ie, prednisolone for AH), and no widespread early detection programmes have been developed.48 Our study should increase awareness in the public health agencies and academic institutions, especially in Europe, to devote more resources to face this prevalent and devastating liver disease. The development of a European Agency for Alcohol Studies, similar to the National Institute for Alcohol and Alcoholism (NIAAA) in the USA, seems a timely initiative. Contributors: NN participated in the collection and analysis of data on disease burden, efficacy of current pharmacological therapies, as well as in the interpretation of data, writing, critical revision and final approval of the manuscript. TGL participated in the collection of data from drug companies, ongoing clinical trials, as well as in the analysis of data and final approval of the manuscript. KJ participated in the collection of all data from major liver meetings (EASL and AASLD), analysis of data and final approval of the manuscript. JC participated in the statistical analysis, data presentation and interpretation, and the final approval of the manuscript. NV participated in the data collection and analysis of the published papers on each liver disease, and final approval of the manuscript. ASB participated in the conception and design of the work, and critical revision and final approval of the manuscript. RB participated in the conception and design of the work, data interpretation, drafting of the article and critical revision and final approval of the manuscript. Funding: This work was supported by a grant from NIAAA (1U01AA021908). Competing interests: None declared. Provenance and peer review: Not commissioned; externally peer reviewed. 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==== Front BMJ OpenBMJ OpenbmjopenbmjopenBMJ Open2044-6055BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bmjopen-2016-01369910.1136/bmjopen-2016-013699NeurologyProtocol150617131726Towards personalised intra-arterial treatment of patients with acute ischaemic stroke: a study protocol for development and validation of a clinical decision aid Mulder Maxim J H L 1Venema Esmee 1Roozenbeek Bob 1Broderick Joseph P 2Yeatts Sharon D 3Khatri Pooja 2Berkhemer Olvert A 145Roos Yvo B W E M 4Majoie Charles B L M 4van Oostenbrugge Robert J 5van Zwam Wim H 5van der Lugt Aad 1Steyerberg Ewout W 16Dippel Diederik W J 1Lingsma Hester F 1 1 Erasmus University Medical Center, Rotterdam, The Netherlands 2 University of Cincinnati, Cincinnati, Ohio, USA 3 Medical University of South Carolina, Charleston, South Carolina, USA 4 Academic Medical Center, Amsterdam, The Netherlands 5 Maastricht University Medical Center, Cardiovascular Research Institute Maastricht (CARIM), Maastricht, The Netherlands 6 Leiden University Medical Center, Leiden, The NetherlandsCorrespondence to Esmee Venema; e.venema@erasmusmc.nlMJHLM and EV contributed equally. 2017 22 3 2017 7 3 e01369931 7 2016 25 10 2016 13 12 2016 Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/Introduction Overall, intra-arterial treatment (IAT) proved to be beneficial in patients with acute ischaemic stroke due to a proximal occlusion in the anterior circulation. However, heterogeneity in treatment benefit may be relevant for personalised clinical decision-making. Our aim is to improve selection of patients for IAT by predicting individual treatment benefit or harm. Methods and analysis We will use data collected in the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) trial to analyse the effect of baseline characteristics on outcome and treatment effect. A multivariable proportional odds model with interaction terms will be developed to predict the outcome for each individual patient, both with and without IAT. Model performance will be expressed as discrimination and calibration, after bootstrap resampling and shrinkage of regression coefficients, to correct for optimism. External validation will be conducted on data of patients in the Interventional Management of Stroke III trial (IMS III). Primary outcome will be the modified Rankin Scale (mRS) at 90 days after stroke. Ethics and dissemination The proposed study will provide an internationally applicable clinical decision aid for IAT. Findings will be disseminated widely through peer-reviewed publications, conference presentations and in an online web application tool. Formal ethical approval was not required as primary data were already collected. Trial registration numbers ISRCTN10888758; Post-results and NCT00359424; Post-resultsc. ischaemic strokeintra-arterial therapymechanical thrombectomyprediction modeldecision aidpersonalised treatment ==== Body Strengths and limitations of this study Multiple characteristics will be evaluated simultaneously to show clinically relevant heterogeneity in treatment benefit between patients. Multivariable prediction modelling substantially increases statistical power compared with other approaches and is more robust, especially in small data sets. We will use a relatively small cohort for the development of a prediction model. Using a proportional odds model requires the assumption that the ORs are the same for each cut-off of the modified Rankin Scale. Introduction In 2015, five consecutive randomised controlled trials (RCTs) showed that intra-arterial treatment (IAT) improves functional outcome in patients with a proximal occlusion in the anterior circulation.1–6 This was a major breakthrough in the field, and IAT is now implemented in updated guidelines on acute ischaemic stroke (AIS) management.7 Ideally, IAT will be targeted at patients who are expected to have optimal benefit: personalised treatment. In this study protocol, we present seven steps for development and validation of a clinical decision aid to predict which individual patients with AIS will benefit most from IAT.8 9 Methods and analysis Step 1: problem definition and data inspection Problem definition RCTs provide estimates of treatment effects for average patients. However, it is important to take potential heterogeneity of treatment effects into account. Clinically relevant differences in the absolute effect of a treatment can be caused by (1) differences in the relative treatment effect (predictive effects) and (2) differences in baseline risk on the outcome of interest (prognostic effects).10 11 For example, in the Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands (MR CLEAN) trial, there is no predictive effect of age; the relative treatment effect is constant across age subgroups.1 This is demonstrated by a non-significant test for interaction between age and treatment (figure 1A). However, variation in baseline risk on favourable outcome according to age results in a larger absolute treatment benefit in younger patients (figure 1B). Figure 1 Relative risk (A) and absolute risk difference (B) for good functional outcome (mRS 0–2) in MR CLEAN sort by age. MR CLEAN, Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands; mRS, modified Rankin Scale. Conventional subgroup analyses are focused mainly on predictive effects and assess the effect of only one variable at a time. If predictive and prognostic effects of multiple characteristics are evaluated simultaneously in multivariable prediction modelling, it is likely that larger heterogeneity in treatment benefit between individual patients will be found. Our aim is to improve selection of patients for IAT by predicting treatment benefit or harm for individual patients with stroke. Development data We will use data of the MR CLEAN trial (n=500), which was a phase 3 multicentre clinical trial with randomised treatment group assignment, open-label treatment and blinded end point evaluation. IAT plus usual care (which could include intravenous administration of alteplase) was compared with usual care alone. IAT consisted of arterial catheterisation with a microcatheter to the level of occlusion and delivery of a thrombolytic agent, mechanical thrombectomy or both.1 Severity of stroke was assessed at baseline with the National Institutes of Health Stroke Scale (NIHSS; range 0–42). Baseline CT was evaluated with the Alberta Stroke Program Early CT Score (ASPECTS; range 0–10). Baseline imaging (CT angiography) was used to determine the location of occlusion and to grade the quality of collateral flow to the ischaemic area with a four-point scale. Detailed information about the MR CLEAN trial can be found in the study protocol and the publication of the main results.1 12 End points of interest The primary outcome will be the modified Rankin Scale (mRS), a seven-point scale ranging from 0 (no symptoms) to 6 (death) at 90 days after stroke.13 We will provide estimates of treatment benefit as the absolute increase in probability on functional independence (defined as mRS 0–2) and survival (defined as mRS 0–5). Step 2: coding of variables As variables, we will use patient characteristics that are expected to predict outcome, or that are expected to interact with treatment, based on expert opinion and the recent literature (table 1). Non-linearity of continuous variables will be tested by comparing the two log likelihood of models with linear and restricted cubic spline functions.14 Table 1 Patient characteristics that are expected to predict outcome (prognostic), or that are expected to interact with treatment (predictive) Per cent of data complete in MR CLEAN Prognostic Predictive Clinical Age6 15 100% x Baseline NIHSS16 17 100% x History of diabetes mellitus18 100% x History of previous stroke19 100% x History of atrial fibrillation20 100% x Prestroke mRS score19 100% x Systolic blood pressure21 100% x IV treatment with alteplase22–24 100% x Time from onset stroke to groin puncture25 26 100%* x x Radiological ASPECTS6 27 99.2% x Location of intracranial occlusion on non-invasive vessel imaging28 29 99.8% x Collateral score on CTA29 30 98.4% x x *Of patients undergoing intra-arterial treatment. ASPECTS, Alberta Stroke Program Early CT score; CTA, CT angiography; IV, intravenous; NIHSS, National Institutes of Health Stroke Scale; MR CLEAN, Multicenter Randomized Clinical Trial of Endovascular Treatment for Acute Ischemic Stroke in the Netherlands; mRS, modified Rankin Scale. Timing of treatment is an essential predictor of outcome. Since time to randomisation was not a reliable indicator for time to treatment in the MR CLEAN trial and will not be applicable in clinical practice, we will use time from stroke onset to groin puncture. Since time to groin puncture is not observable in the control group, we will explore imputation approaches based on the correlation with time to randomisation. All other baseline variable values are more than 98% complete in the MR CLEAN data, so we choose simple imputation by the mean for continuous variables and simple imputation by the mode for categorical variables. Steps 3 and 4: model specification and estimation We will test the effect of variables on functional outcome and treatment effect with proportional odds regression modelling. All variables from table 1 will be tested for effect on outcome and interaction with treatment effect. Prognostic variables (main effects) and predictive variables (interaction effects) with a p value of 0.15 in univariable and multivariable analyses will be included in our final model. A p value of 0.15 was chosen to make the predictor selection less data driven and prevent overfitting.14 31 We will perform shrinkage of all regression coefficients with ridge regression to prevent overfitting of the model.14 Predicted probabilities for each of the mRS categories, with and without treatment, will be derived from the ordinal model. All statistical analyses will be performed within the computing environment R V.3.2.2 (The R Foundation) Step 5: model performance Model performance will be expressed in discrimination and calibration. Discrimination will be quantified with the c-statistic. The c-statistic is similar to the area under the curve for binary outcomes and estimates the probability that out of two randomly chosen patients, the patient with the higher predicted probability of a good outcome will indeed have a better outcome. Calibration refers to the agreement between predicted and observed risks and will be assessed graphically with validation plots, and expressed as calibration slope and an intercept. The calibration slope describes the relative overall effect of the variables in the validation sample, and is ideally equal to 1. The intercept indicates whether predictions are systematically too high or too low, and should ideally be 0.32 We will calculate a general c-statistic to express the performance of our ordinal model and additional calibration plots with specific c-statistics for the predictions of favourable functional outcome (mRS 0–2) and survival (mRS 0–5). Step 6: model validity The c-statistic will be internally validated with a bootstrap procedure (500 samples with replacement) to estimate the degree of optimism in parameter estimates.8 After penalisation of the regression coefficients, we will externally validate the model on data of patients in the Interventional Management of Stroke III trial (IMS III) with an occlusion in the anterior circulation on non-invasive vessel imaging.33 Coefficients of the final model will be fitted on the combined development and validation data sets. After validation, we will assess whether the model can be used to discriminate between patients with low and high expected benefit by making individual predictions of outcome for all patients included in the development and validation data. Step 7: model presentation The final model will be digitally available for use in clinical practice, both for mobile devices and as a web application. It will provide predictions of all mRS categories for each individual patient, both with and without IAT. Ethics and dissemination Findings will be disseminated widely through peer-reviewed publications, conference presentations and in an online web application tool. Formal ethical approval was not required for this study as primary data were already collected. Discussion Compared with the current subgroup analyses on the effect of IAT, our modelling approach has multiple advantages. First, it accounts for the fact that patients have multiple characteristics that simultaneously affect the likelihood of treatment benefit.34 Thus, our model will show more clinically relevant heterogeneity in treatment benefit between patients. Second, a multivariable prediction model substantially increases statistical power to identify heterogeneity in treatment effects compared with other approaches.35 These include neural network and decision trees. We use regression modelling since it is considered more robust, especially in relatively small data sets.36 37 There are some differences between patients included in the MR CLEAN trial and the IMS III trial that may influence the external validity of our model. IMS III had different inclusion criteria, used older devices and older treatment paradigms than MR CLEAN. In order to overcome these limitations, we will use only those patients in IMS III with an occlusion in the intracranial anterior circulation on non-invasive vessel imaging. We will compare the baseline characteristics of the derivation and validation cohort and describe relevant differences that might lead to an underestimation or overestimation of the model performance. Interestingly, a substantial treatment effect in the IMS III patients with proven intracranial large vessel occlusion has been reported.38 Furthermore, even though the MR CLEAN trial has included most patients of the recent RCTs, the cohort remains relatively small for the development of a prediction model, especially for the selection of the main effect and interaction effects. We will reduce regression coefficients to prevent overfitting and also perform external validation. In the future, we will further validate and update our model in the pooled individual patient data of the Highly Effective Reperfusion evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration, harbouring data of all patients from recent randomised trials regarding IAT (over 1700 patients in total). Moreover, we aim to investigate the validity of our model predicting outcome after treatment in clinical practice. Our model will therefore be tested by applying it to recently treated patients in all Dutch neurovascular centres participating in the MR CLEAN Registry (mrclean-trial.org). We will use a proportional odds model to analyse the full mRS score as outcome. Formally, this model requires the assumption that the ORs are the same for each cut-off of the mRS. However, previous studies have shown that even if the proportionality assumption is violated, proportional odds analysis is still more efficient than dichotomisation.39 In addition, all recent RCTs on the effect of IAT used the full mRS and analysed their results with proportional odds regression. Conclusion The proposed study will provide an internationally applicable clinical decision aid for the selection of patients for IAT. We consider this study an important next step towards personalised treatment of patients with AIS. Contributors: MJHLM and EV were involved in literature search, study design, writing (authors contributed equally). BR and HFL were involved in study conception and design, and writing. EWS and DWJD were involved in study conception and design, and critical review of the manuscript. JPB, SDY, PK, OAB, YBWEMR, RJvO, WHvZ, CBLMM and AvdL were involved in study conception and critical review of the manuscript. Competing interests: Erasmus MC received funds from Stryker®, Bracco Imaging® for consultations by DWJD. AMC received funds from Stryker® for consultations by CBLMM, YBWEMR and OAB. MUMC received funds from Stryker® and Codman® for consultations by WHvZ. JPB received study medication for intra-arterial tissue-type plasminogen activator supplied by Genentech. Catheters were supplied during early years of the IMS III trial by EKOS Corp, Concentric Medical, Cordis Neurovascular. He currently receives research monies to Department of Neurology and Rehabilitation Medicine from Genentech for Role on Steering Committee for A Study of the Efficacy and Safety of Activase (Alteplase) in Patients With Mild Stroke (PRISMS) trial. SDY research monies from Genentech for statistical role in the PRISMS trial. PKs department of Neurology received research support from Genentech, Inc for her role as lead principal investigator (PI) of the PRISMS trial and Penumbra, Inc for her role as neurology PI of the Assess the Penumbra System in the Treatment of Acute Stroke (THERAPY) trial; she has also received royalties from UpToDate, Inc and provided consultation for Grand Rounds Experts, St Jude’s and Biogen (DSMB). Ethics approval: Medical and Ethical Review Committee Erasmus MC. Provenance and peer review: Not commissioned; externally peer reviewed. ==== Refs References 1 Berkhemer OA , Fransen PS , Beumer D A randomized trial of intraarterial treatment for acute ischemic stroke . N Engl J Med 2015 ;372 :11 –20 . 10.1056/NEJMoa1411587 25517348 2 Campbell BC , Mitchell PJ , Kleinig TJ Endovascular therapy for ischemic stroke with perfusion-imaging selection . 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==== Front BMJ Case RepBMJ Case RepcasereportsbmjcasereportsBMJ Case Reports1757-790XBMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR bcr-2016-21831010.1136/bcr-2016-218310ArticleFindings That Shed New Light on the Possible Pathogenesis of a Disease or an Adverse Effect15061517Female19-30 yearsWhiteEurope (West)Case ReportHaemophagocytic lymphohistiocytosis associated with fulminant hepatitis and multiorgan failure following primary Epstein–Barr virus and herpes simplex virus type 1 infection http://orcid.org/0000-0002-3051-2686Honsig Claudia 1Beinhardt Sandra 2Tomasits Josef 3Dienes Hans Peter 4 1 Division of Clinical Virology, Department of Laboratory Medicine, Medical University of Vienna, Vienna, Austria 2 Department of Internal Medicine III, Medical University of Vienna, Vienna, Austria 3 Kepler Universityclinic, Med Campus III, Linz, Austria 4 Medical University of Vienna, Institute of Clinical Pathology, Vienna, AustriaCorrespondence to Dr Claudia Honsig, claudia.honsig@meduniwien.ac.at2017 29 3 2017 29 3 2017 2017 bcr201621831017 1 2017 2017 BMJ Publishing Group Ltd2017This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/We present a case of severe fatal hepatitis in a young patient presumably triggered by two ubiquitous viral diseases which occurred in close succession. This case is unusual because of the exceptional chronological sequence of primary Epstein–Barr virus and herpes simplex virus type 1 infection causing systemic immune dysregulation associated with rapidly developing liver failure and consecutive multiorgan failure. Clinical, laboratory and histopathological findings indicated the development of secondary haemophagocytic lymphohistiocytosis triggered by these closely succeeding viral primary infections. special-featureunlocked ==== Body Background Haemophagocytic lymphohistiocytosis (HLH) is a potentially fatal syndrome characterised by an uncontrolled hyperinflammatory response with heterogeneous aetiology.1 2 HLH is categorised as primary HLH (or familial HLH) in patients with underlying genetic causes and as secondary HLH (SHLH) when family history or known genetic causes are absent. SHLH is associated with a wide spectrum of underlying conditions: viral infections have been reported as the most common triggers (29%), followed by other infections, malignancies, autoimmune disorders and immune suppression.1 Among viral infections, Epstein–Barr virus (EBV) has been described as the most frequent virus that associates with SHLH, herpes simplex virus 1 as the next most common virus.3–6 In healthy, immunocompetent persons at any age, EBV and herpes simplex virus (HSV) infection are usually self-limiting, rarely lead to complications and are both uncommon causes of acute liver failure (ALF).7–11 The case presented in this report highlights the possibility of a synergistic effect of these two closely succeeding viral primary infections in the development of a severe systemic disease in an immunocompetent person. In addition, it emphasises that SHLH should be suspected routinely when severe systemic illness with multiorgan failure develops following a viral infection and that the diagnosis should be confirmed rapidly by laboratory and histopathological investigations. In addition to suppression of the severe hyperinflammation which is the main therapeutic aim in HLH, early diagnosis and treatment of the potential underlying disease may also influence the clinical outcome.12 Case presentation A 21-year-old patient presented at a peripheral hospital with a protracted febrile urinary tract infection. The patient did not have any significant medical history, and on admission physical examination was normal. Mild thrombocytopenia and elevated liver enzymes were explained by the serological diagnosis of primary EBV infection. MRI of the kidneys revealed no abnormalities, however, splenomegaly and multiple, smallest, inconclusive hepatic lesions were detected. Owing to the inconclusive MRI of the liver, the antibiotic therapy was stopped immediately and paracetamol was replaced by metamizole. Neither microbiological urine culture nor blood culture revealed a causative microorganism. By day 4 after admission the liver function had decreased dramatically and ALF followed by acute renal failure developed. Leucopenia, thrombopenia and a significantly elevated ferritin level indicated the beginning of severe immune dysregulation (table 1). The patient was transferred to the University Hospital Vienna where on admission genital lesions suggestive of HSV infection were detected and intravenous acyclovir was started immediately. Within only a few hours, the patient's condition rapidly deteriorated, the patient developed multiorgan failure and died—despite intensive care treatment—only 6 days after the initial admission to hospital. Table 1 Course of laboratory and virological findings during hospital stay Day of hospitalisation 1 3 4 5 6 ALT (U/L) 375 822 1213 1831 2650 AST (U/L) 475 1929 3387 6319 11 150 γGT (U/L) 198 230 248 336 326 ALP (U/L) 263 352 570 648 Total bilirubin (mg/dL) 1.9 3.1 4.32 Ferritin (ng/mL) 1844 7058 Creatinine (mg/dL) 0.8 0.9 2.5 4.01 WCC (G/L) 11.2 6.32 4.29 1.52 HgB (g/dL) 12 11,5 10.5 6.5 Platelet (G/L) 130 122 113 23 sCD25 (U/mL) 338.6 HSV-1 (cp/mL serum) 1.48E+07 1.88E+08  Anti-HSV IgM Negative Negative  Anti-HSV IgG Negative Borderline/positive EBV (cp/mL serum) 1.77E+04 2.14E+04  Anti-EBV VCA IgM Positive Positive  Anti-EBV VCA IgG Positive Positive  Anti-EBV VCA IgG avidity Low  Anti-EBV EBNA1 Negative Borderline/negative ALP, alkaline phosphatase; ALT, alanine transaminase; AST, aspartate transaminase; EBNA, Epstein–Barr virus nuclear antigen 1; EBV, Epstein–Barr virus; HgB, haemoglobin; HSV, Herpes Simplex virus type 1; IgG, immunoglobulin G; IgM, immunoglobulin M; VCA, viral capsid antigen; WCC, white cell count; γGT, γ-glutamyltransferase. In a serum sample taken on day 5, EBV DNA was detected by PCR and primary EBV infection was again confirmed by serology, as VCA IgM antibodies and VCA IgG antibodies of low avidity were detected. In addition, HSV1 PCR was also highly positive in this serum sample and the detection of HSV IgG antibody seroconversion confirmed additional primary infection with HSV1 (table 1). As expected, postmortem analysis of small tissue samples of liver, spleen, kidney and gallbladder by PCR revealed HSV1 and EBV DNA in all of the samples. Particularly high concentrations of HSV1 DNA were detected in liver and spleen tissues (8.40E+06 and 7.20E+06 copies/mg, respectively). EBV DNA concentration in these tissues was 1.76E+03 copies/mg (liver) and 7.60E+04 copies/mg (spleen). Histopathology of the postmortem liver samples displayed the typical necrosis pattern of HSV hepatitis with confluent necroses in a geographical pattern without zonal binding (figure 1A) and a mixed reactive inflammatory infiltrate including a substantial number of polymorph nuclear leucocytes (figure 1B). Hepatocytes showed typical nuclear inclusions with the virus (figure 1B). Immunoperoxidase staining confirmed the diagnosis of HSV1 hepatitis (figure 1C). In some areas, the characteristic features of EBV-hepatitis could still be found (figure 1D). The diagnosis was confirmed by the detection of EBV LMP1 by alkaline phosphatase staining (figure 1E) and the detection of EBV by PCR after extraction of EBV DNA from the liver tissue (figure 1F). In portal macrophages, a trapping of erythrocytes was found and in the sinusoids the activated Kupffer cells showed a conspicuous erythrophagocytosis, consistent with SHLH (figure 2). Figure 1 (A) Large confluent areas of necrosis without zonal binding (H&E staining, ×60). (B) In the margin of the necrosis, hepatocytes display nuclei with typical viral inclusions (arrow). The necroinflammatory infiltrate consists of lymphocytes and a lot of polymorph nuclear leucocytes (H&E staining, ×400). (C) HSV1-infected hepatocytes detected with immunoperoxidase staining (×240). (D) In some areas of the liver, typical features of EBV hepatitis with abundant lymphocytic infiltrates in the sinusoids were still present (H&E staining, ×240). (E) EBV LMP1 detected by immunostaining with alkaline phosphatase, ×240 (arrow). (F) After extraction of EBV DNA and subsequent PCR, viral DNA could be demonstrated. (a and f) DNA ladder; (b) empty; (c) patient; (d) negative control; (e) positive control. Figure 2 Acute hepatitis: haemophagocytosis with trapped erythrocytes in activated Kupffer cells (arrows), besides many inflammatory infiltrates and damaged hepatocytes (H&E staining, ×500). These characteristic histopathological changes in liver tissue along with the laboratory and clinical findings indicated the initiation of SHLH by these two closely succeeding viral primary infections. Unfortunately, histopathological investigation of bone marrow and spleen, as suggested in the diagnostic guidelines used in the HLH-2004 trial,13 could not be performed because the relatives denied further postmortem investigations. Nevertheless, regarding the clinical, laboratory and histopathological findings, five out of the eight diagnostic criteria defined by the Histiocyte Society1 were fulfilled. With the considerably elevated level of soluble CD25 (sCD25) retrospectively detected in the serum sample of day 6 (table 1), a sixth diagnostic criterion was fulfilled which further supported the diagnosis of SHLH. Outcome and follow-up In summary, laboratory, virological and pathological findings together with the clinical presentation suggest multiorgan failure due to SHLH initiated by EBV and closely succeeding HSV1 primary infection in a previously healthy young person. Discussion Systemic immune dysregulation triggered by an external agent has been described as a cause of a disease continuum including HLH, sepsis, multiple organ dysfunction syndrome and systemic hyperinflammatory syndrome.13 Here we report a case of foudroyant immune dysregulation following closely succeeding viral primary infections with EBV and HSV1. Clinical findings (fever, splenomegaly), laboratory parameters (cytopenia in two blood cell lines, elevated ferritin and sCD25) and haemophagocytosis in liver tissue suggest the diagnosis of SHLH based on the HLH-2004 criteria.13 In our patient serological findings indicated that primary EBV infection preceded primary HSV infection. The impairment of the immune response caused by primary EBV infection, especially the suppression of the T-cell function, may have enabled the vicious course of primary HSV1 infection in a previously healthy young adult, and both the viruses may have been subsequent triggers for the hyperinflammatory syndrome.3 13 Viral infections have been reported as common triggers of SHLH,1 2 and the possible synergistic effects of two viral infections in the initiation of SHLH have been described in a previous report of SHLH after the close occurrence of EBV and Hepatitis A infection.14 SHLH after EBV or HSV1 infection has been described previously, and also induction of SHLH by coinfection with EBV and HSV1 has been observed before in two patients. In these cases of SHLH following EBV and HSV1 coinfection, however, EBV viraemia was due to reactivation of latent infection.3 Therefore, initiation of SHLH by primary infections with EBV and HSV1 seems to represent a unique feature of our case. Diagnosing HLH or SHLH as defined by the Histiocyte Society1 is challenging because of its rare occurrence, variable presentation and non-specific findings and should be suspected routinely in patients with unexplained multiorgan failure.2 12 13 Early diagnosis and appropriate treatment including supportive intensive care, elimination of the triggers and suppression of the inflammatory response are essential to improve the outcome of this syndrome.13 Our case highlights that in a patient with unexplained fever and elevated liver function tests, HSV in addition to EBV and cytomegalovirus (CMV) should be taken into consideration as causative agent. As reported before, the absence of mucocutaneous lesions—which initially was the case in our patient—does not exclude HSV hepatitis. Owing to the rapid and malignant course of the disease in our patient, the diagnosis of SHLH could only be established retrospectively. Although the severe immune dysregulation may have been untreatable already on initial admission, we would like to emphasise that a delay in diagnosis and initiation of specific antiviral therapy and immunosuppressive treatment in addition to supportive intensive care may have contributed to the poor outcome.7 13 Learning points Primary infection with two different herpes viruses may occur simultaneously or in close succession, adversely affecting the course of the disease. Herpes simplex virus (HSV) and Epstein–Barr virus (EBV) should be considered in the differential diagnosis of fulminant hepatitis. Early virological diagnosis and immediate initiation of specific antiviral therapy is of high importance. EBV and HSV may cause severe disease in immunocompetent persons and secondary haemophagocytic lymphohistiocytosis should be suspected routinely when severe systemic illness develops. The authors thank Professor Ingrid Simonitsch-Klupp (Institute of Clinical Pathology, Medical University of Vienna) for pathological investigations and Professor Winfried Pickl and Doris Trapin, MSc (Institute for Immunology, Medical University of Vienna) for carrying out the sCD25 assay. Contributors: CH is responsible for acquisition of patient data, study of literature, virological diagnosis, analysis and interpretation of findings and creating the manuscript. SB is responsible for access to medical history, critical discussion and revision. JT is responsible for access to medical history, critical discussion and revision. HPD is responsible for pathological examination of the liver, photographic documentation (Figure 1A–D), discussion of the case and critical review of the manuscript. Competing interests: None declared. Patient consent: Not obtained. Provenance and peer review: Not commissioned; externally peer reviewed. ==== Refs References 1 George MR Hemophagocytic lymphohistiocytosis: review of etiologies and management . J Blood Med 2014 ;5 :69 –86 . 10.2147/JBM.S46255 24966707 2 Jordan MB , Allen CE , Weitzman S How I treat hemophagocytic lymphohistiocytosis . Blood 2011 ;118 :4041 –52 . 10.1182/blood-2011-03-278127 21828139 3 Chen J , Wang X , He P Viral etiology, clinical and laboratory features of adult hemophagocytic lymphohistiocytosis . J Med Virol 2016 ;88 :541 –9 10.1002/jmv.24359 26287378 4 Hashemi-Sadraei N , Vejpongsa P , Baljevic M Epstein-Barr virus-related hemophagocytic lymphohistiocytosis: hematologic emergency in the critical care setting . Case Rep Hematol 2015 ;2015 :491567 10.1155/2015/491567 25755898 5 Sato S , Kawashima H , Oshiro H Virological and immunological characteristics of a 19-year-old Japanese female with fatal outcome with Epstein-Barr virus-associated hemophagocytic syndrome . J Clin Virol 2004 ;31 :235 –8 . 10.1016/j.jcv.2004.06.004 15465418 6 Suzuki N , Morimoto A , Ohga S Characteristics of hemophagocytic lymphohistiocytosis in neonates: a nationwide survey in Japan . J Pediatr 2009 ;155 :235 –8 e1 10.1016/j.jpeds.2009.02.050 19446847 7 Rimawi BH , Meserve J , Rimawi RH Disseminated herpes simplex virus with fulminant hepatitis . Case Reports Hepatol 2015 ;2015 :463825 10.1155/2015/463825 26290760 8 Riediger C , Sauer P , Matevossian E Herpes simplex virus sepsis and acute liver failure . Clin Transplant 2009 ;23 (Suppl 21 ):37 –41 . 10.1111/j.1399-0012.2009.01108.x 19930315 9 Kaufman B , Gandhi SA , Louie E Herpes simplex virus hepatitis: case report and review . Clin Infect Dis 1997 ;24 :334 –8 . 10.1093/clinids/24.3.334 9114181 10 Okano M , Gross TG Acute or chronic life-threatening diseases associated with Epstein-Barr virus infection . Am J Med Sci 2012 ;343 :483 –9 . 10.1097/MAJ.0b013e318236e02d 22104426 11 Devereaux CE , Bemiller T , Brann O Ascites and severe hepatitis complicating Epstein-Barr infection . Am J Gastroenterol 1999 ;94 :236 –40 . 10.1111/j.1572-0241.1999.00806.x 9934763 12 Drori A , Ribak Y , van Heerden PV Hemophagocytic lymphohistiocytosis due to acute primary herpes simplex virus 1 infection . J Clin Virol 2015 ;68 :6 –10 . 10.1016/j.jcv.2015.04.013 26071327 13 Ramos-Casals M , Brito-Zeron P , Lopez-Guillermo A Adult haemophagocytic syndrome . Lancet 2014 ;383 :1503 –16 . 10.1016/S0140-6736(13)61048-X 24290661 14 Pinto-Patarroyo GP , Rytting ME , Vierling JM Haemophagocytic lymphohistiocytosis presenting as liver failure following Epstein-Barr and prior hepatitis A infections . BMJ Case Rep 2013 ;2013 10.1136/bcr-2013-008979

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30028-110.1016/j.sjbs.2017.01.019Original ArticleAnalysis on regulatory network linked to Hpa gene in invasion and metastasis of colon cancer Zhang Lei Wu Huili huiliwu183@sina.com⁎Xiao Xingguo Li Kunkun Zhang Yang Zhang Li Wen Tingyu Department of Gastroenterology, Zhengzhou Central Hospital Affiliated to Zhengzhou University, No.195, Tongbai Road, Zhongyuan District, Zhengzhou, Henan Province, China⁎ Corresponding author. huiliwu183@sina.com22 1 2017 3 2017 22 1 2017 24 3 504 507 7 12 2016 28 12 2016 6 1 2017 © 2017 King Saud University2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Our purpose was to discuss the biological function of Hpa gene and its regulatory network in invasion and metastasis of colon cancer. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database were used to perform functional annotation and pathway analysis on Hpa gene. Gene Ontology analysis results showed that Hpa plays a significant role in cellular component, molecular function and biological process; and combined with Kyoto Encyclopedia of Genes and Genomes database, regulatory network of angiogenesis of colon cancer was drawn out. Through analysis of regulatory network linked to angiogenesis in invasion and metastasis of colon cancer, the study lays foundation for further prevention, diagnosis and treatment of colon cancer. Keywords Invasion and metastasis of colon cancerHpa geneAngiogenesisGO analysisNetwork pathway ==== Body 1 Introduction Colon cancer, a common malignant digestive tract tumor, has a high incidence ranking second on the list of digestive tract tumor, greatly threatening human life and health. With the improvement of living standard, people’s diet structure becomes more complicated, and the incidence of colon cancer increases year by year; besides, it tends to attack younger people (Blazeby et al., 2010). In recent years, China has made great progress in treating colon cancer, and in addition to conventional surgical treatment, various protocols have been updated. The postoperative survival rate, however, is still low, and the main reason is that the invasion and metastasis of the tumor lead to recurrence, which finally results in death of patients (Del Pulgar et al., 2007). Since invasion and metastasis of tumor is a complex process with multistage, multi-gene involvement and multi-factor accumulation, it is significant for treatment and prognosis of patients with colon cancer to study the potential molecular mechanism linked to invasion and metastasis of colon cancer. Heparanase (Hpa), a kind of glucuronic acid enzyme, is commonly seen and over expressed in lots of malignant tumors, and more and more studies indicated that it plays a significant role in invasion and metastasis of tumor (Jiang et al., 2009). Hpa can degrade basement membrane and heparan sulfate proteoglycan (HSPG) on extracellular matrix, resulting in destruction of the basement membrane and extracellular matrix and opening channel for the invasion and metastasis of tumor; most importantly, it promotes angiogenesis of tumor with invasion and metastasis, thereby accelerating growth of tumor cells (Vaday and Lider, 2000). To date, there are few reports on analysis of regulatory network correlated to Hpa in colon cancer. Therefore, this study aims to study molecular regulation gene related to colon cancer from the perspective of bioinformatics, and to systematically and comprehensively analyze the regulatory network of Hpa linked to angiogenesis in process of invasion and metastasis of colon cancer, thus providing significant theoretical foundation for improvement of therapy and prognosis of patients with colon cancer. 2 Material and methods 2.1 Gene Ontology (GO) analysis Online website Gene Ontology Consortium (http://geneontology.org/) was employed to conduct GO analysis for Hpa gene. GO analysis began with entering the homepage of Gene Ontology Consortium followed by typing in screening conditions, “Hpa” and “Homo sapiens” in order, and then the primary GO functional annotation for Hpa gene correlated to colon cancer cells was performed. 2.2 Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis The online website KEGG PATHWAY Database (http://www.kegg.jp/kegg/pathway.html) was adopted to analyze network pathway linked to Hpa gene in colon cancer. KEGG analysis began with entering the homepage of KEGG PATHWAY Database before typing in screening conditions, “hsa” for organism, and “Hpa” for keywords, and then regulatory network of the gene in angiogenesis of patients with colon cancer was searched. 3 Results 3.1 GO analysis of Hpa gene Gene Ontology, mainly used in studying specific functions of gene, covers three domains: molecular function, cellular component and biological process. GO analysis results of Hpa gene are shown in Table 1. As shown, Hpa possesses all the three functions, among which molecular function involves heparan sulfate proteoglycan binding, heparanase activity, beta-glucuronidase activity, and so on; cellular component involves proteinaceous extracellular matrix, nucleus, nucleoplasm, and so on; biological process involves positive regulation of vascular endothelial growth factor production, positive regulation of osteoblast proliferation, cell proliferation, glycosaminoglycan catabolic process, and so on. 3.2 KEGG analysis of Hpa gene According to the KEGG analysis of Hpa gene based on KEGG PATHWAY database, Hpa participated in development of angiogenesis in colon cancer, and regulatory network of the angiogenesis which involved Hpa was established, as shown in Fig. 1. 4 Discussion Invasion and metastasis is not only the essential biological characteristic of malignant tumor cells but also a primary reason for the death of patients with tumor. Hpa was firstly found correlated to metastasis of tumor in 1980s. Later, more and more studies indicated that it plays a positive-regulation role in invasion and metastasis of tumor. Friedmann et al. (2000) systematically analyzed the expression of Hpa in human colon cancer, which finally found Hpa had a high expression in the process of colon cancer cells metastasizing to lung, liver and lymph node. Besides, Hpa in tumor tissues of colon cancer had higher expression level and activity as compared to normal colon tissues. Monoclonal antibody was adopted to track the activities of Hpa in colonic epithelial adenoma carcinoma sequence, and results indicated that the expression, processing and location of Hpa in development of colon cancer were closely related to colonic adenoma carcinoma sequence (Doviner et al., 2006). Study by El-Assal et al. (2001) on patients with liver cancer found that mRNA expression of Hpa was significantly correlated to tumor size and invasion of tumor, and they further found that Hpa promoted the growth, invasion and angiogenesis of tumor. In addition, results of study on breast cancer also indicated that Hpa’s correlation to tumor size and metastasis of sentinel lymph node of breast cancer. Koliopanos et al. (2001) conducted over-expression transfection of Hpa in pancreatic carcinoma cells cultured in vitro and results indicated that over expression of Hpa enhanced invasion capability of pancreatic carcinoma cells, thereby promoting the potential of tumor metastasis; besides, the expression of Hpa was negatively correlated to survival rate of patients with pancreatic carcinoma. Research by Goldshmidt et al. (2002) proved the key role that Hpa’s expression and secretion outside the cell played in promoting tumor angiogenesis and metastasis. Elevation of Hpa gene induces tumor angiogenesis and tissue angiogenesis (Elkin et al., 2001). In gastric cancer, study also showed that with the inhibition of Hpa’s expression by siRNA, invasion capability of gastric cancer decreased as well (Zhang et al., 2007). In this study, GO analysis found that Hpa had not only molecular function (including heparan sulfate proteoglycan binding, heparanase activity, etc) but also biological process (including positive regulation of vascular endothelial growth factor production, etc), which is in line with previous studies and intuitively illustrates that Hpa is involved in biological process including tumor metastasis and angiogenesis. Since all above studies showed that Hpa had positive correlation to invasion and metastasis of tumor, researchers have been seeking for an Hpa inhibitor for tumor treatment. For the time being, study of the inhibitor mainly focuses on sulfated polysaccharides or negative ion polymer structurally similar to polysaccharide, among which PI-88 is now the only Hpa inhibitor that enters phase III clinical trials. The invasion and metastasis of tumor needs two essential conditions: one is the break of natural barrier composed of extracellular matrix and basement membrane; the other is tumor angiogenesis. Angiogenesis can maintain and promote normal growth of tumor, and tumor cells can invade surrounding tissues and spread with distant metastasis until forming metastatic lesions in secondary parts; besides, the growth and metastasis of metastatic lesions also need angiogenesis (Fidler and Ellis, 1994), in which the role of angiogenesis is more significant than that in primary lesion (Tanaka et al., 2001). Apparently, angiogenesis is important for invasion and metastasis of tumor. Angiogenesis consists of four steps, which are endovascular dissolution of endothelial basement membrane and endothelial cell activation, endothelial cell migration, endothelial cell proliferation, and formation of blood vessels. The mechanism of angiogenesis is complicated, and it is regulated by various oncogenes and tumor suppressor genes. Through the online website, KEGG PATHWAY Database, this study analyzed the pathways of Hpa on angiogenesis in process of invasion and metastasis. In addition to Hpa, the pathways in this study also involve other angiogenesis-associated factor, such as hypoxia inducible factor 1 alpha (HIF-1α), phosphatase and tensin homolog deleted on chromosome ten (PTEN), cyclooxygenase-2 (COX-2), urokinase plasminogen activator (uPA), and VEGF. Hpa can directly act on vascular endothelium and promote vessel formation by budding; at the same time, it enhances the activity of other growth factors through the degradation of HS so as to speed up the formation of blood vessels (Uno et al., 2001). On the one hand, HIF-1α is a hypoxia inducible factor, which plays an important role in the promotion of tumor angiogenesis and invasion and metastasis of tumor cells. On the one hand, HIF-1α promotes the proliferation and angiogenesis of cells by elevating expression of vascular endothelial growth factor (VEGF) (Liu et al., 2015, Bakirtzi et al., 2014); on the other hand, hypoxia induced by HIF-1α reduces the activity of angiogenesis inhibiting factor so as to provide an appropriate environment for angiogenesis (Ruan et al., 2009). Similarly, uPA and urokinase plasminogen activator receptor (uPAR), through stimulating the migration of vascular smooth muscle cell (Kiyan et al., 2009), degrade extracellular matrix and microvascular basement membrane of adjacent tissues, clear a variety of obstacles for the migration of endothelial cells within a certain range, and provide suitable microenvironment for the formation of new blood vessels, thereby promoting the proliferation of tumor endothelial cells. PTEN is a tumor suppressor gene found and confirmed in 1997 (Li and Ross, 2007), and its inhibition effect on angiogenesis manifests as follows: PTEN dephosphorylates the PIP3 on cell membrane and generate PIP2, and it antagonizes formation of PIP3-mediated proangiogenic pathways (Kuramochi et al., 2016). VEGF is the first protein found to stimulate tumor angiogenesis and also now the unique growth factor that works on vascular endothelial cells only, and it is directly involved in the induction of tumor angiogenesis and also enhances vascular permeability (Hanrahan et al., 2003). 5 Conclusions Through analysis of regulatory network linked to Hpa gene in the process of invasion and metastasis of colon cancer, this study not only facilitates deep understanding of onset and development of colon cancer but also lays theoretical foundation and provides direction for further researches on colon cancer. Furthermore, it offers evidence for prevention, diagnosis, and treatment of colon cancer. Acknowledgments The authors acknowledge the financial support from the Scientific and Technological Development Program in year 2016 in Henan Province China (Grant: 162102310228). Peer review under responsibility of King Saud University. Figure 1 Regulatory network on development of angiogenesis in colon cancer. Table 1 GO analysis results of Hpa gene. Gene Name Ontology Accession Hpa Heparan sulfate proteoglycan binding Molecular_function GO:0043395 Beta-glucuronidase activity Molecular_function GO:0004566 Protein binding Molecular_function GO:0005515 Heparanase activity Molecular_function GO:0030305 Syndecan binding Molecular_function GO:0045545 Nucleoplasm Cellular_component GO:0005654 Proteinaceous extracellular matrix Cellular_component GO:0005578 Nucleus Cellular_component GO:0005634 Intracellular membrane-bounded organelle Cellular_component GO:0043231 Positive regulation of vascular endothelial growth factor production Biological_process GO:0010575 Positive regulation of osteoblast proliferation Biological_process GO:0033690 Positive regulation of protein kinase B signaling Biological_process GO:0051897 Angiogenesis involved in wound healing Biological_process GO:0060055 Positive regulation of cell proliferation Biological_process GO:0008284 Glycosaminoglycan catabolic process Biological_process GO:0006027 ==== Refs References Bakirtzi K. West G. Fiocchi C. Law I.K. Iliopoulos D. Pothoulakis C. The neurotensin-HIF-1α-VEGFα axis orchestrates hypoxia, colonic inflammation, and intestinal angiogenesis Am. J. Pathol. 184 2014 3405 3414 25307345 Blazeby J.M. Soulsby M. Winstone K. King P.M. Bulley S. Kennedy R.H. A qualitative evaluation of patients’ experiences of an enhanced recovery programme for colorectal cancer Colorectal Dis. 12 10 Online 2010 e236 e242 19888959 Del Pulgar T.G. Bandrés E. Espina C. Differential expression of Rac1 identifies its target genes and its contribution to progression of colorectal cancer Int. J. Biochem. Cell Biol. 39 2007 2289 2302 17766170 Doviner V. Maly B. Kaplan V. Gingis-Velitski S. Ilan N. Vlodavsky I. Sherman Y. Spatial and temporal heparanase expression in colon mucosa throughout the adenoma-carcinoma sequence Mod. Pathol. 19 2006 878 888 16607375 El-Assal O.N. Yamanoi A. Ono T. Kohno H. Nagasue N. The clinicopathological significance of heparanase and basic fibroblast growth factor expression in hepatocellular carcinoma Clin. Cancer Res. 7 2001 1299 1305 11350898 Elkin M. Ilan N. Ishai-Michaeli R. Friedmann Y. Papo O. Pecker I. Vlodavsky I. Heparanase as mediator of angiogenesis: mode of action FASEB J. 15 2001 1661 1663 11427519 Fidler I.J. Ellis L.M. The implications of angiogenesis for the biology and therapy of cancer metastasis Cell 79 1994 185 188 7525076 Friedmann Y. Vlodavsky I. Aingorn H. Aviv A. Peretz T. Pecker I. Pappo O. Expression of heparanase in normal, dysplastic, and neoplastic human colonic mucosa and stroma. Evidence for its role in colonic tumorigenesis Am. J. Pathol. 157 2000 1167 1175 11021821 Goldshmidt O. Zcharia E. Abramovitch R. Metzger S. Aingorn H. Friedmann Y. Schirrmacher V. Mitrani E. Vlodavsky I. Cell surface expression and secretion of heparanase markedly promote tumor angiogenesis and metastasis PNAS 99 2002 10031 10036 12097647 Hanrahan V. Currie M.J. Gunningham S.P. Morrin H.R. Scott P.A. Robinson B.A. Fox S.B. The angiogenic switch for Vascular endothelial growth factor (VEGF)-A, VEGF-B, VEGF-C, and VEGF-D in the adenoma-carcinoma sequence during colorectal cancer progression J. Pathol. 200 2003 183 194 12754739 Jiang F. Cui D.W. Su S.Y. Yuan Y. Wang L. Lü C. The expression of HPA in nasopharyngeal carcinoma and its clinical significance Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 22 2009 21 23 Kiyan J. Haller H. Dumler I. The tyrosine phosphatase SHP-2 controls urokinase-dependent signaling and functions in human vascular smooth muscle cells Exp. Cell Res. 315 2009 1029 1039 19133257 Koliopanos A. Friess H. Kleeff J. Shi X. Liao Q. Pecker I. Vlodavsky I. Zimmermann A. Büchler M.W. Heparanase expression in primary and metastatic pancreatic cancer Cancer Res. 61 2001 4655 4659 11406531 Kuramochi H. Nakamura A. Nakajima G. Kaneko Y. Araida T. Yamamoto M. Hayashi K. PTEN mRNA expression is less pronounced in left- than right-sided colon cancer: a retrospective observational study BMC Cancer 13 2016 366 Li L. Ross A.H. Why is PTEN an important tumor suppressor? J. Cell. Biochem. 102 2007 1368 1372 17972252 Liu M. Du K. Fu Z. Zhang S. Wu X. Hypoxia-inducible factor 1-alphaup-regulates the expression of phospholipase D2 in colon cancer cells under hypoxic conditions Med. Oncol. 32 2015 394 25432699 Ruan K. Song G. Ouyang G. Role of hypoxia in the hallmarks of human cancer J. Cell Biochem. 107 2009 1053 1062 19479945 Tanaka T. Konno H. Baba S. Kanai T. Matsumoto K. Matsuda I. Ohba K. Ohta M. Kamiya K. Nakamura S. Prevention of hepatic and peritoneal metastases by the angiogenesis inhibitor FR-118487 after removal of growing tumor in mice Jpn. J. Cancer Res. 92 2001 88 94 11173549 Uno F. Fujiwara T. Takata Y. Ohtani S. Katsuda K. Takaoka M. Ohkawa T. Naomoto Y. Nakajima M. Tanaka N. Anti sense-mediated suppression of human heparanase gene expression inhibits pleural dissemination of human cancer cells Cancer Res. 61 2001 7855 7860 11691803 Vaday G.G. Lider O. Extracellular matrix moieties, cytokines, and enzymes: dynamic effects on immune cell behavior and inflammation J. Leukoc. Biol. 67 2000 149 159 10670574 Zhang Y. Wang Z.N. Zhang X. Xu H.M. Jiang L. Luo Y. Xing L.L. Xu M.D. Li J. Inhibitory effect of siRNA on heparanase expression and invasion ability of gastric cancer cells: an in vitro experiment Zhonghua Yi Xue Za Zhi 87 2007 1717 1720 17825158

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30026-810.1016/j.sjbs.2017.01.017Original ArticleAngiotensin II-accelerated vulnerability of carotid plaque in a cholesterol-fed rabbit model-assessed with magnetic resonance imaging comparing to histopathology Sun Beibei aZhao Huilin huilinzhao2013@163.coma⁎Li Xiao aYao Hong bLiu Xiaosheng aLu Qing aWan Jieqing bXu Jianrong renjixjr@163.coma⁎a Department of Radiology, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, Chinab Department of Neurosurgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200127, China⁎ Corresponding authors. huilinzhao2013@163.comrenjixjr@163.com27 1 2017 3 2017 27 1 2017 24 3 495 503 3 11 2016 28 12 2016 6 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).This study sought to reveal the effect of angiotensin II (Ang II)-induced atherosclerotic vulnerability in rabbits and to determine whether in vivo magnetic resonance imaging (MRI) can determine the effect of Ang II on atherosclerotic development over time. In total, 24 elderly male New Zealand white rabbits underwent an intravascular balloon injury in the left common carotid artery (LCCA) and were subsequently fed a high cholesterol diet for 12 weeks. At 8 weeks, rabbits were randomly assigned to receive either Ang II (1.4 mg/kg/d, Ang II group) or vehicle (phosphate-buffered saline, control) via a subcutaneous osmotic minipump for 4 weeks. The rabbits were imaged three times: at baseline and at 8 and 12 weeks. After the 12-week MRI scanning, rabbits were euthanized to obtain pathological and histological data. Atherosclerotic plaques were identified in the 21 rabbits that survived the 12-week trial. Typical feature of vulnerable plaques (VP), intraplaque hemorrhage, were observed in 6 of 10 animals (60.0%) in the Ang II group. The Cohen K value of MR imaging between the AHA classifications was 0.82 (0.73–0.91; P < 0.001). MRI revealed that the change in carotid morphology were significantly different between the Ang II and control group plaques. Our results support an important role for Ang II in plaque vulnerability by promoting intraplaque neovascularization and hemorrhage as well as inflammation. The vulnerable features induced by Ang II in rabbit carotid plaques could be accurately monitored with MRI in vivo and confirmed with histomorphology. Keywords Angiotensin IIRabbit modelVulnerabilityMagnetic resonance imagingCarotid artery ==== Body 1 Introduction Carotid vulnerable plaques (VP) may result in rapid worsening of stenosis and thrombus formation leading to stroke in patients with carotid atherosclerosis. Therefore, early detection of VP to allow early and effective interventions has become a major research interest. Pathologically, a typical VP often contains intraplaque hemorrhage (IPH) and a large lipid-rich necrotic core (LRNC) covered by a thin fibrous cap and is infiltrated by inflammatory cells, such as macrophages. In recent years, a VP of the aorta can be successfully established through a balloon-induced vascular endothelium injury or by transfecting the abdominal aorta with the p53 gene followed by pharmacological triggering in rabbits given a high-fat diet (Qi et al., 2015, Phinikaridou et al., 2010). These types of models have been the most common models used for magnetic resonance imaging (MRI) studies, but few studies have been related to the rabbit carotid model, which is anatomically suitable for further investigation of the stroke caused by carotid atherosclerosis. In addition, some studies aimed at creating a carotid VP model, such as a high cholesterol diet combined with a balloon injury or cast placement, are both unable to create a human-like vulnerable plaque in a limited period (Ma et al., 2008, Den Dekker et al., 2014) because hepatic failure may develop in New Zealand white (NZW) rabbits after the long period of high cholesterol diet. So creating an accelerated VP plaque model in the carotid artery is our major research interest. Angiotensin II (Ang II) is involved in various vascular events, such as endothelial activation and dysfunction (Pueyo et al., 2000, Laursen et al., 1997), cell proliferation (Kohno et al., 2000) as well as proinflammatory effects (Mervaala et al., 1999) of atherosclerotic lesions. Daiana et al. found that Ang II–induced hypertension can specifically increase the development of atherosclerosis in apoE knockout mice. Others have also reported that the carotid atherosclerotic lesions from Ang II–treated mice display a more pronounced vulnerable plaque phenotype, such as intralesional neovasculature and hemorrhage (Da Cunha et al., 2006, Cheng et al., 2006, Du et al., 2016). Based on these murine models, this study is aimed (1) to test whether Ang II act as an accelerating factor of the lesion by promoting plaque vulnerability in a rabbit atherosclerotic model and (2) to apply MRI measurements to monitor the effect of Ang II on atherosclerotic development over time and to see whether MRI is a useful modality for accurate assessment and follow-up evaluation of plaques. 2 Materials and methods 2.1 Animal model This experimental protocol was approved by our institutional committee for animal use and care. In total, 24 male elderly NZW rabbits(Department of Laboratory Animal Science of Fudan University, Shanghai) weighing 3.0–3.5 kg were recruited and were fed an atherogenic diet containing 1% cholesterol (120–140 g/day) for 1 week and then subjected to a balloon-induced intimal injury of the left common carotid artery (LCCA). A balloon catheter with diameter of 2.5 mm and a length of 20 mm (Medtronic, AVE, Santa Rosa, CA) was inserted into the LCCA and inflated and deflated three times for 180 s after rabbits had been anesthetized with an intravenous injection of pentobarbital. After balloon injury of the LCCA, rabbits were maintained on a high cholesterol diet until sacrifice at the end of 12 weeks. Four weeks before the end of the trial, animals were randomly assigned to receive subcutaneous implantation of a minipump filled with either angiotensin II (1.44 mg/kg/d, AngII group) or phosphate-buffered saline (control). We choose an infusion rate of angiotensin II previously shown to accelerate atherosclerosis in apoE-KO mice (Daugherty et al., 2000). 2.2 Blood pressure and lipid determination After anesthetization through the auricular vein but prior to sacrifice, catheters were placed in the right femoral artery for recording of the rabbit’s arterial blood pressure. Arterial pressure was measured in a conscious rabbit using a pressure transducer and recorded on a thermal array recorder (RTA 1200 M; Nihon Kohden, Tokyo, Japan). Blood samples were collected from the ear artery at 0 week and the end of the 8-week cholesterol diet prior to the animal being sacrificed. Plasma total cholesterol (TC), triglyceride (TG), high density lipoprotein-cholesterol (HDL-C) and low density lipoprotein-cholesterol (LDL-C) were measured with enzymatic reaction kits from BioVision (Mountain View, Calif) and Wako Chemicals Co (Richmond, VA), respectively. 2.3 Histopathological analysis Immediately after sacrifice, the injured LCCA tissues were removed from the rabbit and washed in saline. The distances from the surgical cut and the carotid bifurcation were used as internal reference points to co-localize between the MRI findings and the histological specimen. The carotid arteries were marked with suture ligatures on the left bifurcation over the total length imaged by MRI. The specimens were fixed in 10% formalin, sectioned in 3.0-mm transverse slices, decalcified, and embedded in paraffin. The paraffin-embedded specimens were sectioned at 10 μm thickness, stained with hematoxylin and eosin (HE), and subjected to histopathological and immunohistochemical analyses. HE staining was used for routine histopathological examination and compared with the MRI. Atherosclerosis formation was histomorphologically evaluated according to the AHA classifications (Stary et al., 1995). 2.4 Immunohistochemical (IHC) staining All of the tissue samples were pre-treated as previously described (Torzewski et al., 1998). For Pentraxin3 (PTX3), RAM11 and CD31 staining, slides were first incubated with one of the following primary antibodies: PTX3 (1:200, code LS-B6679, Life span), RAM11 (1:100, code M0633, Dako), CD31 (1:200, code ab9498, Abcam) at 4 °C overnight. After a wash in PBS, sections were incubated with biotinylated anti-mouse secondary antibody (MaiXin Bio, Fuzhou, China) at room temperature for 30 min and then with the avidin–biotinylated horseradish peroxidase complex (ABC Elite kit, Vector, Burlingame, CA, USA) for 30 min. Peroxidase labeling was visualized using 0.2% (v/v) 3, 39-diaminobenzidine as a chromogen. When using the antibodies, the sections were lightly counterstained with hematoxylin. All of the sections were observed and photographed with an Axioskop 2 microscope (Carl Zeiss, Oberkochen, Germany). PTX3 and RAM11 expression levels were interpreted based on the ratio and intensity of positive-staining cells: <5% scored 0; 5–24% scored 1; 25–49% scored 2; 50–74% scored 3; and >74% scored 4 (Zhang et al., 2015). These scores were determined independently by two senior pathologists. CD31+ neovessels in the plaque were counted by light microscopy. Six high-power fields (40×) were acquired for each slice, and the numbers of positively-stained cells in the intima and media were counted. The count was acquired six times and then averaged (Mao et al., 2010). 2.5 High-resolution multi-sequence MR imaging Serial MRI examinations were performed 3 times, at baseline and at 8 and 12 weeks on a 3.0 T MRI scanner (Achieva; Philips Healthcare, Best, Netherlands) with an 8-channel phased-array carotid coil (Chenguang Medical Technologies, Shanghai, China). Rabbits were sedated with an intravenous injection of 40 mg/kg pentobarbital (Shanghai Chemical Reagent Co., Shanghai), allowing spontaneous respiration throughout the examination. MRI were performed with the rabbits placed in a prone position. A standardized imaging protocol was performed to obtain multi-contrast cross-sectional MRI scans: 3D TOF (TR/TE, 20/4.9 ms; flip angle, 20°), quadruple inversion recovery T1-weighted sequence (TR/TE, 800/10 ms), T2-weighted sequence with multi-double inversion recovery 18 (TR/TE, 4800/50 ms), and 3D MPRAGE sequence (TR/TE, 8.8/5.3 ms, flip angle, 15°). All MRI axial scans were acquired with a section thickness of 3 mm, an FOV of 140 × 90 mm, and a matrix size of 336 × 336. Fat saturation was applied to the acquisition of the black-blood sequences to enhance the tissue contrast between the carotid vessel wall and the surrounding tissues. 2.6 MR imaging analysis The MR findings were interpreted independently by three radiologists (ZHL, SBB, and HPP) who were experienced with vascular imaging but blinded to the histological findings. Quantitative MRI analyses were also performed by them by consensus. The wall thickness of the injured LCCA or the distance between the outer and inner borders of the cross-section on the carotid artery was measured using commercial software (Vessel Mass, Netherlands). The measurements were usually made on the T1W images, with the T2W images, 3D-TOF and MP-RAGE cross-referenced. The morphological measurements, including the vessel wall area (VWA) and max wall thickness (max WT), were measured for 6 slices of each artery. The presence or absence and the area of each carotid plaque component (e.g., LRNC and IPH, calcification [CA]) in each slice were identified based on a previous study (Saam et al., 2005, Zhao et al., 2013). According to the modified American Heart Association (AHA) criteria (Cai et al., 2002), type I and II = nearly normal vessel wall thickness; type III = diffuse intimal thickening or a small eccentric plaque with no calcification; Type IV–V = plaques characterized by a lipid or necrotic core surrounded by fibrous tissue with possible calcification. Type VI = complex plaque with a possible surface defect, hemorrhage, or thrombus. 2.7 Statistics All obtained quantitative data were expressed as mean ± standard deviation (SD). Statistical analyses were performed with the SPSS software (SPSS for Windows, version 11.0, 2001; SPSS, Chicago, IL). Cohen’s κ coefficient was used to assess agreement between the MRI findings and the histological analysis. Kappa ranges from −1 (perfect disagreement) to +1 (perfect agreement), and kappa ⩾0.75 was defined as strong agreement, 0.4 < kappa < 0.75 was defined as moderate agreement and kappa ⩽0.4 was defined as poor agreement (Curvo-Semedo et al., 2011), P < 0.05 were considered statistically significant. The effect sizes of plaque parameters to detect plaque development were calculated as the ratio of the mean difference between 8 weeks and follow-up measurements divided by the pooled standard deviation. Effect sizes were compared using a paired t-test (Den Hartog et al., 2013). The continuous variables were compared via an independent-samples t-test when normally distributed or the Mann–Whitney U test when non-normally distributed. 3 Results Of the 24 rabbits initially selected, 2 from the Ang II group died at 9 weeks due to a suspected multi-organ dysfunction. In the control group, 1 died from an anesthesia accident at 8 weeks. 3.1 Artery pressure and serum lipid assay At the end of week 12, lipid disorders were led by a high-cholesterol diet, including significantly increased TC, TG, HDL-C and LDL-C compared with the baseline levels (P < 0.01) (Fig. 1). In addition, the body weights of the rabbits in the two groups gradually increased over time (P < 0.05). However, all these parameters showed no significant difference between the two groups (P > 0.05). At 12 weeks, the rabbits’ mean systolic pressure in the Ang II group was higher than that in control group rabbits (175.8 ± 7.5 vs 120.8 ± 6.4 mmHg). 3.2 Accelerated effect of Ang II in rabbit carotid atherosclerosis model Histologically, the 21 rabbits that survived until the end of the study period all had formed various degrees of atherosclerotic plaques. Six rabbits in the Ang II group (6/10, 60.0%) developed VPs (containing IPH) in the LCCA, but no IPH was observed in the control group. Ang II had a dramatic effect on the development of atherosclerosis in the carotid artery. Representative examples of the LCCA after 4 weeks of Ang II and vehicle treatment in the Ang II and control groups are shown in Fig. 2. Note that other than more prominent neointima augmentation and decreased lumen diameter, intimal changes including irregularly developed intralesional neovasculature and hemorrhages were more evident in the Ang II–treated rabbits compared with the control group (Fig. 2). Additionally, intimal lesions were characterized by the presence of RAM 11-positive macrophage foam cells in both groups, but the most intense staining occurred in the Ang II–treated animals (Fig. 3). Fig. 3 also demonstrates that the expression of inflammatory marker PTX3 and neovascularization marker (CD31) were significantly higher in the Ang II group than in the control group. 3.3 Agreement between the MRI and the histopathological findings Among the 126 carotid segments assessed histologically, 112 showed atherosclerosis ranging from AHA type I to VI. Forty-three segments displayed the early stages of atherosclerosis (AHA I–III), while 69 segments had advanced plaques (AHA IV–VI). Advanced atherosclerotic plaques were found more frequently in the Ang II group than the control group (68.1% vs 31.9%, P < 0.001). Based on the above results, the Cohen K value between the MR imaging and the AHA classification was 0.82 (0.73–0.91) (P < 0.001) (Table 1). Fig. 4 shows representative MR images and histopathology of a IV–V plaque (Fig.4a at 12 weeks) and a VI plaque (Fig.4c at 12 weeks). Fig.4a at 12 weeks shows an MR image of an eccentric type IV–V plaque. The corresponding histological section confirmed the presence of an intact fibrous cap overlaying a lipid-core (Fig.4b). The MR image of the type VI plaque shows a hyperintense signal in all sequences in the thickened vessel wall (Fig.4c at 12 weeks). The corresponding histology (Fig.4d) revealed the hyperintense site and confirmed the presence of intraplaque hemorrhage and abundant foam cells. 3.4 MRI measurements of plaque development in the Ang II and control groups At 12 weeks, 90 of the total 112 axial vessel wall images contained plaque (AHA III–VI) according to the modified AHA criteria for MRI; 48 (53.3%) were in the Ang II group, and 42 (46.7%) were in the control group. At 8 weeks, no IPH were found in these two groups, and the plaque composition (LRNC area) and plaque burden (Max WT and VWA) were not significantly different between these two groups. In order to testify the accelerated effect of Ang II on atherosclerosis, the effect sizes of plaque composition and burden to detect plaque development during 8–12 weeks were calculated in these two groups (Table 2), which showed that significant changes were exhibited in VWA, max WT, and LRNC area in these two groups. In addition, the Ang II group has a greater effect size of the plaque parameters (VWA, max WT and LRNC area) than those of the control group. The morphologic changes of these two groups’ LCCAs are illustrated in Fig. 4. 4 Discussion The present study revealed the followings: (1) Ang II short-term administration prompts the vulnerability of carotid atheroma and creates more extensive plaque lesions by inducing intralesional hemorrhage and neovascularization as well as abundant inflammation in our rabbit atherosclerosis model; and (2) in vivo MRI is capable of accurately identifying and quantifying the major components of the carotid atherosclerotic plaques in the Ang II-treated rabbit model and can also monitor the development of carotid atherosclerotic plaques over time. Plaque vulnerability is closely associated with the incidence of stroke and acute myocardial infarction. So, early and accurate detection of VPs is very important. In our study, Ang II was used to enhance plaque vulnerability, which induced neovascularization, IPHs, and inflammation. IPHs are considered prominent markers of plaque progression and instability, which are closely correlated with plaque disruption (Kolodgie et al., 2003). While, Valdeci et al. showed that besides Ang II-induced neovascularization, hemorrhage and inflammation, the plaque lesion is also accompanied by further expansive remodeling in apolipoprotein E–deficient mice. Ivan et al. demonstrated a close relationship between intimal inflammation and expansive remodeling in ligated arteries of apoE-KO mice (Ivan et al., 2002). Others have also claimed that expansively remodeled atherosclerotic vessels often give rise to more rupture-prone lesions (Schoenhagen et al., 2000). So our study, for the first time, explores the effect of Ang II on the combination of a high cholesterol diet and balloon injury in the LCCA, which is different from the ligation of the LCCA (aim to trigger the remodeling) in the study by Valdeci. Through these design differences, we included that Ang II-induced vulnerable features of plaques may be independent of the expansive remodeling process. Ang II administration increases mechanical strain on the lesions by raising blood pressure, and Ang II–induced hypertension can accelerate the development of atherosclerosis in ApoE-deficient mice (Weiss et al., 2001). Ang II infusion into hyperlipidemic mice also augments lesion formation independent of elevation in blood pressure by eliciting a proinflammatory Th1-like phenotype or by increasing the angiogenic properties of the plaque (Mazzolai et al., 2004). Inflammatory mechanisms are known to play central roles in the pathogenesis and progression of atherosclerosis, plaque rupture and subsequent thrombosis and stroke. PTX3 is the prototypic member of the long pentraxin family that is produced at the site of inflammation in response to primary inflammatory stimuli by various cell types, including monocytes/macrophages, endothelial cells, vascular smooth muscle cells, fibroblasts, and adipocytes (Garlanda et al., 2005). PTX3 has been suggested as a marker of inflammatory activity and plaque instability (Matsuura et al., 2012). A number of studies have suggested PTX3 was associated with plaque vulnerability in the carotid and coronary arteries (Shindo et al., 2014, Hollan et al., 2013), Akihiro Shindo et al. have shown that serum PTX3 levels in both systemic and intracarotid samples before and after carotid artery stenting were higher in the vulnerable group than in the stable group. Neovessels within plaques characterized by fragility and high perfusion have also been considered to be an additional feature of VPs. These neovessels can increase the permeability of inflammatory cells, especially the presence of extensive macrophage accumulation (Chen et al., 2005, Lin et al., 2007). Multiple studies have emphasized a critical link between intravascularization and intralesional hemorrhage and instability by stimulating infiltrated inflammatory cells, which all play important roles in plaque progression and destabilization (Chen et al., 2005). In our study, plaque lesions in the Ang II group had extensive neovascularization; moreover, abundant macrophage cells as well as extensive staining for inflammation marker PTX3 were diffusedly distributed in the Ang II-treated vessel wall. So our data showed that the IPHs that occurred in the lesions in response to angiotensin II stimulation may be correlated with disrupted intralesional vessels and abundant inflammatory cell infusion. Our findings greatly substantiate the multipotent ability of Ang II to affect plaque vulnerability. High-resolution MRI is a non-invasive technique to monitor the natural progression of atherosclerotic plaques not only including general features of a plaque but also some characteristics of a vulnerable plaque: a thin or ruptured fibrous cap, a large lipid-rich necrotic core, and intraplaque hemorrhage. With the rapid development of MRI in recent years, MRI studies on atherosclerosis in various vulnerable atherosclerotic animal models have been described. Our results showed that an MR vessel wall image can accurately identify and quantify the rapid carotid atherosclerotic progression in the Ang II-accelerated rabbit model over time. Ang II induced more extensive lesions that enabled us to determine that there was a good correlation between MRI classifications and histopathological classifications in our rabbit models. Even these correlations have also been studied by others, prior authors either mainly focused on the relationship between the measurements of vessel wall thickness with MRI and histopathology (Wang et al., 2006) or have scarce plaques fulfill the criteria for advanced human atherosclerotic plaque. In addition, different from other MRI studies involving vulnerable atherosclerosis models, which were either long-term experiments with rare advanced lesions or involving a toxic-triggered thrombus that does not represent true plaque rupture (Ma et al., 2008, Ma et al., 2012), in our study, vulnerable plaques were induced by Ang II administration based on a classical high cholesterol-diet and endothelial injury. In particular, the intraplaque hemorrhage which is currently viewed as the driving force in plaque progression (Virmani et al., 2005) and regarded as prone to disruption (Kampschulte et al., 2004, Milei et al., 2003, Zhang, 2016), can be accurately and directly detected by MRI at 12 weeks even when plaque rupture, and an occlusive thrombus is not observed. Based on these observations, MRI analysis also showed that the Ang II accelerates atherosclerotic progression, which is indicated by a larger effective size of VWA, max WT, and LRNC area. Our study is the first to substantiate the proatherosclerotic effect of Ang II with MR vessel wall images. 4.1 Limitations of this study Our study has some limitations. First, the Ang II accelerated model of atherosclerosis resulted in a greater extent of atherosclerosis and a higher percentage of IPH but plaque CA, rupture and thrombosis were absent, perhaps because of short follow-up period in our study design. Another important issue involving our analysis of the consistency between MRI classifications and histopathological classifications, the MRI slice thickness (3 mm) is greater than that of the histological section (10 μm). The MR image represents a composite of 300 histology sections. To lessen this mismatch, we sectioned the tissue in 3.0-mm transverse slices corresponding to the MRI slice thickness and chose the histological sections for HE and IHC staining near the interval level. However, in some complex specimens containing lesions that may change significantly in size or composition from section to section, obtaining precise co-registration can be difficult. Also, due to the tissue degradation and dehydration, artifactual transformation of histological sections may occur. So, we performed HE and IHC staining as soon as possible after animals were sacrificed to reduce those possible influences. In conclusion, as shown in this accelerated atherosclerotic rabbit model, our data further strengthen the effect of Ang II on promoting plaque vulnerability by inducing neovascularization, hemorrhage, and inflammation. Additionally, MR plaque imaging represents a useful modality for accurate assessment and follow-up evaluation of atherosclerotic progression that substantiated roles the roles of Ang II from the view of radiology. Acknowledgements This study was supported by National Natural Science Foundation of China (grants 81401374 and 81571630), Young Researcher Grant from Shanghai Municipal Commission of Health and Family Planning (20144Y0076) and Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (YG2016MS56). Peer review under responsibility of King Saud University. Fig. 1 Body weight and lipid profile of control and angiotensin II-treated rabbits. TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol and HDL-C, high-density lipoprotein cholesterol. Results are expressed as the mean ± S.E.M. “*” Each group compared with 0 weeks (baseline), P < 0.01. “#” Each group compared with 8 weeks, P < 0.01. Angiotensin II, Ang II. Fig. 2 The histopathologic features of angiotensin II–treated rabbits. (Upper panel) Plaque lesions from the control animals showed a thickened intima displaying smooth muscle cells in a matrix background in different regions of the vessel but with rare small intralesional vessels. (Lower panel) Plaque lesions in angiotensin II–treated rabbits display a more complex morphology characterized by large and more irregular intralesional vessels (red arrow) and the presence of intralesional hemorrhages (white arrow). Angiotensin II, Ang II. Fig. 3 Effect of angiotensin II on the expression of RAM11, PTX3 and CD31. Representative sections of the left common carotid arteries from the control (Upper panel) and angiotensin II–treated (Lower panel) rabbits after staining with hematoxylin and eosin and immunostaining for RAM11 and PTX3 expression and capillary density (a). The quantification of immunohistochemistry analysis of RAM11, PTX3, and CD31 staining of sections from artery tissues (b). *P < 0.05. Angiotensin II, Ang II. Fig. 4 MR images showed different development over time between the control group (a) and the angiotensin II-treated group (c), corresponding with histopathology (b and d). (a) In the control group, compared to the baseline MRI, the injured LCCA wall (white arrow) became thicker with hyperintense on T1WI and appeared isointense on T2WI in the 8th week. At the 12th week, the LCCA wall (white arrow) contained a plaque with hyperintensity on T1WI but appeared hypointense on T2WI and there were no hyperintensities in MP-RAGE (AHA IV–V). The histological section (b) corresponding to (a) showed plaques composed of foam cells (blue arrow) with a narrower lumen (black star, HE staining, ×40). (c) In the Ang II-treated group, the MRI showed that the injured LCCA wall (white arrow) contained a plaque with hyperintensity on T1WI and appeared hypointense on T2WI at the 8th week after 4 weeks of the Ang II treatment. The MRI demonstrated an advanced atherosclerotic plaque (white arrow) composed of IPH that appeared hyperintense on T1WI, TOF, MP-RAGE and appeared iso/hypointense on T2WI. The histological section (d) corresponding to (c) showed larger and more extensive plaques (AHA VI) composed of abundant foam cells (blue arrow), neovessels (broad white arrow) and IPH (red arrow) with a significantly narrower lumen (blue star, HE staining, ×40). Angiotensin II, Ang II; left common carotid artery, LCCA. Table 1 The agreement between MRI and histopathologic findings via Cohen’s Kappa test. MRI type Histology type Kappa P I–II III IV–V VI Total I–II 8 4 0 0 12 0.82(0.73–0.91) <0.001 III 4 25 2 1 32 IV–V 0 2 42 0 44 VI 0 0 1 23 24 Total 12 31 45 24 112 Cohen’s Kappa test. Table 2 Comparison of the changes of LCCA over time of the Ang II group and control group. Plaque parameters 8 weeks MRI 12 week MRI Growth t-value Effective size Control group (n = 42 slices) VWA (mm2) 2.37 ± 0.74 4.85 ± 2.21 2.48 ± 2.26 7.12 1.09⁎ max WT (mm) 0.53 ± 0.23 1.10 ± 0.40 0.57 ± 0.42 8.59 1.32⁎ LRNC area (mm2) (12 slices) 0.29 ± 0.35 2.77 ± 1.32 2.48 ± 1.43 6.18 1.78⁎ Ang II group (n = 48 slices) VWA (mm2) 2.60 ± 4.07 8.45 ± 2.66 5.85 ± 4.94 8.19 1.18⁎ max WT (mm) 0.55 ± 0.14 1.29 ± 0.35 0.74 ± 0.41 12.56 1.81⁎ LRNC area (mm2) (23 slices) 0.28 ± 0.46 3.27 ± 1.09 2.99 ± 1.20 11.68 2.43⁎ Effect sizes (t-value/n−2) were compared using a t-test. Data are means±standard deviations. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30029-310.1016/j.sjbs.2017.01.020Original ArticleConfirmation of the abnormal lipid metabolism as a risk factor for the disease of leukoaraiosis Shi Junying Hao Kai Qi Peihong Xie Xiaogang Yang Xinhuan Dong Mei Shang Yingjie Zhang Sijia zhsijia10@163.com⁎Imaging Department, People’s Hospital of Zhengzhou, Zhengzhou 450003, China⁎ Corresponding author. zhsijia10@163.com26 1 2017 3 2017 26 1 2017 24 3 508 513 7 12 2016 28 12 2016 6 1 2017 © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Our purpose is to screen out medical history indicators and test indicators linked to lipid metabolism which is closely correlated to leukoaraiosis (LA), and to build assistant diagnosis model based on support vector machine (SVM), which provided theoretical evidence for genesis and development of LA. One thousand LA patients who underwent magnetic resonance imaging (MRI) examination in Imaging Department was retrospectively analyzed and divided into LA group and non-LA group in accordance with examination results. Detailed clinical statistics of the two groups were collected, including test indicators related to lipid metabolism, such as total cholesterol (TC), triglyceride (TG), low density lipoprotein (LDL), high density lipoprotein (HDL), medical history indicators, age, sex, diabetes, hypertension, hyperlipidemia, history of intracranial infection, history of cerebral hemorrhage, cerebral infarction, lacunar infarction and relevant biochemical indexes. The study shows that patients’ incidence of LA was 31.10%; in accordance with Logistic analysis, the incidence of LA is significantly correlated to factors like age, hypertension, history of cerebral hemorrhage, cerebral infarction, lacunar infarction and triglyceride elevation; two SVMs, one including all variables and the other containing all screened variables were successfully established, and the former’s accuracy, specificity and sensitivity respectively were 85.0%, 85.0% and 85.0% while the latter’s 90.0%, 100.0% and 80.0%. Test indicators and medical history indicators of lipid metabolism correlated to LA were screened out successfully. Meanwhile, an effective SVM model also was built successfully, which is able to predict LA relatively accurately and can be used as assistant diagnostic tool for clinicians. Keywords Lipid metabolismLeukoaraiosisRisk factorsLogistic analysisSupport vector machine ==== Body 1 Introduction Hyperlipemia, known as dyslipidemia in modern medicine, is characterized by excessive total serum cholesterol and triglyceride (Kagoya et al., 2012). Due to no obvious symptoms and latent onset, hyperlipemia is called “the Silent Killer” in the field of medicine (Zhang, 2016) and it is generally found in physical examination. At present, serum total cholesterol (TC), triglyceride (TG), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C) are often used as test indicators of hyperlipidemia, any of which exceeding normal criteria is recorded as hyperlipidemia. Leukoaraiosis (LA), described in iconography, is a disease of abnormal changes in cerebral white matter. It is proposed in 1986 by the Canadian neurologist Hachinski et al. (1986). LA is often found in the medical examination of the elderly so it was believed that the disease was only related to age and drew little attention from people. But in fact, it is a clinical syndrome caused by a variety of pathogenesis (Huo and Feng, 2015). Pathological studies have shown that LA lesions are mainly caused by the changes of demyelination in white matter, which is expressed in CT as white matter mottling or diffuse low density image in periventricular and semi-oval central area (Huo and Feng, 2015), and in magnetic resonance imaging (MRI) showed isointense or hypointense on T1-weighted image and hyperintense on T2-weighted image. Studies have shown that LA is a biomedical marker for brain aging (Ross et al., 2005) and is closely correlated to Alzheimer (Guo et al., 2004, Pantoni et al., 2007). LA may be a different manifestation pattern of stroke in addition to causing slowed thinking, declined cognitive function and mental status changes (Hassan et al., 2004). At present, there is no effective therapy for LA, thus how to prevent LA has been a hot research issue (Hassan et al., 2004, Culebras, 2004, Fujita et al., 2005, Altaf et al., 2006, Chen et al., 2004). Through looking for risk factors inducing LA and treating them targetedly can effectively prevent occurrence of dementia, stroke and death. This study established support vector machine (SVM) based on the risk factors inducing LA and provided certain theoretical basis for prevention of LA’s genesis and development. 2 Materials and methods 2.1 Subjects One thousand patients who received enhanced cranial plain scan MRI examination in Imaging Department from September 2010 to September 2015 were enrolled. According to the MRI findings and LA diagnostic criteria, patients were divided into LA group and non-LA group. After reading documents (Culebras, 2004, Fujita et al., 2005), reported pathogenesis of LA was combined with clinical experience to collect the possible factors related to LA. Finally, risk factors included in the study were age, sex, diabetes, hypertension, history of intracranial infection, cerebral hemorrhage, cerebral infarction as well as lacunar infarction, and test indicators linked to lipid metabolism (TC, TG, LDL and HDL). In addition, detailed medical history of each patient was inquired by the same neurologist, and MRI images of cerebral infarction and lacunar infarction were combined to collect materials of patients. 2.2 Variable definitions ① LA: LA is shown as the punctuate, patchy or fusion flaky long T1 and long T2 signals on MRI around the ventricle. ② Infarction: Infarction is shown as long T1 and long T2 signals on MRI with a diameter >5 mm. ③ Lacunar infarction: Lacunar infarction was shown as long T1 and long T2 signals on MRI with a diameter < 15 mm: level 0 = no; level 1 = 1 ∼ 3; level 2 = 4 ∼ 10; level 3 = more than 10. ④ History of hypertension: According to the WHO/ISH Guidelines and Reports of Hypertension in 2007, Systolic Blood Pressure (SBP) ⩾ 140 mmHg and/or Diastolic Blood Pressure (DBP) ⩾ 90 mmHg and/or who are taking antihypertensive drugs are recorded as hypertension history. ⑤ Diabetes: The diagnostic standards of World Health Organization (WHO) about diabetes in 2005 are: showing diabetes symptoms and fasting blood-glucose ⩾ 7.0 mmol/L (126 mg/dl), or blood glucose ⩾ 11.1 mmol/L (200 mg/dl) 2 h after meal. ⑥ Hyperlipidemia: According to Guidelines for Prevention and Treatment of Chinese Adult Dyslipidemia in 2007, any terms exceeds the four following criteria can be recorded as hyperlipemia: TC ⩾ 5.80 mmol/L, TG ⩾ 1.80 mmol/L, LDL ⩾ 3.60 mmol/L, HDL ⩽ 1.00 mmol/L, any index from the above four terms excesses the standard will be decided as hyperlipidemia. ⑦ Cerebral hemorrhage history: Since a variety of causes lead to cerebral hemorrhage, all patients ever had the disease are recorded as a history of cerebral hemorrhage. 2.3 Statistical analysis SPSS21.0 software was utilized for statistical analysis, χ2 test was used for comparison and P < 0.05 was considered statistically significant. 2.4 Logistic regression analysis Several risk factors of LA have been screened out with forward stepwise Logistic regression analysis by SPSS 21.0 software in this study. 2.5 SVM Two assistant diagnostic models of LA based on all variables and screened variables respectively were established with SVM algorithm. Then the results were divided into two categories and each of them was randomly divided into two groups, one as training set and the other as test set. The SVM models were constructed by MATLAB software and evaluated in terms of accuracy, sensitivity and specificity. 3 Results 3.1 The incidence of LA According to the analysis of 1000 patients’ clinical data, their incidence of LA was 30.10% (311/1000). The percentage of patients who had diabetes, hypertension, hyperlipidemia, history of cerebral hemorrhage, cerebral infarction, lacunar infarction, triglyceride elevation in LA group was greater than that in non-LA group. 3.2 Analysis of LA risk factors χ2 test was applied to eight medical history indicators and four test indicators and the test results are shown in Table 1, Table 2 which indicated that differences of the incidence of LA in age, diabetes, hypertension, hyperlipidemia, history of cerebral hemorrhage, cerebral infarction, lacunar infarction and triglycerides elevation were all statistically significant (P < 0.05), suggesting that age, diabetes, hypertension, hyperlipidemia, history of cerebral hemorrhage, cerebral infarction, lacunar infarction and elevated triglycerides were correlated to the incidence of LA. 3.3 Logistic regression analysis Regrading LA as dependent variable and LA risk factors as covariate, this study used forward stepwise Logistic regression analysis to screen out significant indicators for LA diagnosis, and the results are listed in Table 3. It was found that age, hypertension, history of cerebral hemorrhage, cerebral infarction, lacunar infarction, and elevated triglycerides were significantly correlated with the incidence of LA. 3.4 SVM model All cases were divided into two groups, LA group and non-LA group. 291 cases randomly selected from LA group and 669 cases from non-LA from group were regarded as training sets based on which SVM model was established. Then the rest 20 cases from LA group and 20 cases from non-LA group were taken as test sample and were put into SVM model to perform training and simulating tasks. Two SVM models, one including all variables and the other including screened variables, were established, and their simulation results are shown in Figure 1, Figure 2, in which abscissa represents test sample; ordinate represents, output result of model; “○” represents target output; and “∗” represents actual simulation output of SVM. When all variables were included, SVM’s accuracy, specificity and sensitivity respectively were 85.0%, 85.0% and 85.0%; when screened variables were included, SVM’s accuracy, specificity and sensitivity respectively were 90.0%, 100.0% and 80.0%. It can be concluded that SVM including screened variables was better than SVM including all variables in terms of accuracy and specificity. And SVM simulation results had the highest fit level compared with actual results and had good efficacy. 4 Discussion With the improvement of people's living standard and the change of dietary structure, the prevalence of dyslipidemia is also gradually increasing, and abnormal lipid metabolism is closely related to the increase of hepatobiliary diseases, diabetes, cardiovascular disease and other biochemical indexes (Zhang et al., 2016, Hu, 1997). LA, an imaging concept, is a clinical syndrome induced by various etiologies and it is correlated to many factors. Most scholars believe that LA’s basic pathological changes are diffuse or localized demyelination in white matter and tissue looseness together with edema, reactive glial cell proliferation around cornu occipitale; intima thickening of subcortical white matter deep perforating artery branches together with lipid deposition, glassy degeneration or abercrombie’s degeneration of small vessel; loss of cells of ependymal layer; increase of extracellular fluid content among intervals around endyma and thinner and less axis cylinder (Chimowitz et al., 1992). CT outcomes of LA is closely related to age. As seen from Table 1, the incidence rate of LA in each age group was significantly increased with age, which was respectively 2.05%, 20.31% and 53.44%. Logistic regression analysis showed that relative risk odds ratio (OR) of age to LA was 3.535 (95% CI 2.558 ∼ 4.885), indicating that when patient escalated into a higher age group, his risk of LA increased by 3.535 times, which showed that the incidence of LA increased with age. And the study results are in line with those of Zhou and Zhou (2009), indicating a possible association of partial LA with aging. Among the four indicators causing hyperlipidemia, TC, HDL and LDL were related to the incidence of LA but the difference among their relations had no remarkable significance. But in Logistic analysis, the OR of triglyceride was 2.106 (95% CI 1.456～3.047) and the LA incidence on people with high triglyceride was 2.106 times higher than normal people, which indicated that high-triglyceride hyperlipemia was a risk factor for LA, and this result is consistent with the research of Park et al. (2007) on the relations between abnormal metabolism and LA among healthy people. Therefore, the prevention of high triglyceride is effectively helpful for LA’s prevention and treatment. Table 3 shows that OR of the cerebral hemorrhage history was 6.626 (95% CI 1.206 ∼ 36.418), illustrating there was a 6.626 times higher risk of LA in people with a history of cerebral hemorrhage than people without cerebral hemorrhage ever; the OR of cerebral infarction was 7.179 (95% CI 2.838 ∼ 18.156), showing that the risk of people with cerebral infarction suffering from LA was 7.179 times higher than those without cerebral infarction; the OR of lacunar infarction was 2.712 (95% CI 2.328 ∼ 3.160), suggesting that if lacunar infarction increased by one level, the risk of LA would increase 2.712 times. Therefore, the prevention and control of cerebral hemorrhage, cerebral infarction as well as lacunar infarction helps to reduce the incidence of LA. Hypertension is a risk factor for LA, and compared with normal people, people with hypertension is more likely to have LA, which has been widely recognized (Saba and Mallarlini, 2010). In this study, the OR value of hypertension was 1.64 (95% CI 1.089 ∼ 2.471), which indicated that the risk of people with hypertension suffering from LA was 1.64 times higher than that of those without hypertension. According to its mechanism, cerebral hemispheres white matter is located in terminal zone of the blood-supply area of middle cerebral artery perforating arteries in cortical layer thus it is easier to get chemia injury than cortex. Therefore, the control and prevention of hypertension helps to decrease the incidence of LA. Whether diabetes is correlated with white matter changes is controversial now. Longstreth et al. (1999) found that there was no relation between LA degree and fasting blood-glucose, blood insulin levels or history of diabetes, which was not consistent with study results by Baloh and Vinters (1995). And in this study, the incidence rate of LA in diabetes group (39.04%) was still higher than that in non-diabetes group (29.74%). The possible reason is that diabetes causes cerebral arteriosclerosis inducing blood circulation disorder of while matter in deep brain which leads to hypoxic-ischemic demyelinating and cerebral infarction. SVM has shown advantages in addressing small sample, nonlinearity and high dimensional mode recognition, and it, to a great extent, overcame problems of “dimension disaster” and “over learning” (Ding et al., 2011). SVM model has been well applied to medicine (Voigt et al., 2016, Wang et al., 2016), but it is rarely used in discussion of LA risk factors and the common method is to adopt Logistic analysis to explore relations between LA and risk factors (Liu et al., 2007, Liu, 2016). In this study, a SVM model containing risk factors screened through Logistic analysis was successfully established. And when including all variables, SVM model’s accuracy, specificity and sensitivity were 85.0%, 85.0% and 85.0%, respectively; when including screened factors, SVM model’s accuracy, specificity and sensitivity were 90.0%, 100.0% and 80.0%, respectively. It can be seen from the results that the accuracy and specificity of SVM including screened variables increased by 5% and 15%, respectively, while the sensitivity reduced by 5%. Therefore we say SVM including screened variables is superior to SVM including all variables. Moreover, screening out proper character subsets can not only simplify model but also improve its diagnostic efficacy. The detection rate of LA is increasing year by year. In the case of no specific treatment drugs and methods for LA, effectively controlling various risk factors for LA, slowing the pace of its development, and then postponing a series of functions obstacles can significantly reduce the incidence of LA. This not only can, to some extent, alleviate the burden on patients and families, but also has a positive meaning for all people’s healthy development. In the study, patient with elevated triglyceride, hypertension, cerebral infarction, lacunar infarction or history of cerebral hemorrhage is prone to LA, so the result is instructional for prevention of LA to some extent, and the corresponding preventive measures for different risk factors can be formulated so as to reduce the occurrence of LA. In addition, people without these risk factors can also adopt targeted measures for prevention, thereby indirectly preventing the occurrence of LA caused by the corresponding risk factors. 5 Conclusions Through Logistic regression analysis, this study screened out test indicators linked to lipid metabolism and medical history that are closely correlated with LA, and the study also shows that control of triglyceride level can reduce LA’s incidence. Meanwhile, this study successfully built a SVM model based on the risk factors which can forecast LA relatively correctly and can be regarded as an assistant diagnostic tool for clinician. Acknowledgments The authors acknowledge the financial support from Basic and Frontier Technology Research Projects of Henan Province (Grant: 132300410331). Moreover, the authors would like to thank People’s Hospital of Zhengzhou for providing help in this study. Peer review under responsibility of King Saud University. Figure 1 The simulation results of SVM model including in all variables. Figure 2 The simulation results of SVM model including in screened variables. Table 1 Analysis of medical history indicators on LA. Factor Type LA group Non-LA group χ2 P Age ⩽40 3 143 176.442 0.000 41–60 91 357 61–80 217 189 Sex Male 166 400 1.910 0.167 Female 145 289 Diabetes No 254 600 5.031 0.025 Yes 57 89 Hypertension No 195 483 5.376 0.020 Yes 116 206 History of intracranial infection No 308 678 0.620 0.431 Yes 3 11 History of cerebral hemorrhage No 304 684 4.204 0.040 Yes 7 5 History of cerebral infarction No 269 674 51.154 0.000 Yes 42 15 History of lacunar infarction 0 28 431 326.423 0.000 1 39 106 2 74 78 3 170 74 Table 2 Analysis of test indicators on LA. Test indicator Type LA group Non-LA group χ2 P Total cholesterol ⩽5.8 mmol/L 255 588 1.814 0.178 >5.8 mmol/L 56 101 Triglycerides ⩽1.8 mmol/L 160 447 12.612 0.000 >1.8 mmol/L 151 242 High >1 mmol/L 231 519 0.126 0.723 density lipoprotein ⩽1 mmol/L 80 170 Low ⩽3.6 mmol/L 228 504 0.109 0.741 density lipoprotein >3.6 mmol/L 88 185 Table 3 Logistic regression analysis of LA. Factor Regression coefficient β P OR 95% CI Lower Upper Age 1.263 0.000 3.535 2.558 4.885 Hypertension 0.495 0.018 1.640 1.089 2.471 History of cerebral hemorrhage 1.891 0.030 6.626 1.206 36.418 Cerebral infarction 1.971 0.000 7.179 2.838 18.156 Lacunar infarction 0.998 0.000 2.712 2.328 3.160 Triglyceride 0.745 0.000 2.106 1.456 3.047 ==== Refs References Altaf N. Daniels L. Morgan P.S. Lowe J. Gladman J. MacSweeney S.T. Moody A. Auer D.P. Cerebral white matter hyperintense lesions are associated with unstable carotid plaques Eur. J. Vasc. Endovasc. Surg. 31 2006 8 13 16226900 Baloh R.W. Vinters H.V. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30031-110.1016/j.sjbs.2017.01.022Original ArticleDerivation and characterization of sheep bone marrow-derived mesenchymal stem cells induced with telomerase reverse transcriptase Zhu Xuemin zhuxuemin7195@126.coma⁎Liu Zongzheng bDeng Wen aZhang Ziqiang aLiu Yumei aWei Lan aZhang Yuling aZhou Liutao aWang Yuzhu aa College of Animal Science and Technology, Henan University of Science and Technology, Luoyang 471023, Chinab Animal Husbandry and Veterinary Research Institute of Qingdao, Qingdao 266000, China⁎ Corresponding author. zhuxuemin7195@126.com23 1 2017 3 2017 23 1 2017 24 3 519 525 3 11 2016 23 12 2016 6 1 2017 © 2017 King Saud University2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Bone marrow mesenchymal stem cells (BMSCs) are a type of adult stem cells with a wide range of potential applications. However, BMSCs have a limited life cycle under normal culturing conditions, which has hindered further study and application. Many studies have confirmed that cells modified by telomerase reverse transcriptase (TERT) can maintain the ability to proliferate in vitro over a long period of time. In this study, we constructed a gene expression vector to transfer TERT into sheep BMSCs, and evaluated whether the TERT cell strain was successfully transferred. The abilities of cell proliferation and differentiation were evaluated using the methods including growth curve determination, inheritance stability analysis, multi-directional induction and so on, and the results showed that the cell strain can be cultured to 40 generations, with a normal karyotype rate maintained at 88.24%, and that the cell strain can be transferred and differentiated into neurocytes and lipocytes, proving that it retains the multi-directional transdifferentiation ability. Keywords Sheep BMSCsStem cellsTERTMulti-directional differentiation ==== Body 1 Introduction The bone marrow mesenchymal stem cell (BMSC) is a kind of multi-potent adult stem cell originating from the bone marrow stromal, and is a type of adult stem cell with a wide range of potential applications in the fields of tissue engineering, and cell and gene therapy (Augello et al., 2010, Austin-Page et al., 2010, Dai et al., 2014, Machado et al., 2009, Nakahara et al., 2009, Tögel et al., 2009, Yang et al., 2011). In the present study, we found that the ability to proliferate decreases along with the number of in vitro passage cultures in BMSCs, which limits the application of BMSCs to a certain extent (Bonab et al., 2006, Estrada et al., 2013). In recent years, different kinds of immortalized cells have been obtained by different methods, but there is no safe way to obtain immortalized cells. Telomere is an important structure in maintaining chromosome stability and the life span of cells. Telomere length is inversely proportional to the number of chromosome copies. If the telomere length decreases to an extreme value, it will no longer maintain its function of ensuring chromosome stability, which leads to cell death However, telomere contains a reverse transcriptase known as telomerase reverse transcriptase (TERT), which can catalyze reverse transcription of the telomerase into telomere DNA, which is then synthesized into chromosome ends and added to the length of the telomere, thus resulting in continuous cell growth (Kim et al., 2009). Many studies show that exogenous telomerase reverse transcriptase does not produce canceration, and can maintain stem cell self-renewal and multilineage differentiation potential. Therefore, it is of great theoretical significance to study the effect of TERT on the stable passage and differentiation of MSCs. Therefore, through introduction of the exogenously expressed TERT gene, we further studied the life cycle and biological characteristics of BMSCs as a basis for further application of mesenchymal stems cells in disease treatment and tissue repair technology. 2 Materials and methods All chemicals and culture media used in this study were of cell culture grade and obtained from Sigma Chemicals Co., (St. Louis, US) unless otherwise indicated. The plastic ware was from Nunc (Roskilde, Denmark). 2.1 Tissue materials and cell culture Sheep renal tissue was harvested from 12 month old small-tailed Hen sheep which were provided by a slaughterhouse. BMSCs were provided by the Experimental Center of the College of Animal Science and Technology. Cells were inoculated at a density of 2 × 104 cells/ml in DMEM containing 10% FBS, and cultured at 37 °C in a 5% CO2 humidified incubator after thawing at 37 °C. The culture medium was replaced after 24 h, and every 3 days afterward. When cells had grown to a fusion of 80-90%, subculturing of the cells was performed at a ratio of 1:3 with digestion by 0.25% trypsin. 2.2 Construction of eukaryotic expression vector pcDNA 3.1-EGFP-TERT Total RNA was extracted from the sheep renal tissue, and reversely transcribed into cDNA which was used as a template. A TERT primer was designed containing the Hind III and EcoR I restriction enzyme cutting site, Fwd: CCCAAGCTTGCCACCATGA AGGTGCAGGACTGCG (Hind III), Rev: CGGAATTCTG TCCAAGATGGTCTTGAAGTCT (EcoR I). PCR amplification conditions: 94 °C, 8 min; 94 °C, 40 s; 56 °C, 30 s; 72 °C, 2 min; 35 cycles. The amplified bands were extracted and sequenced after the reaction was terminated. The recombinant plasmid containing the TERT gene and the plasmid pcDNA3.1-EGFP were cut by Hind III and EcoRI restriction enzymes, respectively, and the enzyme fragments were added into T4 DNA ligase to perform the overnight ligation. Double enzyme cutting and sequence identification of the fragments ligated by Hind III and EcoR I were carried out. 2.3 Liposome transfection and Screening of TERT-BMSCs 0.8 μg of normally sequenced plasmids was mixed with 3 μl of liposome in 100 μl of serum-free DMEM culture medium. Then, the mixture was slowly added into a culture containing 70–80% fused cells after 20 min of incubation at room temperature (RT). After the cells were transfected for 24 h, fluorescence was observed under a fluorescence microscope, and the cells were screened by adding G418 with a final concentration of 300 μg/ml. After 7 d, the G418 concentration was reduced by half and cells continued culturing. 2.4 Determination of growth curve P5 and P40 TERT-BMSCs as well as BMSCs were selected and inoculated at a concentration of 2 × 104 in 24-well plates. The growth curve was determined by calculating the number of cells in 3 wells per day for 9 consecutive days. 2.5 Inheritance stability analysis Numerous metaphase cells were selected from P10, P20 and P40 TERT-BMSCs and BMSCs. Then, using BEION chromosome karyotype analysis software, the number of chromosomes was analyzed, and the chromosome number and structural stability of the TERT-BMSCs during subculturing was measured. 2.6 RT-PCR analysis Total RNA was extracted and reversely transcribed into cDNA for use as a template. A primer for the study gene was designed (Table 1). The targeted band was amplified by PCR, and the amplified band was extracted and sequenced after the reaction was terminated. 2.7 Multi-directional induction and differentiation The P30 TERT-BMSCs were selected and inoculated at 2 × 105 cells/ml in 4-well plates. The culture medium supernatant was discarded and replaced with an adipogenic induction culture medium (DMEM-F12 + 10% FBS + 1 μM of dexamethasone + 17 μM of pantothenic acid + 5 mM of indometacin + 1 μM of insulin + 0.5 mM of IBMX) when cells had grown to a fusion of 70–80%, and the culture medium was replaced every 3 d. Cells were cultured for two weeks. For neuroblast induction, the pre-induction medium (DMEM-F12 + 10%FBS + 1 mM BME) was first added, and was then replaced with induction medium (DMEM-F12 + 5 mM BME) after 12 h of induction. The induction continued for 24 h, and changes were observed under a microscope. 2.8 Identification of induction differentiation Identification of adipogenic induction: The culture medium was discarded after two weeks of cell induction. Cells were then rinsed three times with PBS, and then rinsed three times with distilled water after 20 min of fixation with 10% formaldehyde, then stained with Oil-Red O for 20 min at RT. The results were observed under a microscope. RT-PCR were used to detect the expression of the specifically expressed gene PPAR and Leptin. Identification of neuroblast induction: The culture medium was discarded after cell induction was terminated. Cells were fixed for 20 min by adding 95% ethanol, and rinsed twice. After staining with toluidine blue dye solution for 40 min at 50–60 °C, and rinsing with distilled water for 2 min, cells were observed under an inverted microscope. Expression of the specifically expressed gene NSE and GFAP were detected by RT-PCR. 2.9 Statistical analysis Data analysis was performed on SPSS 9.2. The effects of different cryopreservation media on pre-freezing and post-thaw viability of cells were tested by a one-way analysis of variance (ANOVA). 3 Results 3.1 Eukaryotic expression vector pcDNA3.1-EGFP-TERT Three bands (Fig.1A) which were 5 s, 18 s and 28 s could be clearly observed in the extracted sheep renal tissue RNA by electrophoresis detection. A single band (Fig.1B) with higher specificity and a size comparable to that of the anticipated fragment could be seen after PCR amplification, and the sequencing results were the same as the sequence released in NCBI, which proves that the sheep TERT gene was cloned. The enzyme-cut plasmid pcDNA3.1-EGFP was re-ligated and transfected with TERT fragments, from which the expressing plasmid was obtained, and the TERT gene (Fig.1C) was acquired using PCR amplification. The 6132 bp band of pcDNA3.1-EGFP and the 1873 bp band of TERT (Fig.1D) were acquired by double enzyme cutting. 3.2 Derivation of TERT-BMSCs The filtered TERT-BMSCs were subcultured. The cells had a fast growth rate, requiring an average growth period of 3–4 days for each generation. The morphology of the cells was better than that of the BMSCs at higher passages. With the passages increasing, BMSCs gradually grow wider and shorter, eventually taking on a flat polygonal shape, indicating the slow growth caused by cell aging (Fig.2A and B). Meanwhile, TERT-BMSCs maintained their spindle shape, and had no obvious shortening or increase in the number of protuberances, and showed no significant change in growth rate (Fig.2C and D). RT-PCR results show that TERT-BMSCs can express the TERT gene (Fig.2E). 3.3 Growth curve The growth curves of TERT-BMSCs and BMSCs from both P5 and P40, take on an “S” shape (Fig. 3), but there is a significant difference between the growth curves of the BMSCs and TERT-BMSCs. The BMSCs ordinarily remain latent for the first 1–2 days, and then enter a logarithmic growth phase on day 3, and a plateau phase on day 7 or 8, with a reduction in the rate of proliferation. While TERT-BMSCs ordinarily begin to grow rapidly from day 2, and enter the plateau phase in advance of day 5–6 due to the growth space constraints. It is to be noted that there is a big difference between the proliferation rates of BMSCs and TERT-BMSCs. 3.4 The Inheritance stability of TERT-BMSCs Through karyotype analysis, we found that the normal sheep chromosome karyotype is 2n = 54, which includes 26 pairs of autosomes and 1 chromosome pair. Statistical analysis of the chromosome karyotypes of P5, P20 and P40 TERT-BMSCs and BMSCs showed that the normal karyotype rates of different passages of BMSCs were 96.30%, 72.22% and 31.22%, respectively, and the normal karyotype rates of different passages of TERT-BMSCs were 95.35%, 92.00% and 88.24% (Table 2), respectively. This shows that the TERT-BMSCs maintain excellent inheritance stability over a long period of in vitro passage culturing. 3.5 Identification of adipogenic induction The morphology of TERT-BMSCs begins to change after 24 h of adipogenic induction, gradually changing from the spindle shape to a large ovular shape. Small lipid droplets begin to appear in cytoplasm after 3 days of transfection. Larger lipid droplets appear in some of the cells after 5–6 days of transfection, presenting as a round or ovular shape (Fig.4A). The lipid droplets were stained red using oil-red O dye for observation (Fig.4B) after 9 days of transfection. Meanwhile, no red lipid droplets were observed in the stained control group (Fig.4C). The expression of the specific PPAR and Leptin genes can be detected by RT-PCR. 3.6 Identification of neuroblast induction No obvious changes were observed in cell morphology after pre-induction of TERT-BMSCs. Enhanced refraction was observed in the BMSC cell bodies, which began to shrink and become rounder 3 h after addition of the induction agent. After 12 h, protuberances began to appear and project out of the cell bodies, causing the cells to form forked ends with large protruding points that can make contact with other cell bodies and points, resembling a synapse structure. The cells became bipolar, multi-polar and tapered, with a morphology like that of neurons after 24 h of induction. At this time, many cells had already intertwined and interconnected with one another with a reticular appearance (Fig.4D). After staining with toluidine blue, nissl bodies appeared as dark blue particles or patches with blue cell nuclei (Fig.4E). RT-PCR was able to detect the expression of the specific NSE and GFAP genes. 4 Discussion The mesenchymal stem cell (MSC) is a kind of adult stem cell widely applied in tissue repair engineering, and cell and gene therapy. However, MSCs, tend to age and stop proliferating when subcultured in vitro, and there is no way to greatly amplify these cells (Bourgine et al., 2014, Peng et al., 2015, Zimmermann et al., 2003, Røsland et al., 2009, Okada et al., 2016). The present study proves that telomere length may shorten along with cell proliferation. Cells may age and die as the continuously shortened telomere length cannot maintain chromosome stability. Therefore, telomere length is important in guaranteeing cell proliferation stability. Enhancing telomerase activity by introducing the exogenous TERT gene into targeted cells is the primary method used in recent cell immortalization studies (Kaloyianni et al., 2015, Teng et al., 2014, Tsai et al., 2010, Wongkajornsilp et al., 2012). Hamada et al., constructed an hMSC-TERT cell line in 2003, which had biological characteristics that were no different than the original generation of hMSCs, but the detailed molecular mechanism and the function of telomerase remain unclear. Construction of a eukaryotic expression vector, using the pcDNA3.1-EGFP ring-opening as the expression vector by the restriction enzyme EcoRI and Hind III can ensure the proper insertion direction of exogenous fragments, and can prevent the self-ligation of vectors, which improves recombination efficiency. Furthermore, the expression vector pcDNA3.1-EGFP carries the EGFP gene and the Neo resistance gene, which ensures that it can both express green fluorescence after being introduced into the cell, and can be filtered in eukaryotic cells by G418. In the present study, after constructing a eukaryotic expression vector, we examined the vector from the two dimensions of colony PCR and double enzyme cutting of recombinant plasmids (Zhou et al., 2014). Two types of bands were obtained from the results of double enzyme cutting, with sizes comparable to expectations. One of these was the band of the targeted gene, while the other was the band of the vector, which proves that we successfully constructed the vector. Cells normally expressing the TERT gene were acquired by G418 filtering after the introduction of the successfully constructed vector into the BMSCs by liposomes (Wongkajornsilp et al., 2012). The acquired cells showed no obvious difference in cell morphology as compared to normal BMSCs when subcultured by amplification to the 40 th passage, while the normal BMSCs showed obvious cell aging and degeneration in cell morphology when cultured to the 20 th passage. These results are analogous to those obtained by (Yao and Hwang, 2012, Yin, 2012). According to the growth curve, the proliferation rate of the BMSCs is obviously decreased when subcultured to the 20th passage, while TERT-BMSCs maintained a normal proliferation rate when subcultured to the 40th passage, the growth curve of this cell maintained the “S” shape, proving that TERT-BMSCs have vigorous proliferation, as reported by (Simonsen et al., 2002). We selected P10, P20 and P40 cells to study the inheritance stability of TERT-BMSCs. Through chromosome karyotype analysis, we found that the cells maintained a karyotype correction rate of 77.78% when subcultured to the 40th passage, which proves that TERT-BMSCs have higher inheritance stability. However, it is necessary to further verify whether TERT-BMSCs are capable of infinite passage culturing. Adipogenic differentiation assays show that TERT-BMSCs can be differentiated to adipocytes. We find that indomethacin is the most rapid adipogenic supplement, and in 3–4 days of treatment on average, small oil droplets were observed under the inverted microscope. After 7 days of incubation, the cells were stained with Oil-Red O, and red oil vacuoles were obvious in the cytoplasm. The formation of large lipid droplets on the 12th day in adipogenic-induced human MSCs was previously reported. RT-PCR of the differentiated cells shows the adipogenic differentiation-specific genes such as PPAR and Leptin were expressed. Our neural differentiation assays showed that TERT-BMSCs can differentiate to neurons. BME can support the viability and differentiation of fetal mouse brain neurons (Fortino et al., 2013) and is used as an effective inducer of neural differentiation in MSCs (Latil et al., 2012, Sanchez-Ramos et al., 2000). BME induced dramatic modifications of cellular shape and the expression of neural marker NeuN within 5 h. Nestin expression is a necessary step for neural differentiation of MSCs, and serum in culture medium can inhibit the expression of Netein. We found that TERT-BMSCs also could differentiate into neural cells under serum-free conditions. RT-PCR results confirmed that the specific genes for neural differentiation such as ESE and GFAP were expressed. 5 Conclusions In the present study, the TERT eukaryotic expression vector was successfully constructed and BMSCs were transfected. Observation of cell morphology and detection of the biological characteristics of BMSCs showed that no early aging occurred, while the stem cell characteristics of the cells were maintained and their life spans prolonged. TERT-BMSCs maintain the potential for multi-directional differentiation after induction. These results can be referenced in the future research of cell immortalization, helping further the discussion of the immortalization mechanism, and laying a foundation for applying the immortalization mechanism in the fields of tissue regeneration and repair, cell transplantation, and gene therapy, etc. Acknowledgment This research was supported by Chinese National Natural Science Foundation (grant number: 31402153) and PhD Start-up Fund of College of Animal Science and Technology (13480062). Peer review under responsibility of King Saud University. Figure 1 Construction of eukaryotic expression vector pcDNA3.1-EGFP-TERT. (A) The extracted sheep renal tissue RNA under electrophoresis detection; (B) the TERT gene was cloned after PCR amplification; (C) the enzyme-cut plasmid pcDNA3.1-EGFP was re-ligated and transfected with TERT fragments, from which the expressing plasmid was obtained, and the TERT gene (C) was acquired using PCR amplification; (D) The 6132bp band of pcDNA3.1-EGFP and the 1873bp band of TERT (D) were acquired by double enzyme cutting. Figure 2 Derivation of TERT-BMSCs. (A) BMSCs in confluent culture at P3 (×100); (B) BMSCs in confluent culture at P40 (×100); (C) TERT-BMSCs in confluent culture at P3 (×100); (D) BMSCs in confluent culture at P40 (×100). (E) RT-PCR results show that TERT-BMSCs can express the TERT gene, Lane M 2000-bp ladder, lane 1 TERT (191bp), lane 2 negative control. Figure 3 BMSC and TERT-BMSC growth curves, each value is expressed as mean ± standard error of the mean (SEM). Figure 4 Adipogenic (A–C) and Neural (D–F) differentiation potential of sheep TERT-BMSCs. (A) Lipid droplets were seen in the cytoplasm of visible after 9 days of culturing. (B) Oil red O-positive cells. (C) Gene expression profile. Lane 1 250/100bp ladder, lane 2 negative control, lane 3 up: PPAR (175bp); down: Leptin (163bp). (D) Condensed cell bodies and extended dendrites were seen after 24-h culture. (E) Toluidine blue staining, (F) gene expression profile. Lane M 250/100bp ladder, lane 2,3 up: NSE (190bp); down: GFAP (123bp), lane 3,4 negative control. Table 1 Details of primers used for gene expression through RT-PCR. Sr. no. Gene Primer sequence Product size (bp) Annealing temp °C Source Accession no. 1 GAPDH CTTCATTGACCTTCACTACATGG(F) 356 57 Ovis aries NM_001190390.1 TGCAGGAGGCATTGCTGACAA(R) 2 NSE AGACCTCATCCTGCCTGTGC(F) 190 58 Ovis aries XM_004006922.1 GGCGTCCTTGCCATACTTG(R) 3 GFAP CCGCATCACCATTCCTGTTC(F) 123 60 Ovis aries XM_004012992.1 CGCATCTCCACGGTCTTCAC(R) 4 PPAR CGTCAGGGTTCCACTATGGAGTT (F) 175 55 Ovis aries NM_001100921.1 GACATCCCCACAGCAAGGCACTT (R) 5 Leptin CCAGGATGACACCAAAACCCTCA (F) 163 57 Ovis aries KC526876 GATTGCCAATGTCTGGTCCATCT (R) 6 TERT AGCAGGCGGCTGTAGGTG(F) 191 56 GCGTTCTTTCTCCAGGTCATCA(R) Table 2 Sheep MSC diploid normal rate of different generations. BMSCs (%) TERT-BMSCs (%) P5 96.30% (52/54) 95.35% (41/43) P20 72.22% (26/36) 92.00% (46/50) P40 61.22% (30/49) 88.24% (45/51) ==== Refs References Augello A. Kurth T.B. De Bari C. Mesenchymal stem cells: a perspective from in vitro cultures to in vivo migration and niches Eur. Cell Mater. 20 2010 121 133 21249629 Austin-Page L. Thein S. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30020-710.1016/j.sjbs.2017.01.011Original ArticleEfficient purification protocol for bioengineering allophycocyanin trimer with N-terminus Histag Li Wenjun ac1Pu Yang b1Gao Na dTang Zhihong aSong Lufei aQin Song cpra_yic@sina.coma⁎a Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, Chinab School of Agriculture, Ludong University, Yantai 264025, Chinac University of Chinese Academy of Sciences, Beijing 100049, Chinad South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China⁎ Corresponding author at: Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China.Yantai Institute of Coastal Zone ResearchChinese Academy of SciencesYantai264003China cpra_yic@sina.com1 Wenjun Li and Yang Pu are co-first authors. 21 1 2017 3 2017 21 1 2017 24 3 451 458 7 12 2016 30 12 2016 6 1 2017 © 2017 King Saud University2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Allophycocyanin plays a key role for the photon energy transfer from the phycobilisome to reaction center chlorophylls with high efficiency in cyanobacteria. Previously, the high soluble self-assembled bioengineering allophycocyanin trimer with N-terminus polyhistidine from Synechocystis sp. PCC 6803 had been successfully recombined and expressed in Escherichia coli strain. The standard protocol with immobilized metal-ion affinity chromatography with chelating transition metal ion (Ni2+) was used to purify the recombinant protein. Extensive optimization works were performed to obtain the desired protocol for high efficiency, low disassociation, simplicity and fitting for large-scale purification. In this study, a 33 full factorial response surface methodology was employed to optimize the varied factors such as pH of potassium phosphate (X1), NaCl concentration (X2), and imidazole concentration (X3). A maximum trimerization ratio (Y1) of approximate A650 nm/A620 nm at 1.024 was obtained at these optimum parameters. Further examinations, with absorbance spectra, fluorescence spectra and SDS-PAGE, confirmed the presence of bioengineering allophycocyanin trimer with highly trimeric form. All these results demonstrate that optimized protocol is efficient in purification of bioengineering allophycocyanin trimer with Histag. Keywords Efficient purificationBioengineering allophycocyanin trimerOptimizationHistag ==== Body 1 Introduction Nature has been developing and optimizing photosynthesis for billions years. Phycobilisomes, which are the main light harvesting complexes in cyanobacteria and red algae, absorb and transfer solar energy to photosynthetic reaction center with capability of broad range visible spectrum and high efficiency (Lowe, 2007, Nelson and Yocum, 2006, Adir, 2005, Adir et al., 2007). Allophycocyanin (APC) trimer is the primary composition of phycobilisomes core, containing only phycocyanobilin (PCB), for efficient lower-energy photons funneling pathway. Moreover, it is also the basic unit to finish bio-functions in photosynthesis in cyanobacteria and red algae (MacColl, 2004, MacColl et al., 2003). APC trimers (αβ)3 are self-assembled by three monomers (αβ) which were initiated with the docking process of α and β subunits (Adir, 2005, Adir et al., 2007). Recently, the relatively high-resolution images with X-ray crystallography and electron microscopy have revealed the details of native APC trimer structures from cyanobacteria (McGregor et al., 2008, Arteni et al., 2009). The direct structural proof of the APC trimer from Synechocystis sp. PCC 6803 by electron microscopy revealed a flattened disk structure with proximately 3 nm in short axis, 12 nm in long axis and a central hole of 3 nm in diameter (MacColl, 2004, Arteni et al., 2009). Recent study showed that the photon energy transfer in APC trimer from the core into reaction center chlorophylls may follow the mechanism of Förster resonance on a time scale 430–440 femtoseconds with unbelievable efficiency (MacColl, 2004). Moreover, APC trimer was wide used as fluorescent tags, antioxidant and anti-enterovirus, except for the bio-functions in photosynthesis (Su et al., 2010, Liu et al., 2010). However, in cyanobacteria, APC is a minor constituent in three types of phycobiliproteins, compared with phycocyanin (PC) and phycoerythrin (PE) (Su et al., 2010, Bermejo et al., 1997). Thus, the difficulty of purification of APC trimer with high efficiency is a current major inhibitor of its applications. For this reason, in 2009, Liu et al. reported highly soluble self-assembled bioengineering allophycocyanin (bAPC) trimer with N-terminus polyhistidine (14 unstructured residues including 6 × Histag) from Synechocystis sp. PCC 6803 for the first time, which were expressed in Escherichia coli strain. For the construction of engineering strain, two constructed expression vectors were co-transformed into E. coli with an entire biosynthesis pathway of bAPC trimer (Liu et al., 2010, Tooley et al., 2001). Specifically, the full-length gene of apoprotein α subunit (apcA) was cloned into one cassette in the pCDFDeut-1 expression vector and the full-length genes of heme oxygenase 1 (hox1) and 3z-phycocyanobilin ferredoxin oxidoreductase (pcyA) were cloned into another cassette in the same expression vector; the full-length gene of apoprotein β subunit (apcB) was cloned into one cassette in the pRSFDeut-1 expression vector and the full-length genes of heterodimeric lyases (cpcS-1 and cpcU) were cloned into another cassette in the same expression vector. Native structure, stability and fluorescent characteristics of the bAPC trimer have been demonstrated by fluorescence and absorption spectrometry analyses, supporting previous reports (Liu et al., 2010). In addition, the presence of N-terminus which extends out from the globular domains for both α and β subunit has a great advantage of being significantly sensitive to histidine-tagged proteins (McGregor et al., 2008, Liu et al., 2010, Schirmer et al., 1985, Schirmer et al., 1986). Meantime, previous modeling studies showed that the longer His tag near N-terminus did not significantly interfere with trimer assembly and had a great chance to improve the sensitivity by presenting the histidine side chains on the surface of the trimer (Liu et al., 2010, Cai et al., 2001). The fused His6-tag provides a convenient method to purify the recombinant APC trimer from the cell lysate on a large-scale (Ge et al., 2009, Liu et al., 2009, Sugiura and Inoue, 1999). In addition, the large-scale fermentation of functional recombinant APC trimer has also been accomplished successfully. After ultrasonic treatment, conventional protocols of purification for APC trimer with His-tag included three steps: immobilized metal-ion affinity chromatography (IMAC) with chelating transition metal ion (Ni2+), Sephadex G25 size-exclusion chromatography and Superdex 200 size-exclusion chromatography. Three columns were utilized for binding the His-tag proteins, desaltification and obtaining target protein with exact molecular weight, respectively. However, there are several problems in conventional methods. Thus, there is an urgent need to develop efficient methods for purifying the APC trimer. The first step of separation is the most important to the recombinant APC trimer yield, which is based on the interaction between histidine side chain and Ni2+. But recombinant APC trimer can be disassembled into monomers following an imidazole gradient elution, because of monomer-promoting function of imidazole (Liu et al., 2010, Belew and Porath, 1990). Furthermore, electrostatic interactions including both polar and hydrophobic interactions are responsible for the assembly of monomer and trimer, and the hydrogen bonds which are the strongest noncovalent forces in biological system through functional groups such as carbonyl, amide, or aromatic residues, may play an important role as reported earlier (MacColl et al., 2003, McGregor et al., 2008, McConnell et al., 2010). So, several factors may influence the purification efficiency, among which the most important ones are: imidazole concentration, pH of potassium phosphate and NaCl concentration. Combination effects of the varied factors have not been thoroughly studied. For seeking optima and identifying the combination effect of the individual variables in elution, response surface methodology (RSM) was performed previously (Lee and Gilmore, 2006). Traditional “one-factor at a time” method used to optimize a multivariable system is time-consuming and often missing alternative effects among different components (Bandaru et al., 2006). Therefore, we examined the interactive influences collectively between the three parameters (imidazole concentration, pH of potassium phosphate and NaCl concentration) and purity ratio of APC trimer, using Box-Behnken design by responding surface methodology (RSM) to maximize the trimerization of bAPC trimer (Box and Behnken, 1960, Ragonese et al., 2002, Box and Wilson, 1951). The second step, using size-exclusion chromatography for removal of imidazole is time consuming, low protein recovery, high cost and not adequate to large scale purification (Su et al., 2010). Therefore, replacing the Sephadex G25 size-exclusion chromatography with centrifugal filter device will help in solving this problem, which is speediness, lower cost, simplicity and fitting for large-scale purification. It is widely accepted that APC monomers and trimers have distinctively different spectral characteristics because of the PCBs in different microenvironments (MacColl et al., 2003). Monomers exhibit a maximum absorption peak at 615 nm, and trimers exhibit absorption maximum at 650 nm with shoulder at around 620 nm (MacColl, 2004). The significant red-shift indicated the trimerization process, and it could provide an easy method for determination of trimerization ratio (MacColl, 2004, MacColl et al., 2003, Liu et al., 2010). In this case, the main peak of 620 nm was observed because of uncompleted trimerization (MacColl et al., 2003, Liu et al., 2010). In order to detect the trimerization ratio of recombinant APC trimer, the A650 nm/A620 nm ratio was performed previously (MacColl, 2004, MacColl et al., 2003, Liu et al., 2010). The aim of the present study was to provide an optimized method, which included the optimized elution buffer in immobilized metal-ion affinity chromatography and centrifugal filter device, for obtaining higher trimerization ratio of recombinant APC trimer. Following additional Superdex 200 size-exclusion chromatography step, the purification of recombinant APC trimer can be completed efficiently. In addition, we firstly report the method for optimization of purification protocol of Histag trimer using response surface methodology. 2 Materials and methods 2.1 Recombinant APC trimer over expression For preparing the bioengineer APC trimer, the identical strain was conducted according to the previous description, with slightly modification (Liu et al., 2010). The expression vectors of pCDFDeut-1 (spectinomycin resistance) and pRSFDeut-1 (kanamycin resistance) were supplied by Novagen Inc. All materials in this study were from Sigma–Aldrich. Fig.1A shows the predicted amino acid sequences of apoprotein α subunit and apoprotein β subunit. We tagged six N-terminus polyhistidine on α subunit and β subunit, both of which were amplified from the cyanobacteria Synechocystis sp. PCC 6803 by designed PCR primers. In this study, other four genes (hox1, pcyA, cpcS-1 and cpcU) were also from Synechocystis sp. PCC 6803. The bioengineer E. coli was cultured at 37 °C in LB medium with 50 mg/L spectinomycin and 50 mg/L kanamycin to 0.4–0.6 A600 nm value. After adding IPTG to a final concentration of 0.5 mM, expression was induced for 14 h at 28 °C. Cells of approximately 100 g were obtained by centrifugation (7000×g, 30 min) at 4 °C (Fig.1B). 2.2 Isolation of recombinant APC trimer by IMAC and centrifugal filter device Cell paste of approximately 100 g was suspended in 1000 mL of binding buffer A1 (20 mM sodium phosphate, 500 mM NaCl, and 20 mM imidazole, pH 7.4) before sonication. After centrifugation (10,000×g, 30 min), the supernatant of sonicated harvest passed through a 0.45 μm filter. A series of equal amounts of filtered supernatant (50 mL) was loaded into 5 mL HisTrap HP chromatography (GE Healthcare Bio-Sciences, USA) at 1 mL/min, using an AKTA fast-performance liquid chromatography system (GE Healthcare Bio-Sciences, USA). Before loading supernatant, the HisTrap HP column was equilibrated to 4 °C with buffer A1. HisTrap HP columns were eluted with 25 mL binding buffer A1 to remove unbound proteins, and then most bound contaminants were eluted by washing with 25 mL buffer A2 (20 mM sodium phosphate, 500 mM NaCl, and 50 mM imidazole, varieties of pH following elution buffer A3) at 1 mL/min. The recombinant APC trimers were finally eluted with a flow rate of 1 mL/min, using varieties of elution buffer A3 to optimize imidazole concentration, pH of potassium phosphate and NaCl concentration. Each eluted fraction at the main peak was collected as target protein. The fractions containing Histag proteins were filled to centrifugal filter device (Millipore Amicon-Ultra-15, USA), in which every unit contained 15 mL sample, with centrifugation 5000×g, 20 min) at 4 °C. Every sample was concentrated to 1 mL and removed the imidazole, and then 14 mL potassium phosphate (50 mM, pH 7.0) was added to the tube with centrifugation (5000×g, 20 min) at 4 °C. Subsequently, above processes of dilution were repeated three times. After dilutions, the final fractions were investigated by absorbance spectra, fluorescence emission spectrum and SDS-PAGE. 2.3 Optimization A model of 33 Box-Behnken design was utilized as an optimization of trimerization ratio within the experimental range (Box and Behnken, 1960, Ragonese et al., 2002). As shown in Table 1, Table 2, pH of potassium phosphate (X1), NaCl concentration (X2), and imidazole concentration (X3) was used as three independent input variables, while the trimerization ratio (Y1) was chosen as the output variable. Totally, 15 experiments were conducted and the second-order polynomials (Eq. (1)) were calculated to estimate the dependent variable response with SAS (version 8.0): (1) Y1=A0+A1X1+A2X2+A3X3+A12X1X2+A13X1X3+A23X2X3+A11X12+A22X22+A33X32 where X1, X2, X3 are the independent variables, A0 is the offset term, A1, A2, A3 are the linear coefficients, A12, A13, A23 are the interaction terms, A11, A22, A33 are the quadratic coefficients and Y1 is the anticipated response. 2.4 Absorbance and fluorescence spectrometry Absorbance spectra were measured using a computer-controlled spectrophotometer (Thermo Nanodrop 2000c, USA). The fluorescence emission spectrum were obtained by FluoroMax-4 fluorescence spectrophotometer (HORIBA Jobin Yvon) using 600 nm as excitation wavelength, 620 nm to 750 nm as emission wavelength range and 5 nm as emission slit width. 2.5 SDS-PAGE and Zn2+-UV fluorography Denatured samples were separated by 15% SDS polyacrylamide gel electrophoresis. After electrophoresis, the protein bands were analyzed by UV-fluorography subsequent to soaking gel in 20 mM zinc sulfate with 10 min. After then, the gels were stained with Coomassie blue R-250. 3 Results 3.1 Optimization of formulation Fifteen experiments were accomplished using different variables combinations according to the central composite design. Eq. (2) was obtained using Eq. (1) to fit the empirical evidence of trimerization ratio (Y1) with pH of potassium phosphate (X1), concentration of Na+ (X2), and concentration of imidazole (X3). (2) Y1=0.982333+0.049125X1+0.0335X2+0.023625X3-0.102X1X2-0.02425X1X3+0.0145X2X3-0.192167X12+0.01825X22-0.028917X32 The summary of variance of the predicted results of Y1 and the regression model by Eq. (2) is shown in Table 3. The fitness of the model could be checked against different criteria. In this case, R2 is 93.49% which indicated that 93.49% of the total variations in response could be explained using this model for coefficient of determination. In addition, the F-value of the model at 6.834309 indicated that the model was reasonable. The value of “Probe > F” less than 0.05 suggests that the model terms are fitting. Fig. 2 represents three-dimensional contour plots for the optimization of formulation. In the design bounds, each plot of response surface had a significant top point and the corresponding contour plot had a significant peak point, which clearly indicated that the highest point of Y1 could be involved in the design bounds. RSM is often applied in engineering and manufacturing for several important advantages. For example, researchers could easily use considerable less experimental efforts with determining the impact of a factor, identifying of factors, finding optima, offering well precision and facilitating system. In this experiment, using Box-Behnken experimental design, we determined the optima near the central pH of potassium phosphate, NaCl concentration and imidazole concentration. The optimum formulation obtained was pH of potassium phosphate at 7.38, NaCl concentration at 491.4 mM and imidazole concentration at 332.5 mM, respectively. A predicted maximum trimerization ratio of approximate A650 nm/A620 nm at 0.993 was obtained at these optimum parameters. We verified the predicted maximum in a trial run, in excellent agreement with predicted value. The trimerization ratio of A650 nm/A620 nm was 1.024 and the relative error was 3% with the predicted value. Absorbance spectrometry of the optimization experiments is shown in Fig. 3, which shows the desired trimerization ratio of bAPC trimer, compared with the main absorbent peak at 650 nm with that at 620 nm from Liu’ s previous report in 2009. 3.2 Fluorescence emission spectra and gel electrophoresis examination After purification following these optimum parameters, a serial of further examinations were carried out. Fig. 4 exhibits fluorescence spectra with emission maximum at 660 nm with shoulder at around 640 nm, which demonstrated the formation of recombinant APC trimer complexes, consistent with the previous reports (MacColl, 2004, MacColl et al., 2003, Liu et al., 2010). Moreover, the results of APC trimer purification were confirmed by Coomassie staining and Zn2+-UV-fluorography of SDS polyacrylamide gel electrophoresis (Fig. 5). The SDS-PAGE images of Fig. 5 reveal that there were two main bands in lane 2 at the expected position, both Coomassie stain and orange fluorescence. The excited fluorescence was visualized under ultraviolet rays, because of the linkages of zinc ion and chromophore (Tooley et al., 2001, Berkelman and Lagarias, 1986). The special fluorescent property was direct proof of the presence of covalently bonded chromophore. As compared to molecular mass standard, two main bands were obtained at 21 kDa and 19 kDa with Coomassie stain, closely matching the theoretical molecular weights of α (band a) and β (band b) subunits with Histag (Liu et al., 2010). In addition, the bands of orange fluorescence were consistent with the bands of Coomassie stain. Meantime, the same results were observed, comparing to background of E. coli extract in lane 1 (cell lysate). Most importantly, the intensity analysis of Coomassie stain showed that the amount of α subunits was equivalent approximately to β subunits, in despite of slight contaminant protein bands. 4 Discussion As the previous reports, the assembly of an APC monomer is due to the hydrophobic interactions and electrostatic interactions (charge-charge interactions, hydrogen bonds) (McGregor et al., 2008, McConnell et al., 2010, Wiegand et al., 2002). As evidenced by X-ray crystallography, the hydrophobic interactions between α-helices X and Y of same subunit and the globular domain of its partner subunit are prominently responsible for the formation of APC monomer (McGregor et al., 2008). There are also many types of hydrogen bonds in the formation of APC monomer, respectively, hydroxyl-hydroxyl, hydroxyl-carbonyl, amide-carbonyl and amide-hydroxyl. The formation of APC trimer is similar to the above pattern. But the interaction surface between monomers is only proximately 50% of the interaction surface between α and β subunit, hinting the weaker stability of APC trimers than monomers (McGregor et al., 2008, Huang et al., 1987, Kupka and Scheer, 2008). In addition, the hydrogen bonds between polypeptide backbone and water, between polypeptide backbone and polar side chains, between two polar side chains and between polar side chains and water are present in hydrophobic core and near the surface. It is possible that there are more stably internal hydrogen bonds without the competition with water molecules in APC monomers than in APC trimer. Using the native APC as a model, above descriptions provided a reasonable explanation of the phenomenon that bAPC trimers were dissociated into monomer rather than subunits, under the same elution conditions. bAPC trimer binding was based on the formation of coordination bonds between unoccupied coordination sites of Ni2+ ion and histidine tags of the protein surface, which Ni2+ ion coordinated six ligands with polyhistidine and chelater in an octahedral complex (Belew and Porath, 1990, Davankov and Semechkin, 1977). Elution was mainly due to competing ligands from imidazole (Sulkowski, 1996a, Sulkowski, 1996). In the meantime, sodium chloride can affect the elution by reducing electrostatic interactions and interactions between chloride ions and Ni2+ ion; the increase of pH is associated with an increased competition from hydroxyl ions for coordination sites of Ni2+ ions. However, following elution, trimer dissociation could occur as a result of a disruption of hydrogen bonds, which was caused by direct interaction between imidazole and polypeptide backbone contacts. The competition with imidazole nitrogen for hydrogen bond sites of bAPC trimer which located contact surface between monomers might be an explanation of the disruption. Although lower imidazole concentration in elution buffer may reduce the disruption, the elution time is longer than the elution with higher imidazole concentration. A longer elution time may even increase risk of dissociation of bAPC trimer, which is consistent with our results. It may thus be necessary to keep an appropriate imidazole concentration, as our works. Chloride ions, as counter ligand of Ni2+, is usually used for a decrease of nonspecific electrostatic interactions and contribution to the elution efficiency by interactions between chloride ions and Ni2+, but high-ionic-strength might increase the dissociation of bAPC trimer. In addition, protonation of histidine at lower pH under pH 6.0 could be beneficial to an effective desorption, but bAPC trimer is very easy to form monomers due to sensitive to the acid conditions (MacColl, 2004). Moreover, higher pH above pH 8.0 also decreases histidine coordination strength, but there will be a great possible monomerization because of an increased competition from hydroxyl ions for hydrogen bond sites of bAPC trimer which located contact surface between monomers. From the above discussion, it is reasonable that the varied factors are correlative and imidazole concentration play a main role. Intrinsic properties of the bAPC trimer such as stability in neutral pH, natural function in low-ionic-strength aqueous solution, also support our suggestions for the potentially best possible conditions. In conclusion, this work suggests an optimized protocol to achieve the desired trimerization ratio of bAPC trimer. The method could be served as a new paradigm for the purification protocol of the polymeric form of protein complex with fused histidine peptides and will help in designing the purification protocol of a lager sample on a large column. Acknowledgments The authors gratefully thank the financial support provided by the Science Foundation of the Chinese Academy of Sciences (XDA1102040300) and Ocean Public Welfare Scientific Research Project, State Oceanic Administration of China (201205027). This work was also supported by the National Key Technology R&D Program of China (Grant No. 2013BAB01B01) and Scientific Research Foundation of Ludong University (ly2014041). Dr. Hainan Su (Shandong University) is acknowledged for the help with spectra analysis. Peer review under responsibility of King Saud University. Figure 1 (A) Amino acid sequences and Cell paste of recombinant APC trimer with His tag (red). (B) Protein expression strain (left) and blank control (right). Figure 2 Three dimensional contour plots for the maximum Y1. RSM plots were generated using the data shown in Table 3. Inputs were the 15 experimental runs which carried out under the conditions established by the Box-Behnken experimental design. (A) X1 vs. X2 on trimerization ratio (B) X1 vs. X3 on trimerization ratio (C) X2 vs. X3 on trimerization ratio. Figure 3 Absorbance spectra of the APC trimer for optimization. Figure 4 Fluorescence emission spectrum of the bAPC trimer in solution. Figure 5 Electrophoresis imaging of bAPC trimer purification. (A) Gels were stained with Coomassie blue R-250. (B) UV-exited fluorescence binding with zinc ions. Line1: background of E. coli extract (cell lysate). Line2: APC trimer purification. M: Protein Molecular Weight Marker. Table 1 Independent variables in the experimental plan. Variables Coded values −1 0 1 pH of potassium phosphate 7 7.5 8 NaCl concentration (mM) 400 500 600 Imidazole concentration (mM) 200 300 400 Table 2 Box-Behnken experimental design with three independent variables. Run No. X1 X2 X3 Y1 1 7(−1) 400(−1) 300(0) 0.852 2 7(−1) 600(1) 300(0) 0.683 3 8(1) 400(−1) 300(0) 0.643 4 8(1) 600(1) 300(0) 0.571 5 7.5(0) 400(−1) 200(−1) 0.756 6 7.5(0) 400(−1) 400(1) 0.780 7 7.5(0) 600(1) 200(−1) 0.706 8 7.5(0) 600(1) 400(1) 0.803 9 7(−1) 500(0) 200(−1) 0.866 10 7(1) 500(0) 200(−1) 0.801 11 7(−1) 500(0) 400(1) 0.871 12 8(1) 500(0) 400(1) 0.864 13 7.5(0) 500(0) 300(0) 0.985 14 7.5(0) 500(0) 300(0) 0.969 15 7.5(0) 500(0) 300(0) 0.993 Table 3 ANOVA for the entire quadratic model. Factors Degrees of freedom Sum of squares Mean square F-value Probe > F Model 9 0.202705 0.022523 7.973006 0.017089 X1 1 0.019306 0.019306 6.834309 0.047426 X2 1 0.008978 0.008978 3.178184 0.134718 X3 1 0.004465 0.004465 1.58064 0.264197 X12 1 0.039108 0.039108 13.84423 0.013701 X1X2 1 0.002352 0.002352 0.832689 0.403349 X1X3 1 0.000841 0.000841 0.297711 0.608771 X22 1 0.13635 0.13635 48.26735 0.000949 X2X3 1 0.001332 0.001332 0.471612 0.522792 X32 1 0.003087 0.003087 1.092934 0.343706 Lack of fit 3 0.013826 0.004609 30.86105 0.03155 Pure Error 2 0.000299 0.000149 Total 14 0.21683 ==== Refs References Adir N. Elucidation of the molecular structures of components of the phycobilisome: reconstructing a giant Photosynth. Res. 85 1 2005 15 32 15977057 Adir N. Dines M. David L. Klartag M. McGregor A. Melamed-Frank M. Sendersky E. Schwarz R. Structural aspects of the assembly and disassembly of the Phycobilisome Photosynth. Res. 91 2–3 2007 157-157 Arteni A.A. Ajlani G. Boekema E.J. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30022-010.1016/j.sjbs.2017.01.013Original ArticleDifferences between flocculating yeast and regular industrial yeast in transcription and metabolite profiling during ethanol fermentation Li Lili llli@yic.ac.cnWang Xiaoning 1002591179@qq.comJiao Xudong xdjiao@yic.ac.cnQin Song SQ0535@163.com⁎Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, 17 Chunhui Road, Laishan District, Yantai 264003, China⁎ Corresponding author. Fax: +86 0535 2109000. SQ0535@163.com24 1 2017 3 2017 24 1 2017 24 3 459 465 27 10 2016 31 12 2016 6 1 2017 © 2017 Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Objectives: To improve ethanolic fermentation performance of self-flocculating yeast, difference between a flocculating yeast strain and a regular industrial yeast strain was analyzed by transcriptional and metabolic approaches. Results: The number of down-regulated (industrial yeast YIC10 vs. flocculating yeast GIM2.71) and up-regulated genes were 4503 and 228, respectively. It is the economic regulation for YIC10 that non-essential genes were down-regulated, and cells put more “energy” into growth and ethanol production. Hexose transport and phosphorylation were not the limiting-steps in ethanol fermentation for GIM2.71 compared to YIC10, whereas the reaction of 1,3-disphosphoglycerate to 3-phosphoglycerate, the decarboxylation of pyruvate to acetaldehyde and its subsequent reduction to ethanol were the most limiting steps. GIM2.71 had stronger stress response than non-flocculating yeast and much more carbohydrate was distributed to other bypass, such as glycerol, acetate and trehalose synthesis. Conclusions: Differences between flocculating yeast and regular industrial yeast in transcription and metabolite profiling will provide clues for improving the fermentation performance of GIM2.71. Keywords Ethanol fermentationGene expressionJerusalem artichokeMetabolic analysisSelf-flocculating yeast ==== Body 1 Introduction Bioethanol production by Saccharomyces cerevisiae is currently, by volume, the single largest fermentative process in industrial biotechnology. The major portion of total expenditure in today’s bioethanol industry is allotted to feedstock costs (Galbe et al., 2007). A global research effort is under way to expand the substrate range of S. cerevisiae to include nonfood feedstocks, such as Jerusalem artichoke. Jerusalem artichoke (Helianthus tuberosus L.) can grow well in non-fertile land and is resistant to frost, drought, salt-alkaline and plant diseases (Yu et al., 2011). It is superior to the other inulin-accumulating crops in terms of its output of biomass production, inulin content, and tolerance of a relatively wide range of environmental conditions. The tuber yield of Jerusalem artichokes can be up to 90 t/ha resulting in 5–14 t carbohydrates/ha (Stephe et al., 2006). Besides its economic value, it also has a function of soil remediation, such as salt adsorption. To date, Jerusalem artichoke has predominantly been cultivated in North America, Northern Europe, Korea, Australia, New Zealand and China (Li et al., 2013). The principle storage carbohydrate of Jerusalem artichoke is inulin, which consists of linear chains of β-2, 1-linked d-fructofuranose molecules terminated by a glucose residue. It preserves carbohydrate in a 9:1 average ratio of fructose to glucose. Improving of fermentation performance with Jerusalem artichoke would have significant impacts on profits in large scale ethanol production. Flocculating yeast separated from fermentation broth by self-flocculating at the end of fermentation and was re-used in consecutive fermentation, and therefore high density cell was obtained without increasing operating costs. High density cells exponentially shortened the fermentation time and increased cells resistance to ethanol stress (Li et al., 2009a). This work provides the first demonstration of the differences in transcriptic and metabolic profiles between flocculating yeast and regular industrial yeast. The result will provide clues to improve fermentative performance of flocculating yeast. 2 Materials and methods 2.1 Strain and cell culture Industrial S. cerevisiae YIC10 is presented by Bincheng alcohol company (Shandong Province, China), self-flocculating S. cerevisiae GIM2.71 is obtained from Guangdong Microbiology Culture Center. Yeasts were grown overnight before inoculated in fresh medium (1% yeast extract, 2% peptone, 0.4% glucose, 3.6% fructose, ratio of fructose/glucose is 9 in order to stimulate hydrolysates of Jerusalem artichoke) to an initial OD600 of 0.1. Samples for microarray analysis were collected at exponential growth phase (7 h) and total RNA was then isolated. Samples for monitoring cell growth and fermentation were taken at 0, 2, 4, 5, 6, 7, 8, 10, 12, 14, 16, 18, 20, 21 and 23 h. 2.2 RNA extraction After the sample was taken, it was immediately centrifuged at 4000 rpm for 3 min at 4 °C, the cells were then stored in liquid nitrogen until total RNA was extracted. Total RNA was extracted using Yeast RNAiso Kit (TaKaRa, Japan) after partially thawing the samples on ice, and RNA was purified using NucleoSpin Extract II kits (Machery-Nagel, Germany) according to the manufacturers’ instructions. Then total RNA was assessed by formaldehyde agarose gel (1.2%, w/v) electrophoresis and was quantitated spectrophotometrically (A260 nm/A280 nm ⩾ 1.80). 2.3 DNA microarray assays An aliquot of 2 μg of total RNA was used to synthesize double-stranded cDNA, and cDNA was used to produce biotin-tagged cRNA by MessageAmpTM II aRNA Amplification Kit (Ambion, USA). The resulting biotin-tagged cRNA were fragmented to strands of 35–200 bases in length according to the protocols from Affymetrix. The fragmented cRNA was hybridized to Affymetrix GeneChip Yeast Genome 2.0 Arrays. Hybridization was performed at 45 °C using Affymetrix GeneChip Hybridization Oven 640 for 16 h. The GeneChip arrays were washed and then stained by Affymetrix Fluidics Station 450 followed by scanning with Affymetrix GeneChip Scanner 3000. 2.4 Microarray data processing Hybridization data were analyzed using Affymetrix GeneChip Command Console Software. An invariant set normalization procedure was performed to normalize different arrays using DNA-chip analyzer 2010 (http://www.dchip.org, Harvard University). A multiclass method for analysis of microarray software (Significant Analysis of Microarray method, developed by Stanford University) was used to identify significant differences. Genes with false discovery rate <0.05 and a fold-change >2 were identified as differentially expressed genes. Differentially expressed genes were clustered hierarchically using Gene Cluster 3.0 (Stanford University). Gene ontology (GO) analysis of differentially expressed genes was done with DAVID (http://david.abcc.ncifcrf.gov/list.jsp). 2.5 Real-Time quantitative PCR Based on microarray results, seven genes (HXT1-7) were selected for quantitative transcription analysis. The primers used in RT-qPCR analyses are listed in Table 1. Real-Time quantitative PCR (RT-qPCR) was performed according to the method described by Ye et al. (2009). ACT1 was used as an internal reference for normalizing gene expression (Liu et al., 2007). 2.6 Metabolites preparation and analysis Intracellular and extracellular metabolites including glucose, fructose, ethanol, glycerol, acetate and trehalose were prepared by methods reported by our previous study (Li et al., 2009b). Samples were analyzed by a high-performance liquid chromatography (HPLC, Waters, USA) system with an Aminex HPX-87H column (Bio-Rad), 2414 refractive index detector and 515 HPLC pump. Column was kept at 50 °C and 5 mM H2SO4 was used as eluent at a flow rate of 0.5 ml/min. 3 Results and discussion 3.1 Fermentation behavior YIC10 was superior to GIM2.71 in cell growth rate, sugar consumption and ethanol production performance (Fig. 1). YIC10 and GIM2.71 reached their highest ethanol yield at 12 h (16.2 g/L) and 21 h (16.0 g/L), respectively. Both strains showed indeed a similar behavior in terms of ethanol yield. 3.2 Overview and GO analysis of microarray data Microarray analysis showed that the number of down-regulated (YIC10 vs. GIM2.71) and up-regulated genes were 4503 and 228, respectively. It is the economic regulation for YIC10 that non-essential genes were down-regulated, and cells put more “energy” into growth and ethanol production. GO analysis was carried out with the up-regulated genes and the significant GO terms obtained were sorted according to their corresponding GO categories (Table 2). According to that analysis, most of genes focused on monosaccharide, hexose and glucose metabolic process, generation of precursor metabolites and energy and ion transport (Table 2), which indicated that these pathways may have some contributions for fermentative performance. 3.3 Hexose transport Gene expression analysis using RT-qPCR method was well corresponded with microarray means (Fig. 2a). Transport is suggested as the rate-limiting step of glycolysis in metabolic control analysis and transport exerts a high degree of control on glycolytic flux (Oehlen et al., 1994). The results showed that the detected transporter genes were all down-regulated in YIC10 vs. GIM2.71 comparisons, except HXT5 (Fig. 2a). It was consistent with the report that HXT5 was regulated by the growth rate of cells, where the growth rate of YIC10 was significantly higher than GIM2.71. However, different from HXT5, HXT1-4 and HXT6/7 were regulated by extracellular glucose (Diderich et al., 2001). Investigations using single transport mutants also showed that Hxt1-4, 6 and 7 are the major hexose transporters in yeast transporting glucose and fructose (Reifenberger et al., 1997, Reifenberger et al., 1995). Furthermore, analysis of intracellular glucose and fructose showed that both sugars levels were always higher in GIM2.71 than in YIC10 (Fig. 2b and c), which was consistent with the higher expression of major genes involved in hexose transporter. It concluded that hexose transport was not the limiting-step in sugar consumption and ethanol production for GIM2.71, compared to YIC10. 3.4 Central carbon metabolism Once sugars have been imported into cells, they are phosphorylated by one of three sugar kinases, Hxk1, Hxk2 and Glk1. Glucose and fructose are both phosphorylated by hexokinases Hxk1 and Hxk2 but with different efficiencies, and the glucokinase Glk1 phosphorylates glucose but not fructose (Rodriguez et al., 2001). The three genes were all down-regulated in YIC10 to GIM2.71 comparisons, which indicated that hexose phosphorylation was not the limiting steps in sugar consumption and ethanol production for GIM2.71. Most genes in central carbon metabolism were down-regulated, only 3-phosphoglycerate kinase encoding genes PGK1, pyruvate decarboxylase encoding genes PDC6, alcohol dehydrogenase encoding genes ADH5 were up-regulated (Fig. 3). During S. cerevisiae growth on fermentable carbon sources, six PDC genes were identified out of which three structural genes (PDC1, PDC5 and PDC6) were encoded for active Pdc enzymes, independently (Milanovic et al., 2012). Pdc6p is the predominant isoenzyme form that catalyzes an irreversible reaction in which pyruvate is decarboxylated to acetaldehyde. Additionally, there are four genes (ADH1, ADH3, ADH4 and ADH5) that encode alcohol dehydrogenases involved in ethanol synthesis. ADH5 gene product is the major enzyme that is responsible for converting acetaldehyde to ethanol. It suggested that the most limiting steps of ethanol fermentation were the reaction of 1,3-disphosphoglycerate to 3-phosphoglycerate, the decarboxylation of pyruvate to acetaldehyde and its subsequent reduction to ethanol. 3.5 Expression of genes involved in glycerol and its intracellular level Glycerol was the major by-product in ethanol fermentation. The first step in glycerol synthesis is the most important as glycerol-3-phosphate dehydrogenase (encoded by GPD1 and GPD2) activity controls the amount of glycerol produced. In this experiment, GPD1 and GPD2 were down-regulated significantly, and other genes involved in glycerol both synthesis (RHR2 and HOR2) and degradation (GUT1 and GUT2) were all down-regulated (Fig. 4a). Intracellular metabolic analysis showed that glycerol was at relatively low levels both for YIC10 and GIM2.71 at the onset of fermentation, whereas it was accumulated 83-fold compared to its initial level in GIM2.71 when ethanol was exponentially synthesized and carbon resource was exhausted (Fig. 4b). And this response was significantly stronger than YIC10. 3.6 Expression of genes involved in acetate and its intracellular level Among genes encoding acetate synthesis, only ALD4 was up-regulated and the other three genes (ALD2, ALD5 and ALD6) were down-regulated (Fig. 4c). It was reported that the deletion of ALD4 had no effect on the amount of acetate formed (Remize et al., 2000). Intracellular metabolic analysis showed that acetate in YIC10 was always at a relatively low level, whereas acetate in GIM2.71 was accumulated quickly at late-logarithmic phase (Fig. 4d). 3.7 Expression of genes involved in trehalose and its intracellular level Genes both were encoding trehalose synthesis (TPS1 and TPS2) and hydrolysis (ATH1 and NTH1) were all down-regulated (Fig. 4e). The intracellular trehalose in YIC10 was always at a relatively low level throughout the fermentation, whereas trehalose in GIM2.71 was accumulated rapidly at 10 h and 16 h (Fig. 4f). Glycerol, acetate and trehalose were significantly accumulated in response to environmental stress in GIM2.71. Glycerol formation is the results of redox balance and stress response (Nevoigt and Stahl, 1997) and the observed differences suggest that the two strains could have a different stress response. This hypothesis is also supported by the formation of acetate, another significant redox-driven product, and the accumulation of trehalose, other potential stress protectants like glycerol. Achieving high fermentative performance is a major challenge, particularly when it comes to modifications of the central carbon metabolism which is inherently coupled to energy and redox issues. Glycerol is the major by-product accounting for up to 5% of the carbon in S. cerevisiae ethanolic fermentation. Decreasing glycerol formation may redirect part of the carbon toward ethanol production (Nissen et al., 2000). Pagliardini et al. (2010) reported that fine-tuning the glycerol synthesis pathway allowed the strains to keep their initial ethanol tolerance. Conflict of interest The authors declare that they have no conflict of interest. Acknowledgements This work was supported by National Natural Science Foundation of China (21306221) and Science and Technology Development Program of Shandong Province (2010GSF10208). Peer review under responsibility of King Saud University. Figure 1 Fermentative performance of YIC10 and GIM2.71. Cell growth (a), fructose (b), glucose (c) and ethanol concentration (d) were determined. Figure 2 Expression of genes encoding hexose transport (A) and intracellular fructose (B) and glucose (C) levels. Figure 3 Expressions of genes involved in central carbon metabolism. “+” and “–” before regulation fold represented up-regulated and down-regulated genes, respectively. Figure 4 Expression of genes involved in glycerol (a), acetate (c) and trehalose (e), and intracellular levels of glycerol (b), acetate (d) and trehalose (f). Table 1 Genes and primers used in RT-qPCR. Gene ID Primer Sequence 5′ → 3′ Amplicon (bp) ACT1 F AACATCGTTATGTCCGGTGGT 144 R ACCACCAATCCAGACGGAGTA HXT1 F GTTGCTTTCGGTGGTTTCAT 101 R TCGTGGTGCTTCATACCAAA HXT2 F ATTCGCTACTAGCCGCGTT 140 R TTGGCTTTGCTGGGAGTTCA HXT3 F GGCCGACCAAGTACTTACCA 85 R ACCGAAGGCAACCATAACAC HXT4 F TACCGTTTTCACTGCTGTCG 145 R GGAAGCAGCACCCCATAATA HXT5 F TCTGAAGTGTCGCCTAAGCA 139 R ATGGTACCCTCCATTGGACA HXT6/7 F GGGCTGTTTGGTCTTCATGT 94 R TTCTTCCCACATGGTGTTGA Table 2 The GO analysis of up-regulated genes (Top 10). Term Count % P-value Metabolism DNA metabolic process 26 13.3 2.90 × 10−2 Monosaccharide metabolic process 15 7.7 4.40 × 10−4 Hexose metabolic process 13 6.6 1.70 × 10−3 Glucose metabolic process 11 5.6 3.60 × 10−3 Cellular carbohydrate catabolic process 10 5.1 1.80 × 10−3 Hexose catabolic process 8 4.1 4.20 × 10−3 Monosaccharide catabolic process 8 4.1 6.20 × 10−3 Alcohol catabolic process 8 4.1 9.60 × 10−3 Glucose catabolic process 7 3.6 9.60 × 10−3 Oxidoreduction coenzyme metabolic process 7 3.6 2.80 × 10−2 Energy Generation of precursor metabolites and energy 16 8.2 6.20 × 10−2 Energy reserve metabolic process 5 2.6 6.80 × 10−2 Transport Ion transport 17 8.7 4.20 × 10−3 Cation transport 15 7.7 5.10 × 10−4 Metal ion transport 9 4.6 1.40 × 10−2 Di-, tri-valent inorganic cation transport 7 3.6 7.90 × 10−3 Transition metal ion transport 7 3.6 1.90 × 10−2 Carboxylic acid transport 7 3.6 4.40 × 10−2 Siderophore transport 5 2.6 2.00 × 10−4 Anion transport 5 2.6 3.20 × 10−2 Iron assimilation by chelation and transport 4 2 9.00 × 10−4 Siderophore-iron transport 4 2 9.00 × 10−4 Protein Protein modification by small protein conjugation or removal 11 5.6 2.70 × 10−2 Protein modification by small protein conjugation 10 5.1 1.80 × 10−2 ==== Refs References Diderich J.A. Schuurmans J.M. Van Gaalen M.C. Kruckeberg A.L. Van Dam K. Functional analysis of the hexose transporter homologue HXT5 in Saccharomyces cerevisiae Yeast 18 2001 1515 1524 11748728 Galbe M. Sassner P. Wingren A. Zacchi G. Process engineering economics of bioethanol production Adv. Biochem. Eng. Biotechnol. 108 2007 303 327 17541520 Li F. Zhao X.Q. Ge X.M. Bai F.W. An innovative consecutive batch fermentation process for very high gravity ethanol fermentation with self-flocculating yeast Appl. Microbiol. Biotechnol. 84 2009 1079 1086 19475405 Li L.L. Ye Y.R. Pan L. Zhu Y. Zheng S.P. Lin Y. The induction of trehalose and glycerol in Saccharomyces cerevisiae in response to various stresses Biochem. Biophys. Res. Commun. 387 2009 778 783 19635452 Li L.L. Li L. Wang Y.P. Du Y.G. Qin S. Biorefinery products from the inulin-containing crop Jerusalem artichoke Biotechnol. Lett. 35 2013 471 477 23242496 Liu X.Y. Zhang X.H. Wang C. Liu L.Y. Lei M. Bao X.M. 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Decrease in glycolytic flux in Saccharomyces cerevisiae Cdc35-1 cells at restrictive temperature correlates with a decrease in glucose-transport Microbiology 140 1994 1891 1898 7921242 Pagliardini J. Hubmann G. Bideaux C. Alfenore S. Nevoigt E. Guillouet S.E. Quantitative evaluation of yeast’s requirement for glycerol formation in very high ethanol performance fed-batch process Microb. Cell Fact. 9 2010 Reifenberger E. Freidel K. Ciriacy M. Identification of novel HXT genes in Saccharomyces cerevisiae reveals the impact of individual hexose transporters on glycolytic flux Mol. Microbiol. 16 1995 157 167 7651133 Reifenberger E. Boles E. Ciriacy M. Kinetic characterization of individual hexose transporters of Saccharomyces cerevisiae and their relation to the triggering mechanisms of glucose repression Eur. J. Biochem. 245 1997 324 333 9151960 Remize F. Andrieu E. Dequin S. Engineering of the pyruvate dehydrogenase bypass in Saccharomyces cerevisiae : role of the cytosolic Mg2+ and mitochondrial K+ acetaldehyde dehydrogenases Ald6p and Ald4p in acetate formation during alcoholic fermentation Appl. Environ. Microbiol. 66 2000 3151 3159 10919763 Rodriguez A. de la Cera T. Herrero P. Moreno F. The hexokinase 2 protein regulates the expression of the GLK1 , HXK1 and HXK2 genes of Saccharomyces cerevisiae Biochem. J. 355 2001 625 631 11311123 Stephe A.M. Phillips G.O. Willians P.A. Food Polysaccharides and their Applications 2006 CRC Press Florida Ye Y.R. Zhu Y. Pan L. Li L.L. Wang X.I. Lin Y. Gaining insight into the response logic of Saccharomyces cerevisiae to heat shock by combining expression profiles with metabolic pathways Biochem. Biophys. Res. Commun. 385 2009 357 362 19463789 Yu J. Jiang J.X. Ji W.M. Li Y.Y. Liu J.P. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30024-410.1016/j.sjbs.2017.01.015Original ArticleChemical signals and their regulations on the plant growth and water use efficiency of cotton seedlings under partial root-zone drying and different nitrogen applications Li Wenrao aJia Liguo nndjialiguo@163.comb⁎Wang Lei aa State Key Laboratory of Cotton Biology, College of Life Sciences, Henan University, Kaifeng, Henan 475004, Chinab College of Agronomy, Inner Mongolia Agricultural University, Huhhot 010019, China⁎ Corresponding author. nndjialiguo@163.com27 1 2017 3 2017 27 1 2017 24 3 477 487 3 11 2016 28 12 2016 6 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Partial root-zone drying during irrigation (PRD) has been shown effective in enhancing plant water use efficiency (WUE), however, the roles of chemical signals from root and shoot that are involved and the possible interactions affected by nitrogen nutrition are not clear. Pot-grown cotton (Gossypium spp.) seedlings were treated with three levels of N fertilization and PRD. The concentrations of nitrate (NO3−), abscisic acid (ABA) and the pH value of leaf and root xylem saps, biomass and WUE were measured. Results showed that PRD plants produced larger biomass and higher WUE than non-PRD plants, with significant changes in leaf xylem ABA, leaf and root xylem NO3− concentrations and pH values, under heterogeneous soil moisture conditions. Simultaneously, high-N treated plants displayed larger changes in leaf xylem ABA and higher root xylem NO3− concentrations, than in the medium- or low-N treated plants. However, the WUE of plants in the low-N treatment was higher than that of those in the high- and medium-N treatments. PRD and nitrogen levels respectively induced signaling responses of ABA/NO3− and pH in leaf or root xylem to affect WUE and biomass under different watering levels, although significant interactions of PRD and nitrogen levels were found when these signal molecules responded to soil drying. We conclude that these signaling chemicals are regulated by interaction of PRD and nitrogen status to regulate stomatal behavior, either directly or indirectly, and thus increase PRD plant WUE under less irrigation. Keywords Partial root-zone drying (PRD)Nitrogen levelsDrought stressWater use efficiency (WUE)Abscisic acid (ABA)Nitrate (NO3−) ==== Body 1 Introduction Soil water availability is one of the major environmental factors limiting crop growth and yield formation (Wu et al., 2008, Gonzalez-Dugo et al., 2010). The plant hormone abscisic acid (ABA) has been suggested many physiological roles in the plant when the soil water potential dropped below optimum levels, including control of stomatal behavior and limiting ineffictive water use (Davies and Zhang, 1991, Tardieu and Davies, 1993, Dodd, 2003, Verslues et al., 2006, Pinheiro and Chaves, 2011). The increase of ABA biosynthesis in both root and shoot and the enhancement of ABA transportation as a root-to-shoot or shoot-to-root signal, accompanied with the subsequent weakening of gas exchange capacity and decrease of growth rate, have been observed in many studies when water availability decreased from optimal level (Davies and Zhang, 1991, Schurr et al., 1992, Tardieu and Davies, 1993, Comstock, 2002, Chaves and Oliveira, 2004, Verslues et al., 2006, Jia and Davies, 2007, Qin et al., 2011, Wang et al., 2012). However, except the effect of ABA on plant shoot phenotype, the elongation of root length and increase of the root surface area were improved by ABA (indicating a indirect role) to ensure the growth-based water and (nitrogen) nutritions uptake (Wittenmayer and Merbach, 2005, Dodd et al., 2009). ABA-deficient mutants (e.g. flacca, aba-1 and aba-2 etc.) and/or grafted technique have been used to explain the physiological role of shoot-synthesized and root-sourced ABA in plants under suboptimal environmental conditions (Dodd et al., 2008). These studies suggested that the movement of ABA between symplast and apoplast can be inhibited by the alkalization of the xylem sap and the decrease of pH gradients over the cells membrane in stem, leaf or root tissues when the soil dries (Wilkinson et al., 2007, Xue et al., 2014, Dodd et al., 2008), eventually leading to ABA accumulation in mesophyll cells, the guard cells dehydration and then stomatal closure (Jia and Davies, 2007, Netting et al., 2012). As a signaling molecule, ABA regulates plant physiological behaviors under suboptimal environmental conditions, for example the increased ABA levels weakens gas exchanges capability, declines stomatal conductance and transpiration rates as well as maintains leaf water potential and root water-uptake ability by increasing root: shoot ratio and decreasing leaf surface etc. under drought stress (Wittenmayer and Merbach, 2005, Dodd et al., 2009). However, due to convenience of measurement, many researches focused on shoot growth and regulation of shoot-synthesized ABA. Additionally, stomatal closure regulated by ABA can be caused by soil nitrogen (N) deficiency, a same mechanism induced by drought (Wilkinson et al., 2007). It has been suggested that N deficiency increases tissue ABA concentrations (Rahayu et al., 2005). It is also well known that water fluxes in the xylem sap are generally reduced by limited water supply. Mineral N fluxes are also declined because N uptake by plants is limited under these conditions; this alters plant N nutrition and can even lead to N deficiency (Wilkinson et al., 2007, Haefele et al., 2008, Wu et al., 2008, Gonzalez-Dugo et al., 2010). NO3− is one of the main forms of nitrogen (N) uptake and utilization by plants, and its changes in the xylem sap can alter the pH of xylem sap (Dodd, 2003, Wilkinson et al., 2007). Therefore, NO3− concentration in the xylem sap can be reduced under water stress situation (Dodd, 2003, Wilkinson et al., 2007). Studies indicated that the concentration of NO3− in the xylem sap affected stomatal sensitivity to both ABA and soil dryness (Jia and Davies, 2007, Wilkinson et al., 2007), leading to stomatal opening/closure and even changes in WUE, as the apoplastic pH changed (Davies and Zhang, 1991, Haefele et al., 2008, Qin et al., 2011). It has been proved that the effects of NO3− on plant growth were mediated by pH-based ABA redistribution (Wilkinson et al., 2007). It is recently reported that the nitrate transporter 1/peptide transporter (NRT1/PRT) family comprises two ABA transporters (Yann et al., 2013), which suggests the close relationship of ABA and NO3−. Therefore, much work should be focused on the available interaction between ABA and supplied N/NO3−. Furthermore, numerous studies have been focused on ABA not only because of its key regulation to leaf stomatal conductance especially during the early stages of plant response to water deficit, but also its role in increasing water use efficiency (WUE) in agriculture (Schachtman and Goodger, 2008). An innovative partial root-zone drying model (PRD) has been used to remarkably increase the WUE of various crops including grape, fruit trees, and maize, in the regions of limited water supply (Wang et al., 2009, Jovanovic et al., 2010, Wang et al., 2010, Wang et al., 2012). In the field-grown grapevines study, the use of PRD or deficit irrigation increased WUE by about 40% with a yield decreasing by only 15% (Schachtman and Goodger, 2008). It has been confirmed that PRD can promote the increase of xylem sap ABA concentrations as compared with full irrigation, which can be explained by the changes of root water potential (Dodd et al., 2010, Puértolas et al., 2014) and other physiological parameters (Dodd, 2009, Sepaskhah and Ahmadi, 2010). Therefore, the increase of WUE and reductions in stomatal conductance are correlated with increased ABA concentrations under PRD treatment (Schurr et al., 1992, Gonzalez-Dugo et al., 2010, Dodd et al., 2010). On the other hand, studies on N fertilization effect under water stress have indicated that N supply can reduce the negative effects of drought stress on plant growth (Wu et al., 2008, Haefele et al., 2008, Cramer et al., 2009, Gonzalez-Dugo et al., 2010). A typical example is that Sophora davidii seedlings subjected to severe water deficit under low nitrogen conditions had a higher WUE than well-watered seedlings under high nitrogen conditions (Wu et al., 2008). Most experiments showed positive effect of WUE after supplying of N fertilizer, while few results reported about N effect to WUE (Brueck, 2008). Therefore, although PRD or N supply plays a role in enhancing plant WUE, the involved root-sourced chemical signals (ABA, pH and NO3−) and the possible interaction between N nutrition and PRD remain unclear. The aim of this study is to examine how cotton (Gossypium spp.) seedling growth and WUE are affected by the interaction of PRD treatment and different nitrogen levels via cooperatively regulating root-sourced ABA signal and NO3− under soil water change. The cotton seedlings supplied with different N levels were subjected to a drought-rewatering treatment, with PRD or full water supplying before drought and after rewatering. The xylem sap in both shoot and root was collected for the analysis of ABA, NO3−, and pH, and for the measurement of the biomass, leaf water potential and whole plant water consumption. These results can illustrate the mechanism(s) by which plants adapt to changes in water availability. It is also important to explore how to improve WUE with appropriate N supply, especially for crops cultivated in drought-prone areas or in alternating wet-dry growth conditions. 2 Materials and methods 2.1 Plant materials and culture Cotton (Gossypium spp., HX-25) was selected for in this study. Seeds of HX-25 were sowed in a substrate with distilled water. After emergence, half of HX-25 seedlings were transplanted into 0.06 × 0.06 × 0.25 m pots (A) (one seedling per pot) filled with 0.35 kg soil (total N 0.78 g kg−1, nitrate N 67.66 mg kg−1, total K 22.11 g kg−1, solution K 17.63 mg kg−1, total P 0.81 g kg−1, solution P 31.50 mg kg−1, Organic matter 13.15 g kg−1, respectively, and pH = 7) and grown in a controlled environment with following conditions: day/night temperature 30/23 °C, relative humidity 65%, photoperiod 12 h with a photosynthetic photon flux density of 330 μmol m−2 s−1. The remaining HX-25 seedlings were transplanted into separated pots (B). Pot (B) had the same total volume and shape as the above pot (A). However, each pot (B) was separated equally and vertically in two in the medium by a watertight light plastic barrier before seedlings were transplanted. Soil (0.35 kg) was placed in both sides of each separated pot (B) equally and an emergent seed was transplanted into the medium of this pot (B), to allow equal opportunity for the growth of roots on both sides of the pot (B). These seedlings were grown under the conditions as described above. The water status of soil and seedlings during experiment is shown in Table 1, Table 2. 2.2 Treatments HX-25 seedlings were supplied with three levels of nitrogen fertilizer (urea, N 45.16%) mixed with soil as base fertilizer: 25 kg N/667m2 (high N), 17.5 kg N/667m2 (medium N) and 10 kg N/667m2 (low N), respectively. The actual amount of nitrogen fertilizer applied was adjusted according to the amount of nitrogen in the original soil. The same amount of nitrogen fertilizer was applied to the entire rootzone of pot (A) while to the left side of the split-rooted pot (B). The right side of pot (B) received no extra nitrogen supply. Water was supplied to pots (A) and pots (B) (entire rootzone) for saturating soil after transplanting. Subsequently, Pots (A) was supplied with water once every 3 days. It was defined as non partial root-zone irrigation/drying (non-PRD) in this paper. Pots (B) were supplied with appropriate water on alternate sides of the pot once every 3 days. It was defined as partial root-zone fertilization with alternate partial root-zone irrigation/drying (PRD) in this paper. Three weeks after transplanting of HX-25 seedlings, water control occurred and all pots were stopped water supply. After 12 days of soil drying, all plants were rewatered to the same level as before drought treating. The daily water consumption per pot was recorded. Parameters were measured every 2–3 days during the periods of drought and rewatering. Each treatment was replicated four times. 2.3 Collection of leaf blade and de-topped root xylem sap Xylem sap from the de-topped root and leaf blade of the same seedling was collected by exudation according to Jia and Davies, 2007, Liang and Zhang, 1997 and Netting et al. (2012) with minor modification. In brief, the base of the third fully expanded leaf from top was cut off in petiole with a sharp razor blade, leaving at most 0.25 mm on the plant. The leaf was immediately placed in pressure chamber (Model: 3005, Soil Moisture Equipment Co., USA) with sealed by a silicone rubber bung so that only the petiole of the blade through the pressure chamber lid and was left outside. After placing the leaf in a whole pressure chamber, the cut petiole surface was cleaned 2–3 times with distilled water to remove all contaminating cell debris. Then, the pressure in the chamber was raised very slowly (0.05 MPa increments). An overpressure (nearly more than 0.2 MPa than leaf water potential) was applied to the leaves (following measurement of leaf water potential) so that about 0.05 ml of sap pre plant could be collected within less than 10 min. Individual whole plant from the full-soil pots was sealed in the pressure chamber, after which the shoot was cut off from the stem base. The cut surface was rinsed just like described above. Root xylem sap was collected into pre-weighted eppendorf tubes for calculating sap flow rate with a series of overpressure. To collect about 0.05 ml root xylem sap per plant sap flow rate was closely matched with transpiration rate. The collected root xylem sap was immediately used for pH measurement or stored at −80 °C for ABA and NO3− analyses. 2.4 Abscisic acid (ABA) assay Analysis of ABA concentrations in leaf and root xylem sap from HX-25 plants was carried out using the radioimmunoassay (RIA) method as described by Zhang et al. (1997) and Netting et al. (2012) with minor modification. Briefly, 50 μl of xylem sap was mixed with 200 μl PBS buffer (phosphate-buffered saline, 50 mM Na2HPO4/NaH2PO4, pH 6.0, with 100 mM NaCl), 100 μl 3H-(+)-ABA diluted solution (about 20,000 dpm, 2.5 g/L γ-globulin in PBS buffer, 1:100) and 100 μl diluted ABA antibody solution (about 1:100–300, MAC252, 5 g/l BSA + 4.5 g/l PVP in PBS buffer). The reaction mixture was incubated at 4 °C for 60 min and aqueous extracts were obtained as supernatants after centrifuging the extraction mixtures. After that saturated (NH4)2SO4 and 50% saturated (NH4)2SO4 were used to precipitate protein sequentially. The bound radioactivity was measured in 50% saturated (NH4)2SO4-precipitated pellets with 1.2 ml of a liquid scintillation cocktail using an Ls6500 multi-purpose scintillation counter. The assay sensitivity was calculated based on a standard curve (via measuring different 3H-(+)-ABA concentrations) according to Zhang et al. (1997). 2.5 Measurement of nitrate concentrations in the xylem sap Xylem nitrate concentrations were determined using a Low Range Lab Nitrate Test kit (Nitrate Elimination, catalog No. L-NTK-202) with minor modification. Firstly, 50 μl xylem sap was mixed with 100 μl reaction buffer (25 mM KH2PO4, 0.025 mM EDTA, pH 7.5), 50 μl 2 mM NADH and 20 μl (1 unit) nitrate reductase. After incubation for 30 min at room temperature, 100 μl 1% sulfanilamide in 3 mol L−1 HCl and 100 μl 0.02% N-naphthylethylenediamine were sequentially added to the reaction mixture. After incubation for a further 20 min, the absorbance at 540 nm was read in a spectrophotometer for the sample and nitrate standards. The nitrate content was calculated according to a standard curve. 2.6 Measurement of pH value, stomatal resistance, biomass, water dissipation by transpiration, whole plant water use efficiency (WUE) and leaf water potential and soil water content The pH value of leaf xylem or root xylem sap was determined immediately after collection using digital pH meter (Model 60, JENCO, USA). Stomatal resistance of the top fully expanded leaves was measured at 10:00–12:00 am using a steady state porometer (Li-1600, LiCor Inc, NE, USA). Daily water dissipation by transpiration and the evaporation from soil surface was determined by weighing every day. At the beginning of this measurement, the plant weight per pot was determined to avoid prejudicing the amount of water use per pot. The cumulative amount of water dissipation by transpiration per pot d−1 during the whole experiment was the total water dissipation by transpiration of the potted plants. The water use efficiency of biomass production (WUE) was determined for each potted plant by dividing the total biomass production (above- plus belowground biomass) by cumulative water use throughout the test period (i.e. the total water dissipation by transpiration). The water potential of top third fully expanded leaf were measured by pressure chamber (Model: 3005, Soil Moisture Equipment Co. U.S.A) as described above. The soil water content per pot was measured by HH2 Moisture Meter (Cambridge, England). 2.7 Statistical analysis All data obtained from the experiment were subjected to ANOVA (analysis of variance) using the SigmaPlot 8.0, the Dome statistical package and the SAS (statistical analysis system software). Means were compared using Duncan’s multiple range tests at the 5% level of probability. Linear regression was used to determine relationships between variables and differences between parameters of fitted models were evaluated with an F-test. 3 Results 3.1 ABA signaling in response to the interaction between PRD and nitrogen under drought stress In all treated plants, the ABA concentration in the xylem sap increased soon along with the decrease of soil water content (Fig. 1, Table 1, Table 2, p < 0.05), reached to the top point after 6–12 days drought and then rapidly declined to the pre-drought level after rewatering. ANOVA showed significant effects of PRD and/or N level on ABA (Table 3). During 12 days of drought, the partial root-zone fertilization with alternate partial root-zone irrigation/drying treatment (PRD) induced obvious larger increase (6.66–7.34 folds control levels) but slower reaction of ABA concentration in leaf xylem sap (ABAleaf) compared with non-PRD treatment (4.00–7.12 folds control levels) regardless of N levels. However, ABA concentration of root xylem sap (ABAroot) in PRD and non-PRD treatment showed similar changes on ratio and tendency (about 5.00 folds control levels at the 12th day after drought), irrespective of N level. Moreover, high N improved most for the ABAleaf content (more than 7.00 folds control level), irrespective of irrigation methods, followed by the medium- and low-N treatment (5.38–6.69 folds control levels in PRD-treated plants and 3.70–5.20 folds control levels in non-PRD-treated plants, Fig. 1, p < 0.05). Importantly, high or low N levels did semblable changes of ABAroot with medium N treatment facing to water deficiency, in both PRD and non-PRD treatments. In addition, there were larger changes in ABAleaf than ABAroot during drought stress, irrespective of irrigation methods or N levels. 3.2 NO3− concentrations of xylem sap changes facing to PRD and nitrogen level under drought stress ANOVA also showed significant effects of PRD and/or N level on NO3− concentrations (Table 3). PRD induced a obvious slower but larger NO3− concentrations increase in leaf xylem sap (4.19–5.49 folds control levels, NO3−leaf) compared with non-PRD treatment (3.52–3.74 folds control levels) during water stress and rewatering in cotton seedlings (Fig. 2, Table 1, Table 2, p < 0.05). However, the increase of NO3− concentrations in root xylem sap (NO3−root) of non-PRD-treated plants was larger (2.06–3.39 folds control levels) than in PRD-treated plants (0.45–0.82 folds control levels). At the same time, over N supply induced more increase of NO3−leaf, while low N supply did not change NO3−leaf obviously, compared with medium-N treatment after 12 days drought. Only low-N improved NO3−root in PRD but decreased NO3−root in non-PRD. High- and medium- N showed similar value in response to soil drought 12 days. The data also indicated that there were larger changes in NO3−leaf than in NO3−root. 3.3 Alkalization in leaf and root xylem sap induced by PRD and different N levels As drought stress became more severe, pH was alkalined in leaf and root xylem sap in all treatments except for the leaf xylem sap in non-PRD plants, in which pH showed relative constant during the drought treatment (Fig. 3). Similarly, ANOVA showed significant effects of PRD and/or N level on pH value (Table 3). To be more specific, PRD-treated plants showed similar pH increase (only 1.11–1.20 folds of control) with the decrease of soil water content both in leaf and root xylem sap (p < 0.05). High nitrogen level induced the increase in pH of both leaf xylem sap and root xylem sap. Whereas, in medium N, pH in leaf xylem sap was the highest, regardless of irrigation methods; while in root xylem sap, different N levels did not change pH with different ratio during 12-day drought stress. Additionally, the pH of leaf xylem sap was higher than that of root xylem sap. Xylem sap pH returned to pre-drought values after rewatering. 3.4 Water consumption, biomass accumulation and plant growth affected by interaction of PRD and nitrogen levels under drought stress As shown in Fig. 4, although PRD did not change whole plant water consumption in high- and medium-N levels subjected to 12-day drought stress, it improved biomass accumulation more (1.32-fold in high N, 1.41 folds in medium N and 1.54 folds in low N treatment, respectively) compared with non-PRD treatment, irrespective of N levels. Therefore, the WUE of PRD-treated plants were higher than those of non-PRD-treated plants (1.39, 1.49 and 1.08 folds, respectively, in high, medium and low N treatment). However, single leaf water potential in PRD plants were slightly lower than that of non-PRD plants subjected to 12-day water stress (Table 2). Plants in the high- and medium-N treatments showed obviously more water consumption and biomass accumulation than did plants in the low-N treatment, which was observed in both PRD-treated and non-PRD-treated plants. Plants in the low-N treatment showed a higher WUE than those plants in the high- and medium-N treatments. However, single leaf water potential did not show obvious difference among N levels, irrespective of irrigation methods. ANOVA also confirmed that the significant effects of PRD, N level and their interaction on biomass and WUE (Table 3). In PRD-treated plants, the decrease of growth rate started at the later period of drought and recovered immediately after rewatering; whereas in non-PRD-treated plants, it started at the early period of drought and the recovery was delayed (Table 4, p < 0.05). Additionally, PRD-treated plants had higher root to shoot ratio (R/S) than non-PRD-treated plants, even in low-N level. High-N treated seedlings showed the highest growth rate, followed by medium-N and low-N treatment during soil water change. The R/S of medium-N treatment plants displayed smaller changes in response to the drought treatment than those in the high- and low-N treatments. 4 Discussion ABA regulates stomatal opening and closure when soil water content and N availability change, which makes plants maintain water balance in cell, tissue and individual level through regulating growth and development as well as water use (Zhang et al., 1997, Comstock, 2002, Chaves and Oliveira, 2004, Haefele et al., 2008, Jia and Davies, 2007, Li et al., 2011, Poorter et al., 2012). We confirmed that in cotton seedlings, the interaction of N levels and PRD induced obvious changes in plant water status, plant growth and WUE (Table 3) via cooperative regulating of leaf-sourced and root-sourced ABA, NO3− and pH (Fig. 1, Fig. 2, Fig. 3) to stomatal behaviors in different levels of soil water. The growth and WUE of cotton seedlings were concerned in this study. The PRD-treated plants exhibit larger biomass and R/S, higher growth rate and WUE, rapider recovery rates from drought stress etc. (Table 4), compared with non-PRD-treated ones during drought and subsequent rewatering. This was consistent with other studies (Li et al., 2010, Poorter et al., 2012, Sampathkumar et al., 2013, Nouna et al., 2016), in which alternate partial root-zone drying/irrigated maize, potato or cotton had the higher WUE than traditional-irrigated plants. On the other hand, 1.5 folds N fertilization did not increase biomass and WUE obviously but raise water consumption of per cotton seedlings; While 0.6-fold N fertilizer significantly declined biomass and water consumption but increased WUE, irrespective of irrigation methods, compared with medium N supplied cotton seedlings under drought stress and subsequent rewatering. It can be attributed that the appropriate N fertilizer is useful to promote vegetative growth of plants. Nitrogen deficiency is not beneficial for growth maintaining, while N overuse is not helpful to WUE improving. However, Wang et al. (2012) reported that N fertilization did not affect WUE in maize grown in the PRD system. In this experiment, the increase of WUE in low-N level is due to that the decrease of drought-induced biomass was less than water consumption (Table 4 and Fig. 4). It also suggested that even less signal substances accumulations (ABA and NO3−) or change (pH) could decrease inefficient water consumption, increased WUE at low N supplied plants under drought finally. Stomatal opening/closure regulates water consumption and water use via controlling transpiration (Zhang and Davies, 1987, Li et al., 2010). Therefore, the phenotypic changes of cotton seedlings during soil water varied as mentioned above (biomass, leaf water potential, stomatal behavior, R/S etc), are close related to the cooperative regulation of xylem sap ABA, NO3− and pH to stomatal opening and closing in PRD and different N levels (Fig. 1, Fig. 2, Fig. 3). ANVOE also confirmed this (Table 3). The decline of soil water availability promotes the increase of ABA and NO3− as well as alkalization both in leaf and root xylem sap, irrespective of treatments. Afterwards, the rising of soil water availability recovered them to the control levels (Fig. 1, Fig. 2, Fig. 3). The original role of ABA in controlling stomatal opening and closing induced by root environments water shortage has been clarified on many species plants (Zhang and Davies, 1987, Davies and Zhang, 1991, Chaves and Oliveira, 2004, Jia and Davies, 2007, Wilkinson et al., 2007, Dodd et al., 2008, Kaldenhoff et al., 2008). Most of them thought that the pH fluctuation in apoplast or symplast of leaf changed ABA concentrations near guard cell’sapoplas, regulated the stomatal opening and closing finally (Jia and Davies, 2007, Dodd et al., 2008). In PRD, the dried zone of root system improved ABA-dependent pH signal and/or hydraulic signal in root xylem sap, induces stomatal closure and transpiration decrease, while the wet zone of root system maintains water uptake to protect plants avoiding severe water deficit (Dodd, 2003, Dodd et al., 2008). Therefore, the “adapting” to alternative water supply before the onset of long period drought stress promotes the synthesis of root-sourced chemical signals and their transfer from xylem sap to shoot, which stimulates leaf-sourced chemical increase or re-synthesis. This process is responsible for the better growth, higher WUE and rapider recovery from “long-time” drought stress in PRD-treated cotton seedlings. Xylem sap flow alkalization and the changes of ion concentrations (Ca2+, K+ and Cl− etc, which caused turgor pressure reduce and then stomatal closure) might be the original root-sourced drought signals that induce stomatal closure (Dodd et al., 2008). Accompanied these processes, ABA synthesis was stimulated and combined ABA (such as ABA-GE) in the root xylem sap could be transformed to the free-ABA under a weak acid condition, catalyzed by β-glucosidase and more free-ABA components might promote higher ABA accumulation (Netting et al., 2012). Dodd et al. (2008) confirmed that the ABA concentration in root xylem sap increased as the soil water potential decreased, regardless of whether sap was collected from the wet or dry root system in PRD-treated plants, or from a drought-stressed plant. Alkalinization of the leaf apoplast resulted from drought conditions is attributed to the altering of biochemical functions for maintaining a charge balance in cell (Jia and Davies, 2007, Poorter et al., 2012) and inducing of root-sourced chemical or hydraulic signals. The increased pH delays decomposition metabolism of ABA, blocks ABA diffusion to outside of stomatal cell, then accumulates ABA in apoplast connected with guard cells or re-synthesized ABA in leaves (Wilkinson et al., 2007, Dodd et al., 2008). Although the interaction of PRD and N levels is obvious but independent effects of PRD and N levels on signal substances are significant while soil water content changes (Fig. 1, Fig. 2, Fig. 3, Table 3). Compared with non-PRD, irrespective of N levels, PRD induces obvious the larger increase of ABA, NO3− and pH in leaf xylem sap, but with significant smaller NO3− and pH in root xylem sap while drought stress occurred. Leaf-sourced ABA can stay the site or be loaded into the phloem and then transported to the roots. Some of ABA arrived in roots might be deposited in the tissue or metabolized, or with root-synthesized ABA be loaded to the xylem vessels and recirculated to the shoots (Davies et al., 2005, Dodd et al., 2009). The alternation of drying and wetting of soil water status in PRD might promote sensitivity of stomata to ABA by accelerating synthesis of leaf-sourced ABA or improving recirculation of ABA from root to shoot (Davies et al., 2005, Dodd et al., 2009). Moreover, root-sourced ABA can be regulated by lower root xylem sap pH. We observed nearly unchanged root-sourced ABA and lower root xylem sap pH during long period drought in PRD in the experiment. This process follows by transporting more NO3− to shoot with transpiration flow (meant decline of NO3− in root xylem sap) and alkalizing leaf xylem sap (more shoot biomass and leaf area means stronger transpiration power). Additionally, leaf xylem sap NO3− increase also might be attributed to the N metabolism location change between root and shoot. The reduced N contents increase in xylem sap induced by drought. It might be contributed to the enhancement of pH in apoplast of leaves and the accumulation of ABA in apoplast nearby guard cells (Jia and Davies, 2007, Dodd et al., 2008). It suggested that PRD was beneficial for NO3− absorption via root system because of relative stronger transpiration power. Absorbed NO3− by roots transfers to the leaf with the transpiration flow, participating in the regulation of ABA synthesis in leaf as reported by Gonzalez-Dugo et al. (2010) and Qin et al. (2011). Simultaneously, in leaf xylem sap, high N stimulates obvious larger increase of ABA and NO3− but lower pH in comparison with medium N both in PRD and non-PRD at drought stress; N deficiency has similar ABA and NO3− but lower pH compared with medium N. In root xylem sap, there are no obvious difference on ABA and pH among N levels, but low N improves NO3− accumulation in non-PRD and inhibits NO3− accumulation in PRD. It suggested that xylem sap alkalization is promoted by both N overdose and deficiency, but it is weaker than medium N under drought stress in this experiment, especially in leaf xylem sap. Higher nitrogen supply increased NO3− resource (Fig. 2), produced larger biomass (Table 4) and faster nitrogen use for growth. Together with alkalization of the xylem sap, NO3− plays an important signaling role in regulating ABA concentrations and triggering ABA distribution (Jia and Davies, 2007). Wilkinson et al. (2007) reported that a small increase in N supply could increase the pH of sap, thereby indirectly affecting the ABA accumulation. This process of NO3− transferring to the leaf tissues might be easier in PRD or high-N supplied plants because of larger leaf areas and transpiration power (more biomass, Table 4), which also explains why pH values did larger changes in leaf xylem sap than in root xylem sap, both in PRD-treated and non-PRD-treated plants. The NO3− concentrations in the xylem sap are a pH-dependent root and shoot signals (Dodd, 2003). Declined root-sourced NO3− in low N level in PRD might be due to changed composition of organic acids, such as the increase of malic acid, which was used to neutralize the hydroxide produced during N transformation and then induced alkalization of xylem sap (Wilkinson et al., 2007, Wang et al., 2012). Thus, the decrease in pH and the decline in ABA and NO3− concentrations after rewatering might also be related to a decrease in the malate level, or contributed to maintaining the charge balance of cations and anions in the xylem sap. In the study on maize of PRD, Hu et al. (2009) showed that increased root N absorption in the irrigated zone had a significant compensatory effect on N uptake of whole plant. Gonzalez-Dugo et al. (2010) also reported that although mineral N fluxes in the xylem sap generally decreased under water deficiency, N supplied in one part of the root-zone could complement and provide resources to other parts with no N supply for plant growth in a split-root irrigation treatment. That is why NO3− concentrations in root xylem sap in low-N showed larger changes in PRD-treated plant in this study. Earlier research on cotton by Radin and Ackerson (1981) reported that ABA accumulation (in leaves) occurred at a higher water potential in N-deficient cotton plants compared with normal plants. This tendency was more significant in PRD plants than in non-PRD seedlings (see above) in this experiment (before 12-day drought stress, Table 1, Table 2), illustrating an improvement of N deficiency to ABA accumulation at earlier drought stress, which suggests a positive role of PRD treatment and/or N levels. 5 Conclusion Better growth, larger biomass and higher WUE were shown in PRD plants subjected to soil water deficiency, compared with non-PRD, irrespective of N levels. Higher leaf xylem ABA, NO3− and pH, the lower root xylem NO3− and pH as well as similar root xylem ABA, in comparison with non-PRD, are responsible for maintaining of phenotype characteristics in PRD. These phenotypic characteristics are controlled by different leaf and root xylem chemical signals, by regulating stomatal behavior under drought stress condition. Obvious significant interaction of PRD and nitrogen levels on chemical signals was exhibited. Whereas high N is benefit to PRD plants growth and biomass increase through stimulating only leaf xylem ABA and NO3− increase under relatively low pH, although their WUE decreases. Nitrogen deficiency fails to promote both leaf and root xylem chemical signals to regulate better biomass and growth, however, WUE increases via inhibiting root xylem NO3− accumulation in PRD. Thus, leaf-sourced chemical signal perhaps is sufficient to confer PRD phenotype, especially under high N condition. We conclude that these signaling chemicals are regulated independently and interacted by soil drying conditions, PRD and nitrogen status to regulate stomatal behavior, either directly or indirectly, and thus increase plant WUE in PRD under less irrigation. Acknowledgements The study was supported by State Key Laboratory of Cotton Biology Open Fund (CB2014A24), the National Natural Science Foundation of China (31300327), and Excellent Young Scientist Foundation of Henan University (CX0000A40378). The authors would like to thank Professor Ian C Dodd for valuable advice. Peer review under responsibility of King Saud University. Fig. 1 Change of ABA concentrations in leaf and root xylem sap in PRD and non-PRD cotton seedlings treated with different nitrogen levels during drought stress (S) and subsequent rewatering (R). Two factors (water treatments and nitrogen levels) ANOVA was carried out to the ABA concentrations values but only compared results after drought stress 12 days were showed on this figure. Capital letters refer to ABA concentrations of leaf xylem sap and italic letters to ABA concentrations of root xylem sap. For this and subsequent figures and tables, bars (with standard errors) with the same letters are not significantly different (p < 0.05). Data are shown as mean ± SE of four independent measurements (p < 0.05). Fig. 2 Change of NO3− concentrations in leaf and root xylem sap in PRD and non-PRD cotton seedlings treated with different nitrogen levels during drought stress and subsequent rewatering. Capital letters, small letters and italic letters above error bars represent ANOVA results of high nitrogen, medium nitrogen and low nitrogen treatments, respectively. Data are shown as mean ± SE of four independent measurements (p < 0.05). Fig. 3 Change of pH value in leaf and root xylem sap in PRD and non-PRD cotton seedlings treated with different nitrogen levels under drought stress and subsequent rewatering. S and R on the horizontal axis represent days of drought stress and rewatering, respectively. Capital letters, small letters and italic letters above error bars represent ANOVA results of high nitrogen, medium nitrogen and low nitrogen treatments, respectively. Data are shown as mean ± SE of four independent measurements (p < 0.05). Fig. 4 Accumulated water consumption (square columns), water use efficiency of biomass production (WUE, ‘○’) and biomass accumulations (●) of cotton seedlings treated with different nitrogen level under drought stress and subsequent rewatering. Capital letters refer to accumulated water consumptions, small letters to WUE and italic letters to biomass accumulations, respectively. Data are shown as mean ± SE of three independent measurements (p < 0.05). Table 1 Water contents of soil during water stress and subsequent rewatering. S and R represent drought stress and rewatering, respectively. Capital letters refer to treatments of high nitrogen (HN), small letters to treatments of medium nitrogen (MN) and italic letters to treatments of low nitrogen (LN). Data are showed as mean ± SD of six independent measurements (p < 0.05). (1) and (2) represent the first water supply side and the second water supply side, respectively. Without water supply With water per 3 days (Mv) S0 S3 S6 S12 R9 Non-PRD HN 686 ± 11A 583 ± 13B 452 ± 7C 292 ± 17D 566 ± 10B MN 697 ± 18a 528 ± 32b 427 ± 33c 241 ± 29d 536 ± 9b LN 671 ± 23A 548 ± 11B 404 ± 37C 204 ± 7D 519 ± 23B PRD HN (1) 691 ± 22A 598 ± 18B 492 ± 45C 338 ± 29D 542 ± 19BC (2) 576 ± 20B 465 ± 33C 389 ± 39D 272 ± 11E 385 ± 9D MN (1) 676 ± 15a 571 ± 20b 465 ± 16c 314 ± 11d 537 ± 16bc (2) 567 ± 25a 432 ± 10b 374 ± 18c 288 ± 70d 371 ± 6c LN (1) 679 ± 21A 555 ± 17B 460 ± 10C 328 ± 6D 536 ± 14B (2) 536 ± 21B 447 ± 27C 368 ± 12D 287 ± 9AE 378 ± 13D Table 2 Leaf water potential of cotton seedlings during drought and subsequent rewatering. S and R represent days of drought stress and rewatering, respectively. Capital letters refer to treatments of high nitrogen, small letters to treatments of medium nitrogen and italic letters to treatments of low nitrogen. Data are shown as mean ± SD of four independent measurements (p < 0.05). Without water supply With water per 3 days (MPa) S0 S3 S6 S12 R9 Non-PRD High nitrogen −0.37 ± 0.033D −0.53 ± 0.010C −0.89 ± 0.073B −1.10 ± 0.127A −0.43 ± 0.010CD Medium nitrogen −0.39 ± 0.009e −0.60 ± 0.108d −0.92 ± 0.049b −1.15 ± 0.029a −0.45 ± 0.029e Low nitrogen −0.44 ± 0.071D −0.58 ± 0.015D −0.87 ± 0.007BC −1.12 ± 0.052A −0.43 ± 0.118D PRD High nitrogen −0.40 ± 0.028D −0.74 ± 0.038C −0.91 ± 0.049B −1.22 ± 0.049A −0.43 ± 0.090D Medium nitrogen −0.43 ± 0.075d −0.65 ± 0.110c −0.89 ± 0.062b −1.25 ± 0.270a −0.41 ± 0.056d Low nitrogen −0.47 ± 0.051D −0.68 ± 0.072C −0.87 ± 0.093B −1.22 ± 0.057 A −0.38 ± 0.043D Table 3 The effects of PRD, nitrogen supply levels and their interaction on ABA and NO3− concentration and pH under drought situation (two factors analysis of variance). PRD treatment Nitrogen levels PRD × N levels Leaf xylem sap ABA <0.0001*** 0.0443* 0.0312* NO3− 0.0482* 0.0187* 0.0304* pH <0.0001*** 0.0472* 0.0092** Root xylem sap ABA 0.0422* 0.3890 0.0436* NO3− 0.0003*** 0.0010*** 0.0042** pH 0.0235* 0.9968 0.9461 Biomass <0.0001*** 0.0162* 0.0162* WUE 0.0271* 0.0369* 0.0369* Note: “*” means that the difference was significant at p = 0.05 level; “**” means that the difference was significant at p = 0.01 level; “***” means that the difference was significant at p = 0.001 level. Table 4 Effect of drought stress and subsequent rewatering on biomass and ratio of root/shoot in cotton seedlings treated with different nitrogen level. S and R represent drought stress and rewatering, respectively. Data are shown as mean ± SD of four independent measurements (p < 0.05). (g. dry weight) S0 S3 S6 S12 R9 Non-PRD High nitrogen Biomass of per plant 0.61 ± 0.01D 0.68 ± 0.01D 0.87 ± 0.03C 0.92 ± 0.00C 1.26 ± 0.01A Increased biomass – 0.07 0.19 0.05 0.14 Ratio of root/shoot 0.54 ± 0.01C 0.56 ± 0.01C 0.63 ± 0.01 AB 0.67 ± 0.01 A 0.53 ± 0.02C Medium nitrogen Biomass of per plant 0.53 ± 0.03f 0.58 ± 0.00ef 0.65 ± 0.02de 0.73 ± 0.02d 1.16 ± 0.04a Increased biomass – 0.04 0.07 0.08 0.21 Ratio of root/shoot 0.55 ± 0.02a 0.56 ± 0.01a 0.58 ± 0.02a 0.57 ± 0.03a 0.47 ± 0.02b Low nitrogen Biomass of per plant 0.50 ± 0.03E 0.55 ± 0.05DE 0.66 ± 0.01 CD 0.71 ± 0.02C 1.04 ± 0.07A Increased biomass – 0.05 0.10 0.05 0.14 Ratio of root/shoot 0.45 ± 0.01C 0.47 ± 0.02BC 0.51 ± 0.02AB 0.54 ± 0.02A 0.47 ± 0.02BC PRD High nitrogen Biomass of per plant 0.83 ± 0.02E 0.97 ± 0.03DE 1.11 ± 0.09 CD 1.14 ± 0.02 CD 1.67 ± 0.09 A Increased biomass – 0.14 0.13 0.03 0.31 Ratio of root/shoot 0.54 ± 0.01B 0.59 ± 0.01AB 0.63 ± 0.03A 0.65 ± 0.04A 0.60 ± 0.00AB Medium nitrogen Biomass of per plant 0.80 ± 0.05f 0.94 ± 0.01ef 1.07 ± 0.02de 1.17 ± 0.02 cd 1.63 ± 0.06a Increased biomass – 0.14 0.13 0.10 0.16 Ratio of root/shoot 0.61 ± 0.01cd 0.63 ± 0.02bcd 0.66 ± 0.02abc 0.68 ± 0.01a 0.62 ± 0.03bcd Low nitrogen Biomass of per plant 0.85 ± 0.01F 0.98 ± 0.02E 1.10 ± 0.03D 1.16 ± 0.03 CD 1.58 ± 0.05A Increased biomass – 0.13 0.12 0.06 0.18 Ratio of root/shoot 0.65 ± 0.02BC 0.69 ± 0.01AB 0.74 ± 0.03A 0.71 ± 0.01AB 0.62 ± 0.02C ==== Refs References Brueck H. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30023-210.1016/j.sjbs.2017.01.014Original ArticleEstablishment of apoptotic regulatory network for genetic markers of colorectal cancer Hao Yibin abShan Guoyong bNan Kejun nankejun2013@yeah.neta⁎a Department of Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710077, Chinab Department of Oncological Radiotherapy, People’s Hospital of Zhengzhou, Zhengzhou 450003, China⁎ Corresponding author. nankejun2013@yeah.net26 1 2017 3 2017 26 1 2017 24 3 466 476 3 11 2016 25 12 2016 6 1 2017 © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Our purpose is to screen out genetic markers applicable to early diagnosis for colorectal cancer and to establish apoptotic regulatory network model for colorectal cancer, thereby providing theoretical evidence and targeted therapy for early diagnosis of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers applied to early diagnosis of colorectal cancer were searched to perform comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were employed to establish apoptotic regulatory network model based on screened genetic markers, and then verification experiment was conducted. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, p53, APC, DCC and PTEN, among which DCC shows highest diagnostic efficiency. GO analysis of genetic markers found that six genetic markers played role in biological process, molecular function and cellular component. It was indicated in apoptotic regulatory network built by KEGG analysis and verification experiment that WWOX could promote tumor cell apoptotic in colorectal cancer and elevate expression level of p53. The apoptotic regulatory model of colorectal cancer established in this study provides clinically theoretical evidence and targeted therapy for early diagnosis of colorectal cancer. Keywords Colorectal cancerGenetic markerMeta analysisGO analysisKEGG analysis ==== Body 1 Introduction Colorectal cancer, as a common malignant tumor in digestive system, ranks third among male common malignant tumors and ranks second among female common malignant tumors in terms of morbidity in worldwide. In 2008, there were 1.2 million new cases of colorectal cancer globally, among which 609 thousand died of the disease (Jemal et al., 2011). In China, colorectal cancer mainly attacks people aged 40–60 years old and due to its occult onset and low awareness, most patients have been in advanced stage when diagnosed, and metastasis has occurred in about 25% patients when first diagnosed. Therefore, elevation of early diagnostic rate for early treatment and improvement of prognosis for colorectal cancer are focuses in current and further colorectal cancer prevention and control. Meta analysis refers to a quantitative literature review which takes multiple independent research results for the same topic as objects, and based on strict design, it employs proper statistical methods to perform systematic, objective, quantitative and comprehensive analysis, aiming to promote statistical test efficacy, evaluate inconsistency or contradiction of research results and discover disadvantages in individual research. In addition, it can process a large quantity of literatures without number limitation. Therefore, Meta analysis plays a significant role in clinical diagnosis, treatment, risk assessment, prevention and intervention, health service as well as decision-making (Zhou et al., 2010). Meta analysis not only promotes efficacy of statistical inference thus lessening inconsistency of single research and draw more comprehensive and reliable conclusions (Zhang et al., 2013, Chaiyakunapruk et al., 2014), but also puts forward some novel research subjects, guiding direction for further study. Gene Ontology (GO) database (Hill et al., 2016) is a structured standard biological model established recently by GO organization, aiming to build a standard system of genes and their biological productions to analyze genes and their cellular component, molecular function and biological processes they are involved in. Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa and Goto, 2000) is a database that integrates genome, chemistry, and information of system function, which links gene catalogs obtained from genome that has been completely sequenced to system function of higher level of cell, species and ecosystem. It is characterized by powerful image function, enabling people to have an intuitive and comprehensive understanding of the metabolic pathways they study. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers that are applied to early diagnosis of colorectal cancer were searched to perform comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers which can be used in early diagnosis of colorectal cancer. Regarding the screened seven genetic markers, including WWOX, K-ras, COX-2, P53, APC, DCC and PTEN, their apoptotic regulatory network model in colorectal cancer was established by GO analysis and KEGG analysis, and then verification experiment was conducted. The model defines programed death regulatory mechanism for colorectal cancer cell, hence directing the individual diagnosis and targeted therapy of colorectal cancer. 2 Material and methods 2.1 Subjects With CNKI, VIP and Wanfang databases were regarded as primary sources for Chinese literatures retrieval, literatures published between 1st January 1990 and 31 December 2013 were searched under key words of colorectal cancer, genetic markers, and early diagnosis. Regarding English literatures, Pub Med and MEDLINE were considered as main sources, and literatures published between 1st January 1990 and 31 December 2013 were searched with key words “colorectal cancer”, “genetic markers” and “diagnosis”. All literatures meeting inclusion criteria were carefully read, including the whole text and references, and related literatures were searched as well. The full text of included literatures were either in Chinese or in English and concerning researches made by the same institution or on the same subject but published on different journals, the latest and the most complete report was adopted. Inclusion criteria for literatures: (1) the literature should be in English or in Chinese, with content of application of genetic markers in early diagnosis of colorectal cancer; (2) the research type is retrospective study; (3) the gold standard in literature is histopathology or operative diagnosis, and the literature takes patients with colorectal cancer as experimental group and healthy people or patients with benign tumor as control group, and objects with no restriction of nation, age as well as sex; (4) literature should provide diagnose results of colorectal cancer separately diagnosed by genetic markers; (5) true positive (TP), false positive (FP), false negative (FN) and true negative (TN) of patients with colorectal cancer that is separately diagnosed by genetic markers can be obtained directly according to the literature or by calculation; (6) the literature employs correct methods and the study has normative process, and regarding researches multiply made by the same institution or on the same subject but published on different journals, the latest and the most complete report was adopted. All included literatures in this study were published full text in Chinese or in English and all data were obtained from the original text. Exclusion criteria for literatures: (1) the literature involves either an unoriginal or repetitive research, or serious design defect, or incomplete data; (2) the type of literature is review or abstract; (3) cases are not diagnosed by gold standard; (4) subject is colon cancer or rectal cancer; (5) no control group is set in the study; (6) the literature studies application of genetic markers in postoperative recurrence diagnosis of colorectal cancer; (7) the literature shows no results of separate diagnosis but only combined diagnosis results of genetic markers for colorectal cancer. 2.2 Data extraction and quality assessment Data extraction of included in literatures: (1) general data, including authors, published time, published journal, title, the numbers of cases in experimental group and in control group; (2) methodological characteristics: cutoff value; (3) characteristics of research results: diagnostic results of genetic markers for colorectal cancer, including TP, FP, FN and TN. Quality assessment of included literatures: included literatures were separately and independently assessed and performed cross-check by two professional reviewers using quality assessment of diagnostic accuracy studies (QUADAS) developed by Whiting et al. (2003). QUADAS consists of 14 assessment indicators. Regarding each indicator, “Yes” indicates meeting the standard; “No” indicates not meeting the standard, “Not clear” indicates insufficient information can be got from the literature to determine whether the standard is met. 2.3 Meta analysis All data were performed two-sided test of Meta analysis, in which P < 0.05 indicates statistical difference and P < 0.01 indicates extremely significant difference. Meta analysis of related results was performed by Meta-Disc software and the results were shown as forest graph and SROC figure. 2.4 GO analysis To conduct GO analysis, the home page of AmiGO (http://geneontology.org/) was visited. With “Homo sapiens” as filter criteria, preliminary analysis of GO annotation was performed on seven genetic markers obtained from Meta analysis and the seven genetic markers are WWOX, K-ras, COX-2, p53, APC, DCC and PTEN. 2.5 KEGG analysis To perform KEGG analysis on genetic markers, the homepage of KEGG signaling pathway database (http://www.kegg.jp/kegg/pathway.html) was visited. Then, taking “hsa” as screening criteria, and seven genetic markers including WWOX, K-ras, COX-2, p53, APC, DCC, and PTEN as keywords, signaling pathways of genetic markers in patients with colorectal cancer or linked to apoptosis were searched. 2.6 Verification experiment for apoptotic regulatory network of colorectal cancer 2.6.1 Experiment materials pcDNA4.0/Myc-WWOX recombinant plasmid was constructed and preserved at laboratory in the First Affiliated Hospital of Xi’an Jiaotong University, and it was purified by Qiagen plasmid purification kits before being determined purification and concentration by ultraviolet spectrophotometer. Human colorectal cancer cell line Colo205 was purchased in China Center for Type Culture Collection, and verification experiment was conducted after cells’ arrival. The cells were grown in RPMI 1640 supplemented with 2.5 g/L glucose, 1.5 g/L sodium bicarbonate, 0.11 g/L sodium pyruvate and 10% fetal bovine serum; and they were routinely screened for Mycoplasma contamination. The primers were synthesized by Shanghai Shenggong Bioengineering Co., Ltd. The sequences of primers were as follows: WWOX, upstream primer was 5′-GATAATCCGACCAAGCCAAC-3′, downstream was 5′-ACTGCTTCACTCGCCCTTG-3′, length of amplification product was 209 bp; p53, upstream primer was 5′-GGCCCACTTCACCGTACTAA-3′, downstream primer was 5′-TAAAACGCAGCTCAGTAACAGTCCG-3′, length of amplification product was 186 bp; housekeeping gene β-actin, upstream primer was 5′-TGGAATCCTGTGGCATCCATGAAAC-3′, downstream primer was 5′-TAAAACGCAGCTCAGTAACAGTCCG-3′. Transfection reagents were from Qiagen. 2.6.2 Cell transfection Cell transfection was conducted referring to the introduction of liposomes transfection reagent when culture fluids for Colo205 cell line reached to 60–80%. The ratio of liposome and plasmid was 10:1; meanwhile, the empty vector pcDNA4.0/Myc-His and the non transfected Colo205 cell lines were set as control groups. 2.6.3 Apoptosis detection by flow cytometry With pcDNA4.0/Myc-WWOX as transfection group, pcDNA4.0/Myc-His empty vector-transfected group and Colo205 cell line control group, cell apoptosis rate was analyzed with the FACSalibur software of flow cytometry. 2.6.4 RNA extraction Total RNA was extracted according to instructions of total RNA extraction kit by Trizol method. Ultraviolet spectrophotometer was employed to detect ultraviolet absorption of total RNA at 260 nm, 280 nm, and 230 nm to determine purification and concentration of total RNA. Integrity of total RNA was tested on 1% agarose gel. 2.6.5 Synthesis of cDNA by reverse transcription One μg RNA with good purification and integrity was taken respectively from each group as template for reverse transcription which was carried out in accordance with steps on Takara RT-PCR kit. 2.6.6 Polymerase chain reaction (PCR) reaction Twenty-five μL PCR reaction system was added according to kit instructions:cDNA template 2 μL 10 mM primer I upstream primer (20 p mol/μL) 1 μL 10 mM primer II downstream primer (20 p mol/μL) 1 μL 2 × Master mix 12.5 μL ddH2O 9.5 μL The reaction was performed under 94 °C for 45 s, 55 °C for 45 s and 72 °C for 60 s for 33 cycles before being extended at 72 °C for 7 min. 3 Results 3.1 Meta analysis on genetic markers for early diagnosis of colorectal cancer 3.1.1 Included literatures A total of 394 Chinese literatures and 1030 English literatures were retrieved by computer, among which 44 literatures (see Appendix) were eventually selected and included in Meta analysis. The flow chart of literature search and screening process is shown in Fig. 1. 3.1.2 Meta analysis results of genetic marker p53 3.1.2.1 Data extraction from included literatures linked to genetic marker p53 Taking Meta analysis results of genetic marker p53 for example, a total of 13 literatures, including 11 Chinese literatures and 2 English literatures, were included. Totally, there were 773 patients with colorectal cancer and 524 controls in included literatures and specific data are shown in Table 1. 3.1.2.2 Meta analysis on p53 for early diagnosis of colorectal cancer Taking Meta analysis results of genetic marker p53 for instance, Figure 2, Figure 3, Figure 4 are sensitivity forest plot, specificity forest plot and diagnostic odds ratio (DOR) graph of p53 on colorectal cancer respectively. According to the figures, in 13 literatures, the sensitivity of p53 for colorectal cancer diagnosis was 24–85% and pooled sensitivity 0.57 (0.53, 0.60); the specificity was 80–100%, pooled specificity 0.93 (0.91, 0.95); the diagnostic ratio was 17.42 (9.30, 32.62). Fig. 5 is the summary receiver operating characteristic curve (SROC) of p53 for colorectal cancer, which indicates that area under SROC (AUC) is 0.8305 and standard error 0.0563. 3.1.2.3 Bias analysis Liner regression method was used for bias detection, and DEEK graph was drawn as shown in Fig. 6. Results indicated that P = 0.74 > 0.05, which means there was no bias. 3.1.3 Meta analysis results of seven genetic markers Meta analysis results of seven genetic markers are listed in Table 2. As shown, the DOR of WWOX, K-ras, COX-2, P53, APC, DCC and PTEN in Meta analysis were 7.56 (4.97, 11.50), 12.56 (6.33, 24.90), 10.29 (4.00, 26.45), 17.42 (9.30, 32.62), 25.40 (7.37, 87.50), 4.41 (11.28, 262.54) and 22.39 (10.69, 46.88), respectively, indicating that all seven factors had high diagnostic efficiency for colorectal cancer, among which DCC had the best diagnostic efficiency. 3.2 GO analysis results of genetic markers In reference to GO analysis, the roles that gene and protein play in cell are classified into three parts, biological process, molecular function and cellular component. Table 3, Table 4 show GO analysis results of genetic markers DCC and PTEN. As shown, DCC plays a key role in biological process and cellular component and participates in apoptotic signaling pathway with positive regulation. And PTEN covered biological process, molecular function as well as cellular component, significantly acting in T cell receptor signaling pathway and biological process such as inositol phosphate metabolic process and phospholipid metabolic process. Table 5, Table 6 demonstrate GO analysis results of genetic markers COX-2 and p53. As indicated, COX-2 played a crucial part in biological process and molecular function and participates in cyclooxygenase pathway, inflammatory response, regulation of blood pressure and other biological processes. And p53 was included in biological process and cellular component, getting involved in apoptotic process and playing positive regulation in apoptotic signaling pathway. Table 7, Table 8 exhibit GO analysis results of gene markers APC and WWOX. As presented, both APC and WWOX covered biological process, molecular function and cellular component functions. APC was involved in apoptotic process with positive function; WWOX participated in intrinsic apoptotic signaling pathway by p53 class mediator, Wnt signaling pathway and so on; whereas functional annotation of human gene K-ras was not found in GO database. 3.3 Establishment of apoptotic regulatory network of genetic markers for colorectal cancer Through KEGG analysis of seven genetic markers based on KEGG signaling pathway database, it was found that p53, APC, DCC and K-ras were involved in regulatory network of colorectal cancer, as shown in Fig. 7. Combining results of GO analysis and KEGG analysis, genetic markers PTEN and COX-2 were added to establish primary apoptotic regulatory network of genetic markers for colorectal cancer, as shown in Fig. 8. WWOX is a newly discovered tumor suppressor factor, and the apoptotic regulatory signaling pathway of colorectal cancer that WWOX was involved in is still unclear. It was found in GO analysis that WWOX was involved in apoptotic signaling pathway by p53 class mediator, so related verification experiment was supplied. 3.4 Verification experiment for apoptotic regulatory network of colorectal cancer 3.4.1 Effect of WWOX transfection on cell apoptosis of colorectal cancer As indicated in Fig. 9, after transfection of gene WWOX into Colo205 cell line, apoptosis rate of pcDNA4.0/Myc-WWOX transfection, pcDNA4.0/Myc-His empty vector-transfected group and Colo205 cell line control group were (12.63 ± 0.43)%, (2.31 ± 0.58)%, (2.20 ± 0.36)%, respectively. pcDNA4.0/Myc-WWOX group was different from the two control groups in apoptosis rate with statistical significance (P < 0.01), indicating that the transfection of pcDNA4.0/Myc-WWOX elevated apoptosis rate of Colo205 cell line and that gene WWOX promoted apoptosis of colorectal cancer tumor cells. 3.4.2 RT-PCR analysis on effect of WWOX on p53 mRNA expression Fig. 10 demonstrates RT-PCR analysis on effect of WWOX on p53 mRNA expression. As indicated, after transfection of pcDNA4.0/Myc-WWOX into the tumor cell, Colo205 cell, mRNA expression level of gene WWOX in transfected cell Colo205 was higher than that in pcDNA4.0/Myc-His empty vector-transfected group and Colo205 cell line control group. Normally, wild-type p53 expresses low in Colo205 cell line; after transfection of pcDNA4.0/Myc-WWOX, however, it expressed higher in transfected Colo205 cell line than that in pcDNA4.0/Myc-His empty vector-transfected group and that in Colo205 cell line control group, which indicated that WWOX can elevate expression level of p53. 4 Discussion Colorectal cancer, a genetic disease, is caused by the multi-phase and long-term process in which proto-oncogene is activated and suppressor gene is inactivated under the environmental effect. The onset of colorectal cancer is latent, leading to a low degree of symptom awareness, so most patients have already been in advanced phase when diagnosed. Up to 50% of newly diagnosed patients eventually developed into metastatic colorectal cancer, with five-year survival rate less than 5%. Besides, patients with intermediate and advanced colorectal cancer always have poor therapeutic results and the bad prognosis impairs their life quality, at the same time, imposes them large economic burden (Al-Shuneigat et al., 2011). Colorectal cancer growth is correlated to pathways like gene mutation, gene repair, signal transduction and metastasis and invasion. At present, commonly used serum markers for early diagnosis of colorectal cancer in clinic have low diagnostic value because many patients have already been in advanced phase when diagnosed, which severely affected their treatment and prognosis. Thus it’s necessary to find diagnostic methods with high sensitivity and specificity. The molecular model of colorectal cancer morbidity was posed by Fearon and Jones (1992) in 1992, and with the development of molecular biology techniques, molecular mechanism in the model was expanded and it’s found that mutations in growth and development of colorectal cancer are sequential, which makes it feasible to diagnose colorectal from respects of oncogenes and tumor suppressor genes. Sugai and Habano (2016) discussed the genetic mechanisms of colorectal cancer and the relationship of these alterations with emerging biomarkers for pathological diagnosis, patient prognosis, and the prediction of treatment responses, which provided significant evidence for early diagnosis and treatment of tumor. Related literatures about genetic markers used in early diagnosis of colorectal cancer were searched through CNKI database, VIP database, Wanfang database, Pub Med database and MEDLINE database. And then through Meta-analysis of diagnostic test, it was found that the DORs of WWOX, K-ras, COX-2, P53, APC, DCC and PTEN respectively were 7.56 (4.97, 11.50), 12.56 (6.33, 24.90), 10.29 (4.00, 26.45), 17.42 (9.30, 32.62), 25.40 (7.37,87.50), 54.41 (11.28,262.54) and 22.39 (10.69,46.88), which suggested that these seven genetic markers had high diagnostic efficacy with DCC highest and WWOX lowest. GO and KEGG databases were used to conduct GO functional analysis and KEGG signaling pathway analysis on the seven genetic markers so as to establish a primary apoptotic regulatory network, which showed that DCC was involved in the apoptotic signaling pathway with positive regulation. And over these years DCC was reported as one of the key tumor suppressor genes (Kazemzadeh et al., 2015) and was closely correlated to growth and development of colorectal cancer. And it was found that DCC gene can inhibit cell proliferation and cause degraded carcinoembryonic antigen (CEA) expression in rectal cancer cell line SW1116 (Jiang et al., 2015). Therefore, detection of DCC protein in caner tissues is important for colorectal cancer patients’ prognosis assessment and usage of assistant treatment. PTEN is a key tumor suppressor gene having phosphatase activity, which involves in biological process, molecular function and cell components and it plays an important role in biological processes including T cell receptor signaling pathway, inositol phosphate metabolic process and phospholipid metabolic process, etc. According to the apoptotic regulatory network in this study, PTEN was involved in apoptotic pathway with negative regulation to serine/threonine protein kinase B(PKB/Akt), which is in line with the report by Zeng et al. (2016). Besides, COX-2, called “quick responsive gene”, is an inducible enzyme. It expresses low in normal tissues while the expression increases rapidly under internal and external stimulus and COX-2 is, in a large part, involved in the development of tumor, reported by Tabriz et al. (2016) In addition, p53 playing an important role in biological process and cell components is involved in apoptotic process with positive regulation. Al-Saran et al. (2016) found that Zinc can up-regulate expression of p53 and p21, resulting in apoptosis of human breast cancer MCF-7 cell. Analysis of genomic data suggested that p53 is linked to incidence of cancer (Stracquadanio et al., 2016). He et al. (2011) found that curcumin can speed up tumor cell apoptosis and improve patients’ health through increasing p53 expression. What’s more, APC and WWOX both play a role in biological process, molecular function and cell components, and APC participates in apoptosis progress with function of promoting cell apoptosis and WWOX is involved in apoptotic signaling pathway and Wnt signaling pathway induced by p53. There was report indicating that as a negatively regulatory protein, APC can abnormally activate Wnt signaling transduction pathway when it’s not expressed, suggesting that APC as one of cancer suppressive factors of colorectal cancer is involved in the development of colorectal cancer (Blundon et al., 2016, Xu et al., 2016). A new cancer suppressor gene named as WWOX was found by Bednarek et al. (2000) in 2000 through Shotgun method. WWOX gene is correlated to tumor infiltration degree, lymphnode metastasis and pathological stage and it always expresses low in many tumor cells but its over-expression may induce tumor cell apoptosis (Xiong et al., 2010, Baykara et al., 2010). Moreover, recent research reported that K-ras mutation is the negative factor for growth, development and prognosis of colorectal cancer and it’s closely correlated to targeted treatment. K-ras is a tumor gene, whose mutation is the early event of colorectal cancer, thus K-ras gene have drawn people’s attention more and more in tumor treatment. And detection of K-ras gene mutation is helpful for individual treatment of cancer, which is crucial in treatment of colorectal cancer (Liu and Fu, 2012). Experimental verification of WWOX showed that WWOX participated in apoptotic signaling pathway of colorectal cancer by activating p53 signaling pathway. According to study by Chang et al. (2001), stable transfection of WWOX gene into L929 cells resulted in an elevated expression of WWOX in L929 tumor cells, a reduced expression of more than 85% of anti-apoptosis factor Bc-1 2 and Bc-1 xL and an increased expression of 200% of pro-apoptotic factor p53 as well as an increased TNF’s cell toxicity which is in good agreement with partial results of this study. 5 Conclusions A primary apoptotic regulatory network of colorectal cancer composed of p53, APC, DCC, K-ras, PTEN, WWOX and COX-2 and other related genes were established in this study by Meta analysis combined with Go functional analysis and KEGG signal pathway analysis. And it’s proved by experiment that WWOX is involved in apoptotic signaling pathway of colorectal cancer through activation of p53 signaling pathway by elevating p53 expression. Apoptotic regulatory network of colorectal cancer can provide a theoretical basis for early diagnosis and targeted treatment of colorectal cancer in clinic. Acknowledgment The authors acknowledge the financial support from the National Key Technology R&D Program (Grant: 2016YFC1303200). Peer review under responsibility of King Saud University. Figure 1 Flow chart of literature search and screening process of meta analysis on genetic markers for early diagnosis of colorectal cancer. Figure 2 Sensitivity forest plot of p53 for diagnosis of colorectal cancer. Figure 3 Specificity forest plot of p53 for diagnosis of colorectal cancer. Figure 4 Diagnostic odds ratio graph of p53 for diagnosis of colorectal cancer. Figure 5 SROC curve of p53 for diagnosis of colorectal cancer. Figure 6 Bias assessment of published literatures on colorectal cancer diagnosis. Figure 7 Primary apoptotic regulatory network of genetic markers for colorectal cancer. Figure 8 Apoptotic regulatory network of genetic markers for colorectal cancer. Figure 9 Effect of transfecting WWOX into Colo205 cell line on cell apoptosis. Figure 10 RT-PCR analysis on effect of WWOX on p53 mRNA expression; Note: 1: Colo205 cell line control group; 2: pcDNA4.0/Myc-His empty vector-transfected group; 3: pcDNA4.0/Myc-WWOX transfection group. Table 1 General data of included literatures related to p53. Number Author Colorectal cancer group (case) Control group (case) TP FP FN TN 1 Chen Haiwei 40 40 29 11 3 37 2 Wang Wenxing 95 57 65 30 9 48 3 Chaar Ines 59 108 20 39 9 99 4 Zhan Qiang 40 20 23 17 0 20 5 Li Weiwei 31 10 10 21 0 10 6 Chung-Chuan Chan 94 54 23 71 1 53 7 Wang Yuhuan 68 40 34 34 1 39 8 Zhao Jianling 35 15 14 21 1 14 9 Zhang Yanxia 80 40 53 27 8 32 10 Hou Hui 80 80 68 12 0 80 11 Xiao Chaowen 40 20 32 8 0 20 12 Zhang Jiping 45 25 31 14 4 21 13 Chen Ling 66 15 38 28 0 15 Table 2 Meta analysis results of seven genetic markers. Genetic marker Number of literatures (case) Number of patients (case) Control group (cases) Pooled sensitivity Pooled specificity DOR K-ras 5 270 76 0.70 (0.64, 0.75) 0.82 (0.71, 0.90) 12.56 (6.33, 24.90) COX-2 8 449 230 0.79 (0.75, 0.83) 0.66 (0.56, 0.72) 10.29 (4.00, 26.45) p53 13 773 524 0.57 (0.53, 0.60) 0.93 (0.91,0.95) 17.42 (9.30, 32.62) APC 5 381 297 0.61 (0.56,0.66) 0.94 (0.91,0.96) 25.40 (7.37,87.50) DCC 6 361 225 0.57 (0.51,0.62) 0.98 (0.95,0.99) 54.41 (11.28,262.54) PTEN 5 256 198 0.58 (0.52,0.64) 0.96 (0.92,0.98) 22.39 (10.69,46.88) WWOX 7 391 225 0.65 (0.60, 0.69) 0.79 (0.73, 0.84) 7.56 (4.97, 11.50) Table 3 GO analysis results of genetic marker DCC. Gene ontology GO number Name Biological process GO:0043065 Positive regulation of apoptotic process GO:0007411 Axon guidance GO:0097190 Apoptotic signaling pathway Cellular component GO:0005829 Cytosol GO:0005886 Plasma membrane Table 4 Go analysis results of genetic marker PTEN. Gene ontology GO number Name Biological process GO:0050852 T cell receptor signaling pathway GO:0048011 Neurotrophin TRK receptor signaling pathway GO:0043647 Inositol phosphate metabolic process GO:0007173 Epidermal growth factor receptor signaling pathway GO:0008543 Fibroblast growth factor receptor signaling pathway GO:0006661 Phosphatidylinositol biosynthetic process GO:0048015 Phosphatidylinositol-mediated signaling GO:0044281 Small molecule metabolic process GO:0006644 Phospholipid metabolic process GO:0038095 Fc-epsilon receptor signaling pathway GO:0045087 Innate immune response Cellular component GO:0005829 Cytosol Molecular function GO:0016314 Phosphatidylinositol-3,4,5-trisphosphate3-phosphatase activity GO:0051717 Inositol-1,3,4,5-tetrakisphosphate 3-phosphatase activity GO:0051800 Phosphatidylinositol-3,4-bisphosphate 3-phosphatase activity Table 5 GO analysis results of genetic marker COX-2. Gene ontology GO Number Name Biological process GO:0019371 Cyclooxygenase pathway GO:0006954 Inflammatory response GO:0008217 Regulation of blood pressure GO:0006979 Response to oxidative stress GO:0055114 Oxidation-reduction process Molecular function GO:0004601 Peroxidase activity GO:0004666 Prostaglandin-endoperoxide synthase activity GO:0020037 Heme binding Table 6 GO analysis results of genetic marker p53. Gene ontology GO number Name Biological process GO:1900740 Positive regulation of protein insertion into mitochondrial membrane involved in apoptotic signaling pathway GO:0007219 Notch signaling pathway GO:0007596 Blood coagulation GO:0006915 Apoptotic process GO:0000075 Cell cycle checkpoint GO:0033554 Cellular response to stress GO:0097193 Intrinsic apoptotic signaling pathway GO:0006977 DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest Cellular component GO:0005654 Nucleoplasm Table 7 GO analysis results of genetic marker APC. Gene ontology GO number Name Biological process GO:0043065 Positive regulation of apoptotic process GO:0008285 Negative regulation of cell proliferation GO:0016477 Cell migration GO:0000281 Mitotic cytokinesis GO:0007026 Negative regulation of microtubule depolymerization GO:0030178 Negative regulation of Wnt signaling pathway GO:0007050 Cell cycle arrest GO:0006974 Cellular response to DNA damage stimulus Cellular component GO:0005737 Cytoplasm GO:0005634 Nucleus Molecular function GO:0008013 Beta-catenin binding Table 8 GO analysis results of genetic marker WWOX. Gene ontology GO number Name Biological process GO:0072332 Intrinsic apoptotic signaling pathway by p53 class mediator GO:0016055 Wnt signaling pathway GO:0001649 Osteoblast differentiation GO:2001241 Positive regulation of extrinsic apoptotic signaling pathway in absence of ligand GO:0097191 Extrinsic apoptotic signaling pathway GO:0048705 Skeletal system morphogenesis Cellular component GO:0005829 Cytosol GO:0005794 Golgi apparatus Molecular function GO:0005515 Protein binding ==== Refs References Al-Shuneigat J.M. Mahgoub S.S. Huq F. Colorectal carcinoma: nucleosomes, carcinoembryonic antigen and ca 19–9 as apoptotic markers; a comparative study J. Biomed. Sci. 18 2011 50 21787404 Al-Saran N. Subash-Babu P. Al-Nouri D.M. Alfawaz H.A. Alshatwi A.A. Zinc enhances CDKN2A, pRb1 expression and regulates functional apoptosis via upregulation of p53 and p21 expression in human breast cancer MCF-7 cell Environ. Toxicol. Pharmacol. 47 2016 19 27 27567443 Baykara O. Demirkaya A. Kaynak K. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30030-X10.1016/j.sjbs.2017.01.021Original ArticleQuantitative evaluation methods of skin condition based on texture feature parameters Pang Hui Chen Tianhua cth188@sina.com⁎Wang Xiaoyi Chang Zhineng Shao Siqi Zhao Jing School of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, People’s Republic of China⁎ Corresponding author. cth188@sina.com26 1 2017 3 2017 26 1 2017 24 3 514 518 7 12 2016 25 12 2016 6 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).In order to quantitatively evaluate the improvement of the skin condition after using skin care products and beauty, a quantitative evaluation method for skin surface state and texture is presented, which is convenient, fast and non-destructive. Human skin images were collected by image sensors. Firstly, the median filter of the 3 × 3 window is used and then the location of the hairy pixels on the skin is accurately detected according to the gray mean value and color information. The bilinear interpolation is used to modify the gray value of the hairy pixels in order to eliminate the negative effect of noise and tiny hairs on the texture. After the above pretreatment, the gray level co-occurrence matrix (GLCM) is calculated. On the basis of this, the four characteristic parameters, including the second moment, contrast, entropy and correlation, and their mean value are calculated at 45 ° intervals. The quantitative evaluation model of skin texture based on GLCM is established, which can calculate the comprehensive parameters of skin condition. Experiments show that using this method evaluates the skin condition, both based on biochemical indicators of skin evaluation methods in line, but also fully consistent with the human visual experience. This method overcomes the shortcomings of the biochemical evaluation method of skin damage and long waiting time, also the subjectivity and fuzziness of the visual evaluation, which achieves the non-destructive, rapid and quantitative evaluation of skin condition. It can be used for health assessment or classification of the skin condition, also can quantitatively evaluate the subtle improvement of skin condition after using skin care products or stage beauty. Keywords Skin beautyCosmetic efficacy evaluationQuantitative evaluation of skin conditionSkin texture featureGray level co-occurrence matrix ==== Body 1 Introduction People are increasingly concerned about their own health and beauty in recent years. There is a direct link between the skin state and beauty of health. The surface of the skin condition depends on skin texture characterization. Every object with a physical form has its own unique texture. In other words, different objects have different texture, so the texture is a significant character for people to observe and identify objects (Shan et al., 2015, Gao et al., 2011, Song and Li, 2014). Scientific assessment of skin texture is an important approach that could analyze image and evaluate effectiveness of cosmetics and cosmetology. The way of mechanical detection, uses a certain intensity probe to explore, and it is mainly adopted in early evaluation of skin texture and coarseness. As the probe may puncture the surface of the skin, the approach not only has a certain degree of risk but also its precision and sensitivity are not very ideal (Dong, 2011, Liu and Liu, 2010). In recent years, silicone film has been widely used to detect the shadow area of skin wrinkle formed by inclined light, and it could get texture index by conversion. This method still has limitations on the sensitivity and accuracy although it could reduce some risks (Xu et al., 2011a, Xu et al., 2011b). The paper achieves objective quantitative assessment of skin texture state by acquiring skin image from the image sensor, calculating the Tamura feature and the GLCM of two-dimensional image and counting skin texture characteristic value in four different orientations. 2 Material and methods Nowadays, there is no uniform definition of texture. The texture often refers to space distribution of regular and interdependent pixels' gray level in an image area. Texture can be also recognized as a partial basic mode unit which repeats itself in a method of closing to periodicity in a certain image area (Song et al., 2009). Currently, both in terms of health or skin cosmetic effects, there has been a particular need of scientific analysis and quantitative evaluation of texture state. In traditional medical cosmetology, skin is mainly judged by experimental knowledge of doctors or associated professional staff, which lacks scientific standards. In fact, there are many factors affecting skin quality including the skin surface sebum membrane, skin types, living habits, the application of cosmetics and so on. Furthermore, human skin is also affected by age, race, gender, personality and other factors. It shows diversity and has little differences among its shape, color and texture. Therefore, only using observation or traditional methods is easy to cause a diagnostic error. With the rapid development of computer and digital image processing techniques, image analysis can be used in quantitative analysis of skin texture features according to coarseness and texture measure theory, in order to achieve quantitative appraisal. 2.1 Texture feature of skin image Currently, there are some main characters of skin texture which are GLCM, LBP (Local Binary Pattern) algorithm, texture spectrum and transform domain method. However, these features have a common shortage of which physical or visual characters are indefinite, having a great possibility to produce inconsistency between checking result and visual feeling. In 1980s, Tamura and others put forward an expression of texture character, according to the psychological research of human perception for texture. There are six components of Tamura texture character corresponding with the six properties in psychological field which are coarseness, contrast, directionality, linearity, regularity and roughness. According to the image process theory, the first three characters can describe the subtle visual features of texture in a better way. It can be used to evaluate skin texture and retrieve content-based image. For the sake of improving the description and identification, this paper proposes a modified Tamura texture features algorithm, using coarseness column chart instead of coarseness to express micro information of skin texture. 2.2 The calculation of GLCM GLCM is one of the effective methods for quantitative analysis of skin texture. This concept is proposed by Haralick, and it focuses on transforming the gray level information to texture information conveniently, which has been proven both in theory and experiments. GLCM describes a frequency correlation matrix of two pixel points which have i and j gray values separately, s distance and in a θ angle direction (Bai et al., 2012, Xu et al., 2011a, Xu et al., 2011b, Qi et al., 2012). GLCM provides a supportive theory for the description of skin texture state. According to the principle of GLCM, if the image consists of pixel block, the diagonal element values of GLCM will be big (Schmidt et al., 2007). If the gray value of skin image pixel has a big change in some fields, the element values which keep away from the diagonal element will be big. As there is an internal mathematical relationship between GLCM and distribution of image texture, therefore GLCM has a good effect on skin texture analysis. It can evaluate skin texture, roughness and degree of consistency of skin condition. At present, the main characteristics parameters of GLCM include angular second moment, contrast, entropy, correlation and other indicators. Texture c in total sets of value of omputing focuses on GLCM calculation. Take any point (x, y) in N × N image and another point (x + dx, y + dy) which deviates a s distance to form point pair and we assume the gray value of the point pair is (g1, g2). To begin with, we move the point (x, y) on the whole image and we will acquire several different values of (g1, g2). Then the series of image gray values is supposed as k, and there will be k2 sets of value of (g1, g2) in total. For the whole picture, we need to count the frequency of every (g1, g2) and arrange them in a matrix. Moreover, according to the total number of appearing (g1, g2), the matrix members need to be normalized transformed into the probability P (g1, g2). We call this matrix, the GLCM matrix. As can be seen from it, GLCM is a function of distance and direction whose order is determined by gray values of the image and it is a symmetrical matrix. We can obtain joint probability matrix in different situations when (dx, dy) has different values. The value of (dx, dy) should be chosen from distribution of texture. Small divided difference need to be acquired for the thinner texture, for example, (1, 0), (1, 1), (−1, 1), (2, 0). At the same time, calculating the four individual orientation (0, 45°, 90°, 135°) reflects the statistics features of skin texture comprehensively, according to the concept of pixel neighborhood. Gray level co-occurrence matrix calculation mainly involves the choices of two key parameters. One is a series of gray levels which need to be confirmed generally by the gray level series of skin image. In order to reduce the amount of calculation, the number of gray values can be reduced properly. The other one is the choice of displacement. There is no consensus method of how to choose the distance S, and the optional range of incremental theory is from 1 to size (image). Various research aims have different selection methods for S. For example, the incremental values of {0, 4, 8,…, 64} for skin texture calculation generally choose 1 or 2 in the horizonal and vertical direction. 2.3 The choices of feature parameters Haralick supposed 14 kinds of texture features based on GLCM parameters (Schmidt et al., 2007, Barata et al., 2014). But there is redundancy between parameters. Theoretical studies and a large number of experiments show that the energy, contrast, correlation and entropy can reflect skin features from different levels. Asm is the sum of squares of GLSM element values, so it can be called as energy. It is the uniformity measurement of texture gray distribution (the thickness measurement of the image texture). If all element values of co-occurrence matrix are equal, the asm is small. On the contrary, it is also true.(1) Asm Angular Second Moment (asm). The definition of Angular Second Moment is (1) Asm=∑g1∑g2[p(g1,g2)]2 (2) Contrast (con). The definition of con is (2) con=∑kk2∑g1∑g2p(g1,g2) in the formula, k=g1-g2. Contrast is also called the moment of inertia which is a part gray level measure of change in the image. The contrast size reflects the sharpness, texture density and the depth extent of groove. The visual effect is clearer if image has deeper groove and bigger contrast. If the element values which are far away from the diagonal are bigger in GLCM, the contrast value will be bigger.(3) Entropy (ent). The definition of ent is (3) ent=-∑g1∑g2p(g1,g2)lgp(g1,g2) Entropy, which is a measurement of information, describes the complexity of image texture, and it reflects the randomness of image texture gray distribution. When all elements of the co-occurrence matrix have the greatest randomness, the entropy will be big.(4) Correlation (cor). Correlation is used to measure the similar extent of GLCM elements in a certain direction. Its definition of cor is (4) cor=∑g1∑g2g1g2p(g1,g2)-μxμyσxσy Correlation is the linear correlation measurement of texture image optical gray level. When elements are identical or uniform, the value of correlation is large. On the contrary, it is also true. According to the properties of the human skin, the mathematical model of the comprehensive evaluation of the skin condition is as follows: Rin=∑g1∑g2p(g1+g2){p(g1,g2)+lg[p(g1,g2)]}∑kk2∑g1∑g2p(g1,g2)×cor=Asm+entcon×cor 3 Results & discussion 3.1 Image acquisition and experiment Use CCD image sensor to collect the image of 52 individuals in different ages, use LED as the light source, and the camera lens enlargement factor is 50 times. The parts of acquisitions include cheeks, forehead, canthus, chin, and there are 208 images in total. The image size is 640 × 480. Each person who is being collected images must wash face carefully with clean water, after which the image collection begins. Fig. 1 shows 8 images of Cheek part. The experiment has calculated all images of 52 individuals, Fig. 2 expresses 8 cheek images in the sample. According to Fig. 1, the results of calculation can be shown at Table 1 (including angle second order moment, and contrast, and entropy and correlation four features parameter and the standard poor). The angular second moment, entropy, contrast and correlation are related to the characteristic values at 0°, 45°, 90°, 135° four direction average of the characteristic values, the standard deviation (std) respective refers to the standard deviation in 4 directions. The last two columns are the Rin values based on Rin index and measured values of Geman CK instrument. According to Fig. 1, image (c) is the best one of the skin situation among these eight images, followed by image (h) while the skin situation of image (a) is the worst. The results of calculation are consistent with the evaluative result of skin images in Fig. 1. It illustrates using the GLCM quantitative evaluation in accordance with real situation. The evaluation method can be regarded as objective, scientific and reasonable. The texture feature parameters reflect the characteristics of skin structure and physiology. The shortage of using GLCM is that its calculation value has great relevance with the scales and gray series of the skin image. For example, if an image at 256 gray level with 512 × 512 scales, the computation of GLCM will be approximately 234 times multiplication. According to the results of experiment from Table 1, we use the texture coarseness theory to calculate human skin texture features, and then the evaluation of effect on the skin coarseness is consistent with human visual perception precisely. That is, using the proposed calculation method, the texture coarseness value is large for coarse texture image, while the value is small for delicate skin texture image. 3.2 The analysis of characteristic parameter Fig.2(a) shows the calculations of angular second moment, which are shown in the 8 images of Fig. 2 in the 0°, 45°, 90°, 135°directions. As can be seen Fig.1(c) shows the maximum ASM value of which the skin texture situation is the best, while Fig.1(a) shows the minimum ASM value of which the skin texture situation is the worst. Angular second moment (ASM) well reflects the uniformity of skin texture coarseness and gray distribution. Fig.2(b) shows the calculation results of entropy in the directions of 0°, 45°, 90°, 135°. The entropy indicates the randomness of skin image gray distribution. The better skin condition is, the less value of entropy is. The results of calculation in Fig.2(b) explain the concept precisely. The results of contrast and correlation are shown in Fig.2(c)–(d). Correlation reflects the similarity of skin texture in a certain direction. The experiment shows that if the contrast of skin image is small, its correlation will be big. However, if contrast is big, its correlation will be small. Meanwhile, we can find that the variation of correlation is similar with the variation of energy. In fact, the change law of relativity is similar to that of Asm. Theoretical analysis and experiments show that the single feature value reflect the state of the skin only from one level, while the Rin index is a comprehensive characterization of state in texture, direction, consistency and other aspects of the skin, which fully reflects the comprehensive information of the state of skin texture. 4 Conclusion The skin texture characteristic means the spatial traits of skin, and skin state analysis have received increasingly more attention in the field of Image Processing and Biomedical Engineering. A total of 208 skin texture images of four different parts from 52 individuals in different ages are collected, analyzed, processed and calculated in this project. Experimental results show that the image preprocessing method based on the median filter, gray mean value and color information can eliminate the influence of the image noise and the hair pixels on texture. The four main parameters including energy, contrast, entropy and correlation of the skin image were calculated by the gray level co-occurrence matrix method. Theoretical analysis and calculation results show that the above characteristic parameters reflect the texture and physical state of human skin from different aspects. However, due to the skin condition not being uniform and consistent, the same characteristic parameters, in different directions, its size are not same. In this paper, the average of four directions (0°, 45°, 90°, 135°) is taken as the eigenvalue, which eliminates the randomness of the characteristic parameters in different directions. According to the basic theory of Image Engineering, although the four parameters of angular second moment, contrast, entropy, and correlation characterize the skin from different aspects, there is a limitation of any single index in the above indicators. The Angle Second Moment mainly reflects the gray distribution of skin texture, which is the thickness of skin texture; The Contrast demonstrates the skin image gray value of the local variation, that is, the clarity of skin texture and groove depth; The Entropy characterizes the randomness and complexity of skin texture distribution. The Correlation shows the local linear correlation of skin texture in gray image, which is the similarity of skin texture in one direction. Therefore, the individual features cannot fully reflect the skin's overall state. The comprehensive characteristic parameters proposed in this paper overcome the limitations of the single feature, and combine the advantages of the above four features, which could better represent the actual state of the skin surface and texture characteristics. The results of the evaluation are basically the same as those of CK instrument based on biochemical principle, which can evaluate the skin condition quickly, accurately and objectively. The method can be used to quantitatively analyze and evaluate the micro-state changes of the skin, to rapidly detect, evaluate and classify the skin condition, and can also be used for quantitative evaluation of skin-care products and cosmetic effects. Acknowledgements The Work was Science and Technology Development Program of Beijing Municipal Commission of Education (No. KZ201510011011), the National College Student’s Scientific Research and Entrepreneurial Action Plan (No SJ201502084.), and comprehensive reform project for promoting personnel training of Beijing Technology and Business University-China cosmetics collaborative innovation research center construction project (No. 19005428069/007). Peer review under responsibility of King Saud University. Figure 1 Texture image of face cheek. Figure 2 The relation of characteristic parameters. Table 1 Texture characteristic parameter computed result. No. asm std ent std con std cor std Rin CK 1.bmp 0.1072 0.0102 2.6625 0.0903 0.3854 0.1166 0.4992 0.0208 73.751 0.1569 2.bmp 0.1659 0.0115 2.1426 0.0705 0.2183 0.0403 0.7538 0.0134 65.168 0.2265 3.bmp 0.2962 0.0112 1.5924 0.0436 0.1692 0.0225 1.6233 0.0411 35.146 0.1182 4.bmp 0.2032 0.0145 1.9655 0.0699 0.2443 0.0468 1.0612 0.0361 44.506 0.1587 5.bmp 0.2014 0.0133 1.9612 0.0661 0.2231 0.0383 1.0371 0.0271 48.602 0.1681 6.bmp 0.2721 0.0129 1.7006 0.0532 0.1867 0.0284 1.4501 0.0414 37.143 0.1367 7.bmp 0.1876 0.0172 2.0379 0.0891 0.2497 0.0561 0.9588 0.0343 48.811 0.2011 8.bmp 0.2446 0.0153 1.8376 0.0649 0.2354 0.0419 1.2362 0.0467 36.008 0.1355 ==== Refs References Bai L. Fang C. Ding X.Q. High resolution face image recognition based on skin texture feature Comput. Eng. 38 1 2012 164 166 Barata C. Ruela M. Francisco M. Two systems for the detection of melanomas in dermoscopy images using texture and colour features IEEE Syst. J. 8 3 2014 965 979 9 Dong X.Y. Study of Fingerprint Image Pre-processing Algorithm 2011 Jilin University Changchun pp. 21–30 Gao Q. Hu L.W. Wang Y. Skin texture parameters of the dorsal hand in evaluating skin aging in China Skin Res. Technol. 17 4 2011 420 426 21338408 Liu H.B. Liu Z.L. Recycling utilization patterns of coal mining waste in China Resour. Conserv. Recycl. 54 12 2010 1331 1340 Qi H.C. Peng W.X. Wu Y.Q. Wu S.B. Xu G.J. Effects of alkaline extraction on micro/nano particles of eucalyptus camaldulensis biology J. Comput. Theor. Nanosci. 9 9 2012 1525 1528 Schmidt S. Koch B. Schulz R. Comparative analy-sis of the applicability of the skeletal age determination methods of Greuli-ch-Pyle and Thiemann-Nitz for forensic age estimation in living sects Int. J. Legal Med. 121 4 2007 293 296 17401574 Shan G.X. Gan P. He L. Study on objectively evaluating skin aging according to areas skin texture J. Biomed. Eng. 32 2 2015 142 145 Song Z.C. Li T.Q. Enhancement of skin texture images based on gabor filter Acta Laser Biol. Sin. 23 5 2014 413 417 Song J.L. Extraction and Analysis of Skin Texture Image Features 2009 Northeastern University Shenyang pp. 8–20 Xu S.C. Zhang S.Y. Zhang Y. Robust algorithm for extracting skin pigment concentration from colour image J. Zhejiang University (Engineering Science). 45 2 2011 253 259 Xu S.F. Wu S.L. Li H. An analysis on hu-man skin texture based on gray-level co-occurrence matrix ACTA Laser Biol. Sin. 20 3 2011 324 328

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30025-610.1016/j.sjbs.2017.01.016Original ArticleHow does stress affect human being—a molecular dynamic simulation study on cortisol and its glucocorticoid receptor Zhang Dan aTian Geng zhang_dan_2016@qq.comb⁎a Jilin Electric Power Company Limited Electric Power Research Institute, Changchun 130021, Chinab Department of Obstetrics and Gynecology, The Second Hospital of Jilin University, Changchun 130021, China⁎ Corresponding author. zhang_dan_2016@qq.com30 1 2017 3 2017 30 1 2017 24 3 488 494 3 11 2016 28 12 2016 6 1 2017 © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Stress can be either positive or negative to human beings. Under stressful conditions, the mental and physical conditions of human can be affected. There exists certain relation between stress and illness. The cortisol and other glucocorticoids bind to the same receptor, which is called glucocorticoid receptor. Some evidences indicated that cortisol molecule binding to its glucocorticoid receptor was necessary for the stress response. Up to now, the structure–function relationships between cortisol molecule and its glucocorticoid receptor have not been deliberated from the atomic-level. In order to get a detailed understanding of the structure–function relationships between the cortisol molecule and glucocorticoids receptor, we have carried out molecular dynamic (MD) simulations on glucocorticoid receptor (Apo system) and cortisol with its glucocorticoid receptor complex (HCY system). On the basis of molecular dynamic simulations, a couple of key residues were identified, which were crucial for the binding of cortisol molecule. The results of binding free energy calculations are in good agreement with the experiment data. Our research gives clear insights from atomic-level into the structural–functional aspects of cortisol molecule and its glucocorticoid receptor, and also provides valuable information for the design of drug which can treat stress related illnesses. Keywords CortisolGlucocorticoid receptorMolecular dynamics simulationBinding free energyCalculations ==== Body 1 Introduction As the pace of life grows faster and faster and when we feel everything has become too much, we generally use the word “stress”. We are overloaded and wondering whether we really can cope with the pressures. That is how we feel the stress in daily life. From the physiological or biological point of view, the stress can be described in such way as when there is a stressor, like an environmental condition or stimulus, the organisms themselves can respond to it. Using this method, the body can react to stress. Regarding stressful event, the body's response is to activate sympathetic nervous system. As it is impossible for the body to keep this state for a long time, the body will return to a normal physiological condition with the help of parasympathetic system. For human beings, stress can be either a positive or a negative condition. Under such conditions, the mental and physical properties of human can be affected. There exists certain connection between stress and illness. Several studies (Schneiderman et al., 2005) pointed out that both acute and chronic stresses can cause illness. Stress can also make human more susceptible to physical illness such as common cold (Cohen et al., 1997). It is of great importance to develop certain drugs which can relieve stress. The cortisol and other glucocorticoids bind to the same receptor, which is called glucocorticoid receptor. Like other steroid receptors (Kumar and Thompson, 1999), the glucocorticoid receptor represents as a modular structure (Kumar and Thompson, 2005) and consists of the following domains (marked A–F) including A/B-N-terminal regulatory domain; C-DNA-binding domain; D-hinge region; E-ligand-binding domain and F-C-terminal domain. The ligand binding domain is the region where cortisol binds. It has been reported that cortisol molecule binding to its glucocorticoid receptor is necessary for the stress response (Kolodkin et al., 2013). Unfortunately, the detailed information about the cortisol molecule binding to its glucocorticoid receptor has not been studied yet. To this end, we have investigated the atomic-level structural characterization of glucocorticoid receptor (Apo system) and cortisol with its glucocorticoid receptor complex (HCY system). Molecular dynamics (MD) simulations can be used as an effective way to study the conformational changes on atomic level (Wang et al., 2013). In this study, MD simulations for Apo and HCY systems were carried out. The aims of this work are to figure out the details about cortisol molecule binding to glucocorticoid receptor and to identify the key residues which are responsible for the cortisol binding. Our work provides detailed atomistic insights into the structure–function relationships between cortisol molecule and its glucocorticoid receptor, and also provides valuable information for the design of drug which can treat stress related illnesses. 2 Computational methods 2.1 Initial structures The crystal complex structure of cortisol and its glucocorticoid receptor was retrieved from the RCSB Brookhaven Protein Data Bank (PDB entry: 4P6X (He et al., 2014), which served as the starting structure for the following molecular dynamic (MD) simulations. Only the chain A of the crystal complex remained. The protonation states of ionizable residues were determined at pH = 7.0 using H++ server (Gordon et al., 2005), which can predict the pKa value of protein residues at a given pH. The prepared complex structure (HCY system) was used as the starting structure of the subsequent MD simulations. The cortisol molecule was removed from this prepared structure to create the Apo form of glucocorticoid receptor. 2.2 Molecular dynamic (MD) simulations MD simulations for both HCY and Apo systems were implemented in AmberTools15 by using sander.MPI module. The 99SB force field (Hornak et al., 2006) was chosen to be the force field for the protein. The force field parameters for cortisol molecule were supplied by general AMBER force eld (Wang et al., 2004). Two sodium ions (Na+) were added to each of the two systems using coulomb potential grid in order to keep the whole system neutral. TIP3P water model (Jorgensen et al., 1983) was selected to solvate both systems using a truncated octahedron box. The size of the water box was set to 10 A distance around the solute molecule. The two systems were first carried out for 2000 steps minimization by employing the decent method and then for 3000 steps conjugate minimization of the entire systems. Then the two systems were heated from 0 to 300 K. The time scale for this process was 1000 ps. The ensemble for the heating process was the canonical ensemble (NVT ensemble). During this process, a force constant of 10.0 kcal mol−1 and a harmonic restraint were applied on the protein and small cortisol molecules. The Langvie thermostat was employed to maintain the temperature, and then the two systems were equilibrated for 2000 ps. During this process, the NPT ensemble was adopted and the constant pressure was set to 1.0 bar. The total relaxation time for the barostat bath was set to 2.0 ps. In the end, the Apo and HCY systems were both simulated for 100 ns. The periodic boundary conditions were employed in this research. The long range electrostatics was handled by the particle-mesh Ewald (PME) method (Darden et al., 1993). The cut-off value for short range interactions was set to 10.0 A. Shake algorithm was employed to hold fixed bonds involving hydrogen. The time step for all the simulations was all set to 2.0 f s. 2.3 MD trajectories analysis The MD simulations were carried out for both Apo and HCY systems for 100 ns. To obtain thoughtful insights into the motion behavior during the 100 ns simulation time, the trajectory obtained by MD simulation was analyzed. These trajectories were processed with AmberTools1.5 module. Root-mean-square deviation (RMSD) was employed to quantify the conformational changes of the same protein. This value was an important criterion in judging the structures of protein. In this study, C-RMSD was calculated for all systems with the first frame as reference structure. Root-mean-square fluctuations (RMSF) were used to evaluate the fluctuation of each residue during the simulation time. Hydrogen bonds are of great importance to biological molecules. We employed the following criteria for the hydrogen bonds analysis: The cut-off value of distance between the two heavy atoms was set to 3.0 A; the angle between acceptor and donor atom for hydrogen bonds employed a 120 cut-off value. The cluster analysis was also employed for the trajectories analysis. In order to visualize the trajectory and to present the structures, VMD (Humphrey et al., 1996), Chimera (Pettersen et al., 2004) and PyMOL (DeLano, 2002) softwares were used. 2.4 MM-GB/SA calculations The MM-GB/SA methods (Wang et al., 2013) were applied to estimate the binding free energies between the ligand and its receptor. The binding free energy (Gbind) in MM-GB/SA between a ligand (L) and a receptor (R) to form a complex RL can be calculated as below (1) Gbind=Gcomplex(Greceptor+Gligand) (2) G=EMM+GsolT S (3) EMM=Eint+Eele+Evdw (4) Gsol=GGB+GSA In the Eq. (2), the EMM represents the molecular mechanics component, which was determined in gas phase. Gsol is the stabilization energy caused by solvation. TS represents the vibrational entropy term. The EMM term is a sum of three terms as shown in the Eq. (3). Eint, Eele, and Evdw are internal, Coulomb and van der Waals interaction terms, respectively. The Eq. (4) gives the solvation energy, Gsol. As shown in this equation, the solvation energy can be divided into two terms including the electrostatic solvation free energy (GGB) and the nonpolar solvation free energy (GSA). The GGB term is obtained by the Generalized Born (GB) method. The GSA term can be proportional to the molecular solvent accessible surface area (SASA) method (Hou et al., 2008). The binding free energies were obtained by averaging over the values calculated for 3000 snapshots from the last 30 ns of the trajectories at 5 ps intervals for the complex structure. Energy decomposition was carried out to anchor the pivotal residues which are crucial and responsible for the binding process of the cortisol molecule from the energetic point of view. We only performed the pre-residue decomposition calculation with the aid of MMGBSA module in AMBER11, wherein the decomposition energy was calculated by the following equation: (5) Gcortisol residue=Evdw+Eele+Gele;sol+Gnonpol; sol 3 Results and discussion 3.1 The overall structural features or Apo and HCY systems As a crucial and fundamental criterion for MD simulation, Root-mean-square deviation (RMSD) can provide an overall assessment of the simulated structures on the conformational changes. By means of contrasting the variations of positions of all atoms of the system with a selected or reference structure, we can obtain the RMSD value of the system. In this study, the reference structure during the RMSD calculations was set as the initial frame of each system. The RMSD plots for Apo and HCY systems are manifested in Fig. 1, from which we can see the structures of Apo and HCY systems reach equilibrium during the 100 ns simulation time. However, there are some details to which we should pay attention. The structure of Apo system changes significantly referenced to initial structure during the time of the first 20 ns while the structure of HCY system keeps fairly stabilized during the same period of time. From 20 to 70 ns, the RMSD value of Apo system increases rapidly, with the highest RMSD value almost reaching 3A. The structure of HCY system during this time still remains stable. Both systems reach equilibrium in the last 30 ns. Root-mean-square fluctuation (RMSF) is also an important criterion for simulated structures. This criterion can afford specifics on the fluctuation of every single residue of the simulation system. Residues with high RMSF values are of large flexibility, which are marked in Fig. 2. As illustrated in Fig. 2, the overall structural flexibility is quite similar. All residues with high RMSF values locate in the loop region of the structure, staying far away from the cortisol binding region. Therefore, these flexible residues may not affect the function of the Apo and HCY systems. 3.2 Hydrogen bonds analysis As we all know, hydrogen bonds make an important contribution to the structural and functional fields of biological macromolecules. To this end, hydrogen bonds between cortisol molecule and surrounding residues in HCY system are supervised during the 100 ns. The results of the analysis are shown in Fig. 3 and Table 1. As can be seen in Fig. 3 and Table 1, cortisol molecule in HCY system totally forms seven hydrogen bonds with its adjacent residues. The residues which are responsible for the binding of cortisol molecule are Asn39, Gln45, Gln117 and Thr214. From Fig. 3 we can see that the hydrogen bond Asn39@OD1-HCY265@HO2 and Thr214@OG1-HCY265@H5 exist during almost whole simulation time, with occupancies of 97.27% and 96.03%, respectively. These two hydrogen bonds are mainly in charge of the binding of cortisol molecule. The oxygen atom of amide group of Asn39 and Thr214 form hydrogen bonds with cortisol molecule. With the help of these two hydrogen bonds, the cortisol molecule stays stable in the binding pocket. For the sake of getting a better comprehension of the electrostatic properties of cortisol molecule, electrostatic potential surface analysis was carried out. The multiwfn software (Lu and Chen, 2012, Ha, 2016) was used for electrostatic potential analysis and the VMD (Humphrey et al., 1996) was employed for depiction. The results of electrostatic potential analysis are shown in Fig. 4, from which we can see that the cortisol molecule itself is a negative and positive clearly molecule. That is, cortisol possesses a positive maximal and negative minimal point. It has been reported that cortisol molecule binding to its glucocorticoid receptor is necessary for the stress response (Kolodkin et al., 2013). Therefore getting a better understanding of the properties of cortisol molecule is important for further design of drug which can treat stress. Based on the electrostatic potential analysis, we can design the inhibitor of glucocorticoid receptor from the electrostatic point. 3.3 Cluster analysis Clustering analysis is a powerful technique for analyzing the trajectory produced by MD simulation. This technique can provide in-depth insight into the structural specifics of Apo and HCY systems. By means of the average linkage algorithm (Shao et al., 2007, Liu, 2014), the two trajectories were separated into five clusters. Among these five clusters, five snapshots were selected as the representative structures of each cluster. These five structures are named as follows: C0, C1, C2, C3 and C4, wherein C0 is the initial structure. The clustering analysis results are illustrated in Fig. 5 and Table 2. From Table 2 we can see that the most popular cluster for Apo system is C4, with a population of 48.3%, while the most popular cluster for HCY system is C0, with a population of 59.2%. The C4 and C0 were superimposed on the crystal structure (PDB ID: 4P6X) for the purpose of verifying the structural differentiation between each representative structure and crystal structure. As shown in Table 2, Apo system possesses a higher RMSD value (1.33 A) as compared with HCY system, which indicates that the overall structure of glucocorticoid receptor changed when cortisol molecule was absent. A close view was made in order to find out the detailed structural differentiation between the two systems. It has been discussed in hydrogen bond analysis section that Gln45 formed hydrogen bond with cortisol molecule, while inn Apo system, the spatial position of this residue changed quite a lot. This information may be of great importance in the design of glucocorticoid receptor inhibitor to treat stress. 3.4 Relative motions of Apo and HCY systems Principal component analysis (PCA) was designed to separate a conformational space of proteins into essential subspaces. The essential subspace containing different degrees of freedom can give a description of protein motions (Amadei et al., 1993). In this study, PCA was carried out with the aid of the ProDy software package (Bakan et al., 2011). VMD (Humphrey et al., 1996) and its plugin NWWiz (Bakan et al., 2011) were employed for the visualization of 3D structural snapshots. The results of the PCA are shown in Fig. 6, from which it is easy to find the remarkable motions in the two systems points on different parts of the structures. For HCY system, the most distinctive motion was the upper region of the structure. While for Apo system, no significant motions were observed. The relative motion information for HCY system is also helpful to the design of effective inhibitor of glucocorticoid receptor. 3.5 Binding free energy Aspect for HCY system To describe the binding ability of cortisol molecule, MM-GB/SA calculations was performed. The 3000 frames of last 30 ns trajectory were used for binding free energy calculations. The results of binding free energy calculations are illustrated in Table 3, from which we can see the calculated binding free energy (−12.48 kcal mol−1) is in good agreement with the experiment data (−7.096 kcal mol−1). It was worthy to note that the electrostatic interaction (Eele) played an important role in binding cortisol molecule of HCY system. As mentioned above, cortisol molecule possessed a positive maximal and negative minimal point, which enabled the Eele term to be the driving force in binding cortisol to glucocorticoid receptor. The residues which are responsible for the cortisol molecule binding were explored by the per-residue binding free energy decomposition analysis. The results are manifested in Fig. 7 and Table 4. As illustrated in Fig. 7, the residues with a lower than −1.5 kcal mol−1 contribution of binding free energy are labeled. The results of hydrogen bond analysis indicate that Asn39 and Thr214 form high occupancies of hydrogen bonds with the cortisol molecule. The energy decomposition results show that these two residues possess the two highest decomposition energies among all the residues, which are −2.71 kcal mol−1 for Asn37 and −2.63 kcal mol−1 for Thr214. As listed in Table 4, the interactions between the cortisol molecule and these two residues are mainly composed of electrostatic interaction. This result is consistent with the previous electrostatic potential surface and binding free energy analysis. Therefore, the electrostatic interaction is the driving force in the cortisol binding process. The crucial residues identified by energy decomposition analysis are of great importance for further design of inhibitor of glucocorticoid receptor. 4 Conclusions MD simulation and binding free energy calculation have become an important and powerful tool. This tool is extremely effective in computational area applying to the study of receptor-ligand interactions (Wang et al., 2013). For the purpose of investigating the structural details on binding cortisol molecule to glucocorticoid receptor, molecular dynamic simulation was carried out for Apo and HCY systems. According to the MD simulation results, the structure of HCY system was more stable than that of the Apo system. Hydrogen bond analysis identified a couple of residues which were responsible for the binding of cortisol molecule. Gln45 was an important residue, of which the spatial position changed a lot from Apo system to HCY system. It is believed that such position changes may be related to the stress conduction. The results of binding free energy calculation were in good agreement to experiment data (Kolodkin et al., 2013). The energy decomposition analysis identified certain crucial residues which were responsible for the binding of the cortisol molecule. Based on the results of electrostatic potential surface and MM-PB/SA calculations, it is of great significance to take the electrostatic properties of cortisol molecule into consideration. Our research gives clear insights from atomic-level into the structural–functional aspects of cortisol molecule and its glucocorticoid receptor, and also provides valuable information for the design of drug which can treat stress related illnesses. Peer review under responsibility of King Saud University. Fig. 1 Calculated root-mean-square deviations (RMSD) of the backbone atoms referenced to the corresponding starting structure. Black lines represent Apo system and red lines represent HCY system. Fig. 2 Root-mean-square fluctuations (RMSF) of the Apo and HCY system during 100 ns MD simulations. Flexible residues are labeled as Ser92, Leu111 Ser184 and Asn253. Fig. 3 Root-mean-square fluctuations (RMSF) of the Apo and HCY systems during 100 ns MD simulations. Flexible residues are labeled as Ser92, Leu111 Ser184 and Asn253. Fig. 4 The electrostatic potential surface of the cortisol molecule. The positive maximal point and negative minimal point are labeled. Fig. 5 The results of cluster analysis. The representative structure of Apo system is colored in turquoise and that of HCY system in medium purple. A close view for the cortisol binding pocket is on the right. Key residues are labeled. Fig. 6 A porcupine plot in stereo showing the Apo and HCY structures with cones signifying the first eigenvectors movements. Fig. 7 The decomposition binding free energy of HCY system. The residues that contribute over −1.5 kcal mol−1 are labeled. Table 1 The properties of hydrogen bonds between cortisol molecule and its surrounding residues. Hydrogen bonds Occupancies (%) Distances (A) Angle () N39-HCY265 97.27 2.87 15.18 T214-HCY265 96.03 2.93 22.51 HCY265-N39 42.15 3.19 28.01 Q117-HCY265 37.03 2.79 15.69 HCY265-Q45 6.06 3.08 23.64 HCY265-T214 6.05 3.01 44.46 HCY265-Q45 5.17 3.05 31.16 Table 2 Populations of representative structures and their RMSD values to crystal structure. System Structure Population (%) RMSD value to crystal structure (A) Apo C4 48.3 1.33 HCY C0 59.2 0.96 Table 3 Binding free energy (kcal mol−1) and its components of HCY system. System Eele Evdw GGB GSASA GMM GB = SAa −T S GT OTb Gexpc HCY −23.72 5.39 −53.83 2.59 46.89 4.67 −3.34 0.072 −34.00 4.20 −21.51 3.77 −12.48 5.64 −7.096 (Kolodkin et al., 2013) a GMM GB = SA = Eele + Evdw + GGB + GSASA. b GT OT = GMM GB = SA−T S. c Gexp = RTlnKD. Table 4 The decomposition binding free energy (kcal mol−1) and its components of HCY system. Residue van der Waals Electrostatic Polar solvation Non-polar solvation Total Met35 −1.67 −0.58 0.45 −0.093 −1.90 Leu38 −2.51 0.26 0.15 −0.23 −2.33 Asn39 −1.50 −4.47 3.39 −0.14 −2.71 Met76 −1.66 −0.34 0.59 −0.20 −1.61 Met79 −1.85 0.51 −0.17 −0.20 −1.70 Phe98 −1.37 0.38 −0.52 −0.15 −1.67 Gln117 −0.85 −3.73 3.18 −0.10 −1.51 Thr214 −0.56 −2.45 0.42 −0.036 −2.63 ==== Refs References Amadei A. Linssen A. Berendsen H.J. Essential dynamics of proteins Proteins: Struct. Funct. Bioinf. 17 1993 412 425 Bakan A. Meireles L.M. Bahar I. ProDy: protein dynamics inferred from theory and experiments Bioinformatics 27 2011 1575 1577 21471012 Cohen S. Doyle W.J. Skoner D.P. Social ties and susceptibility to the common cold JAMA 277 1997 1940 1944 9200634 Darden T. York D. Pedersen L. Particle mesh Ewald: an n.log (n) method for Ewald sums in large systems J. Chem. Phys. 98 1993 10089 10092 DeLano, W.L., 2002. The pymol molecular graphics system. PyMOL. Gordon J.C. Myers J.B. Folta T. H++: a server for estimating pK as and adding missing hydrogens to macromolecules Nucleic Acids Res. 33 2005 W368 W371 15980491 Ha J. Fitness dance video event analysis based on dynamic programming fusion multimoding J. Mech. Eng. Res. Dev. 39 2 2016 270 277 He Y. Yi W. Suino-Powell K. Structures and mechanism for the design of highly potent gluco-corticoids Cell Res. 24 2014 713 726 24763108 Hornak V. Abel R. Okur A. Comparison of multiple amber force fields and development of improved protein backbone parameters Proteins: Struct. Funct. Bioinf. 65 2006 712 725 Hou T. Zhang W. Case D.A. Characterization of domain peptide interaction interface: a case study on the amphiphysin-1 sh3 domain J. Mol. Biol. 376 2008 1201 1214 18206907 Humphrey W. Dalke A. Schulten K. VMD: visual molecular dynamics J. Mol. Graph. 14 1996 33 38 8744570 Jorgensen W.L. Chandrasekhar J. Madura J.D. Comparison of simple potential functions for simulating liquid water J. Chem. Phys. 79 1983 926 935 Kolodkin A. Sahin N. Phillips A. Optimization of stress response through the nuclear receptor-mediated cortisol signalling network Nat. Commun. 4 2013 1792 23653204 Kumar R. Thompson E.B. The structure of the nuclear hormone receptors Steroids 64 1999 310 319 10406480 Kumar R. Thompson E.B. Gene regulation by the glucocorticoid receptor: structure: function relationship J. Steroid Biochem. Mol. Biol. 94 2005 383 394 15876404 Liu Z.L. The groundwater pollution and environmental protection in China Biotechnology 10 24 2014 1883 1886 Lu T. Chen F. Multiwfn: a multifunctional wave function analyzer J. Comput. Chem. 33 2012 580 592 22162017 Pettersen E.F. Goddard T.D. Huang C.C. UCSF chimera a visualization system for exploratory research and analysis J. Comput. Chem. 25 2004 1605 1612 15264254 Schneiderman N. Ironson G. Siegel S.D. Stress and health: psychological, behavioral, and biological determinants Ann. Rev. Clin. Psychol. 1 2005 607 17716101 Shao J. Tanner S.W. Thompson N. Clustering molecular dynamics trajectories: 1. characterizing the performance of different clustering algorithms J. Chem. Theory Comput. 3 2007 2312 2334 26636222 Wang J. Wolf R.M. Caldwell J.W. Development and testing of a general amber force eld J. Comput. Chem. 25 2004 1157 1174 15116359 Wang Y. Zheng Q.C. Zhang J.L. Highlighting a interaction: a protein modeling and molecular dynamics simulation study on anopheles gambiae glutathione s-transferase 1–2 J. Mol. Model. 19 2013 5213 5223 24122449

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30044-X10.1016/j.sjbs.2017.01.035Original ArticleThe Effect of Leonuri Herba alkaloids on Senile BPH (male and female hormone induced) model rats Miao Mingsan miaomingsan@126.com⁎Wang Tan Liu Jing Li Yan Fu Zhenna Tian Shuo Henan University of Chinese Medicine, Zhengzhou 450008, China⁎ Corresponding author. miaomingsan@126.com26 1 2017 3 2017 26 1 2017 24 3 630 633 11 10 2016 25 12 2016 8 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Objective: To investigate the pharmacological effects of Leonuri Herba alkaloids (LHA) on prostate hyperplasia in older rats and the effect mechanism. Methods: Remove bilateral testes from BPH model rats, and conduct subcutaneous injection of testosterone and estradiol. At the same time, feed corresponding drugs to the rats by gastric perfusion for 30d. In the first 27d, conduct bladder fistula surgery. Three days after feeding, carry out the detection of the urine flow dynamics. Eyeball blood taking, determination of serum E2 levels, and quickly remove the prostate, thymus gland, spleen, kidney, lung, and bladder. 1/3 prostate homogenate, determine the level of PACP, T, DHT. 1/3 prostate was determined by mRNA expression in bFGF. The remaining 1/3 prostate was observed by light microscopy. Results: LHA could significantly decrease the animal prostate index, level of DHT, T, PACP, and elevate levels of E2 in the serum. It could also significantly reduce the maximum voiding pressure, intercontraction interval, and bladder resting pressure. Conclusion: LHA has good therapeutic effect on prostatic hyperplasia model rats induced by male and female hormone. Keywords Leonuri Herba alkaloidsTDHTE2BPHbFGFUrine flow dynamics ==== Body 1 Introduction LHA (Leonurus japonicus Houtt.) is also known as winds, Vallisneri. Research findings have shown that motherwort contains effective components of total alkaloids (total alkaloids) (Miao et al., 2016). LHA mainly includes Leonurine and stachydrine hydrochloride and other ingredients, according to TCM treatment of uroschesis “certificate” of the main treatment. LHA has the effect of detoxification, blood circulation, and diuresis. Pharmacological studies show that LHA can improve blood rheology and microcirculation and has the functions such as anti-inflammatory and analgesic, anti oxygen free radical, potassium sparing diuretic, enhance immunity and protection of myocardial. 2 Material 2.1 Animal Rat, SD, male (270–300 g), male (540–630 g), provided by the experimental animal center of Shandong Province, Qualification No.: 0019881; Laboratory Certificate No.: SYXK (Lu) 2013001. 2.2 Drugs and reagents LHA, Baoji Guokang Biological Technology Co. Ltd., concentration >80%, Production Batch No.: 20090615; Longbishu capsule, Shijiazhuang Kedi Pharmaceutical Co. Ltd., production Batch No.: 130706; Finasteride Capsules, Wuhan Humanwell Pharmaceutical LLC, Production Batch No.:20140202; Testosterone Propionate Injection, Shanghai GM Pharmaceutical Holdings C., Ltd, Production Batch No.: 130614; Estradiol Benzoate Injection, Harbin Three Beast Horse Industry Co., Ltd, Production Batch No.: 20131001; Benzylpenicillin Sodium for Injection, North China Pharmaceutical Co., ltd, specifications: 400 units, Production Batch No.: C1406807; Dihydrotestosterone (DHT) kit, Estradiol (E2) kit, Testosterone (T) kit, Prostatic acid phosphatase (PACP) kit, American R&D company, Production Batch No.: 20140702B. 2.3 Instrument Type 680 eliasa, America BIO-RAD; UV1000 type UV VIS spectrophotometer, manufacturer: Shanghai Techcomp Instrument Co. Ltd.; KDC-160HR type high speed refrigerated centrifuge, manufacturer: Zhongjia Branch of KDCX Holdings Co., Ltd. 3 Method 3.1 Experimental grouping Take 70 clean grade male SD rats aged 18 months, and take another 10 young rats (250–270 g) as a young control group. The eighteen-month old rats were randomly divided into 7 groups (10 in each group), and one of the groups was the control group, the other 6 groups of aged BPH model rats were divided into the model group, the Longbishu control group, the Finasteride group, and the small, middle, and large dose LHA group. 3.2 Modeling and administration The model rats should be removed of bilateral testes, after intraperitoneal injection of 10% hydrate (0.3 ml/100 g), suture skin, and intramuscular inject of penicillin 200 thousand μ/kg, 1 time a day, 7 times a day, which continued for 30d (Liu et al., 2011). On the first day of surgery, for the large, middle small dose LHA (50 mg/kg, 25 mg/kg, 12.5 mg/kg), Longbishu suspension (dose equivalent to 10 times the clinical dosage), Finasteride (dose equivalent to 10 times the clinical dosage), feed the rats equal volume of distilled water. On the 27th day, all animals were operated on bladder stoma. PE pipeline with normal saline was fixed to the bladder at the top of the bladder with the method of purse string suture (5–0 silk thread). The other port of PE pipe was fixed on the back of the neck of rats through rat skin. On the 30th day, cut the line on the back of the rat's neck and take out the PE pipe which was fixed on the back of the rat’s neck and connect it to the urine flow dynamics instrument. After adaptation for about 1 h, the rats were fed sterile saline solution for 2 h at the rate of 120 μl/min, and 3 micturition peak data were collected. After detection, weigh the rats, take the eyeball blood, determine serum E2 levels, and quickly remove the prostate, thymus gland, spleen, kidney, lung, and bladder. 1/3 prostate homogenate was used for determining the level of PACP, T, DHT. 1/3 prostate was used for determination of mRNA expression in bFGF. The remaining 1/3 prostate was observed by light microscopy. 4 Statistical analysis SPSS17.0 was used for statistical processing, and measurement data were expressed by (x¯±s), comparison of the single factor analysis of variance. Homogeneity of variance test was conducted using the LSD method. The variance was tested with Games–Howell method. 5 Result 5.1 Effect of LHA on urine flow dynamics on aged BPH model rats (Male and female hormone induced) Compared with the blank group of aged rats, the voiding peak pressure, the micturition interval, and the bladder pressure of the rats significantly increased in model group. Compared with the model group, the maximum pressure of micturition, micturition interval, bladder pressure (p < 0.01) of the rats in the Finasteride group, the Longbishu group, and the LHA large, small and middle groups significantly decreased (Table1). 5.2 Effect of LHA on the level of T in the prostate tissue, and E2 in serum on aged BPH model rats (Male and female hormone induced) Compared with the blank group of aged rats, the level of T and E2 significantly increased in the model group (p < 0.01). Compared with the model group, the level of T and E2 (p < 0.01) significantly decreased in the Finasteride group, the Longbishu group, and large, middle, small dose groups of LHA (Table2). 5.3 Effect of LHA on the expression of mRNA in prostate bFGF on aged BPH model rats (Male and female hormone induced) Compared with the blank group of aged rats, the expression of mRNA in bFGF was significantly higher in the model group (p < 0.01). Compared with the model group, the expression of mRNA in bFGF significantly decreased in the Finasteride group, the Longbishu group, and the large dose of LHA group (Table3). 5.4 Effect of LHA on the level of DHT, PACP on aged BPH model rats(Male and female hormone induced) Compared with the blank group of aged rats, the level of DHT and the content of PACP in model group significantly increased (p < 0.01). Compared with the model group, the level of DHT and PACP (p < 0.01) in the Finasteride group, the Longbishu group, and the large, middle, small dose of LHA group significantly decreased (Table4). 6 Discussion In this experiment, the rat model of BPH was established through subcutaneous injection of testosterone and estradiol in removed bilateral testes of rats (Guo and Miao, 2016). Abnormal micturition is the most common clinical symptoms in patients with BPH (Wang et al., 2006). The normal micturition process involves the bladder and the urethra. With BPH in the elderly forced structure, the change of function of the bladder and urethra, might lead to lower urinary tract symptoms. Therefore, through the determination of rat bladder pressure, the intercontraction interval was alleviated in BPH model of rats. The changes in the clinical index could also be the most intuitive reaction for the improvement of LHA to the BPH model rats. Testosterone (T) is the main androgen in the human body (Kuang et al., 2012). It is on the 5 alpha reductase function into dihydrotestosterone (DHT). The increase in the DHT level in the prostate could lead to BPH. Basic fibroblast growth factor (b-FGF) plays a role in the regulation by DHT and could be called a regulatory growth factor (Miao et al., 2015). It could lead to proliferation of epithelial cells of the prostate gland, causing the cavity to become larger and the secretion to increase, thus generating BPH (Zhang et al., 2011). Leonuri Herba is a common medicine for activating blood circulation, and it is called “the holy blood family” in the Compendium of Materia Medica (Tian and Miao, 2014). It is regarded as the cure for diseases in the department of gynecology. Modern pharmacological studies show that it contains a wide range of active ingredients with a variety of pharmacological effects and is able to be used for treating a variety of diseases. The functions of promoting blood circulation and removing blood stasis, clearing away heat and toxic materials, and diuresis with Leonuri Herba, accord with the treatment mechanism of BPH. The main component of Leonuri Herba is stachydrine and leonurine, and they are the main ingredients used to treat BPH (Xiao and Miao, 2014). This experiment shows that large, small doses of LHA group could significantly reduce the maximum voiding pressure, the intercontraction interval, the bladder resting pressure, significantly reduce DHT, T, PAC in the prostate homogenate, significantly elevate serum E2 levels, and significantly reduce the expression of mRNA in the prostate of bFGF. The effect of LHA on BPH model rats was very good. This study has provided experimental support for the clinical treatment of LHA to BPH, and also provided new ideas and methods for the prevention and treatment of BPH. Acknowledgments The research work is supported by The National Natural Science Foundation of China (81173474); Henan Science and Technology Innovation Team (2012IRTSTHN011); the Science and Technology Innovation Team of Zhengzhou City (131PCXTD612); and Efficacy rating Engineering Technology Center (Education & Technology [2012] 78-12; Henan University of Chinese Medicine and deep processing of geoherbs Collaborative Innovation Center [2012] 188-2. Peer review under responsibility of King Saud University. Table 1 Effect of LHA on urine flow dynamics on aged BPH model rats (Male and female hormone induced). Group N Voiding peak pressure Urine interval Urinary bladder resting pressure Youth blank group 10 32.68 ± 2.14** 9.525 ± 1.05** 504.77 ± 59.69** Aged blank group 10 56.93 ± 14.06** 9.58 ± 0.99** 506.62 ± 65.26** Model group 10 90.66 ± 8.14△△ 13.94 ± 2.65△△ 1455.934 ± 287.80△△ Finasteride group 10 68.82 ± 7.31** 10.21 ± 1.15** 543.76 ± 81.49** Longbishu group 10 60.27 ± 3.43** 10.34 ± 1.30** 562.64 ± 102.69** Large dose of LHA group 10 62.560 ± 3.97** 10.92 ± 1.62** 723.35 ± 116.06** Middle dose of LHA group 10 65.33 ± 8.57** 10.77 ± 0.99** 792.78 ± 138.57** Small dose of LHA group 10 70.63 ± 6.48** 10.88 ± 0.74** 813.65 ± 139.65** ** Indicate: compared with model group p < 0.01. △△ Indicate: compared with the blank group in the elderly. Table 2 Effect of LHA on the level of T, E2 in serum on aged BPH model rats (Male and female hormone induced). Group N T (pg/ml) E2 (pmol/L) Youth blank group 10 65.61 ± 31.40** 9.98 ± 2.10** Aged blank group 10 134.90 ± 48.17** 19.68 ± 4.92** Model group 10 327.14 ± 16.40△△ 32.92 ± 3.44△△ Finasteride group 10 200.41 ± 17.65** 26.57 ± 2.72** Longbishu group 10 203.37 ± 14.32** 26.95 ± 1.33** Large dose of LHA group 10 215.82 ± 19.84** 27.11 ± 4.43** Middle dose of LHA group 10 222.24 ± 8.40** 27.35 ± 1.94** Small dose of LHA group 10 226.94 ± 8.41** 27.73 ± 2.71** ** Indicate: compared with model group p < 0.01. △△ Indicate: compared with the blank group in the elderly. Table 3 Effect of LHA to the expression of mRNA in Prostate bFGF on aged BPH model rats (Male and female hormone induced). Group N mRNA-bFGF Youth blank group 10 0.0297 ± 0.02068** Aged blank group 8 0.0176 ± 0.00654** Model group 3 0.0733 ± 0.01057△△ Finasteride group 8 0.0377 ± 0.01111** Longbishu group 8 0.0376 ± 0.01756** Large dose of LHA group 16 0.0354 ± 0.01472** Middle dose of LHA group 6 0.0571 ± 0.01637 Small dose of LHA group 6 0.0453 ± 0.01674 ** Indicate: compared with model group p < 0.01. △△ Indicate: compared with the blank group in the elderly. Table 4 Effect of LHA to the level of DHT, PACP on aged BPH model rats (Male and female hormone induced). Group N DHT (nmol/L) PACP (pg/ml) Youth blank group 10 13.93 ± 1.94** 579.50 ± 184.86** Aged blank group 10 17.51 ± 1.27** 707.55 ± 84.32** Model group 10 35.29 ± 3.11△△ 1436.00 ± 170.79△△ Finasteride group 10 19.69 ± 1.74** 820.80 ± 32.88** Longbishu group 10 19.84 ± 1.16** 834.55 ± 46.90** Large dose of LHA group 10 20.98 ± 1.40** 851.30 ± 66.03** Middle dose of LHA group 10 21.19 ± 1.81** 895.60 ± 61.08** Small dose of LHA group 10 17.51 ± 1.29** 926.25 ± 127.53** ** Indicate: compared with model group p < 0.01. △△ Indicate: compared with the blank group in the elderly. ==== Refs References Guo L. Miao M.S. Analysis of animal models of benign prostatic hyperplasia based on its clinical characteristics CJTCMP 31 1 2016 261 264 Kuang Z.J. Cao H.Y. Huang P. Comparison of urodynamical differences by two cystometric methods in rats of different ages Chin. Pharmacol. Bull. 28 4 2012 581 584 Liu S.Y. Bai M. Ji X.N. Effect of total alkaloids extracted from Leonuri Herba on mice benign prostate hyperplasia Chin. J. Experimen. Tradit. Med. Formulae 17 21 2011 177 180 Miao M.S. Li Y. Wang T. Effects of motherwort alkaloids on rat ear acne Bangladesh J. Pharmacol. 11 2016 S26 S30 Miao M.S. Xiao K. Gao J.L. Effect of Leonuri Herba alkaloids on prostatic hyperplasia in older rats Chin. Tradit. Herbal Drugs 36 7 2015 1024 1026 Tian S. Miao M.S. Analysis of the modern study of common motherwort herb China J. Chin. Med. 29 1 2014 64 67 Wang Y. Shao J.C. Zhang S.W. Histomorphological studies on hyperplastic prostate of castrated rat caused by androgen National J. Androl. 8 3 2006 190 193 Xiao K. Miao M.S. Analysis of different characteristics of drug action on prostate hyperplasia China J. Chin. Med. 29 1 2014 61 63 Zhang Y. Ji X.N. Miao M.S. Exploration of treating prostatitis and prostatic hyperplasia with commonly used gynecological Chinese herbs China J. Chin. Med. 26 159 2011 967 969

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30055-410.1016/j.sjbs.2017.01.046Original ArticleAnalysis of digestion of rice planthopper by Pardosa pseudoannulata based on CO-I gene Wang Bo abLi Wenfen bYan Hengmei yanhm2006@163.comab⁎a Life Science College, Hunan Normal University, Changsha 410081, Chinab Department of Biotechnology, Beijing Normal University, Zhuhai Campus, Zhuhai 519087, China⁎ Corresponding author at: Life Science College, Hunan Normal University, Changsha 410081, China.Life Science CollegeHunan Normal UniversityChangsha410081China yanhm2006@163.com23 1 2017 3 2017 23 1 2017 24 3 711 717 3 11 2016 25 12 2016 6 1 2017 © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).In order to systematically study the predatory behavior and digestion regularity of spiders, real-time fluorescence quantification PCR technique was used to detect the number of CO-I genes in Pardosa pseudoannulata after it preyed on rice planthoppers in different temperatures within different periods. At 28 °C, 0, 1, 2, 4, 8, 16, and 24 h after P. pseudoannulata preyed on rich planthopper, DNA was extracted from cephalothorax and abdomen of P. pseudoannulata. Routine PCR and real-time fluorescence PCR techniques were employed for CO-I gene amplification. The results show that: The prey liquid was temporarily stored in the sucking stomach of the spider head within 2 h after prey, and gradually transferred to the midgut of the abdomen with the prolongation of time. After 4 h, CO-I gene residues of rice planthopper in the cephalothorax gradually decreased. The CO-I gene of rice planthopper was basically transferred to the abdomen after 16 h. During 0–1 h, food contained in abdominal midgut and other digestive organs was very small, CO-I gene detection was not obvious. Over time, food entered into the midgut from the sucking stomach for digestion. During 2–4 h, CO-I gene amount increased, at 2–4 h, detected CO-I gene residue reached the peak; but rapidly declined after 8, 16, and 24 h, even it is still detectable. The results at different temperatures reveal that: As the temperature increased from 26 °C to 32 °C, CO-I gene residues of rich planthopper in cephalothorax and abdomen of P. pseudoannulata gradually decreased, which indicated that the digestion rate increased with the increase of temperature with some range. However, when the temperature continued to increase to 34 °C, the digestion rate decreased. Keywords Pardosa pseudoannulataRice planthopperDigestionCO-I geneReal-time fluorescence PCR ==== Body 1 Introduction Spider is one of the most important predators for all kinds of agricultural pests. It is very important to know its prey species and digestion rate. The direct method of trophic analysis includes field observation (Luck et al., 1988) and digestive tract anatomy (Hengeveld, 1980). However, spider has in vitro and in vivo digestion after predation, and takes in formless prey tissue fluid, thus traditional morphological classification is not applicable to trophic analysis of stomach contents for spiders. With the development of molecular biology, DNA taxonomy has been developed. DNA-based trophic analysis is to determine food organisms by identifying DNA sequences in the residual tissues of the stomach (Sheppard and Harwood, 2005). Identification of food organisms excludes degree of biological digestion, which can be used for different morphological organisms at different growth and development stages. Hoss et al. published a paper in Nature about applying DNA to identify presence of residual food organism in feces, which is the first case to adopt DNA in determination of animal diet (Hoss et al., 1992). Deagle used this technique in pyrosequencing of food organisms from arctocephalinae feces, obtaining an unbiased diet composition of arctocephalinae (Deagle et al., 2009). Reed et al. extracted DNA from feces and stomach contents of manatee, and analyzed the compositions and proportion of the food organisms with fingerprint technology, which solved the problem related to the high digestibility of prey and the difficulty in identification of traditional food identification methods (Reed and Tollit, 1995). In addition, when it comes to the spider predation effect, Weber suggested that DNA molecular marker has the advantages of rigorous reaction conditions, stable results, high sensitivity and repeatability, based on which one of the most effective methods to study the predation effect is proposed (Weber and Lundgren, 2009, Hanief, 2016). However, qualitative evaluation can only determine whether predators have the ability to prey on certain pests, while quantitative evaluation is essential to determine control capacity of the natural enemies for target pests. Many studies have focused on the predator–prey qualitative research. On the contrary, few studies have evaluated the predatory behavior quantitatively, which is because the conventional PCR-based molecular detection technique can only qualitatively detect the predator species of the target insects, i.e. it cannot quantitatively detect capacity of predator in predation of target insects. Real-time fluorescence quantification PCR detection technique is to design a fluorescent probe based on the conventional PCR, which can detect quantitatively amount of the initial template in target DNA fragments in the system, and evaluate accurately capacity of predator in predation of target insects (Zhang et al., 2007). Therefore, it is believed to provide the possibility of precise digital quantification of ability of predators in predation of pests. In recent years, although real-time fluorescence quantification PCR has been used for predator–prey studies (King et al., 2008), quantitative and qualitative studies of predator–prey relationships and predator control of prey by fluorescence quantification PCR are rarely reported. Specially, Harper et al. (2005) used multiple real-time fluorescence quantification PCR system to study predator’s diet, finding that ground beetle can feed on aphids, weevils, earthworms and some mollusks, of which, earthworms and mollusks are the main food. Troedsson et al. (2007) studied differences in trapping and digestion efficiency of three species of algae of different size by Oikopleura dioica at different concentrations. Lv et al. (2005) quantitatively evaluated the control effect of field predators on Bemisia tabaci with real-time quantification PCR. Zhang et al. (2007) used TaqMan real-time fluorescence quantification PCR technique to quantitatively detect copy number of target DNA in B. tabaci, nymph, imago. Subsequently, various predators of B. tabaci were selected from the field. Based on the copy number of target DNA of B. tabaci residue in the predator, the number of B. tabaci larvae in the digestive tract of each predator was estimated. For pest and natural enemy communities in maize and soybean fields, using DNA molecular markers and fluorescence quantification PCR technique as tools, Song Xinyuan studied in details about the predation behavior of natural enemies for pests, fitted various influence factors to accurately and quantitatively study control effects of main natural enemies on the target pests (Song, 2008). Wang Guanghua obtained ITS gene sequences of Sogatella furcifera, Nilaparvata lugens and Laodelphax striatellus by PCR cloning. The primers and specific ALLGlo probes of the three planthoppers were designed and synthesized, an ALLGlo probe integrated triple real-time fluorescence quantitative detection system was established and optimized, which was further used to study capacity of common predators in rice field in predation of rice planthoppers (Wang, 2009). Using COI and SCAR molecular marker technology, Meng Xiangqin established a technique system for qualitative detection of predation of Frankliniella occidentalis by local natural enemies, and by TaqMan fluorescence quantification PCR, which quantitatively determined capacity of natural enemies in predation of F. occidentalis (Meng, 2010). Nevertheless, comprehensive research on quantitative analysis and evaluation of insect-controlling efficiency by spider has not been reported so far, demonstrating this study is quite necessary. In this study, to understand predatory behavior and digestion regularity of spiders, real-time quantification PCR technique was used to detect the number of COI genes in Pardosa pseudoannulata after it preyed on rice planthoppers in different temperatures within different periods. 2 Materials and methods 2.1 Experimental materials Mature P. pseudoannulata with similar size were collected in Zhuhai rice field (22° 15 min 253 s north latitude, 114° 12 min 314 s east longitude). They were put in 250 ml Erlenmeyer flask separately, and studied in experiments after hungry feeding for 7 d. Absorbent cotton was placed in the flask, only adequate water was provided to ensure that tarantula could survive under hunger. N. lugens Seal of similar size were also collected from rice fields where P. pseudoannulata lived. They were placed in plastic bottle with rice, and sealed with gauze after the capture for temporary cultivation and spare applications. 2.2 Experimental design 2.2.1 Study on digestion rate of P. pseudoannulata after feeding with rice planthopper The P. pseudoannulata were cultured in a bottle under starvation condition. Each of them was fed with three rice planthoppers before being placed in an incubator at 28 °C. They were killed at 0, 1, 2, 4, 8, 16, and 24 h, respectively. All walking legs and pedipalps were removed, the cephalothorax and abdomen were cut with a scalpel. It should be noted that the abdomen is not squeezed before cutting because of the soft tissue in the abdomen. Otherwise, food juice in the midgut will flow back along the podeon to the cephalothorax, affecting the experimental results. The cephalothorax and abdomen were placed and well-marked in a 1.5 ml centrifuge tube for the subsequent processing in the next step. The negative control P. pseudoannulata was not fed and used directly in experiment. 2.2.2 Study on digestion rate of rich planthopper by P. pseudoannulata at different temperatures The P. pseudoannulata were placed in incubators at 26 °C, 28 °C, 30 °C, 32 °C and 34 °C, respectively. Two hours after being fed with rice planthoppers, they were treated same as mentioned above in Section 2.2.1, with DNA extracted for further experiments. 2.3 Experimental methods 2.3.1 DNA extraction: DNA fast extraction kit (animal) produced by Sangon Biotech (Shanghai) Co., Ltd was used to extract DNA CO-I gene primers (Wang, 2009) were synthesized by Sangon Biotech (Shanghai) Co., Ltd. Upstream primer: 5′-CAACATTTATTTTGATTTTTTGG-3′ Downstream primer: 5′-TCCAATGCACATATCTGCCATATTA-3′ 2.3.2 Routine PCR 25 μL routine PCR System: 2.5 μL 10× PCR buffer, 0.5 μL 10 mmol/L dNTPs, 1 μL 10 μmmol/L upstream primer, 1 μL 10 μmmol/L downstream primer, 0.3 μL 5 U/μL Taq DNA polymerase, MLDNA template, add water to 25 μL. PCR procedure: initial denaturation at 94 °C for 3 min; denaturation at 94 °C for 50 s; refolding for 30 s at annealing temperature 55 °C (to be lowered by 1 °C in each cycle until 50 °C); extension at 72 °C for 1 min (35 cycles); extension at 72 °C for 10 min; save at 4 °C for standby application. After completion of the reaction, take 5 μL for electrophoresis in 1% agarose gel at 70 V for 25 min. View the results with gel imager to save resulting image. 2.3.3 Real-time fluorescence PCR Fluorochrome SYBR Green I was used to establish a 25 μL real-time fluorescence PCR system. The liquid was homogenized and placed in a PCR 8 tube, and put in a 7500 fluorogenic quantitative PCR instrument for gene amplification. The PCR procedure was described as follows: Initial denaturation at 95 °C for 3 min, denaturation at 95 °C for 30 s; refolding for 30 s at annealing temperature 50 °C; extension at 72 °C for 1 min (a total of 40 cycles); save at 4 °C for standby application. The expression difference was calculated with 2−△△Ct method by Ct value and formula v = ΔC/t, wherein, v denotes the digestion rate, ΔC denotes the total amount before the digestion- the total amount after the digestion, t denotes the digestion time. The digestion rate was calculated for each time period. 3 Results 3.1 Detection of digestion rate of P. pseudoannulata at different time after feeding by routine PCR method According to the experimental design, gene amplification was performed by routine PCR method, and electrophoresis detection showed that no target band was found in the negative control group, which proved that the primers did not amplify the DNA of P. pseudoannulata, and specificity indeed exists. After gel extraction of positive results, gene sequencing proved that the amplified band represented CO-I gene of rice planthopper. DNA was extracted from the cephalothorax and abdomen of P. pseudoannulata during 0–24 h after it preyed on rice planthopper, followed by conventional PCR amplification. The results are shown in Figure 1, Figure 2. In Fig. 1, the gel imaging results show the fact that there are obvious bright spots with length of about 900 bp in the cephalothorax at 0, 1, 2, 4 h. Among which, spots at 0, 1, 2 h are more obvious. Over time, the brightness of the electrophoretic bands decreases gradually, and becomes difficult to be observed after 16 h. This indicates that there are many residues of CO-I gene of rice planthopper in the digestive tract of cephalothorax within 2 h after predation, which gradually decrease after 4 h, and basically disappear after 16 h. As shown in Fig. 2, after electrophoresis, there are obvious bright spots in the abdomen at 0, 1, 2, 4, 8 h, and very dark band appear after 16 and 24 h. Thus, it is believed that there were many residues of CO-I gene of rice planthopper in the abdomen within 2 h after predation, which gradually decreased after 4 h with residue remained in the abdomen after 24 h. 3.2 Detection of digestion rate of P. pseudoannulata at different time after feeding by real-time fluorescence quantification PCR technique Reaction melting curves are of single peak, the peak value is single, and Tm value of the amplified products is uniform. The reaction specificity is good, and there is no primer dimer and nonspecific amplification. The CO-I gene of cephalothorax and abdomen of P. pseudoannulata after 0–24 h of predation of rice planthopper was amplified with real-time fluorescence PCR technique. The following amplification curves were obtained, as shown in Figure 3, Figure 4, Figure 5. Fig. 3 results show that CT values of amplification curve of cephalothorax at each time are concentrated between cycle numbers 14 and 16. CT value of 0, 1, 2 h is 14. Similarly, the one of the 4th hour is 15. For that of 8th and 16th hour, it is 16. CT value of the template is linear with the logarithm of the initial copy number of the template. The more the initial copy number is, the smaller the Ct value will be. It indicates that there are many residues of CO-I gene of rice planthopper in the cephalothorax of P. pseudoannulata within 2 h after predation, which decrease gradually with digestion time. Calculation of digestion rates of cephalothorax for each time period yielded a line graph as shown in Fig. 4. As can be seen from Fig. 4, digestion rate of cephalothorax accelerates within 2 h after predation, which slows down during 2–24 h. The results of routine PCR and real-time fluorescence PCR show that residue of CO-I gene of rice planthopper in cephalothorax decreases after 2 h. It is because after capture of prey, P. pseudoannulata will inject venom and primary digestive juice secreted from the midgut, so that soft part of the prey is decomposed into liquid as a result of the in vitro primary digestion. The digestive juice mainly contains amylase, protease which only initially digests carbohydrates, proteins, etc., while DNA is not digested as sucking stomach located in cephalothorax does not digest it after sucking prey liquid, but temporarily stores it. Then, the liquid maintains the original state and constantly enters the abdomen after 2 h, so CO-I gene volume in the cephalothorax decreases, demonstrating a downward trend. Fig. 5 shows CO-I gene amplification of rice planthopper in the abdomen of P. pseudoannulata. As can be seen, CT values of the abdomen at various times are concentrated between 15 and 19. Specially, CT value of the fourth and eighth hours is 15; and that of 0, 1, 2, and 16 h is 16, which significantly increases to 19 after 24 h. Digestion rate of residual CO-I gene of rice planthopper in abdomen is shown in Fig. 6. As can be seen from Fig. 6, less CO-I gene residue of rice planthopper can be detected at the 0th and 1st hour. The detected CO-I gene residue of rice planthopper reaches the maximum at 2–4 h, which decreases at 8, 16, 24 h, but the difference is not obvious. The results show that there are less CO-I gene residue of the planthopper in the abdomen within 2 h after predation, and the food is digested in the abdomen during 2–24 h. Considering the characteristics of the digestive system of P. pseudoannulata, the reason for this phenomenon is probably that just after the predation, a lot of food exists in the esophagus and sucking stomach of the cephalothorax, while there is very little food contained in midgut and other digestive organs of the abdomen, so CO-I gene detection volume is not obvious during 0–1 h. Over time, food successively enters into the midgut from the sucking stomach for massive digestion. During 2–4 h, CO-I gene volume increases, the midgut in the abdomen and developed digestive gland can secrete a variety of digestive enzymes including nuclease, and most macromolecules including CO-I gene are degraded, digested and absorbed here, showing accelerated digestion rate. After massive digestion of CO-I gene, there is not much residue in the abdomen, thus CO-I gene digestion rate remains almost the same during 8–24 h. 3.3 Detection of impact of temperature on digestion rate of P. pseudoannulata by routine PCR method As can be seen from Figure 7, Figure 8, the amplified band of CO-I gene of rich planthopper in the cephalothorax of P. pseudoannulata is significantly brighter than that in the abdomen within 2 h of feeding under the same temperature, which is in agreement with the digestive results of P. pseudoannulata at different times in earlier stage after feeding. It is clearly seen from the results of cephalothorax electrophoresis, electrophoretic band brightness gradually become darken from 26 °C to 32 °C, which becomes the darkest at 32 °C. 3.4 Detection of impact of temperature on digestion rate of P. pseudoannulata by real-time fluorescence quantification PCR technique As can be seen from Figure 9, Figure 10, the total CT value of CO-I residual gene amplification of rich planthopper in cephalothorax of P. pseudoannulata is in the range of 14–17. Specially, CT value is the smallest at 26 °C, which is only 14. On the contrary, CT value is 15 at 28 °C, 16 at 30 °C, and becomes the largest (reaching 17 in this case) at 32 °C. A similar trend is observed in real-time fluorescence PCR amplification curve of the abdomen. The larger the CT value is, the less the residual CO-I gene of rice planthopper in P. pseudoannulata is, and the faster the digestion rate is, vice versa. The results show that temperature can affect digestion rate of rice planthopper by P. pseudoannulata to a certain extent. 4 Discussion 4.1 The relationship between the CO-I gene residue of rice planthopper and the digestion time Experiments were carried out in an incubator at 28 °C, which effectively reduced the effect of temperature on the digestion rate (Hoogendoorn and Heimpel, 2001). At the same time, starvation for 7 days (De León et al., 2006, Harper et al., 2006) before the experiment could ensure that predator had consumed residue food before feeding, which increased its predation probability, with relatively accurate predatory capacity detected. The presence of other prey increased the detection rate of prey DNA (Dodd, 2004), so this experiment only fed single prey, rich planthoppers, to the P. pseudoannulata. Within 2 h after predation, the sucked prey liquid was not completely digested, but temporarily stored in the sucking stomach of spider cephalothorax, which gradually transferred to the midgut of the abdomen with the time. After 4 h, CO-I gene residues of rich planthopper in cephalothorax decreased gradually, which was totally transferred to the abdomen at 16 h. Therefore, during 0–1 h, food amount in the midgut and other digestive organs of the abdomen was very low, so CO-I gene detection volume was not obvious. Over time, food successively entered into the midgut from the sucking stomach for massive digestion. During 2–4 h, CO-I gene volume increased, the midgut in the abdomen and developed digestive gland could secrete a variety of digestive enzymes including nuclease, and most macromolecules including CO-I gene were degraded, digested and absorbed during this range, showing accelerated digestion rate. Detected CO-I gene residue of rice planthopper reached the peak during 2–4 h, which dropped rapidly at 8, 16 and 24 h, but still detectable. Studies pointed out that time of detectability of number and type of prey in indigestive tract of predators depends on the predator itself, while food quality can also affect the spider's metabolic rate (Anderson, 1974). Large prey increases time of detectability of prey, with a longer detection period even in the absence of feeding of alternate preys (Sheppard et al., 2005). The longest time of detectability can range from a few hours to five days (Chen et al., 2000, Ma et al., 2005). 4.2 Effect of temperature on the digestion rate of P. pseudoannulata Temperature can significantly affect the digestion rate of predators (Zhao, 2001, Liu, 2014). As the temperature increased from 26 °C to 32 °C, CO-I gene residues of rich planthopper were decreased gradually in cephalothorax and abdomen of P. pseudoannulata, indicating that digestion rate increased with increasing temperature within a certain range. However, when the temperature continued to increase to 34 °C, the digestion rate decreased. It is possible that enzymatic activities of the spider such as digestive enzymes are affected by different temperatures. As a result, digestive capacity, mobility of Oxyopes sertatus maintain at a better state under 28–32 °C (Wang and Yan, 2006), while excessive temperature will cause a negative effect on physiological activity of spiders (Xu et al., 1995). Acknowledgements This research is supported by the National Natural Science Foundation of China (No. 31372159), National Natural Science Foundation of China (No. 31172107), Hunan Provincial Innovation Foundation for Postgraduate, China (No. CX2014B198). Peer review under responsibility of King Saud University. Figure 1 Results of routine PCR electrophoresis of cephalothorax of P. pseudoannulata. Figure 2 Results of routine PCR electrophoresis of abdomen of P. pseudoannulata. Note: M in the figure represents marker DS2000; 0 h, 1 h, 2 h, 4 h, 8 h, 16 h, 24 h represent different time after P. pseudoannulata preys on rice planthopper. Figure 3 Amplification curve of residual CO-I gene of rice planthopper in cephalothorax. Figure 4 Digestion rate of residual CO-I gene of rice planthopper in cephalothorax. Figure 5 Amplification curve of residual CO-I gene of rice planthopper in abdomen. Figure 6 Digestion rate of residual CO-I gene of rice planthopper in abdomen. Figure 7 Routine PCR electrophoresis of cephalothorax of P. pseudoannulata at different temperatures. Figure 8 Routine PCR electrophoresis of abdomen of P. pseudoannulata at different temperatures. Figure 9 Real-time fluorescence PCR amplification curve of residual CO-I gene of rice planthopper in cephalothorax of P. pseudoannulata. Figure 10 Real-time fluorescence PCR amplification curve of residual CO-I gene of rice planthopper in abdomen of P. pseudoannulata. ==== Refs References Anderson J.F. Responses to starvation in the spiders Lycosa lenta and Filistata hibernalis (HentZ) Ecology 55 1974 576 585 Chen Y. Giles K.L. Payton M.E. Greenstone M.H. Identifying key cereal aphid predators by molecular gut analysis Mol. Ecol. 9 2000 1887 1898 11091324 De León J.H. Fournier V. Hagler J.R. Daane K.M. Development of molecular diagnostic markers for sharpshooters Homalodisca coagulata and Homalodisca liturata for use in predator gut content examinations Entomol. Exp. Appl. 119 2006 109 119 Deagle B.E. Kirkwood R. Jarman S.N. Analysis of Australian fur seal diet by pyro sequencing prey DNA in faeces Mol. Ecol. 18 2009 2022 2038 19317847 Dodd C.S. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30056-610.1016/j.sjbs.2017.01.047Original ArticleEffect of Total Alkali in Leonuri Herba on rat ear acne model of serum IL-6 level, Thymus and Spleen Tissue Morphology Li Yan 1Xi Peng 1Wang Tan Miao Ming San miaomingsan@163.com⁎College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450046, China⁎ Corresponding author. miaomingsan@163.com1 Contributed equally to this work. 25 1 2017 3 2017 25 1 2017 24 3 718 723 3 11 2016 25 12 2016 6 1 2017 © 2017 Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).To investigate the effect of Total Alkali in Leonuri Herba (TALH) on rat ear acne model of serum IL-6 level, Thymus and Spleen Tissue Morphology. The rats were divided into the TALH group at high, medium and low doses (200 mg kg−1, 100 mg·kg−1, 50 mg·kg−1), the tanshinone group (360 mg·kg−1), model group and the blank group. All the rats were administrated once a day for 14 days. At the same time on the first day, the rats in the blank group were injected normal saline in the stomach and also had intradermal injection of sterile saline in the auricle. The rats in other groups (model group were lavaged with physiological saline) were injected intradermally auricle spare Staphylococcus epidermidis bacteria liquid. After the injection, the rat ear swelling rate was calculated for 5 consecutive days in terms of rat auricle thickness measurement. 1 h after the last administration, the eyeball blood was removed, centrifuged to separate serum, and measured to be at serum IL-6 level; The tissue sections of thymus and spleen were observed according to morphology criteria. Compared with the blank group, the level of IL-6 in serum of the rats in the model group was remarkably higher, and those in the thymus and spleen groups were remarkably lower, indicating the rat ear acne model is successful. Compared with the model group, the rats in the TALH group can remarkably make serum IL-6 level decrease, causing significant thymus cortical thickening and increased spleen sections, and remarkably increasing the lymphocytes of thymus and spleen. The effect of large dose in the TALH group is the best and increases with the increase of the dose and curative effect. The TALH treatment of acne may be related to the reduction of serum IL-6 levels and the resistance of the atrophy of the thymus and spleen on the rat ear acne model. Keywords Total Alkali in Leonuri Herba (TALH)Epidermis staphylococcusRatAcne modelIL-6Thymus and spleen ==== Body 1 Introduction Acne is a chronic inflammatory skin disease of the pilosebaceous, often occurring on the face, chest, back and other parts of the body rich in fat. In modern medicine, the disease is usually treated with antibiotics, anti-androgen, estrogen, retinoids and other drugs, but all these treatments will bring side effects at various degrees, which is not easy for the patient to accept. In order to seek safer and more effective treatment methods, in recent years, the fight against acne with the help of Chinese medicine through research has been strengthened. Leonuri Herba is dry or fresh aerial parts of the Leonurus heterophyllus sweet and is commonly used in traditional Chinese medicine. It has a pungent and slightly bitter flavor and makes one feel slightly cold after touching it. When injected into the pericardium or the liver, the latter may swell with regulating menstruation. It also promotes blood circulation and diuresis and has the detoxification effect. Leonuri Herba contains alkaloids, flavonoids, terpene, polysaccharide, ferulic acid, volatile oil and various chemical composition (Li, 2013). Motherwort topical protects and repairs skin photoaging caused by ultraviolet irradiation damage (Xu and Wu, 2012). Large doses of motherwort inhibit certain fungal skin effect, in clinical practice, it is widely used in treating eczema (Miao et al., 2013a, Miao et al., 2013b, Liu et al., 2014, Liu and Wang, 2010, Fan, 2016, Liu, 2010), urticaria, dermatitis, prurigo skin, carbuncles and female skin pruritus. For this project, the effect of Total Alkali in Leonuri Herba (TALH) on rat ear acne model at serum IL-6 level, Thymus and Spleen Tissue Morphology is studied in order to provide experimental evidence for clinical application. 2 Materials and methods 2.1 Animals Wistar rats, male, whose weight were 180–200 g, were supplied by the Experimental Animal Center of Hebei Province (Animal permit number: 812074). Laboratory Certificate of Conformity: SYXK (Henan) 2010-001. 2.2 Experimental reagents and drugs The TALH, provided by the chemical room, concentration is 52%; tanshinone capsule, Hebei Xinglong Force Pharmaceutical Co., Ltd., production batch number 20090819; S. epidermidis strain No. 12228, provided by Zhengzhou University microbiology laboratory, experimental passages before backup; IL-6 radiation immunoassay kit, produced by Beijing Ear Cause Biological Technology Co., Ltd. production batch number 20091226. 2.3 Experimental instruments Electronic balancer, Mettler-Toledo Instruments Co., LTD, model AL204; Micrometer screw, Jiangyin Production Throttling Device Factory Co., LTD. Automatic γ Immune Counter, State-run Twenty-six Plant Nuclear Medical Instrument Factory, model FJ2003PS. 2.4 Modeling and administration According to the weight balance, 60 Wistar male rats, weighing 180–200 g. were randomly divided into the TALH group at high, medium and low doses (200 mg·kg−1, 100 mg·kg−1, 50 mg·kg−1), the tanshinone group (360 mg·kg−1) model group and the blank group (Xu et al., 2009). The corresponding drugs were administered in each group. The model group and blank group were given volumes of distilled water. All the rats were administrated once a day for 14 days. At the same time on the first day, the rats in the blank group were injected normal saline in the stomach and also had intradermal injection of sterile saline in the auricle. The rats in other groups (model group were lavaged with physiological saline) were injected intradermally auricle spare S. epidermidis bacteria liquid. After the injection, the rat ear swelling rate was calculated for 5 consecutive days in terms of rat auricle thickness measurement. 1 h after the last administration, the eyeball blood was removed (without anesthesia), centrifuged at 3000 r/min for 15 min to separate serum, and measured according to the IL-6 immunoradiometric analysis kit instruction manual operation. Meanwhile the thymus and spleen are weighed, and the biopsy was stained with HE (Miao et al., 2013a, Miao et al., 2013b). 2.5 Method statistical analysis SPSS 13.0 for windows has been used for statistical processing. The measurement data are represented by mean ± variance (x‾ ± s), and group comparison has adopted analysis of variance; and the Ridit test has been used to rank the data. 3 Results Previous studies had shown that the effect of Total Alkali in Leonuri Herba on S. epidermidis rats has been caused by acne model (Miao et al., 2016). Through our research, we have found that the effect of Total Alkali in Leonuri Herba (TALH) on rat ear acne model of serum IL-6 level is related to Thymus and Spleen Tissue Morphology. 3.1 Effect on thymus tissue morphology of rat ear acne model For the rats in the blank group, the thymic lobule cortex, medulla clear boundaries, cortical lymphocytes were normal (Fig.1A); For the rats in the model group, the thymic lobule cortex, the medulla clear boundaries, the cortical atrophy thinning, lymphocytes decreased significantly (Fig.1B); The rats in the tanshinone group rat, the thymic lobule cortex, medulla, clear boundary, the cortical thickening, denser lymphocyte (Fig.1C); For the rats in the high dose TALH group, the cortical skin, the medullary thymic lobule, the clear boundary, the cortical thickening, denser lymphocyte (Fig.1D); The rats in the medium dose of TALH group, the cortex, medullary thymic lobule, clear boundary, cortical thickening, denser lymphocyte (Fig.1E); The rats in the low dose TALH group, the cortical, medullary thymic lobule, clear boundary, cortical atrophy thinning dense, lymphocyte (Fig.1F). From Table 1, it can be seen that compared with the blank group and the model group, the thymic cortex thickness and the number of lymphocytes decreased significantly (P < 0.01), indicating the rat ear acne model was successful; Compared with the model group, the high and medium dose TALH groups and the tanshinone group significantly increased the thymus cortical thickness and the number of lymphocytes on rat ear acne model (P < 0.01); the low dose of TALH group can be obviously increased the thymus cortical thickness and number of lymphocytes on rat ear acne model (P < 0.05). 3.2 Effect on spleen tissue morphology of rat ear acne model For the rats in the blank group, the red pulp of spleen white pulp clear boundaries, splenic corpuscle and lymphocyte were normal (Fig.2A); The rats in the model group, the red pulp of spleen white pulp clear boundaries, splenic nodule was obviously reduced, lymphocyte sparse decreased obviously (Fig.2B); The rats in the tanshinone group, the spleen red and white pulp, clear boundary, splenic corpuscle increased significantly, lymphocyte dense (Fig.2C); The rats in the high dose of TALH group, the spleen red and white pulp, clear boundary, splenic corpuscle increased obviously, lymphocyte dense (Fig.2D); The rats in the medium dose of TALH group, the red and white pulp, clear boundary, splenic corpuscle increased, lymphocytosis (Fig.2E); The rats in the low dose of TALH group, the spleen red and white pulp, clear boundary, splenic corpuscle increased, lymphocytosis (Fig.2F). From Table 2, compared with the blank group, in model group the spleen section and the number of lymphocytes decreased significantly (P < 0.01), indicating the rat ear acne model successfully; Compared with model group, the high and medium doses of TALH group and tanshinone group can significantly increase the size of spleen section and number of lymphocytes on rat ear acne model (P < 0.01); the low dose of TALH group can be obviously increased the size of spleen section and number of lymphocytes on rat ear acne model (P < 0.05). 3.3 Effects of serum IL-6 levels on rat acne model From Table 3, compared with the blank group, the rats in the model group increased significantly in terms of the IL-6 serum level (P < 0.01), indicating that the rat ear acne model is successful; Compared with the model group, the high and medium dose TALH group and the tanshinone group can significantly decrease the level of IL-6 in serum on rat ear acne model (P < 0.01); the low dose of TALH group can significantly decrease the level of IL-6 in serum on rat ear acne model (P < 0.05). 4 Discussion Acne often occurs on the head and face as hair follicles and sebaceous glands. It may also occur on the chest and back of the neck. It may cause acute purulent infection, which is easy to expand into subcutaneous tissue, subjecting the skin to varying degrees of damage. Other consequences of acnes include pimples, nodules, pus blisters, cysts, scars. According to relevant statistics, the incidence of adolescent suffering from acne is as high as 82.7% and the prevalence rate of male was higher than female (Huang et al., 2012). The current treatment of acne is usually through the use of antibiotics, anti-androgen drugs such as retinoic acid -based, but this method may cause certain therapeutic effects, adverse reactions of antibiotics drug and strong toxicity in the liver and the kidney (Xu, 2011); Anti-androgen drugs can cause human hormone disorders, particularly affecting the female menstrual regularity, and in severe cases can cause infertility; Retinoids to skin desquamation is serious, and had severe liver toxicity, teratogenicity and ocular toxicity (Yan and Chen, 2010, Wang and Hedner, 2016). Therefore, many scholars are studying how to use traditional Chinese medicine to treat acne, and have achieved satisfactory result. The experimental model has chosen ear sebaceous glands of the rat, intradermal injection of S. epidermidis infections and then expanded to form a micro acne hair follicle cavity. Due to the fact that excessive sebaceous gland secretion is an important condition for the formation of acne, and acne are the final performance of hair follicle enlarged model. This method is in accordance with the clinical symptoms of acne and is easy to operate, and achieve high repeatability. It had been reported that the qingrecuochuang soup (Pugongying, wild chrysanthemum, honeysuckle, violet, giant knotweed etc.) can significantly inhibit the delayed type hypersensitivity and inflammation and significantly reduce the peripheral T lymphocyte of spleen and the immune organ index, has anti-inflammatory effect, inhibit cellular over expression in a certain extent (Kou et al., 2003). In order to further investigate the relationship between acne and the immune regulation, in this paper, on the basis of effect of TALH on rat ear acne model, we have observed the local tissue pathology of thymus, spleen, thymus cortex thickness, spleen section size, the corresponding number of lymphocytes, and the influence of the level of serum IL-6. To observe the change of immune organs of rats, we have often selected the thymus and spleen. General observation of the thickness of thymus cortical is conducted by measuring the narrowest and widest point cortex, the mean value for the thickness, and then calculating the pressure in the micrometer reticle number of lymphocytes. General observation of the spleen is conducted in the spleen size section, with eyepiece micrometer reticule and central arteriole of spleen as the midpoint to calculate pressure in lines on both sides of the lymphocyte count, meaning a central small periarterial lymphatic sheaths of the lymphocyte count (Miao et al., 2010); serum IL-6, mainly produced by macrophages, natural-mediated immune factors, also known as pro-inflammatory cytokines, is the key to the innate immune response of the start. There were reports of some significance of inflammatory cytokines in the pathogenesis of acne (Wang et al., 2013). As the thymic cortex thickening reduced, the spleen section increased, and cortical lymphocytes and spleen lymphocytes increased, the serum IL-6 level decreased, which suggested an improvement in immune function. Meanwhile, this paper has a positive control of drug selection tanshinone because the main active ingredient is Cryptotanshinone, which has a strong inhibitory effect (Li et al., 2012). In addition, there is a mild estrogen-like activity, and anti-male hormone-like effect (Wang, 2011). It had been reported that the cure rate of acne was 31.7%, and the total efficiency was 90% (Zheng, 2011). There was a clinical report (Wu and Liu, 2006) that Danshen Mixture (Hedyotis diffusa, Scutellaria baicalensis Georgi, Salvia miltiorrhiza, Prunella vulgaris etc.) could significantly or obviously change the peripheral blood IgG and IL-2 levels of acne patients, suggested that the regulation of the mixture of cellular and the humoral immunity may be one of the mechanisms of clinical treatment. The results of the study indicate that the acne model has made the spleen and thymus of the rat atrophy significantly, the ear squamous epithelium hyperplasia was thicker obviously, and less emergence of a large number of inflammatory cell infiltration, and reduced the number of lymphocytes. The TALH can make the acne model caused by rat thymus and spleen atrophy reduce significantly, significantly thicken the thymus cortex, significantly increase the spleen sections, the number of the cortex of thymus lymphocyte and spleen lymphocytes, and significantly reduce the serum IL-6 level in serum. In the high dose TALH group, the antagonism thymus and the spleen have atrophied, the serum levels of serum IL-6 have decreased strongly, the TALH increases with the increase of the dose and efficacy, and its concentration–response relationship was positively correlated. This article, based on the serum levels of IL-6, local tissue pathology in thymus and spleen, measures the size and corresponding number of the spleen lymphocytes and thymus cortical, which is the first analysis of the immune system with acne relevance. The Motherwort is a common gynecological medicine. This study also provides new ideas for the treatment of acne. Acknowledgments The research work is supported by the National Natural Science Foundation of China (Grant No. 81173474) and the Collaborative Innovation Center for the creation of new Traditional Chinese medicine and Henan University of Traditional Chinese Medicine for genuine regional drug deep processing (2012) 188-2. Peer review under responsibility of King Saud University. Figure 1 Effect of TALH model of thymus tissue of rats (HE×100). Figure 2 Effect of TALH model of spleen tissue of rats (HE×100). Table 1 Effect of Leonurine rats caused by acne model thymic tissue morphology of Staphylococcus epidermidis. Group N The thickness of thymic cortex (μm) Number of lymphocyte Blank group 10 23.46 ± 4.32** 37.21 ± 9.25** Model group 10 14.22 ± 6.24 21.30 ± 8.12 Tanshinone group 10 35.47 ± 6.20** 40.28 ± 7.26** High dose of TALH group 10 31.28 ± 5.43** 38.12 ± 6.21** Medium dose of TALH group 10 28.30 ± 6.26** 33.25 ± 6.17** Low dose of TALH group 10 17.20 ± 4.26* 29.16 ± 5.14* Note: Compared with the model group. * P < 0.05. ** P < 0.01. Table 2 The results of experimental groups with different pathological changes of spleen determination. Group N The spleen section (μm) Number of lymphocyte (μm) Blank group 10 13.26 ± 2.35** 22.18 ± 3.25** Model group 10 8.34 ± 1.27 12.37 ± 2.15 Tanshinone group 10 22.16 ± 3.15** 36.14 ± 5.20** High dose of TALH group 10 18.25 ± 2.43** 27.24 ± 3.35** Medium dose of TALH group 10 16.34 ± 2.36** 24.18 ± 2.27** Low dose of TALH group 10 10.17 ± 3.24* 18.26 ± 3.24* Note: Compared with the model group. * P < 0.05. ** P < 0.01. Table 3 The effect of TALH on rat ear acne model of serum IL-6 level (x‾ ± s, N = 10). Group N IL-6 (ng/ml) Blank group 10 0.17 ± 0.03** Model group 10 0.29 ± 0.07 Tanshinone group 10 0.18 ± 0.02** High dose of TALH group 10 0.19 ± 0.03** Medium dose of TALH group 10 0.20 ± 0.03** Low dose of TALH group 10 0.25 ± 0.03* Note: Compared with the model group. * P < 0.05. ** P < 0.01. ==== Refs References Fan T.K. On agent-based intelligent touring system model construction J. Mech. Eng. Res. Dev. 39 1 2016 149 156 Huang J.W. Zheng J.C. Zheng J.M. Investigation on the influence of college students of acne pathogenesis factor J. Mudanjiang Med. Univ. 33 2 2012 53 55 Kou X. Pi X.M. Qiu Z.F. Experimental study of anti-inflammation and immunity on inhibition of qingrecuochuang Decoction Chin. J. Tradit. Med. Sci. Technol. 10 3 2003 142 143 Li J.F. Progress in pharmaceutical motherwort Intern. Med. China 8 5 2013 533 536 Li C.Q. Zhao L. Xue Z.P. Antibacterial mechanism of cryptotanshinone Chin. Pharm. J. 47 21 2012 1706 1710 Liu H.B. Recycling utilization patterns of coal mining waste in China Resour. Reserv. Recycl. 12 2010 1331 1340 Liu W.B. Wang H. Review on status quo of acne vulgaris models J. Guangdong Pharm. Coll. 26 2 2010 209 214 Liu W.B. Liang Q. Wang H. A acne composite animal model J. Guangdong Coll. Pharm. 30 2 2014 198 201 Miao M.S. Miao Y.Y. Fang X.Y. Effect of jujube polysaccharide on the rat model with both qi and blood deficiency, spleen thymus tissue morphology and bone marrow Pharmacol. Clin. Chin. Mater. Med. 26 2 2010 42 44 Miao M.S. Wang Q. Cheng B.L. Li P.F. Qiu P.B. Effect of Total Alkali in Leonuri Herba in rabbit ear acne model WIT Trans. Biomed. Health 18 1 2013 185 191 Miao M.S. Zhang X.X. Wu W. Effect of Herba Leonuri on pruritus and eczema animal model Tradit. Chin. Med. New Drug Clin. Pharmacol. 24 6 2013 540 543 Miao M.S. Li Y. Wang T. Wang T. Bai M. Miao J.X. Gong B. Effects of motherwort alkaloids on rat ear acne Bangladesh J. Pharmacol. 11 2016 S26 S30 Wang X. Research and clinical application on tanshinone Guangming J. Chin. Med. 26 7 2011 1514 1517 Wang Q. Hedner J. Path planning research for mobile robot based on immune genetic algorithm J. Mech. Eng. Res. Dev. 39 1 2016 164 172 Wang S.N. Zhang Y.R. Zhang B. Effects of SanHuang gel on serum IL-1α and TNF-α of rats with acne on the auricle West. J. Tradit. Chin. Med. 26 1 2013 7 8 Wu X.H. Liu W.L. The clinical study of She-dan Herbal Decoction in treatment of acne vulgaris and its effects on the levels of IgG and IL-2 in peripheral blood of the patients Chin. J. Dermatovenereol. 20 1 2006 23 25 Xu W.L. Adverse effects of antibiotics Chin. J. Clin. Ration. Drug Use 4 10 2011 75 76 Xu R. Wu J.D. Study on protecting effect of Leonurus Japonicus Houtt (LSH) on rat skin photoaging induced by ultraviolet ray Liaoning J. Tradit. Chin. Med. 39 7 2012 1421 1422 Xu C. Tian J. Yang L. Boil drinking elimination of acne model rats anti-inflammatory experiments J. Jilin Tradit. Chin. Med. 29 4 2009 340 341 Yan F. Chen L.J. Application of retinoids in the treatment of skin diseases Shanghai Med. Pharm. J. 21 4 2010 156 159 Zheng H.Y. Clinical observation on tanshinone capsule in the treatment of acne China Mod. Med. 18 11 2011 84 85

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30037-210.1016/j.sjbs.2017.01.028Original ArticlePeripheral arterial stiffness is associated with higher baseline plasma uric acid: A prospective cohort study Ding Xiaohan abYe Ping yeping301@sina.coma⁎Wang Xiaona aCao Ruihua aYang Xu aXiao Wenkai aZhang Yun aBai Yongyi aWu Hongmei aa Department of Geriatric Cardiology, Chinese PLA General Hospital, Beijing 100853, Chinab Department of Health Care and Geriatrics, Lanzhou General Hospital of Lanzhou Military Command, Lanzhou 730050, Gansu Province, China⁎ Corresponding author. yeping301@sina.com26 1 2017 3 2017 26 1 2017 24 3 574 581 27 10 2016 28 12 2016 7 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).This prospective cohort study aimed at identifying association between uric acid (UA) and peripheral arterial stiffness. A prospective cohort longitudinal study was performed according to an average of 4.8 years’ follow-up. The demographic data, anthropometric parameters, peripheral arterial stiffness (carotid-radial pulse-wave velocity, cr-PWV) and biomarker variables including UA were examined at both baseline and follow-up. Pearson’s correlations were used to identify the associations between UA and peripheral arterial stiffness. Further logistic regressions were employed to determine the associations between UA and arterial stiffness. At the end of follow-up, 1447 subjects were included in the analyses. At baseline, cr-PWV (r = 0.200, p < 0.001) was closely associated with UA. Furthermore, the follow-up cr-PWV (r = 0.145, p < 0.001) was also strongly correlated to baseline UA in Pearson’s correlation analysis. Multiple regressions also indicated the association between follow-up cr-PWV (β = 0.493, p = 0.013) and baseline UA level. Logistic regressions revealed that higher baseline UA level was an independent predictor of arterial stiffness severity assessed by cr-PWV at follow-up cross-section. Peripheral arterial stiffness is closely associated with higher baseline UA level. Furthermore, a higher baseline UA level is an independent risk factor and predictor for peripheral arterial stiffness. Keywords Peripheral arterial stiffnessUric acid, risk factorCommunity-basedFollow-upAbbreviations BMI, body mass indexCr, creatininecr-PWV, carotid-radial PWVDBP, diastolic blood pressureFBG, fasting blood glucoseHDL-C, high-density lipoprotein cholesterolLDL-C, low-density lipoprotein cholesterolMetS, metabolic syndromeOR, odds ratioPWV, pulse-wave velocitySBP, systolic blood pressureUA, uric acid ==== Body 1 Introduction Hyperuricemia, a common clinical situation, has been demonstrated as an independent risk factor for cardiovascular diseases including arterial stiffening, atherosclerosis and hypertension (Chu et al., 2000, Iwashima et al., 2006, Katsiki et al., 2015). The abnormality of uric acid (UA) has also been indicated to be associated with regional arterial stiffness in patients with chronic kidney disease and diabetes mellitus (DM) (Iwashima et al., 2006, Alderman, 2007, Santos, 2012, Zhao et al., 2013). The relationship between normal serum UA and arterial stiffness has also been well documented previously (Lin et al., 2012, Shin et al., 2012). Regarding the mechanism underlying arterial stiffness in which UA participates in, it is involved in thickening vessel wall (intima-media) via proliferation and differentiation of smooth muscle cells as well as dysfunction of endothelial cells (Bian et al., 2012, Elsurer and Afsar, 2014, Ishizaka et al., 2007, Zhang et al., 2014). It is also demonstrated that arterial stiffness is a risk factor for or pre-pathophysiological processes of various cardiovascular and cerebra-vascular diseases (Sun, 2015). Presently, the pulse wave velocity (PWV) has been used as a reproducible and valid non-invasive gold standard indicator in the assessments of arterial stiffness (Covic and Siriopol, 2015, Laurent and Boutouyrie, 2007, Liu, 2013). Nevertheless, PWVs from different arteries usually represent stiffness in distinct regions in vasculature system (Jadhav and Kadam, 2005, Weber et al., 2015), such as carotid-radial PWV (cr-PWV) which is applied to assess stiffness in arterioles (Hughes et al., 2004). Inconsistent results on associations between serum UA level and arterial stiffness have been reported before (Elsurer and Afsar, 2014, Mallamaci et al., 2015, Zhao et al., 2013). However, majority of them were performed primarily based on a basic disease such as DM, hypertension and chronic kidney disease (Elsurer and Afsar, 2014, Zhang et al., 2014) among various ethnic groups (Ishizaka et al., 2007, Liang et al., 2012, Lim et al., 2010, Kristina and Gooyers, 2016). However, there were few follow-up studies that have been performed to identify the roles of baseline level of UA in peripheral arterial stiffness. Thus, we postulated that the higher UA level may also play a critical role in increasing peripheral arterial stiffness and performed this follow-up observational study aiming at identifying the associations between UA level and peripheral stiffness evaluated by cr-PWV, to provide novel index for stratification and risk management of arterial stiffness. 2 Material and methods 2.1 Participants and procedures A total of 1680 health check participants were recruited between September 2007 and January 2009 from Pingguoyuan area, the Shijingshan district in this community-based follow-up cohort study according to the inclusion and exclusion criteria. The exclusion criteria were listed as following: endocrine and metabolic diseases (except DM), infection, neoplastic or severe liver or renal diseases. The inclusion criteria were as follows: residents who received a routine health examination in the community. 2.2 Follow-up and outcome assessment Our study was reviewed and approved by the ethics committee at People’s Liberation Army General Hospital. The study was thoroughly explained to all of the subjects who agreed to participate, and all of the subjects signed informed consent forms before their examinations. The participants were followed up for cardiovascular diseases mortality, all-cause mortality, and the development of DM from the initial screening to September 30, 2013. After a median of 4.8 years’ follow-up for 1680 subjects, 181 participants were lost for follow-up and excluded from analysis. Therefore, 1499 subjects (follow-up rate 89.2%) finished the follow-up and fifty-two of which were excluded because of death. In the final data analysis, 1447 participants were included. 2.3 Clinical data collection The lifestyle factors, prevalent diseases, demographic information, anthropometrics, family history and medication use were recorded using a standardized self-reported questionnaire in our following-up study. Smoking status and alcohol use was categorized as current, former, or never drinking/smoking, respectively. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were also examined on the right arm in a sitting position after a rest of five minutes. 2.4 Biomarker variable determination Between 8 am and 10 am after an overnight fast (at least 12 h), the venous blood samples were obtained from all participants. Plasma aliquots were obtained and stored at −80 °C for further study. Concentrations of plasma UA, total cholesterol (TC), triglyceride (TG), fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) concentrations were measured from venous blood samples using commercially available ELISA kits by Roche enzymatic assays (Roche Diagnostics GmbH, Mannheim, Germany). Concentration of plasma creatinine (Cr) was also measured by enzymatic assay (Roche Diagnostics GmbH) on a Hitachi 7600 autoanalyzer (Hitachi, Tokyo, Japan). The biochemical variables were measured from the blood specimens in the Departments of Clinical Laboratory, Chinese PLA General Hospital, following the criteria of the World Health Organization Lipid Reference Laboratories. 2.5 Assessments of arterial stiffness Caffeine, smoking and alcohol were avoided before the assessment for at least 12 h. Arterial stiffness was assessed by automatic cr-PWV measurements using a Complior SP device (Createch Industrie, France) after a 5–10 min rest in supine position in the morning, in a quiet environment, and at a stable temperature. PWV along the artery was measured with two strain-gauge transducers in the noninvasive procedure using the TY-306 Fukuda pressure-sensitive transducer (Fukuda Denshi Co, Japan) that is fixed transcutaneously over the course of a pair of arteries separated by a known distance on the carotid and radial arteries (both on the right side). Two transducers were used: one positioned at the base of the neck over the common carotid artery and the other over the radial artery. Cr-PWV were obtained simultaneously. The measurement was repeated over 10 different cardiac cycles. PWV was calculated from the measurement of the pulse transit time and the distance traveled by the pulse between the two recording sites: PWV (m/s) = distance (m)/transit time (s) (O'Rourke et al., 2002). 2.6 Definition of variables Smoking status was defined as smoking 1 or more cigarettes per day for at least 1 year. Body mass index (BMI) was defined as weight (kilograms) divided by square of height (meters). DM was defined as a fasting glucose ⩾7.0 mmol/L, glucose ⩾11.1 mmol/L at two hours after an oral 75 g glucose challenge, or both, or use of antihyperglycemic medication. Alcohol users were defined as drinking once a week (white spirit or beer or red wine). 2.7 Statistical analysis Normally distributed baseline continuous variables are expressed as mean ± standard deviation (SD) and analyzed with Student’s t-tests while the baseline dichotomous variables are presented as numbers (percentages) and compared using chi-square test. Non-normally distributed variables such as UA levels and other biomarkers were normalized by natural logarithm transformation as necessary. A Pearson’s regression analysis, a stepwise multivariate linear regression analysis and a multicollinearity analysis were performed to evaluate the associations between UA (natural logarithm transformed) level and arterial stiffness (cr-PWV) and other parameters at both the baseline and the end of the 4.8-year follow-up. Plasma UA levels at baseline were categorized as Quartile 1 (⩽238.95 mmol/L, n = 370), Quartile 2 (239–284.60 mmol/L, n = 352), Quartile 3 (284.61–341.85 mmol/L, n = 372), and Quartile 4 (⩾341.90 mmol/L, n = 353). Further analysis by a stepwise multivariate logistic regression analysis was performed to identify the risk of UA (baseline) on the follow-up arterial stiffness (cr-PWV ⩾8.76 m/s vs. cr-PWV <8.76 m/s) (Lin et al., 2012). Regression models were adjusted for age and sex as the independent variable (Model 1) and additionally adjusted for alcohol use (g/day), smoking, DM, LDL-C, TG, SBP, TC, DBP, HDL-C and Cr as the independent variables (Model 2). All analyses were performed using SPSS 19.0 for Windows (SPSS, Chicago, IL, USA). P values <0.05 were considered as statistically significant. Statisticians from the People’s Liberation Army General Hospital were consulted for all of the statistical methods and results. 3 Results 3.1 Baseline clinical characteristics The mean age of the subjects was 61.40 ± 11.4 years and 59.98% were women. The baseline clinical characteristics were categorized into four groups by quartiles of UA. The cr-PWV were significantly higher in Quartiles 2, 3 and 4 than in Quartile 1 at baseline (all p < 0.05, Table 1). 3.2 Associations between peripheral arterial stiffness and UA level at baseline At the baseline, peripheral artery stiffness index, cr-PWV was strongly related to UA level (r = 0.200, p < 0.001) in the Pearson’s analysis. In addition, cr-PWV was also closely correlated with DBP, SBP, Cr and TG in univariate linear analysis, while it negatively associated with HDL-C (Table 2). However, in multivariate analysis, cr-PWV was uncorrelated to UA level (β = 0.284, p = 0.107) which may be adjusted by other variates such as age, DBP, Cr and TG in multivariate linear analysis (Table 2). 3.3 Follow-up cr-PWV was tightly associated with baseline UA level Pearson’s correlation analyses were employed to identify the association between follow-up peripheral arterial stiffness and baseline UA. As shown in Table 3, both univariate and multivariate linear regression analyses revealed statistically significant association between cr-PWV and UA (r = 0.145, p < 0.001 and β = 0.493, p = 0.013). Furthermore, the significant associations between follow-up cr-PWV and baseline parameters such as age, blood pressure, HDL-C (r = −0.061, p = 0.028), Cr (r = 0.132, p < 0.001) and TG (r = 0.085, p = 0.002) were also present in univariate analysis. 3.4 Logistic regressions for UA and arterial stiffness Logistic regression using univariate UA for peripheral artery stiffness measured as cr-PWV indicated only Quartile 4 were risk for peripheral artery stiffening (OR: 1.260; 95%CI: 1.000–1.588; p = 0.050). In the Model 2 for cr-PWV, Quartile 3 UA range revealed significantly risk for peripheral artery stiffening (OR: 1.781; 95%CI: 1.156–2.744; p = 0.009) (Table 4). Furthermore, the UA level in the Quartile 4 showed significant risk for peripheral artery stiffening (OR: 1.259; 95%CI: 0.942–1.681; p = 0.019). 3.5 Subgroups analyses in MetS and non-MetS We performed the subgroup analyses to identify the affection of MetS on arterial stiffness. However, cr-PWV showed no correlation with UA in the MetS subgroup (all p > 0.05). Interestingly, in the non-MetS group, UA was associated with cr-PWV in both linear analyses (r = 0.190, p < 0.001 and β = 0.561, p = 0.017). Association’s analyses for subgroups are displayed in Table 5. Regarding the prediction roles of UA in MetS group, logistic regressions revealed no predictive values for arterial stiffening. Importantly, in the non-MetS subgroup, UA (Quartile 4 vs. Quartile 1) was an independent risk factor or predictor for arterial stiffening reflected by cr-PWV in Model 2 which was adjusted by age, gender, hypertension, DM, current smoking, DBP, SBP and levels of serum TC, TG, HDL-C, LDL-C and Cr (Table 6). 4 Discussion In the current 4.8 years of follow-up study, we found that UA level was associated with cr-PWV. However, only Quartile 3 and 4 levels of baseline UA were independent predictors for follow-up peripheral arterial stiffness. Furthermore, UA predicts arterial stiffness in a MetS independent way. 4.1 UA and its distribution in the health check up population UA as the end-product of purine nucleotide metabolism participates in many pathophysiological processes in vascular diseases (Sabio et al., 2010). Over the past decades, hyperuricemia has been indicated to be a risk factor for cardiovascular and cerebrovascular diseases such as atherosclerosis and hypertension (Katsiki et al., 2015, Sabio et al., 2010). However, the role of UA in its normal physiological range, such as upper level, was remained elusive. In our present study, we did observe that the higher UA levels were accompanied with higher cr-PWV, indicating that higher range of UA may contribute to peripheral arterial stiffening. This result was partly in consistent with previous studies (Hsu et al., 2013). Furthermore, individuals with higher levels (such as Quartiles 3 and 4) of UA account for almost half of the health check up population. Thus, the higher normal UA levels should be given proper attention with critical importance. Additionally, our observations that both SBP and DBP were lower in the lowest normal UA level interval (Quartile 1), combining with the results on arterial stiffness mentioned-above may provide clue for further stratifications and managements of cardiovascular diseases risk. 4.2 Peripheral stiffness were associated with UA levels It has been demonstrated that arterial stiffness occurs in both aortic and peripheral arteries, and the latter one is prior to the former one (Lee and Oh, 2010, Sun, 2015). There are also a number of studies that have indicated that UA was related to arterial stiffening that assessed by brachial-ankle PWV or heart-femoral PWV (Ishizaka et al., 2007, Lim et al., 2010). In our current study, we evaluated peripheral arterial stiffness using cr-PWV. We also found that cr-PWV was associated with baseline UA which is partly similar to the previous results (Liang et al., 2012). In accordance with many others’ reports, the current study showed that UA was correlated with peripheral stiffening at baseline (Shin et al., 2012, Bian et al., 2012); Homma et al., 2015). These results suggested that the subclinical arterial stiffening may exist in normal healthy persons. Particularly, the association between baseline UA and follow up peripheral arterial stiffness suggests that UA may be used to predict the incident of arterial stiffness which will be discussed in the next section. Up to date, though various studies have focused on the association between UA and arterial stiffness (Lin et al., 2012, Shin et al., 2012, Hsu et al., 2013, Bae et al., 2013, Cicero et al., 2014), the mechanisms underlying the pathogenesis that UA participates in arterial stiffening have not been clearly uncovered. However, several basic investigations demonstrated prominent roles of UA in arterial stiffness (Iwashima et al., 2006, Bobik and Grassi, 2012, Kanellis et al., 2003, Kang et al., 2005, Luft, 2012). First of all, UA has been indicated to thick vascular wall via promoting proliferation and differentiation of smooth muscle cells. The biological behaviors of smooth muscle cells mentioned above were triggered by the activated renin-angiotensin system and reactive oxygen species (Bobik and Grassi, 2012, Jae et al., 2012, Jain et al., 2014, Park and Lakatta, 2012). Those results subsequently lead to the reduction in elasticity of artery wall promoting the arterial stiffness. Secondly, numerous studies also conclude that the essential roles of UA in arterial stiffness prior to hypertension process may be attributed to the inflammatory activation (increased levels of C-reactive protein and other pro-inflammatory factors) (Kang et al., 2005). Thirdly, UA has also implicated in endothelial cell dysfunction which plays crucial roles in arterial stiffening (Bellien et al., 2010). It is elucidated that UA participates in arterial stiffness maybe also via the nitric oxide pathway dysfunction, oxidative stress, insulin resistance which result endothelial cell dysfunction (Bellien et al., 2010). Endothelial cell dysfunction further increases proliferation and migration of smooth muscle cells and the rearrangement of artery wall components. Those changes decrease the complaisance and stiffen arteries functionally and structurally. Lastly, it has also been addressed that UA injures arteries chronically through up-regulating the expression of platelet-derived growth factor and monocyte chemotactic protein-1 (Kanellis et al., 2003). In a word, though UA participates in arterial stiffness primarily through the four mechanisms mentioned above, other potential mechanisms still warrant further study. 4.3 Higher baseline UA level was an independent predictor for peripheral arterial stiffness Further analyses by logistic regressions indicated that higher baseline UA level was an independent risk factor and predictor for central stiffness and peripheral stiffness. In both adjusted models, higher UA levels exhibited risk for arterial stiffness measured by cr-PWV indicating that higher UA level was an independent predictor for peripheral arterial stiffening. In addition, baseline UA level in Quartiles 3 and 4 were still an independent predictor for peripheral arterial stiffening (cr-PWV). Though others’ cross sectional studies have indicated the risk for arterial stiffness ubiquitously (Lin et al., 2012, Bae et al., 2013), the predictive value of UA for arterial stiffness has not been confirmed in a long time longitudinal study. Thus, we performed this follow up survey and demonstrated that higher normal UA levels were risk factors and predictors for arterial stiffness. Particularly, the UA level ⩾341.90 mmol/L was an independent risk factor and predictor for central artery stiffening (vs. ⩽238.95 mmol/L). Our observations confirm the association between UA and arterial stiffness ((Lin et al., 2012, Cicero et al., 2014) and its predictive value for arterial stiffness which may facilitate us to prevent arterial stiffening. 4.4 UA predicts arterial stiffness in a MetS independent pattern The association between UA and MetS is debatable (Santos, 2012, Lim et al., 2010, Sun et al., 2013), though a vast amount of previous studies have indicated that UA was closely related to part components of MetS. Furthermore, no positive relationship between UA and MetS independent of other variables was addressed, though it has elucidated that prevalence of MetS increases with UA level positively and hyperuricemia was an independent predictor of MetS incident (Ishizaka et al., 2007). Thus, we performed the subgroup analysis to identify whether the predictive value of UA for peripheral arterial stiffness depended on MetS. Partly in accordance with the previous studies (Lim et al., 2010, Sun et al., 2013), in MetS subgroup no significant association has been indicated between baseline UA and follow up peripheral arterial stiffness, while, in non-MetS group, UA was significantly associated with cr-PWV in both univariate and multiple linear regressions. Although UA was partly associated with MetS in the cross-sectional studies (Sun et al., 2013), the association between peripheral arterial stiffness and UA was conducted in the MetS independent pattern. The underlying mechanisms for the interesting observation may be caused by other confounding atherogenic risk factors such as hyperglycemia and hypertension which consists of MetS. These results further indicated the predictive value of plasma UA for peripheral arterial stiffness in healthy populations with great power. Thus, in the non-MetS subjects with higher baseline UA level should be given more attention on the risk for arterial stiffness than in the MetS ones. 4.5 Limitations The participants in this study were recruited from two districts in Beijing instead of the random sampling from all over the country; the results may not represent the Chinese individuals from other areas. The unavoidable limitation is that a total of 181 subjects (10.7%) were lost to follow-up which may induce bias to the conclusions. 5 Conclusions Given a higher baseline level of UA in normal range, UA is closely related with peripheral arterial stiffness and it is an independent predictor for peripheral arterial stiffness suggesting that the plasma UA level may be used for stratification and management of risk factors for arterial stiffness. Furthermore, baseline UA predicts peripheral arterial stiffness in a MetS independent pattern. Conflict of interest statement The authors declare that there is no conflict of interest. Authors' contributions Ping Ye and Xiao–Han Ding participated the design of this research. Xiao–Han Ding and Xiaona Wang also have drafted the manuscript and performed the statistical analyses. Ping Ye and Xiao–Han Ding have reviewed and revised this manuscript critically for important intellectual content. Xiaona Wang, Ruihua Cao, Xu Yang, Wenkai Xiao, Yun Zhang, Yongyi Bai and Hongmei Wu finished Clinical data collection and the measurements of BP, HR and the biomarker. Xu Yang and Wenkai Xiao carried out the arterial stiffness measurements. Acknowledgements This research is supported by the grant from the National Nature Science Foundation of China (81270941), the Key National Basic Research Program of China (2012CB517503, 2013CB530804) to Dr. Ye Ping. We are also grateful to all study participants for their participation in the study. We thank colleagues at the Department of Laboratory Medicine, the PLA General Hospital for help with biochemical measurements. Peer review under responsibility of King Saud University. Table 1 Characteristics of the subjects categorized by Uric acid levels at baseline. Variable Overall Quartile 1 Quartile 2 Quartile 3 Quartile 4 ⩽238.95 239–284.60 284.61–341.85 ⩾341.90 No. of subjects 1447 370 352 372 353 Age (years) 61.40 ± 11.4 57.13 ± 10.87 61.64 ± 11.59* 61.68 ± 9.69* 66.16 ± 8.73* Women [n (%)] 868(59.98) 310(83.78) 252(71.59)* 223(59.94) * 83(23.51) * Current smoking [n (%)] 380(26.26) 81(21.89) 55(15.62) ** 96(25.80) 148(42.92) * Current alcohol use [n (%)] 274(18.93) 63(17.02) 49(13.92) 75(20.16) 87(24.64) ** BMI(kg/m2) 25.41 ± 3.32 24.72 ± 3.52 26.41 ± 3.31* 25.68 ± 2.93* 25.37 ± 3.43* TG (mmol/l) 1.90 ± 1.24 1.53 ± 0.93 2.23 ± 1.58* 1.87 ± 1.17* 1.88 ± 1.07 TC (mmol/l) 5.03 ± 0.93 4.88 ± 0.93 4.94 ± 0.96 5.06 ± 0.88** 4.91 ± 0.99 HDL-C (mmol/l) 1.38 ± 0.36 1.51 ± 0.42 1.25 ± 0.33* 1.34 ± 0.31* 1.36 ± 0.41 LDL-C (mmol/l) 2.91 ± 0.71 2.83 ± 0.69 2.89 ± 0.74 3.02 ± 0.69* 2.89 ± 0.72 SBP (mmHg) 128.74 ± 17.71 125.93 ± 17.32 133.17 ± 18.86* 129.58 ± 16.34** 136.79 ± 19.65* DBP (mmHg) 76.92 ± 10.23 75.37 ± 9.93 78.80 ± 10.51* 76.89 ± 9.98 76.93 ± 11.34 FBG (mmol/L) 5.39 ± 1.65 5.69 ± 2.13 5.38 ± 1.29 5.18 ± 1.09* 5.73 ± 1.79** Cr (μmoI/L) 66.14 ± 18.16 57.24 ± 13.22 76.57 ± 16.69* 67.49 ± 14.94* 71.16 ± 19.70* cr-PWV (m/s) 9.38 ± 1.47 9.11 ± 1.41 9.71 ± 1.57* 9.40 ± 1.44** 9.72 ± 1.61* Notes: Characteristics are reported as percentages for categorical variables and means (±SD) or medians (with interquartile range) for continuous variables. The study participants were divided into four groups based on the baseline levels of the quartile of Uric acid (⩽238.95, 239–284.60, 284.61–341.85, ⩾341.90 mmol/L). Categorical variables are presented as counts (percentages). The values outside the parentheses are the number of subjects, and the values inside the parentheses are prevalence. The quartile 1 level of Uric acid was used as the reference and quartiles 2, 3, 4 evaluated vs. quartile 1. * <0.05 vs. Quartile 1, ** <0.01 vs. Quartile 1. Table 2 Univariate and stepwise multiple linear regression analyses at baseline. Pearson correlation Multiple linear correlation r P β 95%CI P cr-PWV UA# 0.200 <0.001** 0.284 −0.061–0.629 0.107 Age −0.027 0.337 0.049 0.037–0.062 <0.001** SBP 0.178 <0.001** 0.003 0.003–0.009 0.271 DBP 0.241 <0.001** 0.020 0.010–0.031 <0.001** LDL-C 0.002 0.942 0.003 −0.234–0.241 0.980 HDL-C −0.091 0.001** 0.207 −0.091–0.505 0.173 Cr 0.201 <0.001** 0.058 0.048–0.068 <0.001** TC 0.028 0.321 0.022 −0.175–0.220 0.824 TG 0.102 <0.001** 0.201 0.010–0.393 0.039* # Natural logarithm transformed. * <0.05; ** <0.01. Table 3 Univariate and multiple linear regression analysis of baseline parameters and follow-up cr-PWV. Pearson correlation Multiple linear correlation r P β 95%CI P cr-PWV UA# 0.145 <0.001** 0.493 0.106–0.880 0.013* Age 0.163 <0.001** 0.000 −0.014–0.014 0.968 SBP 0.108 <0.001** 0.005 −0.002–0.012 0.136 DBP 0.205 <0.001** 0.016 0.004–0.027 0.009** LDL-C 0.028 0.314 0.083 −0.183–0.349 0.541 HDL-C −0.061 0.028 0.390 0.056–0.724 0.022* Cr 0.132 <0.001** 0.031 0.020–0.042 <0.001** TC −0.036 0.202 −0.197 −0.419–0.024 0.081 TG 0.085 0.002** 0.312 0.097–0.527 0.004** # Natural logarithm transformed. * <0.05; ** <0.01. Table 4 Logistic regression analysis for baseline levels of uric acid and follow-up cf-PWV. Quartile 2 vs. Quartile 1 239–284.60 vs ⩽238.95 Quartile 3 vs. Quartile 1 284.61–341.85 vs ⩽238.95 Quartile 4 vs. Quartile 1 ⩾341.9 vs. ⩽238.95 OR (95% CI) p OR (95% CI) p OR (95% CI) p cr-PWV Unadjusted 0.645(0.264–1.579) 0.337 1.390(0.980–1.970) 0.065 1.260(1.000–1.588) 0.050* Model 1 0.534(0.207–1.379) 0.195 1.515(1.058–2.171) 0.023* 1.322(1.045–1.672) 0.020* Model 2 0.453(0.005–1.241) 0.123 1.781(1.156–2.744) 0.009** 1.259(0.942–1.681) 0.019* Notes: Data were presented as ORs (per SD increase in uric acid level) and corresponding 95% CIs. In the logistic regression model, cr-PWV (PWV ⩾ 8.76m/s vs. PWV < 8.76 m/s) were treated as the dependent variable. Model 1: adjusted for age, gender; Model 2: adjusted for age, gender, DM, current smoking, MetS, DBP, SBP and levels of TC, TG, HDL-C, LDL-C and Cr. * : p < 0.05. Table 5 Subgroups analyses by linear regressions in baseline parameters and follow-up arterial stiffness. MetS Non-MetS Pearson correlation Multiple linear correlation Pearson correlation Multiple linear correlation r P β 95%CI P r P β 95%CI P cr-PWV UA# 0.059 0.274 0.271 −0.394–0.935 0.424 0.190 <0.001** 0.561 0.099–1.024 0.017* Age −0.215 <0.001** 0.002 −0.025–0.029 0.869 −0.150 <0.001** 0.002 −0.014–0.018 0.772 SBP 0.112 0.025* 0.007 −0.003–0.017 0.170 0.110 0.001** 0.007 −0.002–0.016 0.133 DBP 0.238 <0.001** 0.016 −0.002–0.034 0.074 0.208 <0.001** 0.015 0.000–0.030 0.045 LDL-C −0.060 0.237 0.478 0.009–0.947 0.046* −0.067 0.040* −0.095 −0.421–0.232 0.570 HDL-C 0.027 0.591 1.160 0.418–1.901 0.002** −0.049 0.128 0.227 −0.147–0.600 0.235 Cr 0.057 0.261 0.029 0.009–0.049 0.005** 0.157 <0.001** 0.032 0.019–0.046 <0.001** TC −0.058 0.249 −0.590 −1.005 to −0.174 0.006** −0.027 0.409 −0.071 −0.336–0.195 0.601 TG 0.055 0.277 0.581 0.134–1.027 0.011* 0.114 <0.001** 0.369 0.108–0.629 <0.001* # Natural logarithm transformed. * <0.05; ** <0.01. Table 6 Logistic regressions for cr-PWV in the subgroups. MetS Non-MetS Quartile 4 vs. Quartile 1 Quartile 4 vs. Quartile 1 OR (95%CI) p OR (95%CI) p cr-PWV Unadjusted 1.441(0.637–3.259) 0.380 0.909(0.633–1.305) 0.604 Model1 1.370(0.491–3.828) 0.548 1.117(0.775–1.653) 0.581 Model2 1.252(0.469–3.345) 0.654 1.034(0.661–1.619) 0.043 In the logistic regression model, cr-PWV (PWV ⩾ 8.76 m/s vs. PWV < 8.76 m/s) were treated as the dependent variable. Model 1: adjusted for age, gender; Model 2: adjusted for age, gender, DM, current smoking, DBP, SBP and levels of TC, TG, HDL-C, LDL-C and Cr. *: <0.05; **: <0.01. ==== Refs References Alderman M.H. Podagra, uric acid, and cardiovascular disease Circulation 116 2007 880 883 17709648 Bae J.S. Shin D.H. Park P.S. Choi B.Y. Kim M.K. Shin M.H. Lee Y.H. Chun B.Y. Kim S.K. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30046-310.1016/j.sjbs.2017.01.037Original ArticleStudy on specificity of colon carcinoma-associated serum markers and establishment of SVM prediction model Li Lu Ma Xuhui guanyichunchun@tom.com⁎Department of Oncology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China⁎ Corresponding author. guanyichunchun@tom.com26 1 2017 3 2017 26 1 2017 24 3 644 648 27 10 2016 28 12 2016 7 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).We aimed to evaluate the specificity of 12 tumor markers related to colon carcinoma and identify the most sensitive index. Logistic regression and Bhattacharyya distance were used to evaluate the index. Then, different index combinations were used to establish a support vector machine (SVM) diagnosis model of malignant colon carcinoma. The accuracy of the model was checked. High accuracy was assumed to indicate the high specificity of the index. Through Logistic regression, three indexes, CEA, HSP60 and CA199, were screened out. Using Bhattacharyya distance, four indexes with the largest Bhattacharyya distance were screened out, including CEA, NSE, AFP, and CA724. The specificity of the combination of the above six indexes was higher than that of other combinations, so did the accuracy of the established SVM identification model. Using Logistic regression and Bhattacharyya distance for detection and establishing an SVM model based on different serum marker combinations can increase diagnostic accuracy, providing a theoretical basis for application of mathematical models in cancer diagnosis. Keywords Colon carcinomaTumor markerLogistic regressionSpecificityBhattacharyya distanceSupport vector machine ==== Body 1 Introduction Colon carcinoma is a common type of malignant tumor of the alimentary system. In recent years, as the daily diet of many individuals has changed, the incidence and mortality associated with colon carcinoma have increased worldwide. In America, the incidence of colon carcinoma has increased dramatically, making this cancer type the third highest among common malignant tumors (Levin et al., 2008). Since the onset of colon carcinoma is insidious with ambiguous symptoms, the opportunity for early treatment is often missed, and most patients have been in middle or late stages when they are diagnosed (Onouchi et al., 2008). Therefore, the early diagnosis of colon carcinoma is particularly important for the management of this disease. There are several diagnostic methods for detection of colorectal cancer. Firstly, fecal occult blood tests (FOBTs), a common method used to detect colon carcinoma, can be used to identify fecal occult blood which is one of the symptoms of early-stage colon carcinoma. Secondly, endoscopy performed generally by a fibrocolonoscope or electronic colonoscope, is the most effective means to identify and diagnose colon carcinoma and is also the primary method for early-stage diagnosis with high accuracy. This method can be used to directly observe colonic lesions and perform qualitative biopsy (Young and Cole, 2007). Thirdly, tumor marker test is common method to diagnose all tumors. To date, carcinoembryonic antigen (CEA) is broadly used as a cancer marker in the clinical setting. Additionally, some carbohydrate antigens are evaluated as indexes of early-stage colon carcinoma; these antigens include CA199, CA242, and CA50. These indexes alone or their combinations are helpful for the early diagnosis of colon carcinoma in the clinical setting. Fourthly, gene-based diagnosis is also adopted for detection of colon carcinoma which is a multigenic disease involving several carcinogenic steps. The occurrence and development of colon carcinoma involve changes in multiple cancer-associated genes. Mutations in genes such as APC, KRAS, p53, and DCC can occur during the process of carcinogenesis and metastasis (Oving and Clevers, 2002). Finally, enzymes such as telomerase (TLMA) (Hauguel and Bunz, 2003) and cyclooxygenase 2 can be used as markers of colon carcinoma. Serum tumor markers usually refer to the substances in blood produced and released by tumor tissues. Analysis of tumor markers has been broadly applied in the clinical setting; however, this method has several limitations (Kawamura, 1996). The optimal serum tumor markers are sensitive and specific. However, among dozens of serum tumor markers currently used for detection of colon carcinoma, most are not sensitive or specific (Ocin et al., 1997). Thus, analysis of the sensitivity and specificity of tumor markers is clinically meaningful. In this report, we present a combined mathematical and bioinformatics analysis of the specificity of a few common tumor markers. 2 Materials and methods 2.1 General data Group with Colon Carcinoma: A total of 100 patients who visited Affiliated Cancer Hospital of Zhengzhou University from January 2013 to December 2013 and underwent surgery for colon carcinoma, were enrolled in this study, including 56 men and 44 women (average age: 59.0 years, range: 25–82 years),. According to the World Health Organization (WHO) standards on pathological types and degrees of differentiation, there were 72 cases of colorectal tubular adenocarcinoma, 17 cases of mucinous adenocarcinoma, and 11 cases of papillary-tubular adenocarcinoma; and there were 12 cases exhibiting poorly differentiated tumors, 79 cases exhibiting moderately differentiated tumors, and nine cases exhibiting well-differentiated tumors. All patients were confirmed by operation and pathology, and imaging and surgical exploration demonstrated that no patients showed metastasis to other tissues or organs. Control Group with Benign Tumors: Fifty patients who were admitted to Affiliated Cancer Hospital of Zhengzhou University within the same time period, including 21 men and 29 women (average age: 52.5 years, range: 32–80 years) were also enrolled in this study. There were 20 cases of colitis, 16 cases of polyposis coli, nine cases of colorectal tubular adenoma, and five cases of rectal-villous-papillary epithelioma. All diagnoses were proven by clinical analysis, endoscopy, and pathological examination. All patients agreed to participate in the study and provided written informed consent. 2.2 Clinical examination of tumor markers All patients were phlebotomized after fasting, and 2 mL isolated serum was cryopreserved at −80 °C, being prepared for centralized serum examination. The levels of CEA, neuron-specific enolase (NSE), heat-shock protein 60 (HSP60), CYFRA21-I, tissue plasminogen activator (TPA), alpha-feto protein (AFP), CA199, CA242, CA724, CA125, CA153, and UGT1A8 in the serum were measured by enzyme-linked immunosorbent assays and a COBAS 6000 automatic electrochemiluminescence immunoassay analyzer (Roche, Switzerland). 2.3 Index screening by Logistic regression Taking 12 indexes as covariate and pathological diagnosis results as dependent variable, Logistic regression analysis was used to screen indexes for benign and malignant tumor differentiation. 2.4 Screening indexes using Bhattacharyya distance Bhattacharyya distance was used to sequence and screen the indexes. Bhattacharyya distances show the upper bounds of the minimum error rate of Bayes in sample normal distributions. This method is linked to error rate, and it can theoretically gain the advantageous features of classifications but hardly obtain analytic solutions. For selection of features, multidimensional and low-dimensional data were both feasible. The definition of the Bhattacharyya distance of each index between colon carcinoma samples and normal samples is shown in Eq. (1) (Xuan et al., 2006). Larger Bhattacharyya distances were associated with better classified effects. In this formula, μi+ and σi+ are the mean and variance of colon carcinoma samples, respectively, and μi− and σi− are the mean and variance of the sample in the control group, respectively. In this study, the calculations for the Bhattacharyya distances were carried out using MATLAB. 2.5 Accuracy validation by SVM The specificity of indexes screened by Bhattacharyya distances was validated using SVMs, and the establishment, training, and validation of SVM models were all implemented based on the MATLAB tools program (Chang and Lin, 2011). First, 150 patients were normalized. The malignant regions of samples were marked as 1, and the benign regions were marked as 0. Eighty out of 100 patients with malignant tumors and 40 out of 50 patients with benign tumors were chosen, yielding a matrix of 120 × 12. The samples were input into the SVM for training. During the training, penalty parameter C and nuclear parameter γ were gradually optimized to achieve better results. The remaining 20 patients with malignant tumors and 10 patients with benign tumors were evaluated as the testing samples and input into the SVM network after training; the corresponding results (1 or 0) were obtained. The accuracy could be determined by comparison with the objective. 3 Results 3.1 Results of serum content analysis The results from tumor marker analyses for the 150 samples in the two groups are listed in Table 1. The 12 indexes were CEA, NSE, HSP60, CYFRA21-I, TPA, AFP, CA199, CA242, CA724, CA125, CA153, and UGT1A8. 3.2 Logistic regression analysis of each index Through Logistic regression analysis, CEA, CA199 and HSP60 were finally screened out, and the corresponding P values were 0.000, 0.000 and 0.008 respectively (Table 2). 3.3 Bhattacharyya distance of each index The Bhattacharyya distance of each index was calculated according to Eq. (1). The results are shown in Table 3. The Bhattacharyya distances of NSE, CEA, CA724, and AFP were larger, followed by those of CYFRA21-I and CA125. 3.4 Establishment of different diagnosis models by SVM Based on the 12 indexes and the six indexes screened out by Logistic regression and Bhattacharyya distance respectively, two SVM models were established. 30 test samples were input into SVM models to perform stimulation, and results are shown in Figure 1, Figure 2. Results show that the accuracy, sensitivity and specificity of the SVM model based on 12 indexes were 73.3%, 75.0% and 70.0%, and those of the SVM model based on six screened-out indexes were 90.0%, 85.0%, and 100.0%. 4 Discussion There are many methods for analyzing the specificity of certain features. The most common method is the measurement of distance. Statistical pattern recognition states that as the distance between two categories becomes larger, the classification becomes easier, and the error rate becomes lower. Distance measure is also called class separability criteria or scatter criteria. The study of class separability in statistical pattern recognition is relatively deep. Distance is an important concept in statistical pattern recognition and is often analyzed using Euclidean distances, Mahalanobis distances, and Bhattacharyya distances (Li, 2009). Euclidean distances and Mahalanobis distances are defined in terms of space, whereas Bhattacharyya distances are defined in terms of probability. With regard to selection of features, subsets that can result in the largest classifying distance and the lowest error rate should be selected. The Bhattacharyya distance is usually applied to the feature analysis of gene expression profiles and is applicable to both multidimensional and low-dimensional data. Serum tumor markers were screened by Logistic regression and Bhattacharyya distance, and six indexes were screened out (CEA, CA199, HSP60, NSE, CA724, AFP), which was consistent with a few previous studies. Several reports have demonstrated that CEA and CA724 are of high value in the diagnosis of colon cancer (Gebauer and Muller, 1997). A study by Wong (2006) found that the serum CEA content in patients with colon cancer increased markedly, with a positive rate of 32.26%. Some studies have also reported that when CEA is used to diagnose alimentary canal neoplasms, colorectal cancer exhibits the highest positive rate (Kim et al., 2003). Chen et al. (2008) demonstrated that the sensitivity of AFP, NSE, CEA, and CA125 is 55.8% when these indexes are combined to detect gastric cancer and colon cancer. In addition, AFP is now recognized as the tumor marker with the highest specificity in primary liver cancer. However, 30–40% of samples are negative for APF (Jia et al., 2012). A study by Dai (2008) showed that AFP is statistically meaningless in the diagnosis of colon cancer. Notably, however, nonspecific serum tumor markers are of certain clinical value for diagnosis, assessment of lesion range and degree, evaluation of surgical outcomes, and examination of metastasis and postoperative recurrence in patients with colon carcinoma (Yamamoto et al., 2001). From the results of our study, we concluded that when 12 indexes were combined to establish an SVM model, the accuracy was 73.33%, which was not optimal. However, when six indexes were combined to establish the SVM model, the accuracy was 90%. These data indicated that if too many indexes were used, the effective indexes could be influenced by the redundant indexes, thus decreasing the accuracy. A study by Fu et al. (2012) also found that when a single index was used to detect cancer, the difference between the indexes was not statistically significant. However, when five indexes, including CEA, CA199, CA724, and others were combined to detect cancer, the specificity and sensitivity were dramatically improved. Thus, these findings demonstrated that fewer indexes do not necessarily indicate better results but may lead to instability and unreliability of the results. (1) Bi=14(μi+-μi-)2(δi+2+δi-2)+12lnδi+2+δi-22δi+δi- 5 Conclusion High accuracy can’t be achieved using too many tumor marker indexes. The application of Bhattacharyya distance can effectively screen out indexes with high specificity, and the combination of specific indexes can be used to establish an SVM diagnosis model with high accuracy. However, it is not necessarily good to use fewer indexes. The number of indexes should be controlled properly to avoid occasionality of the results. Acknowledgments The authors thank all individuals who contributed to this study by providing advice and comments. And thanks Affiliated Cancer Hospital of Zhengzhou University for providing research materials and experimental base. And this study is supported by the Open cooperation project of Henan Province, China (Grant No. 132106000064) and by the Program of research in base and cutting-edge technologies of Henan Province, China (Grant No.152300410151). Peer review under responsibility of King Saud University. Figure 1 Analysis of the accuracy of the SVM model established using 12 tumor marker indexes. Figure 2 Analysis of the accuracy of the SVM model established using 4 tumor marker indexes. Table 1 Analysis of 12 serum markers in the two groups (means ± standard deviations). Indexes groups Colon cancer group Control group CEA 29.31 ± 8.31 (ng/mL) 4.28 ± 1.39 (ng/mL) NSE 11.76 ± 2.33 (ng/mL) 2.45 ± 1.01 (ng/mL) HSP60 587.29 ± 477.44 (pg/mL) 201.45 ± 120.97 (pg/mL) CYFRA21-I 8.75 ± 2.22 (ng/mL) 1.98 ± 1.04 (ng/mL) TPA 0.87 ± 1.25 (U/mL) 0.081 ± 0.54 (U/mL) AFP 17.68 ± 5.15 (ng/mL) 2.78 ± 0.98 (ng/mL) CA199 52.03 ± 38.34 (U/mL) 24.03 ± 12.22 (U/mL) CA242 18.55 ± 10.09 (U/mL) 5.06 ± 1.47 (U/mL) CA724 5.87 ± 1.25 (U/mL) 1.06 ± 0.77 (U/mL) CA125 43.05 ± 9.73 (U/mL) 10.31 ± 7.65 (U/mL) CA153 21.40 ± k8.63 (U/mL) 15.14 ± 2.83 (U/mL) UGT1A8 8.52 ± 2.03 (ng/mL) 34.6 ± 12.16 (ng/mL) Table 2 Variables in Logistic regression equation. B S.E Wals df Sig. Exp (B) Step 1a CA199 1.839 0.420 19.158 1 0.000 6.291 Constant 0.024 0.220 0.012 1 0.913 1.024 Step 2b CEA 1.806 0.450 16.138 1 0.000 6.086 CA199 1.922 0.447 18.508 1 0.000 6.834 Constant −0.640 0.283 5.102 1 0.024 0.527 Step 3c CEA 1.721 0.462 13.911 1 0.000 5.592 HSP60 1.252 0.472 7.044 1 0.008 3.496 CA199 1.920 0.459 17.502 1 0.000 6.823 Constant −0.996 0.325 9.371 1 0.002 0.369 a Variable(s) entered on step 1: CA199. b Variable(s) entered on step 2:CEA. c Variable(s) entered on step 3: HSP60. Table 3 Bhattacharyya distances of tumor markers in the two groups. Index CEA NSE HSP60 CYFRA21-I TPA AFP CA 199 CA242 CA724 CA125 CA153 UGT1A8 Bhattacharyya Distance 3.4608 4.2107 1.2176 2.7314 0.9357 3.2135 1.0877 1.7578 3.4332 2.4567 1.0739 2.3742 ==== Refs References Chang C.C. Lin C.J. LIBSVM: a library for support vector machines Acm. T. Intel. Syst. Technol. 2 2011 389 396 Chen, T., Su, X.X., Quan, S., 2008. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30047-510.1016/j.sjbs.2017.01.038Original ArticleStudy of lung cancer regulatory network that involves erbB4 and tumor marker gene Ma Xuhui Li Lu li_lu_01@sina.com⁎Tian Tongde Liu Huaimin Li Qiujian Gao Qilong Department of Oncology, Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China⁎ Corresponding author. li_lu_01@sina.com27 1 2017 3 2017 27 1 2017 24 3 649 657 27 10 2016 28 12 2016 7 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Our purpose is to screen out serum tumor markers closely correlated to the nature of solitary pulmonary nodule (SPN) and to draw a regulatory network containing genes correlated to lung cancer. Two hundred and sixty cases of SPN patients confirmed through pathological diagnosis were collected as subjects, factors closely correlated to the nature of SPN were screened out from eight tumor markers through Fisher discriminant method, and functional annotation and pathway analysis were conducted on erbB4 as well as its tumor marker genes by GO and KEGG databases. Four key tumor markers: CYFRA21-1, CA125, SCC-Ag and CA153 were successfully screened out and the first three proteins’ corresponding gene were KRT19, MUC16 and SERPINB3 while that of CA153 was not found. GO analysis on erbB4, KRT19, MUC16 and SERPINB3 showed that they covered three domains, cell components, molecular function and biological process; meanwhile, combined with KEGG database and based on signal pathway of erbB4, a regulatory network of lung cancer cells escaping from apoptosis was successfully made. This study indicates that serum tumor marker genes play an important role in the occurrence and development of lung cancer, besides, this study primarily discussed the molecular mechanism of these tumor markers in predicting tumor, which provides a basis for in-depth information about lung cancer. Keywords Lung cancerTumor markerErbB4Regulatory network ==== Body 1 Introduction Lung cancer, a malignant tumor with highest morbidity and mortality currently, tops the five most common tumors’ list among males and ranks the second place among females, and it is still the top of the four cancers threatening people’s life according to 2015 cancer statistics report in China (Chen et al., 2016). And it is mainly because of the high misdiagnosis rate and missed diagnosis rate for early lung cancer. Expression of the early lung cancer usually is solitary pulmonary nodule (SPN) (Siegel et al., 2013), therefore, accurate discrimination of benign and malignant SPN is an effective method to reduce the mortality of lung cancer. SPN is a roundlike solid lesion with a diameter no more than 3 cm. Data from American College of Chest Physicians (ACCP) show that among malignant SPN primary lung cancer accounts for 75%, and adenocarcinoma is the most common tumor followed by squamous cell carcinoma (Wahidi et al., 2007). For patients with early primary lung cancer, their five-year survival rate will be up to 80% if they can be diagnosed early (Vazquez et al., 2009, Varoli et al., 2008); due to difficult qualification of SPN, about 50% lung cancer patients, however, miss optimal therapeutic time leaving them a relatively low five-year survival rate. Therefore, it is the key to distinguishing benign SPN from malignant SPN for secondary prevention of lung cancer, which still is a diagnostic difficulty. CT imaging examination is the preferred approach for SPN identification, usually from nodule’s size, location, internal feature and surrounding environment. However, it’s not reliable to identify SPN only through imaging approach and laboratory diagnostic methods like tumor marker diagnosis is necessary. Tumor marker is chemical substance reflecting tumor’s existence and commonly used markers are carcino-embryonic antigen (CEA), carbohydrate antigen (CA) (like CA125, CA199 and CA153) and neuron-specific enolase (NSE). Reasonable usage of these markers will be helpful for early SPN diagnosis and treatment. Currently, however, there are few studies on inside molecular mechanism of these markers participating in lung cancer cell regulation. Our preliminary study analyzed the structural and functional changes of erbB4 before and after mutation (Chen and Zhao, 2016), and based on the original signal pathway of erbB4, tumor marker genes were added to the regulatory network in this study to uncover the internal molecular regulatory mechanism for prediction of tumor occurrence and development, which lays foundation for in-depth cancer suppressor research in the future. 2 Material and methods 2.1 Subjects In this study, continuous data of patients who went to Affiliated Cancer Hospital of Zhengzhou University between 2012 and 2014 were collected. And through analysis of their medical records, 260 cases of SPN patients were selected including 145 cases of malignant SPN (according to the pathological diagnosis there were 88 cases of adenocarcinoma, 32 cases of squamous cell carcinoma, eight cases of adenosquamous carcinoma, two cases of nonsmall-cell lung cancer, four cases of neuroendocrine carcinoma, two cases of mucoepidermoid carcinoma, four cases of mucoepidermoid, one case of carcinoma mucocellulare, one case of metastatic renal cell carcinoma, one case of metastasis breast cancer, one case of carcinoma sareomatodes and one case of unclassified lung cancer) and 115 cases of benign PSN (56 cases of inflammation, 13 cases of tuberculosis, 14 cases of inflammatory pseudotumor, 11 cases of mycotic infection, five cases of hamartoma, four cases of angeioma, three cases of pulmonary abscess, two cases of epithelial tumor, one case of bronchiectasia, one case of glioma peripheral of chest wall, one case of lymphadenoma, one case of secondary osteogenic sarcoma of lung, one case of coccus infection, malignant dyskaryosis and one case of fibrocartilage). All above patients were confirmed by CT imaging diagnosis and pathological diagnosis. 2.2 Content measurement of serum tumor markers In this study eight seismological indexes, including CEA, NSE, cytokeratin 19 fragment (CYFRA21-1), CA125, CA199, CA724, squamous cell carcinoma antigen (SCC-Ag) and CA153, in patients’ blood samples were detected. The detection of every index was in accordance with instruction on kit; CEA and CYFRA21-1 were detected by ELISA method; CA125, CA199, CA724, SCC-Ag and CA153 were detected by Roche E601 automatic immuno-analyzer and NSE was detected by radio immunoassay. 2.3 Methods 2.3.1 Fisher discriminant analysis Fisher discriminant analysis was put in 1930s by British statistician Fisher who first defined Fisher discriminant analysis and applied it to discriminant analysis of iris, and this method has been improved continuously so till now it is still considered as one of the best feature extraction. Its fundamental idea is to conduct linear project on sample data making their between-class scatter maximum and within-class scatter minimum. Fisher discriminant method in SPSS 23.0 software was used to screen serologic tumor markers, aiming to screen out factors correlated to SPN nature. 2.3.2 GO analysis of genes correlated to lung cancer Corresponding genes of tumor markers correlated to SPN nature were searched through literature search and AmiGo homepage (http://geneontology.org/) was visited to conduct Go analysis with screening condition being “Homo sapiens”. The screened out serum tumor markers and erbB4 were primarily analyzed and every gene was conducted functional annotation from cell components, molecular function and biological process, aiming to identify their functions and provide a basis for further researches. 2.3.3 KEGG analysis of genes correlated to lung cancer After visiting KEGG database homepage (http://www.kegg.jp/kegg/pathway.html), key words “lung cancer” and “erbB4” were input to search signal pathway; literature search was used to find key factors running by corresponding genes of tumor markers, and starting from these key factors and based on the signal pathway of erbB4, all signal pathways were linked together through using key nodes, and functions of every tumor marker were added as well. 3 Results 3.1 CT imaging maps and histopathological slice of different pathological type SPN patients Patients highly suspected as malignant SPN not only underwent CT examination but histopathological examination while the benign SPN patients only underwent histopathological examination. Figure 1, Figure 2 respectively are CT imaging map and histopathological slice of lung adenocarcinoma, squamous cell carcinoma and inflammatory pseudotumor. 3.2 Content measurement of serum tumor markers Measurement results of eight tumor markers for totally 260 patients in malignant SPN group and benign SPN group are listed in Table 1 and the eight tumor markers were CEA, NSE, CYFRA21-1, CA125, CA199, CA724, SCC-Ag and CA153. Seen from the table, except CA724, all levels of other seven serum tumor markers in malignant SPN group were higher than that in benign SPN group (P < 0.05). 3.3 Screening results by Fisher discriminant analysis According to clinical test results, CEA, NSE, CYFRA21-1, CA125, CA199, CA724, SCC-Ag and CA153 were regarded as variables included in discriminant analysis model. Wilks’ lambda method was employed to conduct a stepwise discriminative analysis for these variables with F value being discriminative statistics standard and totally four predictors with statistical significance were screened out which were CYFRA21-1, CA125, SCC-Ag and CA153 (see Table 2, Table 3). 3.4 GO and KEGG analysis of genes correlated to lung cancer CYFRA21-1, CA125, CA153 and SCC-Ag were searched through databases like NCBI Database, Wanfang Database, VIP Database and CNKI Database, and so on. CYFRA21-1, a soluble fragment of cytokeratin 19 which is the expression product of KRT19 gene, is mainly applied for detection of tumor marker of non-small-cell lung cancer (Liu et al., 2015). CA125 and CA153 both are carbohydrate antigens. CA125 whose gene is MUC16 is the specific markers of ovarian cancer diagnosis and commonly used in lung cancer diagnosis (Homma et al., 2004); CA153 is the specific marker of breast cancer diagnosis but its genetic expression is still unclear based on current reports. SCC-Ag is squamous cell carcinoma antigen which is significant to non-small-cell lung cancer diagnosis, and two genes producing SCCA have been identified nowadays which are SCCA1 and SCCA2 and their homology in terms of animo acid level was 92% (Yan et al., 2011), and SCCA is the expression of SERPINB3 gene. ErbB4 also called HER4 gene is the fourth oncogene encoding human epidermal growth factor receptor and it was reported to be overexpressed in lung cancer tissues and to be correlative to lymph node metastasis, TNM staging and postoperative survival rate (Starr et al., 2006). KRT19, MUC16, SERPINB3 and erbB4 were input in the homepage of Amigo for their annotation in human. The annotation of KRT19 gene is shown in Table 4 which indicates that KRT19 played an important role in cell component, molecular function and biological process and its protein products distributed in the central filament, the glycoprotein complex and the cell membrane, etc.; erbB4 has function of protein binding and muscle composition and cytoskeleton and is involved in many biological processes like Notch signaling pathway, embryonic cell differentiation, embryonic development and estrogen response. GO analysis results of MUC16 are listed in Table 5, and MUC16 gene mainly participates in cell component and biological process whose protein products can be seen in cell membrane and Golgi apparatus cavity and it plays a crucial role in multiple biological processes including cell adhesion, protein post-translational modifications and protein metabolism in cells, etc. The annotation of SERPINB3 gene is shown in Table 6, similarly, SERPINB3 is involved in cell components, molecular function and biological process. Specifically, the cell components contain the cytoplasm, the nucleus and cytoplasmic vesicle, etc.; molecular function covers activities of virus receptor, binding of protease, activity of serine-type and cysteine-type inhibitor; the biological processes include positive regulation of cell proliferation, regulation of endopeptidase activity, regulation of cell migration and negative regulation of proteolysis; what’s more, it participates in autocrine and paracrine process and penetration of virus into a host cell. Additionally, GO analysis outcomes of erbB4 gene are listed in Table 7 showing that erbB4 gene is involved in multiple functions, and its cell component includes nucleus, mitochondria and plasma membrane; its molecular functions contain protein tyrosine kinase activity, activation of protein tyrosine kinase receptor signal, epidermal growth factor receptor and ATP etc.; besides, it also participates in cell proliferation, ras protein signaling transduction, mitogen-activated protein kinases signaling pathway, transmembrane receptor protein tyrosine kinase signaling pathway, endothelial growth factor receptor signaling pathway and insulin receptor signaling pathway. Inputting the keyword “erbB4” into KEGG signaling pathway database, explicit signaling pathway it participated in were found, and erbB4 was turned out to be involved in the signaling pathway of lung cancer cell escaping from apoptosis through PI3K → PKB/AKt → MDM2 → p53 while nothing about MUC16, SERPINB3 and KRT19 gene was found. And through gene annotation and literature search, these three genes were added to the signaling pathway of lung cancer cell escaping from apoptosis (see Fig. 4). According to the gene annotation, KRT19 is able to act on Notch signaling pathway. Besides, it’s reported (Gong, 2013) that MUC16 can enhance transportation of β-catenin which is the key effector molecule in Wnt signaling pathway from cytoplasm to nucleus, and it can activate Wnt signaling pathway and stimulate and enhance the expression of downstream oncogenes through interaction with β-catenin. As a tumor suppressor gene, the activation of p53 can cause cell apoptosis while its deactivation can help tumor’s development. Moreover, study by Molès found that p53 can inhibit KRT19’s expression (Molès et al., 1994). And SERPINB3 can act on β-catenin and TGFβ, which has influence on the growth and development of tumor (Turato et al., 2014). 4 Discussion SNP is the unique roundlike solitary lesion whose diameter is less than 30 mm, and 150,000 cases of SNP are detected globally every year, among which 10–70% is malignant which is possible to develop into lung cancer anytime. Serological tumor marker is significant for determination of begin and malignant SPN, and the commonly used markers are CYFRA21-1, CA125, CA153 and SCC-Ag. CYFRA21-1 known as cytokeratin 19-fragments is a soluble polypeptide and its principle of being a tumor-detection marker is that when apoptosis of tumor cells happens, the increasing of cytokeratins stimulated by activated protease will elevate patients’ CYFRA21-1 level in serum (Thomas et al., 2015). Currently, CYFRA21-1 is a top priority in detecting non-small-cell lung cancer. As for CA125, it not only is an ovarian cancer associated antigen but also has a high expression in serum of lung cancer patient, especially in lung adenocarcinoma patient. CA153 also is an important specific marker that can be used in detection of lung cancer (Ghosh et al., 2013). SCC-Ag, a squamous cell carcinoma antigen, exists in cytoplasm of squamous cell carcinoma of the uterus, lungs, cervix and head and neck, and is especially rich in non-keratinizing cancer cells. Chu et al. (2011) studied 805 patients with lung cancer and patients with benign lung diseases, even though the area under ROC of individual detection on lung cancer isn’t ideal, 37.3% patients with early stage lung cancer can be detected correctly through detection of SCC combined with other tumor markers, showing potential value of SCC in clinic application. In this study four serological tumor markers which are significant for malignant and benign SPN detection were screened out through clinical cases, and the four markers are CYFRA21-1, CA125, CA153 and SCC-Ag. Then the corresponding genes of CYFRA21-1, CA125 and SCC-Ag respectively were respectively identified as KRT19, MUC1 and SERPINB3 through literature search except CA153. Our previous research mainly studied the structures and function of erbB4 before and after its mutation and found that erbB4 expressed high in non-small-cell lung cancer (Kurppa et al., 2016) and that it’s closely correlated to the metastasis of cancer cells and to patients’ prognosis, which indicates that erbB4 is a possible candidate of targeted molecular therapy (Starr et al., 2006). Therefore, this study conducted GO functional analysis and KEGG pathway analysis on KRT19, MUC16, SERPINB3 and erbB4, aiming to deeply explore the growth and development mechanism of lung cancer. According to the GO analysis of KRT19, MUC16, SERPINB3 and erbB4, these four genes all cover three domains, cell component, molecular function and biological progress. For instance, KRT19 takes part in Notch signaling pathway, MUC16 is involved in protein translational modifications, SERPINB3 participates in cell proliferation and erbB4 takes part in Ras protein signaling transduction, mitogen-activated protein kinase (MAPK) signaling cascades and transmembrane receptor tyrosine kinase signaling pathway, and so on. At present, there are more full-fledged studies on erbB4 and it is known that erbB4 is involved in multiple signaling pathways and has explicit signaling pathway in KEGG pathway data base and it can help tumor cell escape from apoptosis through PI3K-Akt signaling pathway. Based on these current analyses, this study added KRT19, MUC16 and SERPINB3 to KEGG signaling pathway database through gene annotation and literature search and drew a signaling pathway on lung cancer cell escaping from apoptosis. As mentioned, KRT19 is involved in Notch signaling pathway and the cell surface receptor coded by the key gene Notch in this pathway plays an important role in the development of many biological cells. And it was reported that Notch signal has influence on cell proliferation, formation of cell borders, cell apoptosis and multipotent progenitor cell specialization, etc. Notch signaling pathway interacts with other key pathways, having significant influences on tumor’s growth and development (Iso et al., 2003). Moreover, it’s was reported that MUC16 and SERPINB3 both can act on the key factor β-catenin of Wnt signaling pathway (Gong, 2013, Molès et al., 1994, Turato et al., 2014). Wnt signaling pathway is an evolutionarily conserved signaling transduction pathway which also is important to control embryological development, and it contacts with other pathways through complicated networks. Aberrant activation of Wnt signaling pathway plays a crucial role in cell canceration, tumor growth and tumor invasion, thus any change of Wnt gene itself or its any member may induce tumor. In Wnt pathway, β-catenin plays an important role in it and was considered as the key hub, therefore, if it moves from cytoplasm to nucleus, it means that the signaling pathway has been activated and begins to perform its functions. Nowadays, it’s become a hot issue to regard Wnt signaling pathway as the target of gene therapy of tumor in scientific filed (Tai et al., 2015). On the other hand, SERPINB3 was reported to be able to act on TGFβ (Turato et al., 2014) which is a kind of cytokine with multiple biologic activities and it is involved in cell proliferation, differentiation and apoptosis. There has been a study indicating that TGFβ has very complicated influence on tumor. It can inhibit or improve tumor’s development and metastasis by tumor microenvironment, for instance, during early stage of tumor TGFβ can inhibit cell proliferation and induce apoptosis, but if the tumor is in developing stage, TGFβ can promote tumor’s development and metastasis through multiple mechanisms (Brian and Moses, 2006). p53 is a tumor suppressor gene (Meek, 2015) and was thought to be able to inhibit KRT19 expression in study of Molès et al. (1994). Mutant p53 gene can cause its original function loss which means it cannot regulate cell proliferation, growth or DNA repair thus it becomes an oncogene. Signaling pathway mediated by P53 gene plays a crucial role in regulating normal cell life activities and it has complicated connection with other signaling pathway inside cell, and p53 has been the most relevant gene to human tumors till now. 5 Conclusions In this study, four serological tumor markers, CYFRA21-1, CA125, CA153 and SCC-Ag, related to SPN nature were screened out by Fisher discriminant method, and their corresponding genes were identified through literature search. And then correlative genes to lung cancer taking part in the regulatory pathway of lung cancer’s growth and development were analyzed by GO and KEGG analysis. Based on signaling pathway of erbB4, several tumor marker genes were supplied to draw a regulatory network of lung cancer cell escaping from apoptosis, which lays a foundation for supplement and improvement of lung cancer signaling pathway and curves out the way for cancer treatment targeting with oncogenes. Acknowledgments I thank all authors who have contributed to this paper for advice and comments and thank Affiliated Cancer Hospital of Zhengzhou University for providing research materials and experimental base. And this study is supported by the open cooperation project of Henan Province, China (Grant No. 132106000064) and by the Program of research in base and cutting-edge technologies of Henan Province, China (Grant No. 152300410151). Peer review under responsibility of King Saud University. Figure 1 CT imaging map and histopathological slice of lung adenocarcinoma. Figure 2 CT imaging map and histopathological slice of lung squamous cell carcinoma. Figure 3 CT imaging map and histopathological slice of inflammatory pseudotumor. Figure 4 Regulatory network of lung cancer cell escaping from apoptosis. Table 1 Measurement results of eight serum tumor markers of SPN patients. Serum tumor markers Malignant SPN group Benign SPN group t P CEA (ng/mL) 14.61 ± 2.58* 3.23 ± 0.87 4.18 0.000 NSE (ng/mL) 15.01 ± 0.64* 12.03 ± 0.52 3.61 0.000 CYFRA21-1 (ng/mL) 7.25 ± 0.82* 2.18 ± 0.12 6.118 0.000 SCC-Ag (ng/mL) 2.04 ± 0.17* 1.06 ± 0.06 5.49 0.000 CA125 (U/mL) 49.65 ± 5.69* 19.65 ± 2.69 4.76 0.000 CA199 (U/mL) 26.34 ± 3.70* 16.86 ± 1.98 2.26 0.025 CA724 (U/mL) 6.27 ± 0.84 5.43 ± 2.59 0.34 0.736 CA153 (U/mL) 18.27 ± 1.34* 10.58 ± 0.79 4.95 0.000 Note: * represents the difference is statistically significant compared to the benign group. Table 2 Serological variables input /deleted in Fisher discriminant methoda,b,c,d. Step Entered Wilks’ lambda Statistic df1 df2 df3 Exact F Statistic df1 df2 P 1 CYFRA21-1 .896 1 1 258.000 29.897 1 258.000 0.000 2 SCC-Ag .811 2 1 258.000 30.039 2 257.000 0.000 3 CA153 .753 3 1 258.000 27.966 3 256.000 0.000 4 CA125 .726 4 1 258.000 24.068 4 255.000 0.000 Note: At each step, the variable that minimizes the overall Wilks’ lambda is entered. a Maximum number of steps is 16. b Minimum partial F to enter is 3.84. c Maximum partial F to remove is 2.71. d F level, tolerance, or VIN is insufficient for further computation. Table 3 Serological variables included in Fisher discriminant method. Step Tolerance F to remove Wilks’ lambda 1 CYFRA21-1 1.000 29.897 2 CYFRA21-1 0.991 32.331 0.912 SCC-Ag 0.991 27.151 0.896 3 CYFRA21-1 0.989 32.096 0.848 SCC-Ag 0.991 25.358 0.828 CA153 0.998 19.497 0.811 4 CYFRA21-1 0.988 29.358 0.810 SCC-Ag 0.983 27.157 0.803 CA153 0.931 10.973 0.757 CA125 0.925 9.565 0.753 Table 4 Results of GO analysis for KRT19 gene. Gene Gene/product name Direct annotation Ontology GO number KRT19 Keratin, type I cytoskeletal 19 (CK19) Intermediate filament Cellular_component 0005882 Dystrophin-associated glycoprotein complex Cellular_component 0016010 Sarcolemma Cellular_component 0042383 Z disk Cellular_component 0030018 Terminal web Cellular_component 1990357 Plasma membrane Cellular_component 0005886 Costamere Cellular_component 0043043 Extracellular exosome Cellular_component 0070062 Cell periphery Cellular_component 0071944 Protein complex binding Molecular_function 0032403 Structural molecule activity Molecular_function 0005198 Structural constituent of cytoskeleton Molecular_function 0005200 Protein binding Molecular_function 0005515 Structural constituent of muscle Molecular_function 0008307 Notch signaling pathway Biological_process 0007219 Viral process Biological_process 0016032 Cell differentiation involved in embryonic placenta development Biological_process 0060706 Response to estrogen Biological_process 0043627 Sarcomere organization Biological_process 0045214 Table 5 Results of GO analysis for MUC16 gene. Gene Gene/product name Direct annotation Ontology GO number MUC16 Mucin-16 (CA125) Integral component of membrane Cellular_component 0016021 Extracellular space Cellular_component 0005615 Plasma membrane Cellular_component 0005886 External side of plasma membrane Cellular_component 0009897 Golgi lumen Cellular_component 0005796 Extrinsic component of membrane Cellular_component 0019898 Vesicle Cellular_component 0031982 Extracellular exosome Cellular_component 0070062 Protein O-linked glycosylation Biological_process 0006493 Cell adhesion Biological_process 0007155 O-glycan processing Biological_process 0016266 Post-translation protein modification Biological_process 0043687 Cellular protein metabolic process Biological_process 0044267 Table 6 Results of GO analysis for SERPINB3 gene. Gene Gene/product name Direct annotation Ontology GO number SERPINB3 SerpinB3 (SCCA) Extracellular space Cellular_component 0005615 Nucleus Cellular_component 0005634 Cytoplasm Cellular_component 0005737 Cytoplasmic vesicle Cellular_component 0031410 Vesicle Cellular_component 0031982 Extracellular exosome Cellular_component 0070062 Virus receptor activity Molecular_function 0001618 Protease binding Molecular_function 0002020 Serine-type endopeptidase inhibitor activity Molecular_function 0004867 Cysteine-type endopeptidase inhibitor activity Molecular_function 0004869 Positive regulation of cell proliferation Biological_process 0008284 Negative regulation of peptidase activity Biological_process 0010466 Positive regulation of epithelial to mesenchymal transition Biological_process 0010718 Positive regulation of endopeptidase activity Biological_process 0010950 Negative regulation of endopeptidase activity Biological_process 0010951 Positive regulation of cell migration Biological_process 0030335 autocrine signaling Biological_process 0035425 Paracrine signaling Biological_process 0038001 Negative regulation of catalytic activity Biological_process 0043086 Negative regulation of JUN kinase activity Biological_process 0043508 Negative regulation of proteolysis Biological_process 0045861 Viral entry into host cell Biological_process 0046718 Table 7 Results of GO analysis for erbB4 gene. Gene Gene/product name Direct annotation Ontology GO number erbB4 Receptor tyrosine-protein kinase erbB4 Extracellular region Cellular_component 0005576 Nucleus Cellular_component 0005634 Nucleoplasm Cellular_component 0005654 Mitochondrion Cellular_component 0005739 Mitochondrial matrix Cellular_component 0005759 Cytosol Cellular_component 0005829 Plasma membrane Cellular_component 0005886 Basolateral plasma membrane Cellular_component 0016323 Receptor complex Cellular_component 0043235 Integral component of membrane Cellular_component 0016021 Protein tyrosine kinase activity Molecular_function 0004713 Transmembrane receptor protein tyrosine kinase activity Molecular_function 0004714 Epidermal growth factor receptor binding Molecular_function 0005154 Protein binding Molecular_function 0005515 Protein homodimerization activity Molecular_function 0042803 Transcription regulatory region DNA binding Molecular_function 0044212 Receptor signaling protein tyrosine kinase activity Molecular_function 0004716 ATP binding Molecular_function 0005524 MAPK cascade Biological_process 0000165 Activation of MAPKK activity Biological_process 0000186 Neural crest cell migration Biological_process 0001755 Positive regulation of protein phosphorylation Biological_process 0001934 Signal transduction Biological_process 0007165 Transmembrane receptor protein tyrosine kinase signaling pathway Biological_process 0007169 Epidermal growth factor receptor signaling pathway Biological_process 0007173 Small GTPase mediated signal transduction Biological_process 0007264 Ras protein signal transduction Biological_process 0007265 Nervous system development Biological_process 0007399 Axon guidance Biological_process 0007411 Heart development Biological_process 0007507 Lactation Biological_process 0007595 Cell proliferation Biological_process 0008283 Positive regulation of cell proliferation Biological_process 0008284 Negative regulation of cell proliferation Biological_process 0008285 Insulin receptor signaling pathway Biological_process 0008286 Fibroblast growth factor receptor signaling pathway Biological_process 0008543 Embryonic pattern specification Biological_process 0009880 Cell migration Biological_process 0016477 Peptidyl-tyrosine phosphorylation Biological_process 0018108 Central nervous system morphogenesis Biological_process 0021551 Olfactory bulb interneuron differentiation Biological_process 0021889 Regulation of cell migration Biological_process 0030334 Fc-epsilon receptor signaling pathway Biological_process 0038095 Positive regulation of tyrosine phosphorylation of Stat5 protein Biological_process 0042523 Negative regulation of apoptotic process Biological_process 0043066 Positive regulation of phosphatidylinositol 3-kinase activity Biological_process 0043552 Mitochondrial fragmentation involved in apoptotic process Biological_process 0043653 Innate immune response Biological_process 0045087 Positive regulation of transcription DNA-templated Biological_process 0045893 Protein autophosphorylation Biological_process 0046777 Vascular endothelial growth factor receptor signaling pathway Biological_process 0048010 Neurotrophin TRK receptor signaling pathway Biological_process 0048011 Phosphatidylinositol-mediated signaling Biological_process 0048015 Positive regulation of cardiac muscle cell proliferation Biological_process 0060045 Mammary gland epithelial cell differentiation Biological_process 0060644 Mammary gland alveolus development Biological_process 0060749 Cardiac muscle tissue regeneration Biological_process 0061026 Positive regulation of ERK1 and ERK2 cascade Biological_process 0070374 Positive regulation of STAT protein import into nucleus Biological_process 2000366 Transcription, DNA-template Biological_process 0006351 Positive regulation of phosphatidylinositol 3-kinase signaling Biological_process 0014068 Cell fate commitment Biological_process 0045165 Positive regulation of protein localization to cell surface Biological_process 2000010 Negative regulation of neuron migration Biological_process 2001223 ==== Refs References Brian B. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30038-410.1016/j.sjbs.2017.01.029Original ArticleEstablishment of preliminary regulatory network of TRPV1 and related cytokines Zhang Jianhua petermails@zzu.edu.cna⁎Zhou Zheng bZhang Ning aJin Wenwen aRen Yafeng cChen Chuanliang chenchuanliang1@126.comd⁎a Medical Engineering Technology and Data Mining Institute of Zhengzhou University, No. 100 Science Ave., Gaoxin Dist., Zhengzhou 450001, Chinab Department of Respiration, The Second Affiliated Hospital of Zhengzhou University, No. 2 Jingba Rd., Zhengzhou 450014, Chinac Department of Chinese Internal Medicine, The First Affiliated Hospital of Zhengzhou University, No. 1 East Jianshe Rd., Zhengzhou 450052, Chinad Hospital Office, People’s Hospital of Zhengzhou University, No. 7 Weiwu Rd., Zhengzhou 450003, China⁎ Corresponding authors. petermails@zzu.edu.cnchenchuanliang1@126.com26 1 2017 3 2017 26 1 2017 24 3 582 588 27 10 2016 28 12 2016 7 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Our purpose was to investigate the regulatory mechanism of TRPV1 and related cytokines on children bronchial asthma. TRPV1 mRNA level and two SNP genotypes of children in case group and control group were detected by real-time quantitative PCR. Western blot and ELISA were used to measure the levels of cytokines like IgE, IL-2, etc. Their correlations were analyzed by Logistic regression and KEGG analysis. Moreover, tertiary structure of protein and miRNA binding sites were also predicted by online tools. Case group was obviously different from control group in TRPV1 mRNA level, the two SNP genotypes distribution and the related cytokines levels. Logistic regression analysis further demonstrated that TRPV1 mRNA level, EOS, IL-4 and IL-5 may be risk factors for children bronchial asthma. And based on that, the preliminary regulatory network of children bronchial asthma was drawn. What’s more, mutation of rs4790521 and rs4790522 in TRPV1 gene both induced its corresponding miRNA binding site’s change. The preliminary regulatory network of TRPV1 and related cytokines on children bronchial asthma established in this study provides certain theoretical basis for pathogenesis and treatment of children bronchial asthma. Keywords Children bronchial asthmaTRPV1PolymorphisCytokinesPreliminary regulatory networkmiRNAMolecular mechanism ==== Body 1 Introduction Bronchial asthma is a common chronic respiratory disease in childhood. According to the World Health Organization (WHO) statistics, morbidity and mortality of asthma (especially in children) in the United States, Australia, and many developed countries in European have been rising in recent years, and in China, the prevalence rate of children asthma also shows a rising trend (Gu et al., 2012). Asthma has caused serious impact on children, their families as well as social economy and has become a serious public health problem which has attracted great concern in the world (Da Silva et al., 2016). Capsaicin receptor (Caterina et al., 1997) was cloned by receptor activated by capsaicin, which is also known as vanilloid receptor (VR1). Capsaicin receptor has ion channel features and belongs to transient receptor potentia1 channel super family, and its English name is specified as transient receptorpotentia1 vanilloid 1 (TRPV1). TRPV1 is a member of cation channel superfamily, subfamily V, and it expresses widely in mammalian respiratory system (Lieu et al., 2012). Extensive researches suggested that TRPV1 plays a crucial role in children bronchial asthma. With deeper understanding of respiratory system inflammation mechanism by people recently, signaling regulation of TRPV1 gene on asthma has been a research hotspot (Geppetti et al., 2006, Guibert et al., 2011). Bronchial asthma is a chronic airway inflammatory disease with participation of multiple cells and cellular components. Moreover, there are many studies (Liu, 2014, Li et al., 2014) showing that cytokine detection was significant for children bronchial asthma to some extent. Signal transduction pathway of TRPV1 and related cytokines in the pathogenesis of children bronchial asthma, however, is still unclear currently. This study screened the correlated cytokines to children bronchial asthma through analysis of clinical data, and with KEGG database and retrospective analysis, this study also established a preliminary regulatory network of TRPV1 and related cytokines on children bronchial asthma, aiming to provide some theoretical basis for the pathogenesis and treatment of children bronchial asthma. 2 Material and methods 2.1 Subjects Two hundred outpatients and inpatients with children bronchial asthma (new cases or patients with atopy) at the People’s Hospital of Zhengzhou University from May 2012 to December 2015 were collected as case group, and 200 healthy children who came to the hospital for physical examination as control group. The general data of the two groups are listed in Table 1, and their age, gender and weight are similar, which means those materials are comparable. All included children in control group have neither family or personal history of allergic reaction, nor asthma history. And their guardians have agreed and signed the informed consent, besides, this study has been approved by the People’s Hospital of Zhengzhou University ethic committee. 2.2 Sample collection Before and after receiving treatment, 5 ml peripheral vein blood of children in a fasting state of the two groups was collected, from which 1 ml was mixed with EDTA for anticoagulation before being frozen in the refrigerator at −20 °C for total RNA extraction; and the rest of the blood was made into blood smear for serum preparation, then after one-hour quiescence at room temperature it was centrifuged for 10 min at 2000 rpm, and then the serum that remained was placed in refrigerator at −70 °C for the following detection. 2.3 TRPV1 mRNA level of subjects by real-time quantitative PCR detection First, 1 ml peripheral vein blood was mixed with EDTA for anticoagulation, and then RNA was extracted from peripheral leukomonocytes by the total RNA quick extraction kit. With oligo DT as primer and total RNA as template, cDNA was composed by reverse transcriptase according to operation manual. According to TRPV1 gene sequence listed in GenBank, the upstream primer sequence and the downstream primer sequence of TRPV1 mRNA were synthesized. Upstream primer sequence: 5′-GGCTGTCTTCATCATCCTGCTGCT-3′; downstream primer sequence: 5′-GTTCTTGCTCTCCTGTGCGATCTTGT-3′; and the size of PCR product was 117 bp. With GAPDH as reference gene, real-time quantitative PCR composed the upstream primer sequence and the downstream primer sequence of GAPDH; the upstream primer sequence: 5′-TGCACCACCAACTGCTTAGC-3′, the downstream primer sequence: 5′-GGCATGGACTGTGGTCATGAG-3; and its product size was 87 bp. Reaction cycle parameters were as follow: pre-denaturation at 94 °C for 10 min, denaturation at 94 °C for 30 s, annealing at 56 °C for 30 s, extension at 72 °C for 30 s. These procedures were repeated for totally 40 cycles among which 30 cycles later, extension was performed at 72 °C for 7 min. Confidence interval and relative quantification were determined by Wilcoxon Rank Sum Test; 2−ΔΔCT(Licak) was used to compare the TRPV1 relative expression of children in case group and control group. 2.4 Collecting and determining of targeting SNP sites In reference to NCBI SNP data base, assuming MAF > 5% and r2 > 0.8, the specific SNP of UTR-3 gene was screened. SNP detection: PCR reaction product was directly sequenced by Shanghai Yingjun Biotechnical Company Limited. 2.5 Measurement of blood smear cell count and total serum IgE level With help of high magnification (400x), eosinophils count of blood smear was performed; the total serum IgE level was measured by double-antibody sandwich ELISA. 2.6 Cytokine detection ELISA was used to detect the IFN-γ, IL-2, IL-17, IL-4 and IL-5 levels in serum of children in case group before and after the treatment and in control group. All the detection steps were completely consistent with the instruction. 2.7 Data statistics and analysis SPSS16.0 software was adopted for statistical analysis; comparison of means between the two groups was performed by t-test; measurement data was made by χ2 test. P < 0.05 means the difference has statistical significance. Then the correlative factors of bronchial asthma in children were analyzed by Logistic regression analysis, and the corresponding regulatory network was drawn referring to KEGG database and literature retrieval. 2.8 Protein analysis of TRPV1 Swiss-model website online tool was used to predict the tertiary structure of protein; online software of miRBase was employed for prediction of miRNA binding sites. 3 Results 3.1 TRPV1 gene expression level and SNP site analysis TRPV1 was performed real-time quantitative PCR, whose results are listed in Table 2. TRPV1 expression level between the two groups was significantly different (P < 0.01), in particular, TRPV1 expression level in peripheral blood of children with asthma was higher than that in healthy children. According to the gene sequencing, genotype distribution of two SNP sites (rs4790521 and rs4790522) was significantly different in the two groups. According to the gene sequencing and distribution of rs4790521 site, case group was different from control group in distribution of three genotypes, CC, CT and TT; alleles C in case group were much more than those in control group (P < 0.05). While based on genotype distribution of rs4790522 site, three genotypes, CC, AC and AA, were notably different in the two groups, and alleles C in case group was much more than that in control group (P < 0.05) (see Table 3). 3.2 Correlative cytokines levels of bronchial asthma in children and eosinophile granulocyte count As shown in Table 4, IgE content, EOS number, IFN-γ, IL-2, IL-17, IL-4 and IL-5 levels of children in case group before treatment were significantly different from that after treatment and that in control group (P < 0.01). 3.3 Logistic regression analysis of cytokines correlated to bronchial asthma in children and TRPV1 gene Previous χ2 test and t-test showed that differences between case group and control group were statistically significant in aspects of TRPV1 mRNA expression, gene frequency of polymorphic sites rs4790521 and rs4790522, EOS count in peripheral blood and IgE, IFN-γ, IL-2, IL-17, IL-4 and IL-5 levels (P < 0.05). Through Logistic regression analysis on these indexes and step-by-step parameter screening, results showed that the risk factors for bronchial asthma in children were EOS count in peripheral blood, TRPV1 mRNA expression and IL-4 and IL-5 levels (see Table 5). 3.4 Regulatory network of bronchial asthma in children in which TRPV1 gene was included Through KEGG signal pathway database, TRPV1 and cytokines linked to bronchial asthma in children including IL-2, IL-4, IL-5, IL-10 and IL-17 were analyzed, and then based on related literatures, a preliminary regulatory network of bronchial asthma in children was drawn (see Fig. 1). According to Fig. 1, the regulatory network of bronchial asthma in children was quite complicated, and TRPV1 and cytokines of TH1, Th2 and Th17 all can cause bronchial asthma in children. The relationship among TRPV1 and Th1, Th2 and Th17 cytokines was complex and mutually influenced. Specifically, Th1 might promote TNF-β, IFN-γ and IL-2 levels; Th2 might promote IL-4, IL-5 and IL-10 levels; Th17 might promote IL-17 level which would influence IL-1β affecting TRPV1. Besides, some macroelements or tracheal mucosal damage all can affect TRPV1, which could cause immune function disorder or peripheral sensitization, finally resulting in bronchial asthma in children. 3.5 Specific localization of mutant sites rs4790522 and rs4790521 Mutant sites rs4790522 and rs4790521 of TRPV1 gene were both located in 3′UTR of gene (see Fig. 2). 3.6 Prediction of miRNA binding sites before and after mutation The above mutant sites were located in 3′UTR region of TRPV1 gene, which thus had no influence on TRPV1 gene’s animo acid sequence. By changing miRNA bidding sites, however, mRNA level can be regulated. After prediction of miRNA’s binding sites before and after TRPV1 gene, results showed that mutation of rs4790522 caused disappearance of binding site miR-141-3p and that mutation of rs4790521 caused addition of binding sites miR-6802-3p and miR-551b-5p as well as the disappearance of binding site miR-6807-3p. And the specific information on miRNA is shown in Fig. 3. 4 Discussion Over these years, relationship between TRPV1 and children bronchial asthma has been a research hotspot in pathological mechanism of bronchial asthma, and much related achievements has been made. Study by Geppetti et al. (2006) indicated that TRPV1 might be the key signaling regulation factor inducing children asthma. Besides, TRPV1 plays an important role in bronchoconstriction, protein secretion, edema of tunica mucosa tracheae, inflammatory cell chemotaxis (Liu, 2014) and cough reflex (Smit et al., 2012, Patberg, 2011). Recent study further suggested that TRPV1 exists in immunohistochemical T-cells and can regulate the activity of CD4+ and the specificity of inflammatory cells (Baker et al., 2016). Furthermore, McGarvey et al. (2014) found that functional TRPV1 presents in epithelial cell of human airway and has a high expression in airway of patient with refractory bronchial asthma. What’s more, correlation of TRPV1 to bronchial asthma has been proved in some studies. Cantero-Recasens et al. (2010) found that children with functional TRPV1 channel caused by genetic deletion have low risk of exercise-induced asthma. In this study, mRNA level of TRPV1 in case group before treatment was statistically different from that after treatment and that in control group (P < 0.05); and according to TRPV1 gene sequencing, it was found that genotype distribution of the two sites (rs4790521 and rs4790522) was obviously different between case group and control group. Thus it can be concluded that mRNA level of TRPV1 and the two SNP sites all may be a predictor of bronchial asthma in children. Recent study showed that one of key mechanisms in the pathogenesis of asthma is correlated to imbalance of Th1/Th2/Th17/Treg cytokines (Holgate, 2012, Umetsu et al., 2002, Ji et al., 2014, Shi et al., 2011). And Th1 type cytokines mainly participate in cellular immune; Th2 type cytokines always cause some pathological responses like increasing IgE level in plasma, elevating EOS in airway, hyperplasia and hypertrophy of airway smooth muscle, and airway mucus hypersecretion, etc. (Ngoc et al., 2005) Th17 type cytokines play a significant role in pro-inflammatory reaction and autoimmunity; Treg type cytokines can regulate and inhibit immune responses through secretion of IL-10 or cell-contact mechanism(CTLA-4 and GITR). This study shows that both interior-group and inter-group differences of IgE level in peripheral blood and EOS count were significant, which indicates that IgE level and EOS count play certain role in bronchial asthma in children. Besides, interior-group and inter-group differences of IFN-γ, IL-2, IL-4, IL-5 and IL-17 levels were both statistically significant. Through a multi-factor Logistic regression analysis in terms of factors with significant differences between case group and control group, including TRPV1 mRNA, the two SNP gene sites’ genotypes, IgE level in peripheral blood, EOS count and cytokines, four factors (EOS, IL-1, IL-5 and TRPV1 mRNA leve)l were included in Logistic regression equation. The analysis results show that these four factors were the risk factors of bronchial asthma in children, which is different from previous studies by our research team (Chen et al., 2015). And the reason might lie in the sample size and quality; on the other hand it could be that too many factors were included in Logistic regression equation. Based on our previous study, the preliminary regulatory network of genes and cytokines involved in this study was made through searching KEGG database and combing other literatures. The regulatory network for bronchial asthma in children is quite complex, and relations among TRPV1, Th1, Th2 and Th17 are complicated with mutual influence, which all eventually induce immune function disorder or peripheral sensitization and cause bronchial asthma in children. This network is just part of bronchial asthma in children, and this study could contribute to the whole regulatory network of bronchial asthma in children. There are a lot of studies on miRNA and children bronchial asthma home and abroad, and most are about miRNA in signaling pathway of bronchial asthma or miRNA regarded as diagnostic marker for asthma. Huo et al. (2016) found that miR-181b-5p in epithelium and plasma is a potential biomarker for eosinophil increase in airway, and it can participate in eosinophilic airway inflammation through regulating pro-inflammatory cytokines’ expression with targeting SPP1. Kärner et al. (2016) indicated that miR-323-3p participates in negative feedback loop so as to control production of IL-22 in IL-22/IL-17-producing T cells, which may affect T cell response in asthma. According to a study by Maes et al. (2016), miR-223-3p, miR-142-3p and miR-629-3p express high in the sputum of patients with severe asthma and they are correlated to neutrophil airway inflammation, hence this study indicated that these miRNAs can be regarded as markers of this kind of airway inflammatory phenotype. Moreover, it’s found that mutant sites rs4790522 and rs4790521 are located in 3′UTR of TRPV1, thus not affecting the protein structure of gene. 3′ regulatory region plays an important regulating role in gene expression; therefore, mutation in this region might affect gene’s expression. Through prediction of miRNA biding sites before and after the mutation of TRPV1 gene, it’s found that two mutant sites induce changes of four miRNA biding sites including miR-141-3p, miR-6802-3p, miR-551b-5p and miR-6807-3p, which is crucial for studies on posttranscriptional regulation of TRPV1 gene. miRNA is able to act on 3′-UTR region of target gene mRNA to inhibit expression of mRNA and even cause its degradation, and mRNA mainly regulates cell proliferation, differentiation, apoptosis and other cellular processes (Adlakha and Saini, 2016). But for the time being, most studies on the four miRNAs involved in our studies, miR-141-3p, miR-6802-3p, miR-551b-5p and miR-6807-3p, focus on tumor, for instance, miR-145-5p have been reported that it works in kidney cancer, ovarian cancer, liver cancer and prostate cancer (Iorio et al., 2007, Liu et al., 2014, Porkka et al., 2007). miR-551b-3p is closely linked to the growth and development of gastric cancer and pancreatic cancer (Wen et al., 2016, Kuśnierz-Cabala et al., 2015). miR-6802-3p and miR-6807-3p are two new subtypes of miRNA, and there is no relevant study on them at present(Ladewiq et al., 2012). miR-141-3p, miR-6802-3p, miR-551b-5p and miR-6807-3p have not been reported in children bronchial asthma, either, which need deep researches in future. 5 Conclusions Combining with KEGG database and literature searching, this study establishes a preliminary regulatory network on children bronchial asthma in which cytokines correlated to children bronchial asthma screened out by analysis of clinical data were included; meanwhile, through analysis on molecular mechanism of TRPV1 in children bronchial asthma, this study also reveals that TRPV1 gene rs4790521 and rs4790522 mutation sites induce changes in corresponding miRNA, which may be the potential molecular mechanism of significant difference between case group and control group. The outcomes in this study provide certain theoretical basis for the pathogenesis of children bronchial asthma. Acknowledgments I thank all authors who have contributed to this paper for advice and comments. Moreover, the authors would like to thank the People’s Hospital of Zhengzhou University for providing research materials and experimental base. This study was supported by the Open cooperation project of Henan Province, China (Grant No. 132106000064), the Program of research in base and cutting-edge technologies of Henan Province, China (Grant No. 152300410151), and the National 12th “Five-Year” Technology Support Programs (Grant No. 2013BAI02B00). Peer review under responsibility of King Saud University. Figure 1 Partial regulatory network of bronchial asthma in children. Figure 2 Specific localization of mutant sites rs4790522 and rs4790521.Note: gene sequence between two vertical red lines was CDS region of TRPV1 gene; base in blue box was mutant base; gene sequence in dotted line was the disappeared binding site of miRNA after mutation; and gene sequence in straight line was new binding site of miRNA after mutation. Figure 3 Specific information on binding sites of miRNA before and after TRPV1 gene mutation. Table 1 General data of the subjects. General data Case group Control group χ2/t P n 200 200 ⩽3 37 (18.50%) 26 (13.00%) 4.21 0.122 Age >3–⩽7 103 (51.50%) 97 (48.50%) >7–⩽14 60 (30.00%) 77 (38.50%) Gender Male 125 (62.50%) 109 (54.50%) 2.636 1.104 Female 75 (37.50%) 91 (45.50%) Weight (kg) 19.2 ± 7.3 18.7 ± 8.1 0.648 0.517 Table 2 mRNA expression level of TRPV1 in children of case group and control group. Grouping Before treatment After treatment Control group t1 P1 t2 P2 TRPV1 mRNA level 6.63 ± 4.44 4.88 ± 3.21 3.04 ± 2.70 4.517 0 9.742 0 Note: P1 indicates comparison in case group before and after the treatment; P2 suggests comparison between case group before the treatment and control group. Table 3 Genotype distribution comparison of rs4790521 and rs4790522 sites in the two groups. Grouping n Genotype distribution n (%) Alleles n (%) CC CT TT C T rs4790521 Case group 200 64 (32.00) 60 (30.00) 76 (38.00) 188 (47.00) 212 (53.00) Control group 200 17 (8.50) 69 (34.50) 114 (57.00) 103 (25.75) 297 (74.25) χ2 77.288 39.023 P 0 0 Grouping n Genotype distribution n (%) Alleles n (%) CC AC AA C A rs4790522 Case group 200 112 (56.00) 81 (40.50) 7 (3.50) 305 (76.25) 95 (23.75) Control group 200 49 (24.50) 115 (57.50) 36 (18.00) 213 (53.25) 187 (46.75) χ2 50.108 46.354 P 0 0 Table 4 Level of cytokines correlated to asthma and eosinophils count. Category Case group Control group t1 P1 t2 P2 Before treatment After treatment IgE (IU/ml) 104.39 ± 28.13 79.56 ± 20.43 75.30 ± 26.38 10.100 0 10.667 0 EOS (×106number/L) 414.61 ± 93.44 231.24 ± 98.51 192.48 ± 99.62 19.099 0 22.999 0 IFN-γ (ng/L) 31.71 ± 20.10 50.14 ± 19.89 55.64 ± 29.76 −9.271 0 −9.424 0 IL-2 (ng/L) 26.72 ± 13.58 29.74 ± 14.21 31.04 ± 16.07 −2.173 0.03 −2.901 0.004 IL-17 (ng/L) 6.16 ± 2.32 3.27 ± 1.98 2.77 ± 1.41 13.400 0 17.685 0 IL-4 (ng/L) 148.79 ± 22.48 69.14 ± 17.35 64.76 ± 16.12 −39.667 0 −42.96 0 IL-5 (ng/L) 50.93 ± 13.47 26.81 ± 10.84 22.81 ± 9.06 19.729 0 24.498 0 Note: P1 indicates comparison in case group before and after the treatment; P2 suggests comparison between case group before the treatment and control group. Table 5 Logistic regression analysis of the factors correlated to bronchial asthma in children. Inclusive parameters Regression coefficient Wald P OR 95%CI EOS 0.005 4.071 0.044 1.005 1.000–1.009 IL-4 0.06 34.105 0 1.062 1.041–1.084 IL-5 0.039 3.242 0.072 1.039 0.997–1.084 TRPV1 mRNA 0.147 3.846 0.05 1.158 1.000–1.341 ==== Refs References Adlakha Y.K. Saini N. MicroRNA: a connecting road between apoptosis and cholesterol metabolism Tumor Biol. 2016 Baker K. Raemdonck K. Dekkak B. Snelgrove R.J. Ford J. Shala F. Belvisi M.G. Birrell M.A. Role of the ion channel, transient receptor potential cation channel subfamily V member 1 (TRPV1), in allergic asthma Respir. Res. 17 2016 67 27255083 Cantero-Recasens G. Gonzalez J.R. Fandos C. Duran-Tauleria E. Smit L.A. Kauffmann F. Antó J.M. Valverde M.A. Loss of function of transient receptor potential vanilloid 1(TRPV1) genetic variant is associated with lower risk of active childhood asthma J. Biol. Chem. 285 2010 27532 27535 20639579 Caterina M.J. Schumacher M.A. Tominaga M. Rosen T.A. Levine J.D. Julius D. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30043-810.1016/j.sjbs.2017.01.034Original ArticleOptimizing conditions for calcium phosphate mediated transient transfection Guo Ling a1Wang Liyang a1Yang Ronghua a1Feng Rui aLi Zhongguang aZhou Xin aDong Zhilong bGhartey-Kwansah George aXu MengMeng cNishi Miyuki dZhang Qi eIsaacs Williams fMa Jianjie gXu Xuehong xhx0708@snnu.edu.cna⁎a College of Life Science, Shaanxi Normal University, Xi’an 710062, Chinab 2nd Hospital, Lanzhou University, Lanzhou, Chinac Medical Scientist Training Program, Duke University Medical Center, USAd School of Pharmacology, Kyoto University, Japane College of Chemistry and Materials, Shaanxi Normal University, Xi’an 710062, Chinaf School of Medicine, Johns Hopkins University, USAg School of Medicine, Ohio State University, USA⁎ Corresponding author. xhx0708@snnu.edu.cn1 Equal contributor. 11 2 2017 3 2017 11 2 2017 24 3 622 629 10 10 2016 31 12 2016 8 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Background Calcium phosphate mediated transfection has been used for delivering DNA into mammalian cells in excess of 30 years due to its most low cost for introducing recombinant DNA into culture cells. However, multiple factors affecting the transfect efficiency are commonly recognized meanwhile for years, the low transfection efficiency of this approach on higher differentiated and non-tumor cells such as CHO and C2C12 limits its application on research. Results In this paper, we systematically evaluated the possible factors affecting the transfection rate of this approach. Two categories, calcium phosphate–DNA co-precipitation and on-cell treatments were set for optimization of plasmid DNA transfection into CHO and C2C12 cell-lines. Throughout experimentation of these categories such as buffer system, transfection media and time, glycerol shocking and so on, we optimized the best procedure to obtain the highest efficiency ultimately. During calcium phosphate DNA-precipitation, the transfection buffer is critical condition optimized with HBS at pH 7.10 (P = 0.013 compared to HEPES in CHO). In the transfection step, FBS is a necessary component in transfection DMEM for high efficiency (P = 0.0005 compared to DMEM alone), and high concentration of co-precipitated particles applied to cultured cells in combination with intermittent vortexing is also crucial to preserve the efficiency. For 6-well culture plates, 800 µl of co-precipitated particles (11.25 µg/mL of cDNA) in 1 well is the optimal (P = 0.007 compared to 200 µl). For the highest transfection efficiency, the most important condition is glycerol in shock treatment (P = 0.002 compared to no shock treatment in CHO, and P = 0.008 compared to no shock treatment in C2C12) after a 6 h incubation (P = 0.004 compared to 16 h in CHO, and P = 0.039 compared to 16 h in C2C12) on cultured cells. Conclusions Calcium phosphate mediated transfection is the most low-cost approach to introduce recombinant DNA into culture cells. However, the utility of this procedure is limited in highly-differentiated cells. Here we describe the specific HBS-buffered saline, PH, glycerol shock, vortex strength, transfection medium, and particle concentrations conditions necessary to optimize this transfection method in highly differentiated cells. Abbreviations CHO, Chinese hamster ovary cellsC2C12, mouse myoblast cellsPEG, polyethylene glycolFBS, fetal bovine serumPen-Strep, penicillinstreptomycinIntDen, integrated densityKeywords Calcium phosphate transfectionCo-precipitationTransfection efficiency ==== Body 1 Introduction Genetically modified organisms, including transgenic plants, animals and cultured cell lines, have gradually becoming the main experimental models in current food science, agriculture and biology research fields (Liu et al., 2016a, Liu et al., 2016b, Zhou et al., 2016). Delivery of DNA and RNA into cultured cells is a fundamental molecular biology transgenic technique. To explore the function of a gene of interest, transient transfection is normally employed to over-express or knock down gene expression from cells in a controlled setting. To conduct these seminal experiments, recombinant DNA need to be introduced into cell without damaging cell viability. There are currently three main techniques to accomplish this mission. First is lipid-mediated liposome delivery, such as lipofectamine series products manufactured by Invitrogen. This approach displays high efficiency for transient transfection in most cultured cells, but is limited by high cost and is not suitable for large quantity delivery (Junquera and Aicart, 2014, Kaestner et al., 2015, Xiong et al., 2011). The second approach is electroporation, which has the highest efficiency method of the three but also causes the highest death rate of cells (Kalli et al., 2014, Li et al., 2013, Nakamura and Funahashi, 2013). The third method of the transient transfection, and focus of this report, is calcium phosphate mediated transient transfection. In this approach, calcium phosphate forms an insoluble precipitate with DNA, this Ca-DNA complex then attaches to the cell surface where it is transported into cells by endocytosis. Due to its reasonable efficiency and low cost, this technique has been used for delivering DNA into mammalian cells for over 30 years (Dudek et al., 2001, Kwon and Firestein, 2013, Sambrook and Russell, 2001, Sun et al., 2013). However, this approach has largely been neglected in favor of the first two methods due to the myriad of factors affecting transfection efficiency in the hands of experimenters and previous belief that this method does not work well in highly differentiated cells (Inokuchi et al., 2009, Mohammad et al., 2008). In this paper, we systematically documented the optimal conditions for the most effective calcium phosphate mediated transfection in highly-differentiated cell lines, Chinese hamster ovary cells (CHO) and mouse myoblast cells (C2C12). 2 Materials and methods 2.1 Cloning and sub-cloning Four different plasmids were used in this study, EGFP-N1, EGFP-pIRES, EGFP-pIRES-mCnB, and EGFP-pIRES-mCnA. EGFP-N1 is a commercial product from Clontech. mCnB and mCnA genes were amplified from total RNA as previously described (Wang et al., 2008, Yoshiga et al., 2002), and cloned into the EGFP-pIRES plasmid with 5′ Nhe Ⅰ and 3′ Sac Ⅰ restriction sites, which were later used to confirm successful insertion. Recombined plasmid DNAs were transfected into HB101 competent Escherichia coli. Individual colonies were transferred into Kanamycin (20 µg/ml) LB medium and incubated over night at 37 °C with vigorous shaking (250 rpm on a rotary shaker) until the bacteria reached late log phase. Plasmid DNAs were prepared using alkaline lysis with SDS were purified with polyethylene glycol (PEG, 40% PEG6000, 30 mM MgCl2) and were recovered with deionized distilled H2O or 1 × TE buffer (pH 7.6). 2.2 Cell culture Cells for transfection were cultured at 37 °C in a humidified incubator with an atmosphere of 5% CO2, in DMEM high glucose medium (pH 7.4, Gibco) supplemented with 10% inactivated fetal bovine serum (10% FBS, Invitrogen), 100 U/ml penicillin and 100 µg/ml streptomycin (1% Pen-Strep, Invitrogen). Chinese hamster ovary (CHO) cells and mouse myoblast (C2C12) cells were two lines used in the study. Cells were transfected with expression plasmids at 50–50% confluence then sub-cultivated at 80%–90% of cell confluence. The calcium phosphate-DNA co-precipitate transient transfection method was used. 2.3 Calcium phosphate mediated transient transfection of CHO and C2C12 cell-lines In this study, we optimized the procedure of calcium phosphate mediated transient transfection approach using CHO and C2C12 cell-lines (Supplemental Fig. 1 and detailed in Table 1) in 6-wells plates (NUNC). The traditional steps to Calcium phosphate mediated transient transfection are briefly described below: 2.4 Preparation of calcium phosphate-DNA particle or complex within co-precipitation The followings solutions were prepared for transfection experiments including CaCl2 (2.5 M); 15% (v/v) glycerol in 1 × HBS-buffered saline; 2 × HEPES-buffered saline (140 mM NaCl, 1.5 mM Na2HPO4, 50 mM HEPES, pH 7.05); and 2x HBS-buffered saline (280 mM NaCl, 1.5 mM Na2HPO4, 50 mM HEPES, 10 mM KCl, 12 mM Dextrose, pH 7.05, 7.10 or 7.15). 10 µl of 2.5 M CaCl2 and 90 µl of plasmid DNA (25 µg/ml) were mixed and added to 100 µl of HBS or HEPES buffered saline (at ratio of 1:1). Mixture was allowed to incubate at room temperature for one mixture. This total 200 µl of calcium phosphate-DNA particles complex was used for transient transfection as described below. 2.5 Calcium phosphate mediated transient transfection CHO and C2C12 Cells at exponential growth phase were harvested and re-plated at a density of 1–4 × 105 cells/cm2 20–24 h before transfection. Cell media was replaced 1 h before transfection. 200 µl of the calcium phosphate-DNA particles was added to the well drop wise to ensure even addition. The cells were incubated at 37 °C for 6 h before transfection media was replaced with fresh medium. Cells were then culture under standard conditions for 1–3 days for further study. In Table 1 Experiment 9, the glycerol shock applied to CHO and C2C12 cells was conducted by replacing the medium with 1000 µl of 15% glycerol solution directly after transfection and incubating these cells for 1–2 min at 37 °C. Following PBS wash, the glycerol solution was replaced with fresh medium and cells were incubated for an additional 1–3 days. Transfection efficiency was evaluated under inverted fluorescence microscope 48 h after transfection. 2.6 Microscopy and statistics Inverted fluorescence microscopy (Carl Zeiss Microscopy GmbH) and manufacturer software ZEN was used to document fluorescent signals of successful transfection. Images were analyzed using NIH Image J software for transfection efficiency. Numbers of fluorescent cells targeted with plasmid DNA and total cells (the fluorescent cells plus non-fluorescent cells) were counted, and the integrated density (IntDen) and area of fluorescent cells were obtained with the Image J software. SPSS was used to generate figures and statistical analysis on the fluorescent cell number / total cell number ratio. 3 Result and discussion Two categories, calcium phosphate–DNA co-precipitation and on-cell treatments were used to optimize plasmid DNA transfection into CHO and C2C12 cell-lines (Table 1). These two cells lines were selected for their low transfection rates using the calcium phosphate approach. 3.1 Varying preparation of calcium phosphate–DNA co-precipitation Six variables were selected for optimization in preparation of calcium phosphate-DNA complex (Table 1): (1) concentration of plasmid DNA used for preparation of the calcium phosphate-DNA particle (2 µg/200 µl, 4 µg/200 µl and 8 µg/200 µl); (2) PH of solutions used for complex preparation (pH 7.05, pH 7.10 and pH 7.15); (3) calcium phosphate-DNA particle size (varied by strength of vortex used); (4) Size of plasmid used DNA for transfection; (5) buffer solution types (HBS-buffered and HEPES-buffered saline); and (6) Incubation temperature during calcium phosphate–DNA complex preparation. Of these variables, the buffer system played a key role on the efficiency of calcium phosphate mediated transfection. HBS-buffered saline delivered better transfection efficiency than HEPES-buffered saline when used in conjunction with glycerol shock, a variable discussed in detail below. The HBS-buffered saline delivered over 100% more transfected cells than HEPES-buffered saline in both CHO and C2C12 cells (Fig. 1, Table 1 Experiment 5). However, glycerol shock is necessary discussed for this improved performance, as discussed in varying treatments on culture cells. Despite the accepted convention that buffering system pH is critical on transfection [10], our experiments indicated that a pH in the range of 7.05 and 7.10 using the HBS-buffering system is sufficient for optimal transfection. Both pH 7.05 and pH 7.10 induced higher efficiency in the transfection for CHO cells, but a higher pH of 7.15 resulted in lower efficiency (Supplemental Fig. 2, Table 1 Experiment 2). Transfection efficiency was also unaffected plasmid DNA size within the range of 5.3 kb to 6.0 kb (Supplemental Fig. 4, Table 1 Experiment 4). However, transfection efficiency was dependent on vortex strength, indicating that particle size is an important factor. Higher strength vortex generally induced lower efficiency (Supplemental Fig. 3, Table 1 Experiment 3). Surprisingly, increasing plasmid DNA concentration in the calcium phosphate-DNA particle improves transfection efficiency. In experiment 1, 8 µg plasmids DNA per 200 µl particles displayed the highest efficiency transfection for CHO cells (Supplemental Fig. 5, Table 1 Experiment 1). Incubating temperature during calcium phosphate–DNA particles formation was also important with particles prepared at 37 °C demonstrating higher rate of transfection than those prepared at room temperature (Supplemental Fig. 6, Table 1 Experiment 6). 3.2 Varying treatments on cultured cells Five variables were established for optimization during cell transfection. As listed in Table 1: (1) medium-change 1-h prior to transfection; (2) FBS/Pen-Strep supplementation in medium; (3) glycerol shock post transfection incubation; (4) incubation time with the transfection particle, and (5) concentration of particles for transfection incubation (200 µl, 400 µl and 800 µl of particles in 2 ml of transfection medium). Unlike liposome transfection, FBS and Pen-Strep in the transfection media did not disturb transfection efficiency and even improved efficiency given the appropriate conditions. The highest transfection efficiency was achieved when transfection media was supplemented with FBS or FBS plus Pen-Strep in comparison to un-supplemented media. However, this efficiency was depended on glycerol shock (Fig. 2, Table 1 Experiment 8). Increasing calcium phosphate–DNA particles density also improves transfection efficiency (Fig. 3, Table 1).The addition of glycerol shock post-transfection incubation was a significant contributor to transfection efficiency (Fig. 4, Table 1 Experiment 9). Transfection particles incubation time was also an important factor. An incubation time of 6–7 h is critical for efficient transfection and should not exceed 10 h. Over incubation results in reduced efficiency due to cell damage induced by particle-over delivery (Fig. 5, Table 1 Experiment 10). 4 Conclusion Calcium phosphate mediated transfection is the most low-cost approach to introducing recombinant DNA into culture cells. It is commonly used to transfect recombined DNA into cells for packaging of virus-carrying gene of interest (Shunaeva et al., 2015, Yaswen et al., 1993). However, the low transfection efficiency of this approach on higher differentiated cells such as CHO and C2C12 limits its research application. Our study systematically evaluated possible factors affecting successful transfection rate and optimized transfection conditions for the highest efficiency (Table 1). When calcium phosphate–DNA co-precipitations or particles are prepared, an accurate pH value of HBS-buffered saline is critical to guarantee appropriate formation of the calcium–DNA complex. HBS-buffered saline is preferred over HEPES-buffered saline and transfection efficiency is greatly improved when particle assembly is carried out at 37 °C. And a glycerol shock post incubation with calcium phosphate–DNA particles is vital to efficient transfection. Although FBS- and FBS/Pen-Strep free medium is required for liposome transfection (Cui et al., 2012, Felgner et al., 1987), in calcium phosphate mediated transfection, FBS is necessary for high efficiency on higher differentiated cells such as CHO and C2C12. Furthermore, since fresh media just prior to transfection had no significant effect on efficiency (Supplemental Fig. 7, Table 1 Experiment 7), removing this step will conserve time and further lower the cost of our optimized procedure for the calcium phosphate mediated transfection. Competing interests The authors declare that they have no competing interests. Author’s contributions XXH conceived of the study. GL, WLY and YRH developed protocols and collected all data. GL, WLY and YRH analysed the data. MMX, ZX, FR and GGK provided direction and advisement. XXH, GL, WLY and YRH prepared the manuscript and XXH, MMX, MN, WI and JM edited the manuscript. All authors read and approved the final manuscript. Appendix A Supplementary data Supplementary data 1 Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant Nos. 31371256, 31571273), the National Department of Education Central Universities Research Fund (Grant No. GK20130100), the Foreign Distinguished Scientist Program (Grant No. MS2014SXSF038), US Maryland Stem Cell Research Fund (2009MSCRFE008300), Qinba Mountain Developing Center (Grant No. CIC-QBRSD) and the Outstanding Doctoral Thesis fund (Grant Nos. X2014YB02, X2015YB05). Peer review under responsibility of King Saud University. Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.sjbs.2017.01.034. Fig. 1 Comparison of transfection efficiency between HBS- and HEPES-buffer saline for preparation of particles. Summarized in Table 1, Experiment 5. The efficiency of calcium phosphate–DNA particles preparation with HBS-buffer saline is greater than HEPES-buffer saline in both CHO and C2C12 cell-lines, respectively, F = 0.909 and 0.028, P = 0.013 and 0.025. Corresponding fluorescence images B and C are of CHO cell transfections using particles prepared with HBS and HEPES. Images D and E are of C2C12 cell transfections using particles prepared with HBS and HEPES. *indicates p-value < 0.05. Bars in B-E are 150 µm. Fig. 2 Comparison of transfection efficiency between media supplemented with FBS and Pen-Strep to un-supplemented media. Summarized in Table 1, Experiment 8. Panel A, transfection of CHO cell-lines is more efficient in both FBS-DMEM and FBS/Pen-Strep DMEM in comparison to DMEM alone, F = 0.084 and 0.060, P = 0.0005 and 0.004. Fluorescence images showed in B-D. There is no significant difference between FBS-DMEM and FBS/Pen-Strep-DMEM (F = 0.306, P = 0.056). ** indicates p-value < 0.01 and *** indicates P-value < 0.001. Bars in B–D are 150 µm. Fig. 3 Transfection efficiency in relation to particle densities on CHO cells. Summarized in Table 1, Experiment 11. Panel A, transfection efficiency improves as particle density increases in CHO cell-lines. 800 µl-particles are significantly more efficient than 200 µl, F = 0.117, P = 0.007, and 400 µl-particles are significantly more efficient than 200 µl, F = 0.107, P = 0.024. There is no significant difference between 400 µl- and 800 µl-particles (F = 0.579, P = 0.076). Fluorescence images B–D are of transfected CHO cells with 200 µl (B), 400 µl (C) and 800 µl (D) of particles in 2 ml medium. * indicates p-value < 0.05 and ** indicates p-value < 0.01. Bars in B–D are 150 µm. Fig. 4 Influence of glycerol treatment on CHO and C2C12 cells transfection efficiency. Summarized in Table 1, Experiment 9. Panel A, glycerol shock generated significantly more efficient transfection when used with HEPES buffer saline and HBS buffer saline in both CHO and C2C12 cell lines (F = 0.003, P = 0.044 with HEPES in CHO, F = 0.138, P = 0.002 with HBS in CHO, F = 0.082, P = 0.00003 with HEPES in C2C12, and F = 0.092, P = 0.008 with HBS in C2C12). Panels B–E show CHO cells, comparison of transfection efficiency between glycerol shock (B) and no shock (C) in HEPES buffer saline and comparison of transfection efficiency between glycerol shock (D) and no shock (E) in HBS buffer saline. Panels F-I show C2C12 cells, comparison of transfection efficiency between glycerol shock (F) and no shock (G) in HEPES buffer saline and comparison of transfection efficiency between glycerol shock (H) and no shock (I) in HBS buffer saline. * indicates p-value < 0.05, ** indicates p-value < 0.01, and *** indicates p value < 0.001. Bars in B–I are 150 µm. Fig. 5 Comparison of incubation time effect on transfection efficiency of in CHO and C2C12 cells. Summarized in Table 1, Experiment 10. Panel A, the efficiency with 6 h incubation in both CHO and C2C12 cell-lines is significantly more effective than 16 h incubation, F = 0.841 and 0.981, P = 0.004 and 0.039, separately. Panel B and C show fluorescence images of CHO cells. Panel D and E show fluorescence images of C12C12 cells. * indicates p-value < 0.05 and ** indicates p-value < 0.01. Bars in B–E are 150 µm. Table 1 Two categories optimized for establishment of best conditions are listed as experiment 1–11. Notes: The green areas in table indicate the variable options and optimized options are presented in last row. ==== Refs References Cui, S., Zhang, S., Chen, H., Wang, B., Zhao, Y., Zhi, D., 2012. The mechanism of lipofectamine 2000 mediated transmembrane gene delivery engineering 04, 172–175 http://dx.doi.org/10.4236/eng.2012.410B045. Dudek, H., Ghosh, A., Greenberg, M.E., 2001. Calcium phosphate transfection of DNA into neurons in primary culture. Curr. Protoc. Neurosci. Chapter 3: Unit 3 11 http://dx.doi.org/10.1002/0471142301.ns0311s03. Felgner P.L. Lipofection: a highly efficient, lipid-mediated DNA-transfection procedure Proc. Natl. Acad. Sci. USA 84 1987 7413 7417 2823261 Inokuchi J. Narula N. Yee D.S. Skarecky D.W. Lau A. Ornstein D.K. Tyson D.R. Annexin A2 positively contributes to the malignant phenotype and secretion of IL-6 in DU145 prostate cancer cells Int. J. Cancer 124 2009 68 74 18924133 Junquera E. Aicart E. Cationic lipids as transfecting agents of DNA in gene therapy Curr. Top. Med. Chem. 14 2014 649 663 24444161 Kaestner L. Scholz A. Lipp P. Conceptual and technical aspects of transfection and gene delivery Bioorg. Med. Chem. Lett. 25 2015 1171 1176 25677659 Kalli C. Teoh W.C. Leen E. Introduction of genes via sonoporation and electroporation Adv. Exp. Med. Biol. 818 2014 231 254 25001540 Kwon M. Firestein B.L. DNA transfection: calcium phosphate method Methods Mol. Biol. 1018 2013 107 110 23681621 Li L. Allen C. Shivakumar R. Peshwa M.V. Large volume flow electroporation of mRNA: clinical scale process Methods Mol. Biol. 969 2013 127 138 23296932 Liu S.M. Transgenic plants expressing the AaIT/GNA fusion protein show increased resistance and toxicity to both chewing and sucking pests Insect Sci. 23 2016 265 276 25641865 Liu X. N-3 polyunsaturated fatty acids attenuates triglyceride and inflammatory factors level in hfat-1 transgenic pigs Lipids Health Dis. 15 2016 89 27161005 Mohammad H.S. Annexin A2 expression and phosphorylation are up-regulated in hepatocellular carcinoma Int. J. Oncol. 33 2008 1157 1163 19020748 Nakamura H. Funahashi J. Electroporation: past, present and future Dev. Growth Differ. 55 2013 15 19 23157363 Sambrook, J., Russell, D.W., 2001. Introducing cloned genes into cultured mammalian cells. In: Argentine, J. (Ed.), Molecular Cloning a Laboratory Manual, vol 3, 3rd ed., Cold Spring Harbor Laboratory Press, New York, pp. 16.17–16.54. Shunaeva, A., et al., 2015. Improvement of HIV-1 and HTLV-1 replication-dependent vectors via optimization of reporter gene reconstitution and modification with intronic shRNA. J. Virol. http://dx.doi.org/10.1128/jvi.01940-15. Sun, M., Bernard, L.P., Dibona, V.L., Wu, Q., Zhang, H., 2013. Calcium phosphate transfection of primary hippocampal neurons. J. Vis. Exp. e50808. http://dx.doi.org/10.3791/50808. Wang Y.L. Wang Y. Tong L. Wei Q. Overexpression of calcineurin B subunit (CnB) enhances the oncogenic potential of HEK293 cells Cancer Sci. 99 2008 1100 1108 18422742 Xiong F. Mi Z. Gu N. Cationic liposomes as gene delivery system: transfection efficiency and new application Pharmazie 66 2011 158 164 21553643 Yaswen L.R. Stephens E.B. Davenport L.C. Hutt-Fletcher L.M. Epstein-Barr virus glycoprotein gp85 associates with the BKRF2 gene product and is incompletely processed as a recombinant protein Virology 195 1993 387 396 8393232 Yoshiga T. Yokoyama N. Imai N. Ohnishi A. Moto K. Matsumoto S. CDNA cloning of calcineurin heterosubunits from the pheromone gland of the silkmoth, Bombyx mori Insect Biochem. Mol. Biol. 32 2002 477 486 11886782 Zhou, X., et al., 2016. Liver specific NG37 over-expression leads to diet-dependent fatty liver disease accompanied by cardiac dysfunction. Genes Nutr.

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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30053-010.1016/j.sjbs.2017.01.044Original ArticleA novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems Su Weixing aChen Hanning perfect_chn@hotmail.coma⁎Liu Fang aLin Na bJing Shikai bcLiang Xiaodan aLiu Wei da School of Computer Science and Software, Tianjin Polytechnic University, Tianjin 300387, Chinab Beijing Shenzhou Aerospace Software Technology Co. Ltd., Beijing 110000, Chinac School of Mechanical Engineering, Beijing Institute of Technology, Beijing 110081, Chinad College of Information and Technology, Jilin Normal University, Siping 136000, China⁎ Corresponding author. perfect_chn@hotmail.com21 2 2017 3 2017 21 2 2017 24 3 695 702 23 10 2016 23 12 2016 7 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm. Keywords Artificial bee colonyDynamic optimizationPowell’s searchCrossover operationLife-cycle ==== Body 1 Introduction Many real-world optimization problems are subject to changing conditions over time, which can be identified as dynamic optimization problems (DOP) (Ha, 2016). In the DOP cases, changes may affect the object function, the problem instance, or constraints, causing that the optimal solutions of such dynamic problem being considered may change over time (Branke, 2001). From this point of view, most of the real world problems have dynamic characteristics, where one or more elements of the under-lying model for a given problem may change over time. This requires optimization algorithms to not only find the global optimal solution under a specific environment but also to continuously track the changing optima over different dynamic environments. In recent years, investigating swarm intelligence (SI) algorithms for DOPs has attracted a growing interest (Clerc and Kennedy, 2002), due to that SIs are intrinsically inspired from natural or biological evolution, which is always subject to an ever-changing environment, and hence SIs, with proper enhancements, have a potential to be good optimizers for DOPs. Artificial bee colony algorithm (ABC) is one of the most popular members of the family of swarm intelligence, which simulates the social foraging behavior of a honeybee swarm (Karaboga and Basturk, 2007a, Karaboga and Basturk, 2007b). Due to its simple arithmetic and good robustness, the ABC algorithm has been widely used in solving many numerical optimizations (Karaboga and Basturk, 2007a, Karaboga and Basturk, 2007b, Biswas et al., 2014) and engineering optimization problems (Karaboga et al., 2007). However, facing up complex dynamic problems, similar to other EAs, ABC algorithm suffers from the following drawbacks (Karaboga and Basturk, 2007a, Karaboga and Basturk, 2007b): (1) the solution search equation of ABC works well in global exploration but is poor in the exploitation process. (2) With the dimension increasing, the information exchange of each individual is limited in a random dimension, resulting in a slow convergence rate. Several ABC variants have been developed to improve its optimization performance. One significant improvement is the introduction of PSO-based search equation (Zhu and Kwong, 2010), which allows a powerful global search in the early stage by incorporating the information of the gbest solution into ABC. Similarly, Banharnsakun et al. (2011) presented a modified search equation for the onlooker bees. In their method, the new candidate solutions are more likely to be close to the current best solution. Gao et al., 2013a, Gao et al., 2013b proposed an efficient and robust ABC variant based on modified search equation and orthogonal learning strategies, which demonstrated its high effectiveness and efficiency. Another interesting approach by (Gao et al., 2013a, Gao et al., 2013b) is using the Powell’s method as a local search tool to enhance the exploitation of the algorithm. In this method, ABC good at exploration ensures the search is less likely to be trapped in local optima while it enjoys the merits of fine local search by Powell’s method. Hybridization of ABC with other operators has also been studied widely. For example, Kang et al. (2011) used the Rosenbrock’s rotational direction method to implement the exploitation phase and proposed the Rosenbrock ABC algorithm. Coelho and Alotto (2011) developed a novel alternative search equation in which a parameter is responsible for the balance between the Gaussian and the uniform distribution. Inspired by previous works, this paper presents a novel optimization algorithm called comprehensive learning artificial bee colony optimizer (CLABC), which synergizes the idea of extended life-cycle evolving model with a pool of local searching strategies (Liu, 2013). The main motive of CLABC is to enrich artificial bee foraging behaviors in ABC model by combining population initialization based on orthogonal Latin squares approach, Powell’s pattern search method, life-cycle, and crossover-based social learning strategy, which contributes in the following aspects:(1) The orthogonal Latin squares approach can be used for artificial bee colony initialization to cover the search space with balanced dispersion and neat comparability. (2) The crossover operation, which helps bees exchange more information after the early stage of the algorithm. In this case, the neighbor bees with higher fitness can be chosen to crossover, which effectively enhances the global search ability. (3) Powell’s local search method enables the bee exploit around promising area while avoiding search stagnation. (4) Life-cycle, which results in a dynamic population. This means that, the bee can reproduce and quit adaptively throughout the foraging process and the population size varies as the algorithm runs in the dynamic landscapes. This work adopted the moving peaks benchmark (MPB) to illustrative the inherent adaptive mechanism in the proposed algorithm of surviving in a changing environment. The proposed CLABC has been compared with its classical counterpart, the classical ABC algorithm (Karaboga, 2005) over dynamic benchmarks with respect to the statistical performance measures of solution quality and convergence speed. The rest of the paper is organized as follows. In Section 2, the proposed comprehensive learning artificial bee colony (CLABC) algorithm is given. Section 3 presents the experimental studies of the proposed CLABC and the other algorithms with descriptions of the involved benchmark functions, experimental settings, and experimental results. Finally, Section 4 outlines the conclusion. 2 Comprehensive learning artificial bee colony algorithm The main procedures of CLABC, including orthogonal Latin squares population initialization, Powell’s pattern search, life-cycle, and crossover-based social learning strategies, are details as follows. 2.1 Population initialization based on orthogonal Latin squares approach The orthogonal Latin squares approach can be used for population initialization to cover the search space with balanced dispersion and neat comparability. Suppose a population consisting of N individuals (or food sources) has to be initialized in the D-dimensional search space, a orthogonal table LN (tD) is designed deliberately where N represents the number of initial solutions or table rows, t is the factor level of orthogonal table, D donates the dimension of search space or the number of orthogonal arrays. Generally, this approach has a merit of obtaining optimal space coverage by consuming comparatively less tests, which has been proved theoretically by relevant theorem of non-parametric statistics in (Math, 1975). The main steps of population initialization are listed in Algorithm 1. Then the initial solution (i.e., Xi = (xi1, xi2,…,xiD), i = 1,2,…,N) obtained by Algorithm 1 is endowed with promising balanced dispersion and neat comparability. 2.2 Powell’s pattern search Powell’s search, namely Powell’s conjugate gradient descent method, is an extension of basic pattern search method to speed up the convergence of complex nonlinear problems, in which one merit is that the function need not be differentiable, and no derivatives are taken. The method pursues the minimum of the function by a bi-directional search along each search vector, in turn. The new position can then be represented as a linear combination of the search vectors. The new displacement vector becomes a new search vector, and is added to the end of the search vector list. Coincidently, the search vector which contributed most to the new direction (i.e. the one which was most successful), is deleted from the search vector list. The algorithm iterates an arbitrary number of times until no significant improvement can be made. The Powell’s method algorithm is given as follows. 2.3 Crossover operation As described in canonical ABC (Karaboga and Basturk, 2007a, Karaboga and Basturk, 2007b), the search equation for generating the positional change is much like a blind mutation operator to search in a randomly selected dimension of a randomly chosen bee, which means that the information exchange is restricted to a narrow local scope. This causes that, as the dimension of solved problems grows exponentially, the algorithm using this information exchange based on single dimension of single bee will suffer from the following drawback of premature convergence at the early generations. On the other hand, the neighbor bee and dimension are both chosen randomly, which results in the good individuals with higher fitness may likely be abandoned. To address this concerning issue, inspired by genetic algorithm, the crossover-based comprehensive learning is employed in the bee hive. To benefit from information about positions of good sources foraged by the employed and onlooker bees, a given number of elites form excellent employed bees are subjected to crossover operation. The underlying idea behind this method is to facilitate better search in complex dynamic search space as opposed to classical ABC that perturbs a single parameter. The steps for crossover operation phase are given as follows and the schematic diagram is illustrated in Fig. 1. Algorithm 2. Crossover operationStep 1. Select elites to the best-performing list (BPL). A set of competent individuals from current food sources are selected to construct the best-performing list (BPL), the ones with higher fitness have larger probability to be selected. The size of BPL is equal with current population size. These individuals of BPL are regarded as elites. The selection operation tries to mimic the maturing phenomenon in nature, where the generated offspring will become more suitable to the environment by using these elites as parents. Step 2. Crossover operation. To produce well-performing individuals, parents are selected from the BPL’s elites only for crossover. To select parents effectively, the tournament selection scheme is used. Firstly, two enhanced elites are selected randomly, and their fitness values are compared to select the elites. The one with better fitness is viewed as parent. Then, another parent is selected in the same way. Two offspring are created by performing crossover on the selected parents. Here adopts a representative crossover method, namely arithmetic crossover, according to which, the offspring is produced by the following equation:where Snew is newly produced offspring, parent1 and parent 2 are randomly selected from BPL. Step 3. Update with different selection schemes. Not all current bees are replaced by the elites from BPL, we set a selecting rate CR to determine the replaced individuals. Assuming that population size is S, then the replaced individuals number is S∗CR. For the selected individual Sj, the newly produced offspring Snew is then compared with Sj, applying a greedy selection mechanism, in which the better one is remained. We can choose four selecting approaches: selecting the best individuals (i.e. S∗CR individuals), a medium level of individuals, the worst individuals, and randomly individuals. 2.4 Life-cycle model This work assumes that the computational life-cycle model of bee colony has five major stages, namely the born, forage, reproduction, death, and migration. The bee state transition diagram is shown in Fig. 2. The bees are born when they are initialized. Then they will forage for nutrient (nectar). We define the nutrient updating formula as:where fit(Xit) is the fitness of the ith bee Xi at time t for a minimum problem, Ni(t) is the nutrient obtained by the ith bee Xi at time t. In initialization stage, nutrients of all bees are zero. For each Xi at onlooker bee phase, if the new position is better than the last one, it is regarded that the bee will gain nutrient from the environment and the nutrient is added by one. Otherwise, it loses nutrient in the foraging process and its nutrient is reduced by one. Then the information rate Fit deciding to reproduce or die for each bee Xi at time t is computed as: (3) Hi(t)=fit(Xit)-fitworsttfitbestt-fitworstt (4) Fit=ηHi(t)∑j=1StHj(t)+(1-η)Ni(t)∑j=1StNj(t),η⊂[0,1] where fitworstt and fitbestt are the current worst and best fitness of the whole bee colony at time t. In the foraging process, if the bee Xi converts enough information rate Fit as: (5) Fit>max(Freproduce,Freproduce+(St-S)Fadapt) it will reproduce an offspring by using best-so-far solution information in search equation of employed and onlooker bees steps: (6) xnewi,j=xi,j+φ(xbest,j-xi,j) where xnew is the new offspring, xi is the ith bee, xbest is best individual of current colony, j is a randomly chosen indexes; ϕ is a random number in range [−1,1]. If the bee enters bad environment, and its information rate drops to a certain threshold as: (7) Fit<min(0,(St-S)Fadapt) It will die and be eliminated from the population. Here S is the initial population size and St is the current colony size, Fsplit and Fadapt are two control parameters used to adjust the bee reproduction and death criterions. It should be noticed that the population size will increase by one if a bee reproduces and reduce by one if it dies. As a result, the population size dynamically varies in the foraging process. At the beginning of the foraging process, the bee will reproduce when its information rate is larger than Freproduce. In the course of bee foraging, in order to avoid the population size becoming too large or too small, the reproduction and death criterions, namely Eq. (5) and Eq. (7), are delicately designed: if St is larger than S, for each Fadapt of their differences, the reproduce threshold value will increase by one; if St is smaller than S, for each Fdapt of their differences, the death threshold value will decrease by one. The strategy is also consistent with the natural law: if the population is too crowded, the competition between the individuals will increase and death becomes common; if the population is small, the individuals are easier to survive and reproduce. When the nutrient of a bee is less than zero, but it has not died yet, it could migrate with a probability as a scout bee. A random number is generated and if the number is less than migration probability Pe, it will migrate and move to a randomly produced position. Then nutrient of this bee will be reset to zero. In summary, in order to facilitate the below presentation and test formulation, we define a unified parameters for CLABC model in Table 1. 3 Benchmark test 3.1 Dynamic test function The moving peak benchmark (MPB) problem has been widely used as a dynamic benchmark problem in the literature (Morrison and De Jong, 1999). Within the MPB, the optima can be varied by three features, i.e., the location, height, and width of the peaks, which can be defined as follows:where N is the number of peak in the environment. Hi indicates the ith peak height, Ri is the slope control variable, and (Xi, Yi) represents the coordinate of its center. The initialization of parameters is shown in Table 2. But in our experimental studies, the height and range of slope for each peak are set to be constant, and only the positions of the peaks are changing. In this case, xstep and ystep are the step sizes in x and y direction respectively. And for each step i, Xi+1 and Yi+1 are calculated as where Ax and Ay are both a constant, respectively. Dxi and Dyi can be assigned 1 or −1 with probability 0.5, respectively. An example of the dynamic environment, generated with MPB, in four steps, is illustrated in Fig. 3. The experiments based on MPB are designed to evaluate the adaptability of CLABC for various dynamic environments. Various environmental changes are used in our simulation studies, which are divided into three ranges:(1) Range I – Slow level of environmental changes; (2) Range II – Intermediate level of environmental changes; (3) Range III – High level of environmental changes. The level of changes is reflected by the frequency of changes in the environment, which is defined as a probability f. For the environmental changes classified in Range I, f ∈ [0, 0.01], in Range II, f ∈ [0.05, 0.2] and in Range III f ∈ [0.3, 0.8]. In our simulation studies, f indicates the probability of occurrence of environmental changes after each foraging step. 3.2 Performance evaluation criteria 3.2.1 Average best over a period (ABP) The search ability of algorithms is one of the most important factors in optimization domain. In a dynamic environment, the optimum might be varying over time, which means that it is insufficient to only evaluate the fitness found after a certain number of generations. There is an alternative as evaluation criteria, which averages over the best solution found at each step during a period between two environmental changes (Tang et al., 2006). It is concerned with an average of the best values, denoted by average best, found over a period Ti, where Ti denotes the ith period. This average best over a period is denoted as ABP, which represents the best fitness for a given period Ti. Let Si be the first step of Ti, Ei be the last step. Thus, ABP is formulated as: 3.2.2 Accuracy To obtain the accuracy of algorithm A in function F, firstly, we calculate accuracy in each step t, Then, the accuracy as a whole is defined as:where Vw and Vo are the worst and optimum value respectively, N is the number of steps. 3.2.3 Stability Similar to the definition of accuracy, the stability is defined as follows: 3.3 Parameters settings for the involved algorithms With and without the orthogonal Latin squares population initialization strategy, two CLABC variants are tested, as seen in Table 3 with their experimental settings. To fully evaluate the performance of the proposed CLABC variants, the classical artificial bee colony algorithm (ABC) (Karaboga and Basturk, 2007a, Karaboga and Basturk, 2007b) was used for comparison. In all experiments in this section, the values of the initial population size used in each algorithm were chosen to be the same as 50. The number of function evaluations (FEs) was used as a measure criterion. All algorithms were terminated after 100,000 FEs. For the classical ABC algorithm, as referred to (Karaboga and Basturk, 2007a, Karaboga and Basturk, 2007b), the limit parameter of ABC is set to be SN × D, where D is the dimension of the problem, and is set 30. SN is the total number of employed bees and onlooker bees, and can be set 50. 3.4 Results for the dynamic MPB functions To investigate their performances in dynamical different-level environments, the frequency of changes with f = (0.001, 0.01), f = (0.05, 0.1) and f = (0.5, 0.8) are selected, which fall into from Range I to Range III, respectively. Accordingly, the dynamic accuracy and stability results of CLABC-1, CLABC-2 and ABC are given in Table 4 and Table 5, respectively. From the results, both CLABC-1 and CLABC-2 performed powerful when f = 0.001. However, CLABC-1 performed a little worse than ABC when f = 0.01, which belonged Range I. This means that the CLABC didn’t exhibit significant advantage when the dynamic environmental varied slowly. When f = 0.05 and f = 0.1 in Ranges II, both CLABC-1 and CLABC-2 achieved satisfactory performance. In contrast, the performance of ABC decreased obviously, this is due to the CLABC can react to the dynamical environmental changes effectively. When f = 0.5 and f = 0.8 in Range III, it is not surprising that both the CLABC-1 and CLABC-2 still maintain high accuracy and stability, which can be interpreted that, in this case, the dynamical environment changes rapidly and the strategies of life-cycle and crossover operation play an important role in improving the ability of involved algorithm to search in dynamical functions. The differences between the compared algorithms in terms of accuracy on these dynamic benchmarks suggest that the CLABC is better at a fine-gained search than its counterpart ABC. The key difference is mainly due to Powell’s search and crossover operator in the CLABC. The ABC with crossover operator acts as the main optimizer for quickly searching the near-optimal areal while the Powell’s method performs fine tuning the best solutions in a certain time interval. However, for the dynamic optimization cases, we need not only the high optimization accuracy and computation robustness, but also a faster solution speed. Fortunately, the life-cycle mode enables the population size and bee colony behaviors of CLABC can be dynamically adaptive to the complexity of the dynamic objective functions, which reduces the computational complexity of the optimization process. 4 Conclusions In order to improve classical artificial bee colony algorithm for complex dynamic optimization problems efficiently, this paper proposes a novel comprehensive learning artificial bee colony algorithm (CLABC) by combining several optimal foraging approaches, namely the orthogonal Latin squares population initialization, Powell’s search, crossover operation, and life-cycle strategies. This paper substantially extends the previous work on the ABC algorithms that can be distinguished from it from three aspects: (1) to refine the local search behaviors when a bee finds promising area, the Powell’s search is incorporated to emphasize the exploitation process. (2) to refine the bee-to-bee communication mechanism based on crossover that enhances the information exchange among bee colony, dealing with the global exploration. (3) to refine the bee life-cycle behaviors, which contains born, forage, reproduction, death, and migration states. Acknowledgements This research is partially supported by National Natural Science Foundation of China under Grant 61305082, 51378350, 5157518, 61602343 and 51607122. Peer review under responsibility of King Saud University. Fig. 1 The information exchange mechanism based on crossover operation. Fig. 2 Bee state transition in life-cycle model of CLABC. Fig. 3 A MPB example of environmental changes. Table 1 Parameters of the CLABC. CLABC = (S,D, Fsplit, Fadapt, CR, Tp, η) S Population size D Dimensions of optimization problem Fsplit Reproduction and death criterion Fadapt Control parameter to adjust reproduction and death criterion CR Selection rate Tp Parameter to activate Powell’s search Inertia coefficient Table 2 Parameter settings. Parameter Value N 15 H1 0 R1 0 X1 0.5 Y1 0.5 Hi [1,10] Ri [8,20] Xi [−1, 1] Yi [−1,1] i 2,…,N Table 3 Two versions of CLABC algorithms and their parameter settings. Algorithm Strategies Parameters S D η CR Freproduce Fadapt Tp CLABC-1 Life-cycle; crossover Powell’s search 50 30 0.6 1 (Lb-Ub)-2 50 5 CLABC-2 Orthogonal Latin squares population initialization; life-cycle; crossover; Powell’s search 50 30 0.6 1 (Lb-Ub)-1 50 5 Table 4 Accuracy comparison. Freq CLABC4 CLABC7 ABC ABC & CLABC7 CLABC4 & CLABC7 0.001 0.301559 0.082557 0.427601 0.175878 1.705158 0.01 0.39025 0.220559 0.44092 0.049244 0.453024 0.1 0.395654 0.139605 0.418915 0.025615 0.909054 0.05 0.324041 0.120704 0.432145 0.143515 1.124204 0.5 0.396415 0.091319 0.360905 −0.03961 1.264104 0.8 0.395924 0.130154 0.337033 −0.06569 0.721053 Table 5 Stability comparison. Freq CLABC4 CLABC7 ABC ABC & CLABC7 CLABC4 & CLABC7 0.001 0.01396 0.03214 0.001029 −0.41856 −0.4366 0.01 0.009465 0.04096 0.002116 −0.3466 −0.4276 0.1 0.012598 0.049604 0.011733 −0.04099 −0.34194 0.05 0.009299 0.042643 0.006278 −0.12593 −0.38243 0.5 0.014894 0.042692 0.037387 0.693567 −0.0588 0.8 0.018464 0.049709 0.037448 0.460629 −0.10788 ==== Refs References Banharnsakun A. Achalakul T. Sirinaovakul B. The best-so-far selection in artificial bee colony algorithm Appl. Soft Comput. 11 2011 2888 2901 Biswas S. Das S. Debchoudhury S. Kundu S. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30052-910.1016/j.sjbs.2017.01.043Original ArticleA resource-sharing model based on a repeated game in fog computing Sun Yan 1590sy@sina.com⁎Zhang Nan School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China⁎ Corresponding author. 1590sy@sina.com27 1 2017 3 2017 27 1 2017 24 3 687 694 23 10 2016 23 12 2016 7 1 2017 © 2017 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).With the rapid development of cloud computing techniques, the number of users is undergoing exponential growth. It is difficult for traditional data centers to perform many tasks in real time because of the limited bandwidth of resources. The concept of fog computing is proposed to support traditional cloud computing and to provide cloud services. In fog computing, the resource pool is composed of sporadic distributed resources that are more flexible and movable than a traditional data center. In this paper, we propose a fog computing structure and present a crowd-funding algorithm to integrate spare resources in the network. Furthermore, to encourage more resource owners to share their resources with the resource pool and to supervise the resource supporters as they actively perform their tasks, we propose an incentive mechanism in our algorithm. Simulation results show that our proposed incentive mechanism can effectively reduce the SLA violation rate and accelerate the completion of tasks. Keywords Fog computingRepeated gameCrowd-funding algorithm ==== Body 1 Introduction Cloud computing is a new service mode that can provide available and convenient network visits (Mell and Tim, 2011). It only took several years to integrate in people’s lives. At the far cloud end, data centers keep users from the bottom physical framework through virtualization technology and form a virtual resource pool for external services. The cloud data center is composed of many large servers that meet pay-as-you-go demand. These large-scale data centers are constructed by well-capitalized big companies, such as Google, Yahoo, etc. They possess the absolute right of control over resources, and users can only use resources. With the development of mobile internet, more and more heterogeneous devices are connected to the network (Zhang et al., 2011). Although large-scale cloud data centers can meet the complicated requests of users, bandwidth limits may cause network congestion and even service interruptions when many users request services from the data center at the same time. The QoS (quality of service) cannot be ensured if the request has to be processed by the far cloud end. Under this circumstance, fog computing was developed (Bonomi et al., 2012). Fog computing is a new resource provision mode in which the users not only can use the virtualized resources but can also provide services. In fog computing, some simple requests with high time sensitivity could be processed by geographically distributed devices that can absorb some pressure of the cloud data center. All devices with spare resources can be resource supporters of fog computing, even some sensors and smart phones. Since the resource supporter is closer to the resource consumer, fog computing is superior to cloud computing in terms of response speed. The resource supporters are all rational and would like to achieve some benefit for their resource contributions. If there is not an effective incentive mechanism, the resource owners will not contribute their resources (Vaquero and Rodero-Merino, 2014). Based on the above problems, the main contributions of this paper are presented as follows:(1) A system structure based on the neural network of the human body is put forward according to the characteristics of cloud and fog data centers. The reasonability of this system structure is analyzed. (2) Based on the idea of crowd-funding, a reward and punishment mechanism was established by integrating the computing capacity of geographically distributed devices. This mechanism encourages resource owners to contribute their spare resources and monitors the resource supporters to execute tasks positively; it then increases the working efficiency and reduces the SLA violation rate. In Section 2, we present the architecture of fog computing based on the human neural network, and describe related issues about crowd-funding. Then, we elaborate the crowd-funding algorithm flow and analyze it mathematically using repeated game theory. In Section 3, simulations are used to show the effects of this algorithm on reducing the SLA violation rate and decreasing task execution time. Our work is concluded, and future research directions are proposed in Section 4. In Section 5, the related work of fog computing is introduced. 2 Related works Due to continuous development of the internet of things technology, more intelligent devices are used in people’s daily lives. These geographically distributed devices possess tremendous idle resources. There are plenty of resources available for users in data centers. Therefore, coordinated management of these resources in a fog environment for automatic deployment, dynamic expansion and distribution according to user needs is a research hotspot. Many experts and scholars have explored coordinated resources management in the cloud and fog environment. Zhen et al. (2013) introduced the concept of “skewness.” By minimizing skewness, the overall utilization of server resources is improved to enhance the ability of the cloud data centers to provide resources to serve the users. They also developed a set of heuristics that effectively prevent system overload and conserve energy. Beloglazov et al. (2012) investigated the issue of virtual machine consolidation in heterogeneous data centers and presented an energy-efficient virtual machine deployment algorithm called MBFD. The algorithm selects the physical machine that increases the energy consumption of the system the least after placing a virtual machine as the destination host where a virtual machine should be placed. The algorithm plays an energy-saving role. Lee and Zomaya, 2012 generated two heuristic algorithms for task integration, ECTCC and MaxUtil. The goal of these heuristic algorithms is to reduce the energy consumption of data centers by improving resource utilization of the physical machines to turn on as few physical machines as possible. Hsu et al. (2014) proposed an energy-aware task consolidation (ETC) technique. The ETC minimizes energy consumption by restricting CPU use below a specified peak threshold and by consolidating tasks among virtual clusters. The network latency when a task migrates to another virtual cluster has been considered in the energy cost model. Gao et al. (2013) investigated the deployment of virtual machines under the homogeneous data center, regarding it as a multi-objective optimization problem. System resource utilization and energy consumption were optimized and a multi-objective ant colony algorithm was presented. Dong et al. (2013) designed a hierarchical heuristic algorithm that considers the communication between virtual machines when analyzing the virtual machine deployment problem. The energy consumption of physical and network resources is optimized. Wu et al. (2014) presented a green energy-efficient scheduling algorithm that efficiently assigns proper resources to users according to the users’ requirements in the cloud data center. Their algorithm increases resource utilization by meeting the minimum resource requirement of a job and prevents the excess use of resources. The DVFS technique is used to reduce energy consumption of servers in data centers. Aazam and Huh, 2015a, Aazam and Huh, 2015 proposed a resources management model based on fog computing. The model in (Aazam and Huh, 2015a, Aazam and Huh, 2015) considered resource prediction and allocation as well as user type and characteristics in a realistic and dynamic way, thus enabling to adaption to different telecom operators according to requirements. However, their resources management model neglected heterogeneous services, service quality and device movement. In (Aazam and Huh, 2015a, Aazam and Huh, 2015), the authors proposed a high-efficiency resources management framework. Since fog computing involves different types of objects and devices, how many resources will be consumed and whether request node, device or sensor will make full use of requested resources are unpredictable. Therefore, they developed a resources evaluation and management method by comparing the abandonment probability of fluctuating users to service type and service prices as well as the variance of abandonment probability. This method was conducive to determining correct resource demand and avoiding resource waste. Nevertheless, their resources management model analyzed from the perspective of only the service supplier, and neglected the economic benefits of service users. In (Do et al., 2015), the authors studied resource co-allocation in fog computing and reducing carbon emissions. A high-efficiency distributed algorithm based on the near-end algorithm was developed that decomposed large-scale global problems into several sub problems that can be solved quickly. However, this algorithm only focused on a single data center and neglected the fact that there are multiple small data centers in fog computing. SU et al. analyzed how to share or cache resources between servers effectively using the Steiner tree theory (Su et al., 2015). When the fog server is caching resources, a Steiner tree is produced first to minimize the total path cost. Next, the Steiner tree is compared with a traditional shortest path scheme, which proved that the Steiner tree is more efficient. However, they only analyzed resources management issues between servers and did not perform collaborative analyses on distributed user resources in the fog environment. In (Zeng et al., 2016), the authors designed a high-efficiency task scheduling and resources management strategy designed to minimize time to accomplish tasks in the fog environment to enhance user experiences. For this reason, the authors discussed three problems: (1) how to balance loads between user devices and the computing server, or task scheduling; (2) how to place task images on the storage server, or resources management; and (3) how to balance I/O interrupt requests between storage servers. They were abstracted into a mixed integer nonlinear programing problem. However, the authors basically applied concentrated resources management under cloud computing and did not consider the distributed structural characteristics of fog computing. In (Lee et al., 2016), the authors put forward a gateway conceptual model based on a fog computing framework. This framework mainly consisted of host nodes and slave nodes, managing virtual gateways and resources. This model was suspended in the theoretical study. How to limit found resources in actual application scenarios and determine which resources need virtualization and how to integrate virtual resources have to be solved in the future. In (Song et al., 2016), the authors established a load equilibrium algorithm based on dynamic graph division that could allocate system resources effectively and reduce loss caused by node transferring. This algorithm sacrificed system performance for resource management, which influenced the user experience. In (Wang et al., 2016), the authors introduced the concept of multimedia perception of service and put forward a new resource allocation framework at the cloud edge, or fog end. This framework analyzed the dependence of data in the space, time and frequency domains as well as energy efficiency under different resource allocation strategies considering the effect of a flexible information channel coding rate. Multimedia perception-oriented users designed a physical resource allocation strategy. Their study emphasized data analysis, but did not have a specific resource collaborative management scheme. In (Zeng et al., 2016, Guo et al., 2016), the authors considered an embedded system that was defined by fog computing support software. To enable users to accomplish tasks in minimum time, an efficient resource management strategy was designed. However, this strategy had the disadvantage of overly high computing complexity and poor resource management. To solve the computing complexity problem, in (Gu et al., 2016, Liu, 2013), the authors put forward a two-stage heuristic algorithm based on linear programming that was proven to be highly cost-efficient by experimental results. Nevertheless, most existing research is based on a fixed resources supply model, resulting in low resource flexibility. The performance of the resources management system is the key to fog computing technology. Devices connected to the network and user demands increase with the continuous development of fog computing, causing resource bottlenecks at the data center. The cloud data center has difficulty meeting the demands of users with high real-time requests. Therefore, it becomes more important to discuss collaborative resources management of data center and network edge devices. Such collaborative management is more complicated because user resources are often distributed and the same resource is often shared by numerous computational nodes. User resources management not only involves topology, configuration, capacity and other intrinsic properties of the network but is also closely related with computing resources, storage resources and distribution of applications. Therefore, studying collaborative management of data center and user resources is challenging and urgent. 3 Methods For the basic structure of fog computing, most existing research is a three-tiered architecture where fog cloud computing lies between the cloud computing layer and the Internet of Things layer. The fog computing layer is composed of some small data centers, located at the edge of the network where they are closer to users. They can handle relatively simple and high real-time task requirements. We are inspired by Ning and Wang (2011) who proposed a future architecture of the Internet of Things that is similar to a human neural network. The architecture is shown in Fig. 1 and consists of the brain nerve center (cloud data centers), spinal nerve center (fog computing data centers), and the peripheral nerves (smart devices), widely distributed all over the body. The activities of the spinal cord are controlled by the brain. Peripheral nerves are distributed in the body. They feel stimulation and transfer tasks. The spinal nervous system handles the simple unconditioned reflex, such as the knee jerk reflex. If all requests had to be dealt with by the brain, the brain would be extremely tired. Similar to the characteristics of neural structures of the body, we designed a new system architecture. In our architecture, the intelligent devices can be seen as the peripheral nerves that are widespread geographically, such as the phones, tablets, smart watches, or sensors. The Fog computing center will address some simple and time-sensitive requests (such as the spinal cord knee jerk reflex) that can share the resource pressure of the cloud data center. The spinal cord is the connecting pathway between peripheral nerves and the brain, which is similar to the location of the fog data center that is the bridge of the underlying Internet of Things and high-level cloud data centers. 3.1 Game model description In the open and sharing mobile Internet era, many spare resources are underutilized. In fog computing, users will not take the initiative to contribute their spare resources if there is not an effective incentive mechanism. We established a set of incentive mechanisms based on the idea of crowd-funding and repeated games, and some definitions are as follows: 3.1.1 Definition 1: broker The local fog computing data centers constructed by small enterprises or universities that have the ability to provide services for users. However, the computing and storage services that they provide are limited. They are eager to improve by integrating the resources of resource supporters. 3.1.2 Definition 2: resources supporters The resource owners who are willing to contribute some or all of their spare resources and execute tasks assigned by fog data centers are the resource supporters. They can earn rewards by contributing their resource capacities. 3.1.3 Definition 3: crowd-funding reward α is the financial reward that resource supporters get from the fog broker per unit time by contributing resources. 3.1.4 Definition 4: task reward β is the financial reward that resource supporters get from the fog broker by performing tasks. 3.1.5 Definition 5: discount factor δ reflects the degree of patience of players in the game. 3.1.6 Definition 6: self-loss The self loss ϕ indicates the energy costs and risk costs of crowd-funding supporters when they actively execute tasks. The resource utilization of crowd-funding supporters will improve if they fully use their resources to actively perform a task. System utilization and power consumption are linear according to (Fan et al., 2007, Kusic et al., 2009), which indicates that the increase in system utilization leads to improvement in energy costs. Even if a supporter has some spare resources at present, these resources may be used at another time. Contributing resources will increase the risk of resource shortages on their own devices. Our crowd-funding algorithm is designed as shown in Fig. 2. To encourage the resource owners to contribute their resources, the fog broker promises to give the supporters a higher bandwidth if they contribute their spare resources. The additional revenue brought by the higher bandwidth is the crowd-funding reward denoted by α. With the objective of obtaining a higher bandwidth, users will select contributing resources to form a local crowd-funding resource pool. However, after crowd-funding supporters have achieved the benefits, they may refuse to continually provide the resources. To monitor user consistency in contributing resources, we design an incentive mechanism based on the repeated game theory. First, supporters consider whether to accept the task assigned by the fog broker. If the supporter accepts tasks, they will get a higher reward β. If the user refuses, he can only get α because he is contributing resources (α < β). If the supporter accepts the task, he will perform the task positively or negatively. The supporters will bring some self-loss ϕ if they perform tasks positively. Suppose the fog broker is unable to know whether the supporter is active and only knows the result of the task. When the task is executed successfully, the income of the fog broker is θ (θ > 0). If the task failed to finish, the income of the fog broker is 0. Suppose when the supporter actively performs the task, the task will surely succeed. When the supporter passively performs the task, the probability of completing the task successfully will be P, and the probability of failure is 1 − P. If a task was performed in only one stage, rational supporters will choose performing tasks negatively. Given this, a task will be divided into many stages, so the supporters do not know the end time of the task. Thus, the selection process of resource supporters is equal to an infinite repeated game. To ensure that the supporters perform tasks actively, the supporters will be put on a black list if they do not complete the task in time. This means they will no longer get a reward from the fog broker. Therefore, supporters will make full use of their own resources in order to get more rewards. Then, we design a reasonable trigger strategy according to the concept of the repeated game to motivate and supervise supporters to actively complete tasks. The resources pool of the fog broker has been effectively expanded. It increases capacity for the task and alleviates the pressure on the bandwidth of the cloud data center. Supporters also gain a reward. 3.2 Game analysis for our algorithm Next, we use repeated game theory to analyze whether the algorithm can effectively motivate supporters to contribute their spare resources and actively perform tasks. In the incentive mechanism we designed, the game between fog broker and crowd-funding supporters is considered as a repeated game with complete information. Assume that the game has perfect memories meaning game players can remember information about themselves and others. At a random stage G, the player will determine his own strategies based on the strategies of the other side. Here, we first introduce the stage game where a task only executes one stage G. 3.2.1 Stage G The fog broker strategy combination is {β|β⩾0} that gives crowd-funding supporters a reward. The crowd-funding supporter strategy combination is a function from {β|β⩾0} to {actively perform tasks, passively perform tasks, and refuse to perform tasks} that is an infinite strategy dynamic game. In this strategy, actively performing tasks, passively performing tasks or refusing to perform tasks is the response of crowd-funding supporters to how much reward the fog broker has given. Suppose the broker and supporters are rational individuals who are eager to maximize their own benefits. Since the award is paid in advance and contributing their own resources continuously will bring additional costs of energy and risk, if there is not a punitive measure, crowd-funding supporters must select passively performing tasks, and the expected revenue of the fog broker is (pθ − β). We assume that pθ − β < 0 (performing tasks negatively is difficult to complete the task within the required time, therefore, generally p is relatively low). Such being the case, the fog broker will not give any reward to supporters, i.e., β = 0. Therefore, crowd-funding supporters will certainly select performing tasks negatively. Thus, the Nash equilibrium of stage G is: {β = 0, selecting performing tasks negatively when β = 0}. When the model becomes a super game with stage repeats, players can decide their strategies according to the memory of stage G. To avoid fog brokers giving cheap rewards and crowd-funding supporters performing tasks negatively, we design a trigger strategy that is a credible threat to both the fog broker and crowd-funding supporters so that getting rid of this unfavorable situation and reaching Pareto is an excellent outcome. 3.2.2 Trigger strategy T: • On the Fog Broker side: pay a higher reward β∗ at the first stage; if the payoff of the fog broker is always θ in the former (t-1) phase, then continue to pay β∗; otherwise no longer give any reward, i.e., β∗ = 0. • On the Crowd-funding Supporter side: if the reward is higher than α, accept tasks assigned by the broker. If the first (t-1) stage rewards are always β∗, then users continue to actively perform tasks at phase t, otherwise execute tasks passively. The ultimate goal of supporters and the fog broker is to get the highest capital return. Since the supporter does not know at which stage the task ends, equivalently there is an infinite repeated game with no final stage. To ensure the credibility of the trigger strategy, the trigger strategy T needs to satisfy the sub-game refining Nash equilibrium. We will analyze it as follows: If the players do not deviate from the trigger strategy, the fog data center gives a higher reward β∗, and supporters complete tasks actively, the payoff function of the fog broker in the whole repeated games is: (1) fw=θ-β∗+δ(θ-β∗)+δ2(θ-β∗)+… When resource supporters perform tasks positively, the payoff function is: (2) uw=β∗-ϕ+δ(β∗-ϕ)+δ2(β∗-ϕ)+=β∗-ϕ+δ1-δ(β∗-ϕ) If any player deviates from the trigger strategy, the Nash equilibrium will return. Because the fog datacenter does not give any reward, it will get nothing in return. The payoff function of the fog data center is: fz=0. If supporters choose performing tasks negatively, they may complete the task with a weak probability p. Once beyond the longest completion time the requestor can tolerate, the crowd-funding supporter can get no incentive from the fog datacenter, they can only get the basic contribution reward α from the fog broker. The payoff function when supporters perform tasks negatively is: (3) uz=β∗+pδuz+1+(1-p)(δα+δ2α+⋯)≅β∗+pδuz+(1-p)(δα+δ2α+⋯) If the trigger strategy is useful to encourage supporters and brokers, the payoff function uw of supporters for actively performing tasks should be more than uz of users for performing tasks negatively. In addition, the payoff function fw of brokers for giving a high reward should be more than fz of brokers for giving a low reward. (4) fw>fzuw>uz⇒θ-β∗1-δ>0β∗-ϕ1-δ>β∗+δ(1-p)α1-δ1-pδ (4) can be solved as follows: (5) α+ϕ+1-δδ(1-p)ϕ<β∗<θ Therefore, if condition (1.5) is met, the trigger strategy is the Nash equilibrium of the original game. The beginning sub-game between two crowd-funding stages has the same structure with the originally repeated game, which is an infinitely repeated game. Therefore, the triggering strategy is also the Nash equilibrium in the sub-game under condition (1.5). If the stages before the beginning sub-game are all in Nash equilibrium, then the fog broker will still give a high reward β∗. Therefore, the optimal strategy of crowd-funding supporters is to perform tasks positively. Then, supporters must complete the sub-task successfully because of active performance. In subsequent phases, the fog broker will continue to give a high β∗ and continue the trigger strategy. The trigger strategy combination in the sub-game is also a Nash equilibrium. Therefore, this strategy combination is a sub-game perfect Nash equilibrium, which indicates that the trigger mechanism is credible. This strategy combination encourages the game players to maintain cooperation with the fog broker. 4 Results A small crowd-funding platform was established based on the basic framework of the extended distributed system Hadoop that consisted of a fog broker, a cloud data center and crowd-funding supporters. The crowd-funding supporters were 50 smartphones, which formed a virtual resource pool for external services. The configuration parameters of fog broker and supporters are shown in Tables 1 and 2, respectively. Our simulation mainly detected the SLA violation rate and time for completion of a task under different task loads. Application pressure test data were generated by JMeter. Specific parameter configuration is introduced in the following Tables 1 and 2. The SLA violation rate is defined as the proportion of the number of failed tasks to the number of total tasks. “Failed tasks” means that the supporter failed to complete tasks in the time required by the task requester. As a typical dynamic scheduling algorithm, Min-Min algorithm (Braun et al., 2001) chooses resources by calculating the minimum for double and schedules abundant tasks onto corresponding virtual machines the most quickly, thus enabling completion of all tasks in the shortest time. The MBFD algorithm proposed in (Beloglazov et al., 2012) allocates tasks based on CPU utilization rate. Our resource crowd-funding algorithm encourages resource supporters to contribute their spare resources and process task requests while achieving a bonus in return. SLA violation rates and the time of completing tasks under different task numbers are shown in Figure 3, Figure 4. It can be seen from Fig. 3 that the SLA violation rate with our algorithm is always lower than MM and MBFD. This is because the bonus incentive encourages crowd-funding supporters to make use of idle resources to execute tasks positively and accomplish user task requests in the stipulated time. It will not suffer resource shortages, thus reducing the SLA violation rate. With the increase of tasks, the SLA violation rate of the proposed algorithm showed better stability than the other two algorithms. The times to accomplish tasks using the three algorithms under different loads are presented in Fig. 4. The proposed algorithm achieved significantly higher execution efficiency than the other two algorithms. This is because crowd-funding users are at the network edges and beyond the restriction of bandwidth, and tasks do not need to be transmitted to the cloud end. With the increase of loads, the proposed algorithm still took less time to accomplish tasks than MM and MBFD. 5 Discussion and conclusions In this paper, we present a system structure based on the neural network of the human body according to the characteristics of cloud and fog data centers. Then, we design a resource crowd-funding algorithm to integrate sporadic resources to form a dynamic resource pool that can optimize the spare resources in the local network. A comprehensive reward and punishment mechanism is presented for the resource supporters in the resource pool. The simulation results shows that our scheme can effectively increase working efficiency and reduce the SLA violation rate by encouraging resource owners to contribute their spare resources and by monitoring the resource supporters to ensure they execute tasks positively. Through this research, we find that unless these widespread devices can work together to create meaningful services, all the resources from devices may be meaningless. Therefore, the integration must be conducted seamlessly and intelligently. Energy consumption is also an important issue in fog computing systems, and it will be studied in the future. Reducing energy consumption to reduce the costs of service providers is of great significance. Improving the resource utilization of data centers and reducing energy consumption in fog computing data centers will be an important future research direction. Peer review under responsibility of King Saud University. Figure 1 Architecture of fog computing based on the nervous system. Figure 2 The flow of the crowd-funding algorithm. Figure 3 The comparison of SLA violation rates for three schemes with different numbers of tasks. Figure 4 The comparison of completion times for three schemes with different numbers of tasks. Table 1 Configuration parameters of the fog broker. CPU Intel Core 2 DuoE5200 Memory space 4G Operating system Win7 Table 2 Configuration parameters of crowd-funding supporters. CPU Exynos 8890 RAM 2G ROM 32G Operating system Android 4.4.3 Storage space 16G ==== Refs References Aazam, M., Huh, E. N., 2015. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30033-510.1016/j.sjbs.2017.01.024Original ArticleSupport vector machine-based open crop model (SBOCM): Case of rice production in China Su Ying-xue Xu Huan Yan Li-jiao tulipblue16@zju.edu.cn⁎College of Life Sciences, Zhejiang University, 310058 Hangzhou, Zhejiang Province, PR China⁎ Corresponding author. tulipblue16@zju.edu.cn30 1 2017 3 2017 30 1 2017 24 3 537 547 26 10 2016 5 1 2017 9 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability. Keywords Crop modelCrop simulationScaling upSupport vector machineSBOCM ==== Body 1 Introduction Crop model is the general term used to identify a series of methods that use mathematical concepts to describe the process of crop growth. However, based on a combination of previous definitions (Curry, 1971, Edwards and Hamson, 1990, Gao, 2004, Sinclair and Seligman, 1996, Xiong, 2004) and the findings of the present study, a crop model is defined in this paper as a computer program that mathematically describes and models the rules of crop growth and can be used to quantitatively and dynamically explain the process of crop growth, development, yield, and reaction to environmental changes. Crop models can be categorized as crop statistical models and crop simulation models (or crop growth models) based on the basic mathematical method of the modeling. Simulation models were generally considered to be better than statistical models because it facilitated the study of crop growth theory in a physiological sense through the enablement of experimental comparison. However, with increasing dissatisfaction with the fit effect of the large-scale nonlinear problems associated with early statistical models, simulation models were more widely employed (Xie and James, 2002). Famous crop models such as the CERES model (Charles-Edwards, 1986, Jones and Kiniry, 1986, Ritchie, 1972) series of America, SUCROS (Lin et al., 2003) and MACROS (Penning de Vries et al., 1989) models of Netherlands, and RSM model (Luo et al., 1990) of China are all crop simulation models. Through the efforts of several generations of experts, crop models and various agricultural production and decision systems that primarily utilize crop models have contributed to both crop development physiology studies and agricultural production, in which field there has been much achievement. Since the 1990s, a number of large-scale agricultural decision system software packages taking these crop models as kernels have been developed through application modules, human–machine interface optimization, integration of decision systems, and data normalization. Such developments include the Decision Support System for Agrotechnology Transfer (DSSAT) model of America, Agricultural Production Systems sIMulator (APSIM) model of Australia, and Crop Cultivational Simulation Optimization Decision-making System (CCSODS) of China (Gao, 2004). These models have been vigorously promoted and utilize to different degrees in various countries around the world. Although simulation models are more widely employed, it is still difficult to determine whether they are actually presently better than statistical models (Dhungana et al., 2006). This is mainly because of the technological and practicality bottlenecks encountered by the former in the early 21st century when they were promoted as the main crop models. The technological bottleneck regarded how to implement simple operations and scaling up, while the practicality bottleneck was due to the grey system feature in agricultural extension. The weaknesses of crop simulation models gradually became apparent when they were put into practical use in the early 1980s. Meanwhile, statistical models had been found to be practical through several large-scale studies (Stewart and Dwyer, 1990). This led to the emergence of the American school of thought that used statistical models for simulation purposes as need arose. The currently popular American CERES model is a typical American school of thought, being a simulation model in a general sense, but with an integrated statistical estimation method (Swain et al., 2007). While crop modeling was encountering its bottlenecks, non-linear statistical theory, particular with regard to machine learning, was making a huge breakthrough in the 1990s. Since then, artificial intelligence has undergone comprehensive development and application through the use of computer iterative algorithms such as the support vector machines (SVMs) (Vapnik, 1998, Vapnik, 1999, Cortes and Vapnik, 1995). Owing to their good sparsity (Gunn, 1998), ability to fit small samples (Suykens, 2001), and global optimization (Xu et al., 2007), SVMs have outperformed other none-linear statistical models (Gualtieri and Cromp, 1998, Viaene et al., 2001, Van Gestel et al., 2001a, Van Gestel et al., 2001b, Xiong, 2009, Zhang, 2009). In recent years, SVMs have also been applied in agricultural production for purposes such as remote monitoring, moisture prediction, and plant disease and insect pest warning (Gill et al., 2006, Du et al., 2008, Kaundal et al., 2006, Trafalis et al., 2007, Yang et al., 2008, Yu et al., 2008). Rice, which is China’s main food crop, was considered in the present study. An SVM was incorporated into the developed crop model, which is here presented as SVM-based open crop model (SBOCM). The basic idea of this study was the use of basic geographic information obtained from surface weather observation stations in China (i.e., daily published meteorological data and the 1:1000000 soil database published by the Chinese Academy of Sciences [CAS] (Shi et al., 2002)) as input, and the rice development and yield records of all agricultural observation stations in China as output. A dynamic open reading frame was designed to dynamically input the daily meteorological data, and a scheduled developmental stage prediction was obtained by SVM classification (SVC), and yield prediction by SVM regression (SVR). 2 Materials and methods 2.1 Support vector machine The SVM-by-Steve Gunn v2.1 software in the MATLAB kit was adopted in our SVM program. The SVM software was presented by Vapnik in the middle 1990s (Cortes and Vapnik, 1995) and has been widely used for machine learning over the last 15 years (Vapnik, 1998, Vapnik, 1999). The theoretical basis of the SVM is the Structural Risk Minimization Principle in statistical learning theory (Vapnik, 1998). Kernel functions were used to convert the linear inseparability problem in low-dimension space into a linear partition problem in high-dimensional space. The optimal hyperplane was determined to separate the two groups of eigenvectors based on their respective longest distances from the interface. For the given training set T={(x1,y1),…,(xl,yl)}∈(X×Y)l, where xi∈X=R and yi∈Y={-1,1}(i=1,2,…,l), a real-valued function g(x) on X=Rn was sort as the decision function f(x). (1) f(x)=sgn(g(x)) The value of y corresponding to x in any mode could be inferred by f(x). In other words, a rule for dividing the points on Rn into two parts was sought. The linearly separable training set min12w2+C∑i=1lξi was obtained under the following constraint in SVM: (2) yi((w·xi)+b)+ξi⩾1,i=1,…,l where ξi is the slack variable and C is the penalty coefficient, which should be set artificially in practice. After obtaining the optimal solutions ω∗ and b∗, the following separating hyperplane was constructed: (3) (w∗·x)+b∗=0 The decision function was then obtained as (4) f(x)=sgn((w∗·x)+b∗) When the sample training set was non-linear and separable, kernel functions (sometimes denoted by K in SVM programs) were required in SVM to deal with the non-linear classification problem and build a mapping relationship between the input vectors and high-dimensional space vectors. Our study considered the non-linear inseparable problem, wherein, theoretically, the introduction of slack variables and kernel functions did not affect the solution of simple linear separable problems. Hence, both slack variables and kernel functions were introduced into the SVM training. Linear, polynomial, and radical basis kernel functions were adopted in this study. 2.2 Open reading frame Two types of SVM training samples were used: (1) SVC binary classification samples, which were used to investigate the occurrence time of certain developmental stages of rice; (2) SVR samples, which were used to investigate certain yield records of rice; a unit of which comprised a pair of input and output vectors, with each pair constituting a record. Modeling requires consistence of the unit structures within a sample. Five developmental stages of rice were considered in this study, namely, sowing, transplanting, tillering, heading, and milk, and the agricultural production time varied significantly among the stations. Thus, in the development of the training samples, we ensured as much as was possible that the sample rules included the principle of maintaining the biological significance of samples. For the above reasons, the corresponding input vector of each record consisted of both static and dynamic variables. The static variables included basic information and soil information of each station that generated records. Hence, the records obtained from a particular station had the same static variables irrespective of the developmental stage. Meanwhile, the dynamic variables could vary with the developmental stage of a record or yield prediction target. An open reading frame was set to generate the dynamic variables. The open reading frame was a fixed-length input window for the daily meteorological data. The length was fixed for a given sample so that the generated variables would be of a certain length. The frame could read the daily meteorological data for a certain period in accordance with the requirements for sample generation, and could generate input variables using one developmental record and yield record. This is done using the methods for generating positive samples in the developmental stages (Fig. 1) and for choosing two developmental stages for the generation of dynamic variables (Fig. 2). 2.3 Data preprocessing Historical data obtained from two sets of observation systems of the China Meteorological Administration network database (http://www.cma.gov.cn/) were used in this study. Three types of rice plantings were considered, namely, middle-season, early, and late planting. After organization of the station information, soil information, and daily meteorological data, and screening them based on their biological significance, we chose a seven-day open reading frame. It was determined that the variables consisted of the initial input variables (Table 1), which added up to 53 dimensions. The samples were built in five developmental stages, namely, sowing, transplanting, tillering, heading, and milk, respectively. Principal component analysis (PCA) was used to screen the different factors, based on which all the classes of the samples used for the SVM training were built. The samples for modeling the SVC developmental prediction were binary classified; i.e., the output of the samples for determining whether a developmental stage had occurred was labeled as “yes” or “no” The dynamic variables of the positive samples were the daily meteorological factors for seven days before the occurrence of a given developmental stage, while those of the negative samples were generated by off-season (150 days in advance) and 30 days in advance strategies, respectively. For convenient expression, the training samples consisting of the positive samples and off-season negative samples were identified as sample class 1, while the ones consisting of the positive samples and 30-days-in-advance negative samples were identified as sample class 2. Thus, five developmental stage samples for each of the three planting types were generated, adding up to 3 × 5 = 30 different samples. These were respectively used to predict the five developmental stages of sowing, transplanting, tillering, heading, and milk, respectively. In the actual training, each set of samples was randomly divided into five parts of equal sizes. Fivefold cross-validation was then used for the model training and testing. 2.4 Building developmental module By SVC, a developmental module is capable of organizing the data sets of the five developmental stages and separately modeling middle-season rice, early rice, and late rice. In the present study, by separately modeling the two classes of training samples and comparing the models, we finally obtained the best prediction model of the occurrence time of the sowing stage. The specific process was as follows. Linear (linearly separable SVC without the use of kernel functions), polynomial, and radical basis kernel functions were chosen and used to conduct SVC training. Fivefold cross-validation was then used to model and test the prediction of the sowing stage for finding the optimal kernel functions. For polynomial and radical basis kernel functions, the optimal hyperparameters were determined via ergodic tests on the corresponding hyperparameters. The SVC penalty coefficient was subsequently further adjusted to improve the optimal model, which had been tested and found to be partly unsatisfactory. Through comparison of the two optimal models developed by the two different strategies, the more suitable model for sowing stage prediction was finally identified. 2.5 Building yield module By SVR, a yield module is capable of yield prediction based on SVM analysis of the information obtained from a given station and the soil and daily meteorological data during different developmental stages of the particular type of rice. The module then outputs the record of the rice yield for the given year. The SVR models developed in this study were respectively based on samples for the tillering stage, heading stage, tillering and heading stages, milk stage, and heading and milk stages. The optimal yield prediction models for the heading, tillering and milk stages were figured out after a comparison. The specific modeling and optimization process was as follows. The linear (linearly separable SVR without using kernel functions), polynomial, and radical basis kernel functions were used for SVR training, after which fivefold cross-validation was used for modeling and testing, respectively. For polynomial and radical basis kernel functions, the optimal hyperparameters were determined via ergodic tests on the corresponding hyperparameters. The SVR penalty coefficient was subsequently further adjusted to improve the optimal model, which had been tested and found to be partly unsatisfactory. Through comparison of the several optimal models developed by the different strategies, we finally identified the most suitable model for yield prediction. 3 Results 3.1 Development module Two classes of samples, fivefold cross-validation, and different kernel functions and hyperparameters were used for separate training of all the developmental stages of the middle-season rice, early rice, and late rice. Based on the training performances for the same developmental stages, we chose the sample class 1 optimal kernel functions and hyperparameters for all the developmental stages (see Table 2, Table 3, Table 4), for which the SVC penalty coefficient was always 1. Because of the satisfactory F1 value of the sample class 2, the penalty coefficient C was further adjusted to improve all the models after the optimal kernel functions and their hyperparameters had been determined. We realized from the findings of previous studies (Cai et al., 2003, Gill et al., 2006, Gunn, 1998, Suykens et al., 2001, Van Gestel et al., 2001b, Van Gestel et al., 2001c) that the penalty coefficient generally increases at a rate of 102 and that an excessively large value of C significantly decreases the computation efficiency (Fig. 3). Hence, all the models were tested using C = 1, 10, 100, and 10000, respectively (Table 5). Through the adjustment of C, we chose the sample class 2 optimal kernel functions and hyperparameters for the training of all the developmental stages (Table 6). 3.2 Yield module Five classes of samples, fivefold cross-validation, and different kernel functions and hyperparameters were used for the separate training and testing of the three classes of rice plantings. A penalty coefficient C of 1 was used to compute the root-mean-square error (RMSE) (kg/h m2) and relative error (RE) (%) of each training. We then chose the most suitable sample, optimal kernel functions, and hyperparameters for all the developmental stages of the yield simulation (Table 7), for which the SVR penalty coefficient was always 1. Because the yield simulation was not sufficiently accurate, we further adjusted the penalty coefficient C after the most suitable sample, optimal kernel functions, and hyperparameters had been determined (Fig. 4 and Table 8). This was done to improve the accuracy of all the models as we did with the development module. The most suitable prediction models for all the developmental stages and their respective performances (Table 9) were finally determined. 3.3 SVM-based open crop model Based on the above results, an SVM-based open crop model (SBOCM) was designed as an application-focused crop model for regional rice development stage prediction and yield prediction. The emphasis was on simplicity of operation. The framework of the entire SBOCM system was quite simple, comprising five parts, namely, the database module, data calling module, development prediction module, yield prediction module, and human–machine interface (Fig. 5). The SBOCM had the following functions and features:– Perennial simulation: Historical meteorological data was used as the basic input to directly simulate the time and yield of all the developmental stages in the various regions. – One-year prediction: The real-time meteorological data of a particular year was used to dynamically follow and simulate all the developmental stages at all the stations, thus enabling real-time predictions of the developmental stages and yield. – Regional prediction: The input of the SBOCM was regional data and the yield simulation of a particular place only required the integration of the corresponding meteorological data and soil and other information of the place. We could thus simulate an entire region without conversion between point and surface models. Previously, the application of the function required users to write MATLAB scripts by themselves and automatically and repeatedly input all point data into the SBOCM. – Extendibility: The features of the input variables of the SVM imply the capability of the SBOCM for future absorption of more natural and social factor inputs. This is significant for the extension of the applicable functions of the model. 4 Discussion 4.1 Kernel function, hyperparameter, and penalty coefficient The key issue in SVM modeling is the determination of the kernel functions, hyperparameters, and penalty coefficient. In the present study, the SVC of five developmental stage predictions of three rice planting types, namely, middle-season, early, and late rice planting, and the SVR of the yield predictions of three developmental points were separately determined. It was observed from the final models that, as far as the kernel functions were concerned, the developmental stage predictions and yield prediction were all complicated nonlinear segmentation problems. Thus, the performances of the polynomial and radical basis kernel functions were better than those of linear functions. This was especially so for yield prediction, wherein the radical basis kernel function prediction accuracy increased with increasing variable dimensions. For most problems, the hyperparameters were found to be within a rational range. For example, D for the polynomial kernel functions was generally between 2 and 4, while p for the radical basis kernel functions was between 1 and 1.5. In conformity with the empirical range of hyperparameters determined in most previous studies (Cai et al., 2003, Gunn, 1998, Trafalis et al., 2007, Van Gestel et al., 2001c), excessively highly values of D and p were found no to be advantageous to nonlinear space mapping. The contribution of the penalty coefficient to improving SVM segmentation was very limited. It led to a significant increase in the computation complexity (see Figure 3, Figure 4). The actual crop modeling was a complicated nonlinear problem and it was very difficult to achieve optimal segmentation using a high-dimensional SVM. The determination of the optimal interface of the sample classes was thus often quite difficult. A higher penalty coefficient only increased this difficulty of the SVM identifying the optimal interface, which happened to be of no benefit to the present study (Gunn, 1998, Suykens et al., 2001). 4.2 Effect of negative samples In the SVC training, the quality of the negative samples had greater effect on the results than the kernel functions and hyperparameters. The information used for the SVC learning was provided by both the negative and positive samples to ensure optimal interfacing. However, for the developmental SVC, the positive samples were determined by the dynamic variables generated by the daily factors that were inputted to the open reading frame of “the occurrence day of a certain developmental stage.” The use of the negative samples was more difficult. Theoretically, any open reading frame that does not correspond to the occurrence day of a certain developmental stage can generate negative samples. However, in practice, it is necessary to maintain proper “distance” between the positive and negative samples so that the SVC can achieve perfect classified learning. The off-season and 30-day-in-advance samples were used in this study. The positive samples generated by the off-season strategy contributed to the improvement of the SVC sensitivity for developmental stage predictions, while low false positivity was observed in the 30-day strategy, as well as much greater SVC learning difficulty. The question thus arises about whether the use of 30-day-in-advance samples is a proper alternative. Apparently not because the meteorological factors differed significantly for a three-month time difference. The SVC sensitivity would thus increase with the possible increase in false positivity. Actually, the 30-day-in-advance strategy still showed rather high false positivity because development is a complicated ecological, physiological, and biochemical process. The process not only includes metabolism and nutrition and water physiologies related to many enzyme systems, but also involves the cultivation environment (sunlight, temperature, water, fertilizer, soil, air, etc.) and the degree of coordination between the source, sink, and flow of the ecosystem. Furthermore, the thermo-sensitivities, photo-sensitivities, and basic vegetative growths of middle season rice, early rice, and late rice are not identical, being DNA-controlled. Concisely, genotype + environment = phenotype. Incidentally, there were phenotype differences (morphology, plant type, maturity, resistance, fertility, etc.) among the three rice and planting types. It would thus be difficult to improve the SVC accuracy by changing the sampling strategy, hence the need for new factors and methods. 4.3 Error source analysis 4.3.1 Mechanism problems Classic crop models are based on decades of research on the internal growth and developmental mechanisms of crops and are highly accurate for field application. As a typical machine learning method, SVM is outstanding for nonlinear fitting. It is characterized by a simple framework and explicit input and output, although insufficient attention is given to the internal physiology of the investigated object. SVMs were used in this study to explore the feasibility of applying machine learning to recent crop modeling, and the potential of an SVM open framework. The target was to achieve a prediction accuracy comparable to that of a simple SVM within a short time. This was found to be apparently impossible. 4.3.2 Sample defects This study mostly employed historical data obtained from the China Meteorological Administration, generated from stations with uncontrolled data quality. After preprocessing, the data still contained defects such as artificial errors, limited factors, excessive effects of macroscopic soil factors, too short open reading frame, insufficient field experiment data for referencing and correction, features that could not be explained by meteorological factors, and limited sample size. These defects could not be fully offset by statistical quality control and thus affected the SVM training results. 4.3.3 Regional differences The yield prediction results contained only small errors for China’s south-eastern coastal areas, but large errors for the western inland and north-eastern areas. There were two reasons for this: (1) there were more sample records for the south-eastern coastal areas, and this improved the corresponding learning. (2) The effects of other regional factors apart from longitude, latitude, and altitude, which were considered during the learning process; possibly including relative humidity (eliminated after PCA) and light angle. 4.3.4 Effects of water and fertilizer management There is a common problem of current crop modeling, wherein modeling under certain production conditions is inapplicable to a complicated regional simulation. Hence, to simplify regional simulation, production management was excluded from the factors considered in the present study, and this reduced the accuracy of the model. Generally, paddy field rice is more affected by human water and fertilizer management than by natural and meteorological conditions. The soil-related factors too were more affected by human water and fertilizer management than by natural and meteorological conditions. The lack of this type of data reduced the accuracy of the study results. It is certain that the irrigation and water conservation conditions in present China are undesirable. Rainfall is the main source of water supply and this was considered in the regional simulation in this study. In future adjustments of the proposed models, it would be necessary to consider the possibility of combining them for real filed production, and to examine the relationship between natural rainfall and human water and fertilizer management. The water and fertilizer management factors should also be simplified on the regional scale and organically integrated with the SBOCM for enhanced performance. 5 Conclusions The SVM machine learning method was used to develop an SBOCM with simplified data acquisition, suitable for regional simulation, and that can be effectively integrated with multiple scale factors for early-stage theoretical investigations. The model input side is open to future integration of additional natural and social factors to improve the practicability and prediction accuracy. The samples used in this study were built through quality control of mass data. Dimensional reduction was done by factor analysis methods such as PCA and the models were evaluated by fivefold cross-validation. The objective of the SVM modeling was to determine the optimal kernel functions, hyperparameters, and penalty coefficient to enable separate investigations of three types of rice plantings and the several developmental stages. We found that the penalty coefficient made limited contribution to model optimization and therefore first determined the optimal kernel functions and hyperparameters, and then optimized the models by adjustment of the coefficient. The search efficiency was thusly improved fourfold. The SVM modeling method proposed in this paper basically utilizes scale-independent factors and has an open input framework, which facilitates integration with large-scale data for scaling up. Because agricultural production involves both natural and socio-economic inputs, factors such as grain price, fertilizer price, seed price, labor cost, location, traffic conditions, governmental support, real status of agriculture, and scientific and cultural innovations may be further integrated into the proposed model to enable more robust simulation. Acknowledgements This work was supported by a Grant from the National High Technology Research and Development Program of China (863 Program) (No. 2007AA10Z220). The authors wish to thank Ms. Jiang Hong for the help in inproofreading this paper. Peer review under responsibility of King Saud University. Figure 1 Building input vector based on a special development record: Show a flow chart about how to build an input vector based on a special development record in this study. Figure 2 Building input vector based on a special yield record: show a flow chart about how to build an input vector based on a special yield record in this study. Figure 3 Relationship between the C value and efficiency (development module). All the models were tested using C = 1, 10, 100, and 10,000 in the development module. The operation time increased with the increase in the C value. Figure 4 Relationship between the C value and efficiency (yield module). All the models were tested using C = 1, 10, 100, and 10,000 in the yield module. The operation time increased with the increase in the C value. Figure 5 An SVM-based open crop model (SBOCM) was designed as an application-focused crop model for regional rice development stage prediction and yield prediction. The emphasis was on simplicity of operation. The framework of the entire SBOCM system comprised five parts, namely, the database module, data calling module, development prediction module, yield prediction module, and human–machine interface. Table 1 Original components of input vector. Static variables Dynamic variables Station information Soil information Daily information (running days) Longitude (east); Latitude (north); Altitude (m) Soil code, section thickness, soil composition entropy, organic matter, pH, total nitrogen, total phosphorus, total potassium Daily air pressure, average daily temperature, average daily relative humidity, 24-h precipitation, daily wind speed, sunshine hours Table 2 Uses of different developmental samples in modeling. Sample name Sample development strategy Modeling purpose Tillering 1 Eleven consecutive days in tillering stage Yield prediction in tillering stage Heading 1 Eleven consecutive days in heading stage Yield prediction in heading stage Heading 2 Eleven consecutive days in tillering stage + Eleven consecutive days in heading stage Yield prediction in tillering stage Milk 1 Eleven consecutive days in milk stage Yield prediction in milk stage Milk 2 Eleven consecutive days in heading stage + Eleven consecutive days in milk stage Yield prediction in milk stage Table 3 Optimization of each developmental stage for sample class one (with unadjusted C). Middle-season rice Developmental stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Polynomial Polynomial Polynomial Polynomial Hyperparameter D = 3 D = 1 D = 2 D = 1 D = 2 F1 0.8282 0.9908 0.9968 1 0.9968 Early rice Developmental stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Radical basis Polynomial Polynomial Polynomial Hyperparameter D = 3 p = 1 D = 2 D = 2 D = 4 F1 0.7957 0.9665 0.9792 0.9938 0.9876 Late rice Developmental stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Polynomial Radical basis Polynomial Radical basis Hyperparameter D = 3 D = 3 p = 0.75 D = 3 p = 0.75 F1 0.8136 0.9778 0.9921 0.9924 0.9721 Table 4 Optimization of each developmental stage for sample class two (with unadjusted C). Middle-season rice Development stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Linear Polynomial Radical basis Polynomial Hyperparameter D = 2 – D = 2 p = 1.75 D = 2 F1 0.8455 0.8255 0.8000 0.7221 0.6933 Early rice Development stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Linear Polynomial Radical basis Polynomial Hyperparameter D = 1 – D = 2 p = 1.5 D = 2 F1 0.8075 0.8066 0.7990 0.7036 0.6903 Late rice Development stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Linear Radical basis Radical basis Radical basis Hyperparameter D = 2 – D = 2 p = 1.75 D = 2 F1 0.8546 0.8353 0.8075 0.7436 0.7028 Table 5 SVC results for scanning C value (F1). Kernel function Hyperparameter C = 1 C = 10 C = 100 C = 10000 Middle-season rice Sowing Polynomial D = 2 0.8455 0.8219 0.7936 0.7641 Transplanting Linear – 0.8256 0.8113 0.7654 0.7421 Tillering Polynomial D = 2 0.8 0.7959 0.7758 0.7364 Heading Radical basis p = 1.75 0.7221 0.7286 0.7532 0.6213 Milk Polynomial D = 2 0.6933 0.6919 0.6854 0.6534 Early rice Sowing Polynomial D = 1 0.8075 0.8013 0.7874 0.7544 Transplanting Linear – 0.8066 0.8062 0.7639 0.7217 Tillering Polynomial D = 2 0.799 0.7840 0.7710 0.7262 Heading Radical basis p = 1.5 0.7036 0.7341 0.6923 0.6741 Milk Polynomial D = 2 0.6903 0.6873 0.6660 0.6492 Late rice Sowing Polynomial D = 2 0.8546 0.8384 0.7921 0.7635 Transplanting Linear – 0.8353 0.8289 0.7536 0.7305 Tillering Radical basis D = 2 0.8075 0.7926 0.7706 0.7223 Heading Radical basis p = 1.75 0.7436 0.7523 0.7213 0.6921 Milk Radical basis D = 2 0.7028 0.6950 0.6722 0.6431 SVC, support vector machine classification. The bold values means the combinations of kernel functions and parameters which performed best in the same developmental stages. Table 6 Optimization of each developmental stage for sample class two (with unadjusted C). Middle-season rice Developmental stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Linear Polynomial Radical basis Polynomial Hyperparameter D = 2 – D = 2 p = 1.75 D = 2 Penalty coefficient C = 1 C = 1 C = 1 C = 3 C = 1 F1 0.8455 0.8255 0.8000 0.7532 0.6933 Early rice Developmental stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Linear Polynomial Radical basis Polynomial Hyperparameter D = 1 – D = 2 P = 1.5 D = 2 Penalty coefficient C = 1 C = 1 C = 1 C = 2 C = 1 F1 0.8075 0.8066 0.7990 0.7341 0.6903 Late rice Developmental stage Sowing Transplanting Tillering Heading Milk Kernel function Polynomial Linear Radical basis Radical basis Radical basis Hyperparameter D = 2 – D = 2 p = 1.75 D = 2 Penalty coefficient C = 1 C = 1 C = 1 C = 2 C = 1 F1 0.8546 0.8353 0.8075 0.7523 0.7028 Table 7 Optimization of yield prediction at each stage. Planting type Prediction point Sample Kernel function Hyperparameter RMSE (kg/h m2) RE (%) Middle-season rice Tillering stage Tillering 1 Radical basis p = 1 126.8 22.1 Heading stage Heading 2 Radical basis p = 1.25 96.4 17.1 Milk stage Milk 2 Radical basis p = 1.5 109.4 19.2 Early rice Tillering stage Tillering 1 Polynomial D = 4 88.3 20.5 Heading stage Heading 2 Radical basis p = 1.25 68.0 15.8 Milk stage Milk 2 Radical basis p = 1.25 36.4 8.5 Late rice Tillering stage Tillering 1 Radical basis D = 4 89.2 21.0 Heading stage Heading 2 Radical basis p = 1.25 69.7 16.5 Milk stage Milk 2 Radical basis p = 1.5 46.5 11.1 RMSE, root-mean-square error; RE, relative error. Table 8 Results of SVR using scanning C value. Planting type Prediction point Kernel function Hyperparameter C = 1 C = 10 C = 100 C = 10000 Middle-season rice Tillering stage Radical basis p = 1 22.1 21.1 21.1 21.5 Heading stage Radical basis p = 1.25 17.1 16.4 17.9 18.1 Milk stage Radical basis p = 1.5 19.2 18.3 19.3 20.1 Early rice Tillering stage Polynomial D = 4 20.5 19.6 17.8 18.1 Heading stage Radical basis p = 1.25 15.8 16.6 18.1 18.1 Milk stage Radical basis p = 1.25 8.5 8.9 8.9 8.9 Late rice Tillering stage Radical basis D = 4 21.0 21.9 21.9 21.9 Heading stage Radical basis p = 1.25 16.5 17.2 17.3 17.3 Milk stage Radical basis p = 1.5 11.1 10.6 10.2 11.5 SVR, support vector machine regression. The bold values means the combinations of kernel functions and parameters which performed best in the same developmental stages. Table 9 Optimization of each developmental stage for yield prediction (with unadjusted C). Planting type Prediction point Sample Kernel function Hyperparameter Penalty coefficient RE (%) Middle-season rice Tillering stage Tillering 1 Radical basis p = 1 10 21.1 Heading stage Heading 2 Radical basis p = 1.25 10 16.4 Milk stage Milk 2 Radical basis p = 1.5 10 18.3 Early rice Tillering stage Tillering 1 Polynomial D = 4 100 17.8 Heading stage Heading 2 Radical basis p = 1.25 1 15.8 Milk stage Milk 2 Radical basis p = 1.25 1 8.5 Late rice Tillering stage Tillering 1 Radical basis D = 4 1 21.0 Heading stage Heading 2 Radical basis p = 1.25 1 16.5 Milk stage Milk 2 Radical basis p = 1.5 100 10.2 RE, relative error. ==== Refs References Cai C.Z. Wang W.L. Chen Y.Z. Support vector machine classification of physical and biological datasets Int. J. Mod. Phys. C 14 2003 575 585 Charles-Edwards D.A. Modelling Plant Growth and Development 1986 Academic Press London Cortes C. Vapnik V.N. Support-vector networks Mach. Learn. 20 1995 273 297 Curry R.B. 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Support vector machines for hyperspectral remote sensing classification 27th AIPR Workshop on: Advances in Computer-Assisted Recognition vol. 3584 1998 SPIE-International Society of Optical Engineering Washington, D.C. 221 232 Gunn S.R. Support Vector Machines for Classification and Regression: Technical Report 1998 University of Southampton Southampton Jones C.A. Kiniry J.R. CERES-Maize: A Simulation Model of Maize Growth and Development 1986 A&M University Press Texas Kaundal R. Kapoor A.S. Raghava G.P.S. Machine learning techniques in disease forecasting: a case study on rice blast prediction BMC Bioinform. 7 2006 485 Lin Z.H. Mo X.G. Xiang Y.Q. Research advances on crop growth models Acta Agron. Sin. 29 2003 750 758 Luo S.M. Zhen H. Chen C.H. Yang W. Study on application of computer simulation in rice high yield cultivation Guangdong Agric. Sci. 3 1990 14 17 Penning de Vries, F.W.T., Jansen, D.M., ten Berge, H.F.M., Bakema, A., 1989. Simulation of Ecophysiological Processes of Growth in Several Annual Crops. Simulation Monographs. PUDOC, Wageningen. Ritchie J.T. Model for predicting evaporation from a row crop with incomplete cover Water Resour. Res. 8 1972 1204 1213 Shi X.Z. Yu D.S. Pan X.Z. 1:1000000 Soil Database of China 2002 Institute of Soil Science, Chinese Academy of Sciences Nanjing Sinclair T.R. Seligman N.G. Crop modeling: From infancy to maturity Agron. J. 88 1996 698 704 Stewart D.W. Dwyer L.M. A model of spring wheat (Triticum aestivum ) for large area yield estimations on the Canadian Prairies Can. J. Plant Sci. 70 1990 19 32 Suykens, J.A.K., 2001. Nonlinear modelling and support vector machines. In: 18th IEEE Instrumentation and Measurement Technology Conference, Budapest. Suykens J.A.K. De Vandewalle J. Moor B. Optimal control by least squares support vector machines Neural Network 14 2001 23 35 Swain D.K. Heranth S. Saha S. Dash R.N. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30032-310.1016/j.sjbs.2017.01.023Original ArticleNon-invasive diagnosis methods of coronary disease based on wavelet denoising and sound analyzing Chen Tianhua cth188@sina.com⁎Zhao Shuo Shao Siqi Zheng Siqun College of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, People’s Republic of ChinaBeijing Key Laboratory of Big Data Technology for Food Safety, School of Computer and Information Engineering, Beijing Technology and Business University, 100048 Beijing, People’s Republic of China⁎ Corresponding author. cth188@sina.com26 1 2017 3 2017 26 1 2017 24 3 526 536 3 11 2016 25 12 2016 6 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).The heart sound is the characteristic signal of cardiovascular health status. The objective of this project is to explore the correlation between Wavelet Transform and noise performance of heart sound and the adaptability of classifying heart sound using bispectrum estimation. Since the wavelet has multi-scale and multi-resolution characteristics, in this paper, the heart sound signal with different frequency ranges is decomposed through wavelet and displayed on different scales of the resolving wavelet result. According to distribution features of frequency of heart sound signals, the interference components in heart sound signal can be eliminated by selecting reconstruction coefficients. Comparing de-noising effects of four wavelets which are haar, db6, sym8 and coif6, the db6 wavelet has achieved an optimal denoising effect to heart sound signals. The de-noising result of contrasting different layers in the db6 wavelet shows that decomposing with five layers in db6 provide the optimal performance. In practice, the db6 wavelet also shows commendable denoising effects when applying to 51 clinical heart signals. Furthermore, through the clinic analyses of 29 normal signals from healthy people and 22 abnormal heart signals from coronary heart disease patients, this method can fairly distinguish abnormal signals from normal signals by applying bispectrum estimation to denoised signals via ARMA coefficients model. Keywords Heart sound signalsBiomedical signal processingNon-invasive diagnosisARMA modelWavelet Transform ==== Body 1 Introduction Over the past 20 years, the morbidity and mortality of cardiovascular disease have increased constantly, and heart disease has been claimed as a pathema which imperils humankind’s health commonly and frequently (Wang et al., 2015, Zhou et al., 2015, An and Yu, 2016). The mechanical movements in the heart and the cardiovascular system can be reflected by heart sound, which contains the information about each part of the heart and interactions among all different sections in heart in both physiological and pathological fields. The presences of noise and distortion in the heart sound have been classified as a useful and reliable information diagnosing heart and cardiovascular diseases in an early stage (Cheng et al., 2016, Zhou et al., 2005). Since the heart sound diagnosis has to be executed in the noiseless environment in order to acquire accurate heart sound signals, the heart sound detection system publically adopts the analog method to eliminate noise utilizing the hardware, or the FIR digital filter (Wang et al., 2010, Joao et al., 2012, Gan et al., 2016, You et al., 2016). The weakness of heart sound signals, with the strength from 0.5 μV to 5 mV and the frequency from 1 to 1000 Hz, leads to vulnerability to external interferences, resulting in strong background noises in the signal detection process. Moreover, the traditional denoising method is not only undesirable in the elimination of noise, but also greatly impairs wanted signals in heart sound (Zhao et al., 2008, Zhu and Liu, 2006, Chen and Chen, 2005). Comparing with the traditional method, the strategy presented in this paper has effectively denoised the heart sound signals through the Wavelet Transform. Besides, the wavelet filter used in this paper enables to control the cut-off frequency of the filter and reserve useful sections in signals whose frequency exceeds transmission bands according to the frequency distribution of heart sound: decomposing signals into detailed and approximate components on different ranges for the purpose of achieving effective separation between signals and noise (Zhang et al., 2013, Cheng and Li, 2015, Zhu, 2012, Yang et al., 2006, Liu, 2013). The research of early diagnosis of coronary heart disease employs advanced digital signal processing technology to unveil the correlation between modern digital signal processing the heart sound and heart disease (Chen and Guo, 2006, Duan, 2016, Wang, 2014). In practice, heart sound diagnosis also has many advantages, such as, noninvasive operation, speediness, convenience, economy and so on. 2 Material and methods The heart sound is an important biomedical signal of the human body, which contains a lot of information on heart health status. Analyzing the heart sound signal is quite essential to diagnose cardiovascular diseases, and its accuracy and reliability will directly affect the evaluation of patients’ clinical diagnosis and prognosis. Traditional heart sound recognition is less accurate because of the subjectivity and instability of auscultation which completes by doctors. Therefore, the research in non-invasive diagnosis methods based on modern information technology in the prevention and diagnosis of cardiovascular system diseases, like coronary heart disease, has become one of the most important issues in medical profession. 2.1 The compositions of heart sound signal As other creatures in nature, the organs of human perform their physical activities in accordance with certain rules. The vibration caused by such physical activities will produce the sound signals, which contain the physiological and pathological characteristics. The heart sound signal is the weak signal, formed in the cardiac cycle, produced by the vibration of the myocardial contraction and relaxation, the opening and closing of the valve, and the impact of the blood stream on the heart wall and the aorta, which spread through the surrounding tissue to the chest wall. The heart sound signal is a kind of biological weak signals under the strong noise background. It is easily affected by a number of human factors, for the reason that the heart sound signals is a kind of instable natural signals, which is signaled by the complex life. The changes of heart sound and the emergence of the heart murmur are the early symptoms of the organic pathological changes of heart. The change of physical structure of the heart directly leads to alteration in the heart sound signals, so the heart sound analyzing is a vital means in learning the status of the heart and large blood vessels. Each component of the heart sounds is shown in Fig. 1, including the first heart sound (s1), the second heart sound (s2), and under certain circumstances, there are the third heart sound (s3) and the forth heart sound (s4). The first heart sound starts at 0.02–0.04 s after the beginning of the QRS wave on the electrocardiogram (ECG) , accounting for 0.08–0.15 s, caused by blood flowing into the great vessels during ventricular contraction, mitral valve and tricuspid valve closure. The occurrence of second heart sound (S2), starting from the tail of T wave on the electrocardiogram, is aroused by the blood flowing from the atrium into the ventricle when the aortic and pulmonary valves are closing but the atrioventricular valve is opening. The second heart sound occurs at the beginning of the diastolic period of the heart, at a relatively high frequency, which is usually shorter than the first heart sound, and takes about 0.07–0.12 s. The third heart sound has low frequency and small amplitude, lagging 0.12–0.20 s behind the T wave on the electrocardiogram, accounting for 0.05–0.06 s, caused by rapid ventricular filling and ventricular wall vibration. The fourth heart sound, with small amplitude, starts at 0.15–0.18 s of the P wave on the ECG, caused by Ventricular wall vibration when Atrial contraction and the blood flowing into the ventricle. The diagnoses of coronary heart disease is divided into Invasive diagnosis Methods and Non-invasive diagnosis Methods. The Non-invasive diagnosis Method is generally based on electrical activity and pump activity of the heart, including electrocardiogram, dynamic electrocardiogram and phonocardiogram, echocardiography and modern medical imaging techniques such as NMR, CT, PET and so on. However, not all patients with coronary heart disease can be diagnosed by ECG and other methods. Some patients, with mild coronary heart disease, have normal ECG. So, using ECG is difficult to achieve accurate diagnosis of coronary heart disease. Invasive diagnostic methods mainly refer to coronary angiography, which is currently the most reliable method of diagnosing coronary heart disease. However, angiography is a traumatic diagnosis, with certain risks. In some cases, it may cause serious complications or even death. Accordingly, some patients are hesitant, and this treatment has not been accepted universally. 2.2 Diagnosis significance of heart sound The systematic use of heart sounds to diagnose heart health began in 1817. For a long time, cardiac auscultation was one of the oldest methods of diagnosing cardiovascular diseases and understanding the function of the heart. Since the French doctor Laennec invented the stethoscope, medical personnel subjectively analyze and judge the heart sound obtained from stethoscope according to their knowledge and experience. Until now, this technique is still a basic method of diagnosing cardiovascular diseases, yet it has great limitations. And heart sound analysis can improve the accuracy of cardiovascular disease diagnosis. Non-invasive diagnosis method has great value and irreplaceable advantages in diagnosing cardiovascular diseases comparied with what ECG and echocardiography do. Domestic and foreign researchers use heart sound to analyze the coronary artery disease, beginning in the early 90s of the last century. It is generally believed that the heart sound is made up of the voice from the heart valve closure, myocardial stretch, the blood flowing and vocal tone. Vascular stenosis caused by atherosclerosis can induce blood turbulence and vascular vibration. Heart sounds detected from the body surface can be used to diagnose diseases brought by blood clogging. Although the heart sound is quite weak and the cardiac murmur is relatively prominent, signals can still be detected because of the minimum pressure on the coronary artery and the maximum blood flowing in the coronary artery. Besides, clinical practices have proved this theory. Based on the theoretical modeling, simulation and clinical experiment, the research on the diagnosis method of heart sound is carried out for a long time. Cheng To, John R. Burg and Kathlean A. Weaver use selective coronary angiography to demonstrate that diastolic murmurs are associated with coronary clogging. Besides, the aortic diastolic murmur in patients of coronary heart disease has disappeared after coronary artery bypass surgery. Consequently, they put forward the idea and method of using heart sound to diagnose coronary artery disease. L Semmlow and W. Welkowitz in 1983 used Fourier transform researching in the difference of Heart sound spectrum during the period of relaxation between patients with coronary heart disease and normal subjects. They also found that the high-frequency energy increased in patients with coronary heart disease. M. Akay’s study is the most representative one in heart sound. Their research results reveal that the over flow happens when the blocking rate of coronary artery stenosis between 25% and 95% and this flow generates a faint heart signal with high frequency, which indicated the blocking in tubular artery. In 1992, M. Akay uses adaptive filtering method to eliminate the background noise of the heart sound signal. The ARMA and AR model were established for the diastolic heart sound signal. Using the power spectra and poles model as diagnostic parameters has created many valuable results. Experiments show the relation between the high frequency of heart sounds and coronary artery indeed existed. 2.3 Digital filtering of heart sound signal The heart sound signal is a kind of biological weak signals under the strong noise background, and is easily disturbed by noises in detection processes. The collection of heart sound is mixed with manifold noise signals, such as environmental noise, power frequency noise, EMG noise, acquisition equipment noise and skin fricative noise. Therefore, only relying on the hardware filtering in heart sound acquisition system cannot complete the elimination of interference in the signal, and digital filtering is also needed to filter out a variety of noises in heart sound signals as far as possible. In recent years, Wavelet Transform which is studied and valued by many scholars both at home and abroad, has been widely used in many fields including biomedical signal de-noising, speech signal processing and related signal processing because of its excellent denoising characteristics. The Wavelet Transform not only inherits the characteristics of the Fourier transform, but also makes up many deficiencies of Fourier analysis. Therefore, it has made a rapid progress and been used widely. The translation and contraction of Wavelet basis allows a flexible time-frequency window, which becomes narrow at high frequency and wide at low frequency. It is well suited for analyzing non-stationary heart sound signals, as it can focus on any details of the analyzed object. At present, Wavelet Transform has been successfully applied in the fields of Biomedical Engineering, Intelligent Signal Processing, Image Processing, Speech and Image Coding, Speech Recognition and Synthesis, Multi-scale Edge Extraction and Reconstruction, Fractal and Digital Television. Wavelet Transform can be described as follows: (1) WTf(a,b)=1|a|∫-∞∞f(x)ψ∗(x)dx=f,ψa,bψa,b(x)=1|a|ψ(x-ba)a,b∈R,a≠0 where ψa,b(x) represents Wavelet generating function, a represents scaling factor, b represents the time-shifting factor, when b take different values, the wavelet along the timeline move to a different location, ψ∗(t) represents complex conjugation of ψ(t). In order to facilitate the use of computer processing, it is necessary to perform the discrete processing on the above-described transformation, Let’s start from the wavelet generating function: (2) 2j2ψ(2jx-k) (3) Namely:a=12jb=k2j It is called dyadic wavelet. Discrete Binary Wavelet Decomposition Algorithm is shown in Fig. 2: Discrete dyadic Wavelet Transform and reconstruction can be realized by Mallat algorithm (Gan et al., 2016), therefore, the decomposition algorithm of the Wavelet Transform can be described as follows: (4) Cj,k=∑mh(m-2k)cj-1,mdj,k=∑mg(m-2k)cj-1,m m=0,1,2,…N-1 where cj,k represents Scaling factor, dj,k represents Wavelet coefficient, and h, g represents the corresponding coefficients of the filter H and G shown in Fig. 2, j represents Wavelet Transform decomposition level, j represents Discrete sampling points. In the continuous Wavelet Transform, Ordering the parameter a = 2−i, b = k2−j, In which j, k ∈ Z ,so the discrete wavelet is: (5) ψ2-j,k2-j=2j2ψ(2jx-k)=2j2ψj,k(x) Thus the corresponding discrete Wavelet Transform as follows: (6) WTf(j,k)=〈f,ψj,k〉=2j2∫-∞∞f(t)ψ∗(2jx-k)dx The decomposition structure of Mallat fast algorithm of this algorithm is illustrated in Fig. 3. According to the principle of the Wavelet Transform, the heart sound signal are reconstructed as the inverse process of the decomposition algorithm, therefore, the corresponding signal reconstruction formula is as follows: (7) Cj,m=∑kcj+1,kh(m-2k)+∑kdj+1,kg(m-2k) 3 Results and discussion 3.1 The experiments of wavelet de-noising For the heart sound signal de-noising, different wavelet basis, the de-noising effect is inequality. Similarly, for the same wavelet, different decomposition layers, the de-nosing effect is not exactly the same. In this paper, the orthogonal wavelets in commonly used heart sound processing, such as Haar, db6, sym8 and coif5, have been compared, and that result demonstrates that db6 wavelet has the optimal de-noising effect. At the same time, the de-nosing effects of different decomposing layers in the same wavelet are compared. The experiment results show that the de-nosing effects are not ideal when the number of decomposing layers is less than 5. When the number of decomposing layers is 5, the de-nosing effects are ideal. When the number of decomposing layers is more than 5, although the de-nosing effects is quite good, a considerable part of the heart sound signal itself is also filtered out. Therefore, using DB6 wavelet to carry out the 5 layer decomposition gets the best de-nosing effects, and the experimental results are shown in Fig. 4. In the parametric model method, the AR model illustrates the peak value in the spectrum, while the MA model shows the valley value in the spectrum. Consequently, the ARMA model is generally used to calculate the characteristic value of the heart sound signal. The ARMA model is a zero-pole model, which reflects the peak and valley value of the power spectrum. 3.2 Heart sound positioning The location of heart sound signal is the prerequisite of feature extraction. In this paper, using synchronous heart sound signal of ECG as the reference signal locates the heart sound signal. Through the correspondence between QRS signal of the ECG wave and the heart sound signal, the heart sound signal can be located. According to the ECG signal waveform, the QRS group is first detected, then the position of the R wave peak can be determined when the slope of the R-wave equals zero. The first heart sound (S1) is Extracted, which locates from 0 to 120 ms to the vertex of the R wave starting from the right side. 3.3 ARMA model and power spectrum estimation The armaqs and armarts functions, presented in Matlab signal processing toolbox, can be used to estimate the ARMA model parameters, and bi-spectrum estimation of ARMA model can be achieved by using function bispect. The armaqs function applies the q-slice algorithm to estimate ARMA model parameters, and the format is shown as follows:[avec,bvec] = armaqs(y,p,q,norder,maxlag,samp_seg,overlap,flag) The Amars function estimates the value of parameters in the ARMR model using the residual time series. The typical formats of bispect and armas functions are described as follows:[avec,bvec] = armarts(y,p,q,norder,maxlag,samp_seg,overlap,flag) [Bspec,waxis] = bispect(ma,ar,nfft) 3.4 The identification results of the heart sound The collection of the sample signal was completed in the First Affiliated Hospital of Hunan University of Traditional Chinese Medicine and the Air Force General Hospital. The samples were divided into two groups, coronary heart disease and non-coronary heart disease. Each group had 18 patients. The coronary-group was confirmed by the coronary angiography. The ARMA model order number P and Q in the bispectrum estimation are important for the classification of heart sounds. By selecting different parameters of P and Q in MATLAB, the calculation is carried out. When p = 2, q = 1, the bispectrum of normal and abnormal heart sounds are shown in Fig. 5. Fig.5(a) shows a normal heart sound signal of the double spectrum, while Fig.5(b) is a case of abnormal heart sound signal. As illustrated in Fig. (a) and (b), the upper-left one is the bispectrum of the armarts function estimation, and the lower-left is the bispectrum of the armaqs function. The lower right is the armaqs function estimation, and the lower-right is the armaqs function estimation of the bispectrum three-dimensional map. When p = 3 and q = 2, the bispectra of the normal and abnormal heart sounds are shown in Fig.6(a) and (b), respectively. When p = 4 and q = 3, the bispectra of the normal heart sound signal and the abnormal heart sound signal are shown in Fig.7(a) and (b), respectively. When p = 5 and q = 4, the bispectra of the normal heart sound signal and the abnormal heart sound signal are shown in Fig.8(a) and (b), respectively. When p = 6 and q = 5, the bispectra of the normal heart sound signal and the abnormal heart sound signal are shown in Fig.9(a) and (b), respectively. In addition to p = 2, q = 1, the abnormal heart sound signal has a higher frequency component than the normal heart sound signal in the bispectrum, so we can distinguish the normal heart sound signal of healthy people and the abnormal one of patients basing on the bispectrum, and Non-invasive diagnosis can be achieved. 4 Conclusion The heart sound signal is a kind of unstable nature signal emitted from complex beings and also representative biological signal of human, yet that signal could be disturbed and influenced by human factors easily, because of its characters of weakness, strong noise interference and randomness. In order to acquire accurate heart sound signals, filtering interference noises is the foundation and prerequisite of Non-invasive diagnosis of coronary heart disease. In this paper, decomposing five layers of the heart sound by db6 wavelet can filter various random noises, in the detection processing, effectively. Finally, the 29 cases normal signals from healthy people and 22 cases abnormal heart signals from coronary heart disease patients are accurately distinguished, through selecting the appropriate ARMA model parameters of the 51 filtered heart sound signals to conduct bispectrum estimation. Acknowledgements The Work was Science and Technology Development Program of Beijing Municipal Commission of Education (No. KZ201510011011), and the National College Student’s Scientific Research and Entrepreneurial Action Plan (No. SJ201501018). Peer review under responsibility of King Saud University. Fig. 1 Oscillogram of heart sound signals. Fig. 2 Discrete wavelet decomposition structure. Fig. 3 Signal decomposition structure of Mallat algorithm. Fig. 4 (a) Heart sound signal of un-filtered. (b) Filtered heart sound signal by db6. Fig. 5 (a) p = 2, q = 1 the Bispectrum of normal heart sound signals. (b) p = 2, q = 1 the Bispectrum of abnormal heart sound signals. Fig. 6 (a) p = 3, q = 2 the Bispectrum of normal heart sound signals. (b) p = 3, q = 2 the Bispectrum of abnormal heart sound signals. Fig. 7 (a) p = 4, q = 3 the Bispectrum of normal heart sound signals. (b) p = 4, q = 3 the Bispectrum of abnormal heart sound signals. Fig. 8 (a) p = 5, q = 4 the Bispectrum of normal heart sound signals. (b) p = 5, q = 4 the bispectrum of abnormal heart sound signals. Fig. 9 (a) p = 6, q = 5 the Bispectrum of normal heart sound signals. (b) p = 6, q = 5 the Bispectrum of abnormal heart sound signals. ==== Refs References An L.L. Yu L. Heart sound signal recognition based on gauss mixture model J. Jinlin Univ. (Sci. Ed.) 54 5 2016 1096 1102 Chen T.H. Chen Q. A method for elimination of noise in ECG signals by digital filter World Sci. Technol. Modern. Traditional Chin. Med. 1 2005 123 126 Chen X.C. Guo X.S. 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==== Front Saudi J Biol SciSaudi J Biol SciSaudi Journal of Biological Sciences1319-562X2213-7106Elsevier S1319-562X(17)30048-710.1016/j.sjbs.2017.01.039Original ArticleThe influence of stachydrine hydrochloride on the reperfusion model of mice with repetitive cerebral ischemia Miao Mingsan miaomingsan@126.comb⁎Wang Ting aLou Xin aMingBai bXi Peng aLiu Baosong aChang Bingjie aa College of Pharmacy, Henan University of Chinese Medicine, Zhengzhou 450000, Chinab Department of Science and Technology, Henan University of Chinese Medicine, Zhengzhou 450000, China⁎ Corresponding author. miaomingsan@126.com23 1 2017 3 2017 23 1 2017 24 3 658 663 22 10 2016 25 12 2016 7 1 2017 © 2017 The Authors2017This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).To study the influence of stachydrine hydrochloride on the inflammatory cytokines and tissue morphology of the re-perfusion model of mice with repetitive cerebral ischemia and probe into the protection mechanism of stachydrine hydrochloride for cerebral ischemia reperfusion impairment. Build a repetitive cerebral ischemia reperfusion model by first blocking the common carotid artery on both sides for 10 min, then resuming perfusion for 10 min and then blocking the common carotid artery on both sides again for 10 min. Before the operation, all the mice in the Nimodipine group, and the big, medium and small stachydrine hydrochloride dose groups were given corresponding gastric perfusion, the mice in the sham operation group and the modeled groups were at the same time given 0.5% sodium carboxymethyl cellulose for gastric perfusion of the same volume. The medicine was fed daily for 7 consecutive days. The model was built 1 h after the last feed and the perfusion continued for 24 h after the operation. Then the death rate of the mice was calculated. The mouse brains were taken out to test the ICAM-1 level and the TNF-α level, and the serum was taken out to test the NSE level and the MPO level. The tissue morphology changes were also observed. All the repetitive cerebral ischemia reperfusion models were successfully duplicated. The stachydrine hydrochloride in all the dose groups significantly reduced the death rates of big and small mice, reduced the level of ICAM-1 and the level of TNF-α in the brain tissues and the NSE level and the MPO level in the serum, significantly alleviating the pathological impairment in the hippocampus. Stachydrine hydrochloride can significantly reduce the death rate of mice, improve the pathological changes in the hippocampus, inhibit inflammatory reactions after ischemia, thus reducing the re-perfusion impairment after cerebral ischemia. Keywords Stachydrine hydrochlorideCerebral ischemia reperfusion ==== Body 1 Introduction Stachydrine hydrochloride is an important part of the pharmacological basis for alkaloids in the Leonurus japonicus Houtt - motherwort. The motherwort tastes spicy and slightly bittersweet. The property slightly, it can be used for clearing heat and detoxification. Stachydrine hydrochloride is also called Proline Betaine or N-Dimethyl proline and is the simplest pyrrole alkaloid. Its basis structure is l-stachydrine and its molecular weight M = 143.18 and the pH = 7. The Chinese Pharmacopoeia 2015 has already designated the concentration of stachydrine hydrochloride as the standard for testing dry motherwort – the concentration of stachydrine hydrochloride must not be below 0.50%. The pyrrole rings in their chemical structure have very good medical effects. Modern pharmacological studies have shown that the alkaloids in motherwort have extensive pharmacological activities in treating cardiovascular and cerebral diseases and resisting inflammations. Cerebral ischemia is a common disease among old people and the pathological mechanism of impairment caused by cerebral ischemia is very complex, which includes many factors, such as the emergence of inflammatory media, exhaustion of energy (Liu, 2016), the emergence of radicals, and the activation of apoptosis pathway. Early studies have found that motherwort, having the capacity to clear heat, detoxify, activate blood and remove blood stasis, can be used in alleviating the impairment caused by cerebral ischemia. “Activate blood and remove blood stasis” is commonly used in clinical treatment of impairment caused by cerebral ischemia and the effect is significant. “Clear heat and detoxify” is a new viewpoint in alleviating such impairment put forward by traditional Chinese medicine researchers. 2 Materials and methods 2.1 Drugs and reagents Stachydrine hydrochloride, was provided by the Chemical Lab of Henan University of Traditional Chinese Medicine, concentration >9%, batch No: 20091212; Nimodipine pills, by Yabao Pharmaceutical Group Co., Ltd, batch No: 130150; penicillin sodium for injection use, by Huabei Pharmaceuticals Co., Ltd, batch No. C1206807; CMC, by Hengxing Chemical Reagent Production Co., Ltd of Tianjin, batch No.: 20120418; ICAM-1 ELISA Testing Reagent, R&D Company, batch No. 20130901A; TNF-α ELISA testing reagent box, R&D Company, batch No. 20130901A; NSE ELISA testing reagent box, R&D Company, batch No. 20130901A; MPO testing box, Nanjing Jiancheng Bio-engineering Institute, batch No. 20130914. Ultraviolet–visible spectrophotometer, provided by Shanghai Tianmei Scientific Instrument Co., Ltd, type: UV 1000; reader, BIO-RAD Company (US), type: BIORAD-68 (Table 1, Table 2). 2.2 Animals KM mice, SPF level, male, 96 in number, weighted 25–30 g, were provided by the Experiment Animal Center of Henan Province, license No: 41003100000258; lab license No.: SYXK (Henan) 2010-001 (Fig. 1). 2.3 Methods 96 healthy mice, male, weighted 25–30 g, were fed normally for 3 days, and then weighed and classified into 6 groups at random, 16 in each group. The groups were the sham operation group, the modeled group, the Nimodipine Group, the big doze stachydrine hydrochloride group, the medium dose stachydrine hydrochloride group and the small dose stachydrine hydrochloride group. Nimodipine suspension gastric perfusion (positive control drug, fed dose 30 mg/kg, equivalent to 15 times that of clinical dose, formulate the medicine concentration of 3 mg/nl with 0.5% CMC prior to use) were conducted for mice in the Nimodipine group at the concentration level of 0.1 ml/10 g. The mice in the big, medium and small dose groups were fed stachydrine hydrochloride suspension (medicine fed is respectively 60 mg/kg, 30 mg/kg and 15 mg/kg, formulate medicine mixtures with concentrations of 6 mg/ml, 3 mg/ml, and 1.5 mg/ml with 0.5% CMC). The mice in the sham operation group and in the model group were given gastric perfusion with 0.5% CMC of the same volume. The mice were fed once daily for 7 consecutive days. From 8 p.m. on the sixth day, abrosia was conducted for the mice batch by batch. After 12 h, the mice were weighed and fed batch by batch. After 1 h, celiac anesthesia was conducted by injecting 10% chloral hydrate (0.03 ml/10 g), the neck was sterilized with alcohol, and then we used operation knives to conduct median slicing and gradually separated surrounding muscles until the common carotid artery (CCA) on both sides were seen. Then we used the bent precision forcep to peel off surrounding nerves, separated CCA and threaded it for back-up use. Each CCA was sutured with an acupuncture needle whose diameter was around 0.3 mm, and was made 90 degrees, stayed lack of blood for 10 min, and then we let loose of the acupuncture needle, resumed perfusion for 10 min, then blocked the blood for 10 min, and then resumed blood perfusion. After that, we applied penicillin powder on the wounds, and sutured the wound at the cervix. For the sham operation group, except that the CCA was not blocked, other operations were the same as those in the modeled group (Tan et al., 2002). We gave all the mice perfusion for another 24 h, removed the eyeballs to extract blood, kept the blood still for half an hour, conducted centrifugal operation of the blood at 3500 r/min for 10 min, obtained the serum, and tested the concentration of NSE and MPO in the serum. Then we removed the cervical vertebra to execute the mice, obtained the brains quickly, vector cut half of the brain and put in 10% formalin, kept still for a week and imbedded it with paraffin, conducted HE staining and observed tissue morphological changes. For the other half of the brain, we used saline to erase the blood stains, and used the filter paper to fully absorb the saline on it, accurately measured its weight and calculated the amount of cold saline added with this weight, made 10% brain homogenate with glass homogenate machine in a big kiln at a proportion that saline (ml): brain tissue (g) = 9:1, conducted centrifugal operation of the homogenate at 3000 r/min at 4 °C, obtained the supernatant and stored it at a temperature below −20 °C, and tested the concentration of ICAM-1 and that of TNF-α in the homogenate (Fan et al., 2015) (Table 3, Table 4). 2.4 Statistical analysis Statistical analysis was conducted with SPSS 17.0. The calculated materials were represented with mean ± standard deviation, and inter-group comparison was conducted with single factor analysis of variance. For those with the same variance, the LSD method was used. For those with different variances, the Games-Howeel method was used. The level materials were tested with Radit (Zhang et al., 2012). 3 Results & discussion 3.1 The influence on the death rate, serum NSE and serum MPO levels of the modeled mic From the table above, it can be seen that the death rate in the modeled group was the highest, and the death rates in the Nimodipine Group and the big, medium and small dose stachydrine hydrochloride groups were all lower to some extent, indicating that the medicine fed in each group could all reduce the death rates of the modeled mice, reduce cerebral tissue impairment and protect the cerebral tissue. Compared with the sham operation group, the NSE level and the MPO level in the serum both significantly increased (P < 0.05), the NSE levels in the serum in the big dose and medium dose group were obviously reduced (P < 0.05), the serum NSE level in the small dose group revealed a trend of decline (P > 0.05), the serum MPO level in the big dose and medium dose group declined remarkably (P < 0.01), and the serum MPO level in the small dose group remarkably declined, suggesting that the medicine fed had protective effects for the neurological cells of the mice at varied degrees and could reduce the NSE level and the MPO level in the serum. 3.2 Influence on the ICAM-1 and TNF-α level of the cerebral tissue of the mice From the table above, it can be seen that compared with the sham operation group, the level of ICAM-1 and TNF-α in the cerebral tissue of the mice in the modeled group both increased significantly (P < 0.01), suggesting that the modeling was successful; compared with the modeled group, the ICAM-1 level of cerebral tissue of the mice in the Nimodipine group decreased significantly (P < 0.01), the ICAM-1 level in the cerebral tissues of the mice in the small dose group declined significantly (P > 0.05), the TNF-α level of the cerebral tissues of the mice in the Nimodipine and big dose group decreased significantly (P < 0.01), the TNF-α level in the medium and small dose group obviously decreased (P < 0.05), suggesting that the medicine fed had the ability to reduce the ICAM-1 and TNF-α levels in the brain tissues and reduce the impairment inflammations could do to cerebral tissues. 3.3 The influence on the pathological changes in the cerebral cortex of the modeled mice The pathological observation of the cerebral cortex of the modeled mice is as follows (see Fig. 1 for the pictures): in the sham operation group, the nerve cells in the cerebral cortex were normal; in the modeled group, the nerve cells in the cerebral cortex had edema and most neurons were necrotic; in the Nimodipine group, a small amount of nerve cells had edema, a small amount of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic; in the big dose stachydrine hydrochloride group, a small amount of nerve cells in the cerebral cortex had edema, were interspersed, a small amount of neurons degenerated, the cytoplasm was lightly dyed and the structure blurred; in the medium dose group, part of nerve cells in the cerebral cortex had edema, were interspersed, a small amount of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic; in the small dose group, the nerve cells in the cerebral cortex had edema, chunks of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic. After Ridit test, compared with the sham operation group, the modeled group had more statistical significance (P < 0.01), suggesting that prominent pathological changes took place in the cerebral cortex of the mice and that the modeling was successful; compared with the modeled group, the Nimodipine and the big, medium and small dose groups all had prominent statistical significance (P < 0.01), suggesting that the medicine fed in each group could significantly reduce the pathological damages of the cerebral cortex and protect the cerebral tissues. 3.4 The influence on the pathological changes in the hippocampus of the modeled mice The pathological observations of the hippocampus of the cerebral tissues of the modeled mice are as follows: in the sham operation group, the hippocampus nerve cells were normal; in the modeled group, the hippocampus never cells had edema, most neurons were necrotic; in the Nimodipine group, a small amount of nerve cells had edema, a small amount of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic; in the big dose group, a small amount of nerve cells had edema, were interspersed and a small amount of neurons degenerated, the cytoplasm was lightly dyed and the structure blurred; in the medium dose group, part of the nerve cells in the hippocampus had edema, were interspersed, a small amount of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic; in the small dose group, the nerve cells in the hippocampus had edema, chunks of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic. After Ridit test, compared with the sham operation group, the modeled group had prominent statistical significance (P < 0.01), suggesting that prominent pathological changes took place in the hippocampus of the mice and that the modeling was successful; compared with the modeled group, the Nimodipine and the big, the medium and the small dose groups all had prominent statistical significance (P < 0.01); the small dose group had obvious statistical significance, suggesting that the medicine fed in each group could reduce the pathological damages of the cerebral cortex at varied degrees and protect cerebral tissues. 4 Conclusion Ischemia took place twice in the modeling process. In the second ischemia, maybe because of acute loss of blood, the mice were dead. Most deaths took place during the 10 min of the second ischemia and the 10 h after ischemia. The reasons might be that it was difficult for individual mice to form collateral circulation of cerebral veins, the blood flow in the brain declined dramatically, or that the cerebral tissues had a poor resistance against lack of oxygen, causing the respiratory center to be inhibited, or that the blood pipes were pierced open, the nerves were broken in the operation, or that the physical conditions of the mice were weak. Therefore, it can be seen that since the factors that might cause death of the mice were many, death rates could only be used as an auxiliary indicator for (Fan, 2015, Zhou, 2014). After ischemia took place, through inducing IL-1β and other cytokines to release themselves, TNF-α could exacerbate inflammatory reactions, damage the endothelial cells in the capillary, damage blood brain barrier, exacerbate cerebral edema, add to the formation and development of artery thrombus, and cause many other mechanism to have effect, leading to cerebral ischemia reperfusion damages; it could also increase the ICAM-1 gene express by inducing the express of NF-κB to increase, intensify the syntheses of ICAM-1, and merge with corresponding recipients in the leukocyte, causing the leukocyte to be attached to endothelia cells, then emigrate and enter the cerebral substance outside the blood pipes and release inflammatory media and cytokines. Meanwhile, these inflammatory media and cytokines further promoted the gene express of ICAM-1 and caused a vicious cycle, thus exacerbating the damages. The major leukocytes that invaded the brain during an ischemia were neutrophil cells and single-core cells and MPO was a unique enzyme for them both. The activity of MPO reflected the immersed level of the two leukocytes in cerebral tissues. In normal cerebral tissues, NSE only existed in neuron cytoplasm. After ischemia continued for 24 h, NSE would be released from cerebral neurons that were damaged due to ischemia and enter blood circulation by crossing blood brain barriers (Ma and Yang, 2006). The testing of the variance of NSE levels in the serum could reflect the impairment degree of cerebral neurons. The study result suggested that compared with the sham operation group, the TNF-α, ICAM-1, and MPO levels all obviously increased. Stachydrine hydrochloride could reduce the levels of TNF-α, ICAM-1, and MPO and this suggested that stachydrine hydrochloride could inhibit the attachment of leukocyte to endothelia cells and its emigration, thus reducing the release of inflammatory media and cytokines (Wang and Wang, 2012). After observing the pathological tissue slices, we found that the cerebral nerve cells were damaged after ischemia. After ischemia, the metabolism of the cerebral nerve cells became disordered, causing nerve cell damages or apoptosis. In this experiment, we adopted the HE staining method to observe the damages of the nerve cells. The major area we observed was the hippocampus, which was vulnerable to damages caused by ischemia reperfusion. The cerebral tissue slice could directly reflect the volume of cerebral cells and the changes near the cytoplasm, nucleus, and blood pipes. The experiment results suggested that compared with the sham operation group, significant pathological changes took place in the cerebral tissues of the modeled animals, which were mainly the edema and degeneration of the nerve cells in hippocampus. Stachydrine hydrochloride could reduce edema in the cerebral nerve cells and this suggested that stachydrine hydrochloride could soothe the pathological changes in the hippocampus caused by ischemia reperfusion and could be useful in treating ischemia. In terms of treatment effect, the bigger the dose of stachydrine hydrochloride, the better. The experiment result proved that stachydrine hydrochloride could protect nerve cells from inflammatory impairment and the effect of big dose stachydrine hydrochloride was the best (Tan, 2013, Miao and Rui, 2016). Stachydrine hydrochloride, the pharmacological active component of motherwort, a traditional Chinese herb that can clear heat, detoxify, activate blood and remove blood stasis, could reduce ischemia reperfusion impairment from many aspects (Yoo, 2012, Miao and Cheng, 2015). This finding has laid a laboratory foundation for developing medicines that can prevent and treat ischemia diseases in a secure and effective way. Acknowledgements This research was financially supported by National Natural Science Foundation of China (Grant No. 81173474), Outstanding scientific and technological innovation team of Henan province (Grant No. TCJ2014-391) and Zhengzhou science and technology innovation team (Grant No. 131PCXTD612). Peer review under responsibility of King Saud University. Figure 1 The pathological photos of the cerebral cortex of modeled mice. Table 1 The influence on the death rate, serum NSE and serum MPO levels of the modeled mice (X^ ± s). Group Dose (mg/kg) Number of animals modeling Death rate (%) NSE (ng/ml) MPO (U/L) Before Post Sham-operation group – 16 16 0 4.401 ± 0.587** 66.808 ± 24.759** Model group – 16 10 37.5 5.401 ± 0.546 118.964 ± 38.892 Nimodipine group 30 16 12 25 4.848 ± 0.539* 86.806 ± 27.390* Big-dose group 60 16 13 18.75 4.892 ± 0.410* 79.679 ± 23.303** Medium-dose group 30 16 11 31.25 4.929 ± 0.493* 82.838 ± 26.744** Small-dose group 15 16 11 31.25 5.046 ± 0.476 85.493 ± 33.322* Note: Compared with the modeled group, **P < 0.01, *P < 0.05. Table 2 Influence on the ICAM-1 and TNF-α level of the cerebral tissue of the mice (X¯ ± s). Group n Dose (mg/kg) ICAM-1 (ng/ml) TNF-α (pg/ml) Sham-operation group 16 – 63.239 ± 14.605** 85.539 ± 10.305** Model group 10 – 81.204 ± 14.328 104.431 ± 14.311 Nimodipine group 12 30 69.048 ± 9.816** 91.275 ± 8.210** Big-dose group 13 60 69.826 ± 9.783* 90.965 ± 11.694** Medium-dose group 11 30 71.861 ± 10.636* 93.316 ± 9.824* Small-dose group 11 15 72.259 ± 9.162 94.973 ± 8.421* Note: Compared with the modeled group, **P < 0.01, *P < 0.05. Table 3 The influence on the pathological changes in the cerebral cortex of the modeled mice. Group n Dose (mg/kg) − + ++ +++ P Sham-operation group 16 – 16 0 0 0 ** Model group 10 – 0 0 2 8 Nimodipine group 12 30 5 4 2 1 ** Big-dose group 13 60 3 6 3 1 ** Medium-dose group 11 30 3 4 3 1 ** Small-dose group 11 15 2 5 2 2 ** Note: Compared with the modeled group, **P < 0.01, *P < 0.05. “−” the nerve cells were normal; “+” cerebral cortex nerve cells had edema, were interspersed, a small amount of neurons degenerated, the cytoplasm was lightly dyed and the structure blurred; “++” the nerve cells in the cerebral cortex had edema, chunks of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic; “+++” the cerebral cortex nerve cells had edema and most neurons were necrotic. Table 4 The influence on the pathological changes in the hippocampus of the modeled mice. Group n Dose (mg/kg) − + ++ +++ P Sham-operation group 16 – 16 0 0 0 ** Model group 10 – 0 0 3 7 Nimodipine group 12 30 4 3 3 2 ** Big-dose group 13 60 3 4 5 1 ** Medium-dose group 11 30 2 3 4 2 ** Small-dose group 11 15 1 2 5 3 * Note: Compared with the modeled group, **P < 0.01,*P < 0.05. “−” the nerve cells were normal; “+” hippocampus nerve cells had edema, were interspersed, a small amount of neurons degenerated, the cytoplasm was lightly dyed and the structure blurred; “++” the nerve cells in the hippocampus had edema, chunks of neurons degenerated, the cytoplasm was lightly dyed, the structure blurred and certain neurons were necrotic; “+++” the hippocampus nerve cells had edema and most neurons were necrotic. ==== Refs References Fan M.Y. Protective effects of lithium chloride treatment on repeated cerebral ischemia-reperfusion injury in mice Neurol. Sci. 36 2 2015 315 321 25192664 Fan M.Y. 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==== Front Indian J AnaesthIndian J AnaesthIJAIndian Journal of Anaesthesia0019-50490976-2817Medknow Publications & Media Pvt Ltd India IJA-61-18910.4103/ija.IJA_110_17EditorialManaging acute post-operative pain: Advances, challenges and constraints Bajwa Sukhminder Jit Singh Department of Anaesthesiology and Intensive Care, Gian Sagar Medical College and Hospital, Patiala, Punjab, India. E-mail: sukhminder_bajwa2001@yahoo.com3 2017 61 3 189 191 Copyright: © 2017 Indian Journal of Anaesthesia2017This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms. ==== Body Existence of pain is as old as that of humankind. Ever since the beginning of surgical practice, pain has followed surgeons and anaesthesiologists like an inseparable shadow. In spite of our advanced knowledge about pain and pain-relieving practices, it has made an indelible mark and an irreversible penetration into the human psyche, be it that of the patient or the pain physician. Pain physicians of late have opened a larger front to confront the menace of pain, with the availability of newer pain-relieving drugs, equipment and techniques, as well as the formation of pain societies and associations. There is a burgeoning of pain centres and pain management workshops, and greater awareness through pain journals about the existing gap in our knowledge related to pain-relieving practices. India has undergone a drastic transformation over the last three decades, and considerable progress has been made in anaesthesia and pain management. The question still remains: Why is pain still troubling the pain physicians in spite of the many advances in pain medicine? The answer to such a question can be quite daunting considering the various clinical, psychological, biosocial, cultural and economic factors. The first and foremost is the failure to adopt universal practices. The pain-relieving practices in our nation follow the diversity similar to our cultural, regional and religious practices. These gaps are further widened as different specialities follow varied practices in the same institute, leave alone differences between cities and states. These diversities, on the contrary, somehow do help in generation of interest among pain physicians and a quest for more advancement in this field. Although lot of research has been done on molecular mechanisms of pain and pain pathways, the heterogeneity of pain-relieving practices further adds to the poor management of post-operative pain.[1] The poor pain management practices can be potential causes of prolonged hospital stay, gross dissatisfaction among patients, disability in routine life, psychological breakdown, financial and social burden and many more ill effects.[23] Onset of chronic pain can also be partly blamed on poor management of acute post-operative pain. Any patient treated for a surgical procedure has a basic human right to be free of any pain or discomfort. However, such basic rights take a back seat in the backdrop of economical and financial circumstances in resource-challenged nations. The decision-making in such circumstances is often subjective, bypassing the evidence-based protocols.[4] The recent post-caesarean analgesia strategies have been well described in one of the review articles being published in this issue of Indian Journal of Anaesthesia (IJA).[5] However, the application of such techniques, methodologies and drugs for post-caesarean analgesia will only be successful if acute pain services (APS) get uniformly improved as most of these operative deliveries are conducted in hospitals with mediocre facilities and in peripheral health units. There are so many national programs which encourage the antenatal patients to undergo deliveries and operations in hospitals. However, post-operative analgesia is not adequately emphasised in the objectives of these programs, which can possibly cause a greater dissatisfaction among patients. The route of administration of analgesics during the post-operative period is another major issue of contention. A study comparing transdermal buprenorphine with oral tramadol for relief of post-operative pain is being published in this issue.[6] The role of transdermal buprenorphine in relieving post-operative pain can be well appreciated over oral tramadol. The former releases sustained drug molecules into plasma and thus does not lead to peaks and troughs causing intermittent painful periods that occur with fixed duration, intermittent dosing of oral opioids. Such indirect comparisons may sound scientifically and statistically weak, but in clinical practice, such studies do have a larger impact on day-to-day pain-relieving practices. A major landmark Cochrane review of over 350 clinical trials examined the experience of 45,000 patients who underwent a single oral post-operative analgesic intervention.[7] The overview established certain basic facts which include but are not limited to lack of high quality effectiveness of commonly used analgesics, rare use and unavailability of stronger analgesics, the variable effectiveness of analgesics in different surgical conditions and a suggestion to include failure of analgesia also as a part of the clinical outcome of any trial.[7] The most dynamic aspects of pain-relieving practices are the rapidly evolving newer strategies that somehow dilute the effectiveness of existing guidelines and thus create a need for their modification to keep pace with the advancements. This also helps in getting pain medicine diversified as newer drugs, techniques and strategies keep getting evolved. This sometimes proves challenging in laying foundation for newer evidence-based guidelines. This issue of the IJA also carries the results of a major survey study related to current practices of post-operative pain management in tertiary care institutes in the state of Maharashtra.[8] The results of the study have shown that bulk of workload in post-operative pain-relieving practices is carried out by surgeons, whereas anaesthesiologists are mainly involved in the early recovery period and/or post-anaesthesia care unit. The survey has thrown light on a very critical issue, that is, the role of anaesthesiologists in post-operative wards and the strong need for follow-up and post-operative rounds for every surgical procedure. The credit anaesthesiologists are getting in leading pain medicine speciality is somehow diluted by not extending their services into post-operative period uniformly.[9] At present, post-operative services are mainly confined to patients who are part of the research plans and studies or where institute-based pain-relieving protocols are meticulously followed. No medical or surgical speciality can progress unless and until it comprehensively takes care of clinical, scientific, behavioural, attitudinal, cultural, fiscal and psychological aspects associated with it. The future of pain medicine will become much brighter if we take into consideration all the laggards also in the management of post-operative pain into our armamentarium. Maharashtra is considered to be one of the best states in India in delivering quality health care services in tertiary care institutes. Going by the findings of this survey study, the plight of APS in the entire nation, especially in the peripheral health sector can well be imagined.[8] The greatest imbalance that we see today is that in spite of so many achievements in pain-relieving strategies, patient care has not progressed in parallel to the same level. The higher incidence of complications, such as myocardial infarction, arrhythmias, pneumonitis and prolonged hospital stay, can occur when pain is poorly relieved.[10] The concise and updated knowledge of pain-relieving practices should be disseminated widely across the nation so as to bring down morbidity and mortality associated with poor management of pain. Inadequate treatment of pain is not only unethical but also shows that our advanced knowledge and research in pain-relieving field is not satisfactory. For improving APS, there is a strong need for application of existing evidence-based guidelines in a modified form. These modifications should take into consideration our limited resources and behavioural and cultural practices so as to improve the pain index in our nation. The medical fraternity needs to be updated regularly about various aspects of pain, including surgical and non-surgical pain, so as to deal with this menace more precisely and effectively. The best solution in developing nations seems to be combining the evidence-based guidelines with expert clinical practices in different resource-challenged circumstances. This may seem to be a herculean task but with the presence of so many associations and pain societies this can definitely become possible. ==== Refs REFERENCES 1 Visentin M Zanolin E Trentin L Sartori S de Marco R Prevalence and treatment of pain in adults admitted to Italian hospitals Eur J Pain 2005 9 61 7 15629876 2 Mhuircheartaigh RJ Moore RA McQuay HJ Analysis of individual patient data from clinical trials: Epidural morphine for postoperative pain Br J Anaesth 2009 103 874 81 19889750 3 Breivik H Collett B Ventafridda V Cohen R Gallacher D Survey of chronic pain in Europe: Prevalence, impact on daily life, and treatment Eur J Pain 2006 10 287 333 16095934 4 Lohman D Schleifer R Amon JJ Access to pain treatment as a human right BMC Med 2010 8 8 20089155 5 Kerai S Saxena KN Taneja B Post caesarean analgesia: What is new? Indian J Anaesth 2017 61 200 14 6 Desai SN Badiger SV Tokur SB Naik PA Safety and efficacy of transdermal buprenorphine to oral tramadol for treatment of postoperative pain following the surgery for fracture neck of femur; a prospective, randomised clinical study Indian J Anaesth 2017 61 225 9 7 Moore RA Derry S McQuay HJ Wiffen PJ Single dose oral analgesics for acute postoperative pain in adults Cochrane Database Syst Rev 2011 9 CD008659 8 Khatib SK Razvi SS Kulkarni SS Parab S A multicentre survey of the current acute post-operative pain management practices in tertiary care teaching hospitals in Maharashtra Indian J Anaesth 2017 61 215 24 9 Bajwa SJ Takrouri MS Post-operative anesthesia rounds: Need of the hour Anesth Essays Res 2013 7 291 3 25885970 10 Apfelbaum JL Chen C Mehta SS Gan TJ Postoperative pain experience: Results from a national survey suggest postoperative pain continues to be undermanaged Anesth Analg 2003 97 534 40 12873949

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==== Front Indian J AnaesthIndian J AnaesthIJAIndian Journal of Anaesthesia0019-50490976-2817Medknow Publications & Media Pvt Ltd India IJA-61-19210.4103/ija.IJA_143_17Special ArticleRegulatory requirements for clinical trials in India: What academicians need to know Gogtay Nithya J Ravi Renju Thatte Urmila M Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Mumbai, Maharashtra, IndiaAddress for correspondence: Dr. Nithya Jaideep Gogtay, Department of Clinical Pharmacology, Seth GS Medical College and KEM Hospital, Mumbai - 400 012, Maharashtra, India. E-mail: nithyagogtay@kem.edu3 2017 61 3 192 199 Copyright: © 2017 Indian Journal of Anaesthesia2017This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.The academician forms the backbone of any medical college, hospital or university and shoulders the quadruple responsibilities of patient care, teaching, administration and research. Of these, research, though long and difficult, is extremely fulfilling. Academicians often carry out research that is based on observations in practice or in response to their patient's needs. These are called as “Investigator- initiated studies” and these may not have the funding support of the pharmaceutical industry. Hence, the investigator must make sure that he/she complies with the country's regulatory requirements. In the past decade, several changes have dotted the regulatory landscape in the country and have changed the way in which academic research is carried out. The present article outlines regulatory requirements for academic research giving their historical evolution, the key bodies in India that govern or oversee research, along with “must know” and “good to know” facets for the conduct of clinical research in the country. Key words Academic researchclinical trialscomplianceIndiaregulations ==== Body INTRODUCTION The academician is the life line of any medical college, hospital or university as he/she performs the quadruple functions of patient care, teaching, administration and research. While all of these can provide a great measure of satisfaction, a key driver to sating intellectual curiosity remains research. Good research contributes to evidence-based medicine and thus better and improved patient care with the ultimate goal of promoting health. Research, however, is a laborious, time and labour intensive task that can take months or even years to reach fruition. Drug development research, in particular, is long and arduous and bringing a single new drug costs on an average USD 1.78 billion and takes approximately 13.5 years from discovery to the market.[1] Drug development research is primarily funded by the pharmaceutical industry including the process of human testing (Phase I-IV studies). These studies (called clinical trials or regulatory studies) are conducted with the academician as the principal investigator largely in academic centres. The pharmaceutical industry funds or 'sponsors' the studies and ensures compliance with the country's regulatory requirements. Academicians, however, also carry out their own research and these studies are called as 'Investigator initiated studies' (IISs). Here, the academician raises funds for the study through his efforts from various sources including possibly the pharmaceutical industry. In these IISs, he dons the dual mantle of an investigator and 'sponsor' and thus directly becomes responsible for ensuring regulatory compliance. Anaesthesia as a speciality straddles several diverse disciplines that include various branches of surgery and medicine as well as critical care and pain management among others. The past three decades have also seen remarkable advances in the field of anaesthesia, some of which include pulse oximetry, end-tidal gas monitoring, introduction of propofol and the laryngeal mask airway. Anaesthesiologists are uniquely positioned to carry out translational research given the data-rich environment in which they practice[2] and this research can be used successfully to guide evidence-based practice of the discipline as also public health policy.[3] Regardless of the nature of the research (Regulatory Clinical Trials or IISs), knowledge of the regulatory requirements is an essential imperative for researchers. The present article details these requirements giving their historical evolution, the key bodies in India that govern or oversee research along with 'must know' and 'good to know' for the conduct of clinical trials in the country. THE NATIONAL REGULATORY BODY – THE CENTRAL DRUGS STANDARD CONTROL ORGANISATION AND THE DRUGS CONTROLLER GENERAL OF INDIA The Central Drugs Standard Control Organization (CDSCO) is the National Regulatory Authority in India. Its equivalent counterparts elsewhere include the United States Food and Drug Administration (US FDA), Health Canada and the European Medicines Agency. CDSCO is an arm of the Ministry of Health and Family Welfare, Government of India. Its mission is to safeguard and enhance public health by assuring the safety, efficacy and quality of drugs, cosmetics and medical devices.[4] The Drugs Controller General of India (DCGI) is an official of the CDSCO who is the final regulatory authority for the approval of clinical trials in the country. His ambit, in addition, also extends to inspections of trial sites, inspections of sponsors of clinical research and manufacturing facilities in the country, oversight of the Central Drugs Testing Laboratory (Mumbai) and the Regional Drugs Testing Laboratory as also heading the Indian Pharmacopeia Commission among various other roles, responsibilities and functions. THE DEPARTMENT OF HEALTH RESEARCH AND THE INDIAN COUNCIL OF MEDICAL RESEARCH The Indian Council of Medical Research (ICMR) is the apex body that is responsible for the formulation, coordination and promotion of biomedical research. It receives funding from the Ministry of Health and Family Welfare and the Department of Health Research, Government of India.[5] KEY DOCUMENTS IN CLINICAL RESEARCH Drugs and Cosmetics Act (1940) and Drugs and Cosmetics Rules (1945) This act first came into being in 1940 and regulates the import, manufacture and distribution of drugs in the country to ensure that drugs and cosmetics sold in the country are safe, effective and conform to essential quality standards. It has Chapters, Rules and Schedules[67] and is amended at regular intervals to ensure greater safety, efficacy and drug quality. The Schedule Y along with rules 122A, 122B, 122D, 122DA, 122DAC and 122E (see below) is the key document that governs clinical research in the country. Per law, it is mandatory that all clinical research that falls under the ambit of Schedule Y complies with the necessary requirements. It has 12 appendices, formats for clinical trial protocols, informed consent forms, ethics committee (EC) approval templates and a format for serious adverse event (SAE) reporting. Ethical Guidelines of the Indian Council of Medical Research (2006) The revised ICMR guidelines released in 2006 is called the 'Ethical Guidelines for Biomedical Research on Human Participants' and remains valid as of today, and a revised version is expected in 2017. This guideline covers two broad aspects of clinical research – the general principles that need to be followed and guidance regarding special areas of research (e.g., research in children or herbal research).[8] Researchers are expected to be familiar with both these documents and abide by the requirements in the former and the guidance in the latter. Indian Good Clinical Practice Guideline (2001) A good clinical practice (GCP) guideline was released in 2001 by the CDSCO that attempted to be India specific, but unlike the ICH GCP guideline, has not been revised since.[9] REGULATORY CHANGES IN INDIA'S LANDSCAPE (2005–2016) The Schedule Y amendment released on 20th January, 2005 saw dramatic changes that attempted to bring India on par with internationally prevalent regulations.[7] Some of the key changes included the definition of a clinical trial, permitting trials in India to be conducted in the same phase of drug development as elsewhere in the world, demarcation of clear roles and responsibilities of the sponsor, investigator and ECs, underscoring the importance of informed consent, requirement for studies in special populations and mandating that protocol amendments need approval from the office of the DCGI.[10] Over the next 10 years, a slew of changes and reforms dotted the regulatory landscape as outlined in Table 1. Table 1 Evolution of regulatory changes in India (2005–2016 as relevant to clinical trials) REGULATORY REQUIREMENTS FOR THE CONDUCT OF CLINICAL TRIALS IN INDIA – WHAT THE INDIAN RESEARCHER MUST KNOW A - Regulatory definitions What is a 'clinical trial' A clinical trial[11] is defined as the systematic study of new drug(s) (see below for the definition of a new drug) in human subject(s) to generate data for discovering and/or verifying: The clinical pharmacological (including pharmacodynamic and pharmacokinetic) effects And/or adverse effects With the objective of determining safety and/or efficacy of the new drug What is a 'new drug' A 'new' drug[12] is one: That has not been used to a significant extent in the country An already approved drug that is now proposed to be used in a different dosage, different dosage form, a new route or a new indication. An example of this would be the intrathecal or epidural route of use of dexmedetomidine Approved for use but has been on the market for <4 years after approval A fixed-dose combination of two or more drugs, individually approved earlier for certain claims, which are now proposed to be combined for the first time in a fixed ratio All vaccines Drugs made using the recombinant DNA technology B - Conduct of the clinical trial Conduct of the clinical trial The investigator must ensure that clinical trials are conducted as per the rules outlined below[13] In compliance with an EC and a DCGI approved protocol In the case of IISs with 'new drugs', DCGI approval is no longer needed; only an EC approval is required – 16th March, 2016 G.S.R. 313 (E)[14] In compliance with GCP guidelines All applicable regulations Registration of Ethics Committees that approve studies (Rule 122DD)[15] Investigators and Administrators of Academic Institutes should ensure that their Institutional Ethics Committees (IECs) are registered with the central licensing authority and the registration renewed at the end of 3 years.[15] This is mandatory for Regulatory Clinical Trials Approval from Institutional Ethics Committee All clinical trials need to have approval from the IEC A recent regulatory change with respect to IISs is that academicians who carry out trials with 'new drugs' no longer need approval from the DCGI for the conduct of the trial and IEC approval would suffice. This is provided that these studies are not intended for generating data to make a regulatory submission.[14] In the event that the IEC feels that there could be a potential overlap between the academic and regulatory purposes of the trial, they should notify the office of the DCGI. If the IEC does not hear from the DCGI within 30 days, it should be presumed that no permission is needed from the licensing authority Understand the three types of review carried out by the Institutional Ethics Committee and the nature of the research proposal/s in each category of review Institutional Ethics Committees function according to standard operating procedures [SOPS] that are usually available on their websites. Projects submitted essentially undergo two broad types of review- Full board or full committee review [for all projects that present more than minimal risk] or expedited review [for projects that pose no more than minimal risk; e.g., left over clinical samples]. Projects may also be “exempted from review” [research on data in public domain as for example with systematic reviews or meta-analysis]. Investigators who make submissions to the IEC should be familiar with the IEC SOPs and understand the category that their trial/study fits into and the nature of the review that it is likely to undergo. Registration of the clinical trial with the Clinical Trials Registry of India The CTRI[1617] is a free, online portal that allows both investigator-initiated and regulatory studies to be registered. It is recommended that all studies are registered at a public portal. However, for Regulatory Clinical Trials, registration in CTRI is mandatory from June 2009. Registration must be done before the first participant is enrolled Registration is important from a publication standpoint point as editors of many Biomedical Journals will not accept papers that have interventional studies not registered with a Clinical Trials Registry Obtain informed consent from participants Investigators must ensure that written, informed consent is obtained from all participants in a clinical trial For trials that involve vulnerable participants (children or mentally challenged patients for example) and involve a new chemical entity or a new molecular entity, the investigators in addition have to ensure audio visual recording of the informed consent process (gazette notification dated 19th November, 2013).[18] Report serious adverse events that occur during a clinical trial An SAE is defined as an untoward medical occurrence during a clinical trial that is associated with death, in patient hospitalisation (if the study was done on outpatient basis), prolongation of hospitalisation (if the study was conducted on in-patient basis), persistent or significant disability or incapacity, a congenital anomaly or birth defect or is otherwise life-threatening.[19] The timelines for reporting SAEs are given below - The investigator should report all SAEs to the DCGI (for regulatory studies), the sponsor and the IEC, within 24 h of their occurrence (for academic studies, these should be reported only to the IEC within 24 h)[20] - If unable to do so, the reason for delay in reporting the SAE along with the report should be submitted to the DCGI - Send SAE report to DCGI after due analysis. In addition, send to Chairman of IEC and the Head of the institution where the trial has been conducted within 14 calendar days of occurrence of the event - IEC should submit its report on the SAE, after due analysis, along with its opinion on the financial compensation (if any) to be paid by the sponsor or his representative, and to the DCGI within 30 calendar days of occurrence of the event Understand that compensation for trial related death and injury is now required and the implications of compensation particularly when academic studies with 'new drugs' are carried out Compensation in a clinical trial is needed both when death occurs or when there is clinical trial-related injury. The formulae for compensation for both are described below.[21] Compensation for death: B × F × R/99.37, where 'B' is a base amount of 8 lakhs, 'F' is a age factor based on the Workmen Compensation Act and 'R' a risk factor that takes into account the severity, duration of disease and co-morbidities Compensation for permanent disability: (C × D × 90)/(100 × 100), where 'C' is the quantum of compensation which would have been given to the nominee in case of death of the participant and 'D' is the percentage disability suffered by the subject Compensation for an SAE leading to life-threatening disease: 2 × W × N, where 'W' is the minimum wage per day of the unskilled worker (in Delhi) and ‘N’ is the number of days of hospitalisation Compensation for birth defect or congenital anomaly: Medical care to be provided as long as required and a lumpsum amount to be kept in a fixed deposit that would bring in a monthly interest equal to half of the minimum wage of an unskilled worker in Delhi Addressing SAEs and compensation: For dealing with SAEs, some institutions have a SAE subcommittee (over and above the IEC) that meets regularly to review and evaluate SAEs. For institutes that do not have them, this would be a good committee to constitute. Since clinical trial related injury or death is equally possible both with pharmaceutical industry and investigator-initiated (academic) studies, budgetary provisions need to be in place at the institutional level for the medical management of adverse events [AEs], SAEs and provision of insurance to trial participants. Site preparedness (rule 122DAC) Understand that the regulator can inspect the site at any time and that he can cancel the trial permission and discontinue the study. Therefore preparedness of the study site at all times must be ensured Studies with medical devices A draft notification [Medical Devices Rules, 2016] dated 17th October 2016, has been issued for medical devices by the Ministry of Health and Family Welfare, Department of Health and Family Welfare, Government of India [GSR 983 (E)]. Per this notification, medical devices are broadly classified as investigational medical devices and registered or approved medical devices. Chapter VII of this notification states that clinical trials with the former need both IEC and DCGI approval, while academic studies [studies not intended for manufacturing or marketing the device] with the latter, need only IEC approval.[22] Interventional studies in Anaesthesia that are not “drug” trials Clinical studies/trials that are investigator – initiated and involve procedures as interventions [e.g., comparison of effectiveness of two different techniques of brachial plexus block] would need Institutional Ethics committee approval and CTRI registration. Table 2 covers must know and good to know aspects of clinical trial research. Table 2 Key rules of the Drugs and Cosmetics Act and what they mean for the researcher SOURCES OF FUNDING FOR ACADEMIC INVESTIGATOR INITIATED RESEARCH Several governmental and non-governmental organisations within the country fund academic research and the academician needs to make an application to them with application formats and timelines being available on their home pages. Some of these include – ICMR, Department of Biotechnology, Department of Science and Technology and the Council for Scientific and Industrial Research. In addition, several pharmaceutical companies in the country also fund investigator initiated research. The funding from the industry could be by way of provision of drug supplies or monetary support or both. The control of the study including its conception, conduct and analysis remains exclusively with the investigator in these studies and would need a clear memorandum of understanding with the industry funder. WORKING WITH A COLLABORATOR OUTSIDE THE COUNTRY Studies that involve a collaborator from outside India need an additional approval from the Health Ministry Screening Committee, a committee that works out of ICMR and meets quarterly to assess these projects for collaborative merit.[23] THE ROAD AHEAD India accounts for 17% of the world's population and 20% of the global disease burden expressed as disability adjusted life years.[24] The scope for clinical research in the country thus is enormous as it faces the dual burden of both communicable and non-communicable diseases.[25] A recent study has also shown that the regulatory studies done in the country are not commensurate with their health care needs.[26] An understanding of both the disease burden coupled with the regulatory requirements by the researcher will go a long way in alleviating disease associated burden and suffering in the country. CONCLUSIONS The academic investigator needs to be up to speed in reading, understanding and applying regulations and work in tandem with the pharmaceutical industry for greater patient benefit. The ECs now have a larger than ever onus need to appreciate and understand risk – benefit and to empower themselves through repeated training and use of standard operating procedures given that it is known that the quality of IEC review across the country remains variable.[27] Finally, the empowering of IECs by the regulator towards approving studies with 'new' drugs without the need for regulatory approval means that researchers, IECs and institutional administrators should have mechanisms in place for greater participant protection, assessment and analysis of SAEs and budgetary provisions in place for insurance and compensation of participants in these trials. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. ==== Refs REFERENCES 1 Paul SM Mytelka DS Dunwiddie CT Persinger CC Munos BH Lindborg SR How to improve R and D productivity: The pharmaceutical industry's grand challenge Nat Rev Drug Discov 2010 9 203 14 20168317 2 Sanders RD Maze M Translational research: Addressing problems facing the anesthesiologist Anesth Analg 2007 105 899 901 17898363 3 Amin P Fox-Robichaud A Divatia JV Pelosi P Altintas D Eryüksel E The Intensive Care Unit specialist: Report from the Task Force of World Federation of Societies of Intensive and Critical Care Medicine J Crit Care 2016 35 223 8 27444985 4 Central Drugs Standard Control Organization Last accessed on 2017 Feb 20 Available from: https://www.cdscoonline.gov.in/CDSCO/homepage 5 Indian Council of Medical Research, New Delhi Last accessed on 2017 Feb 20 Available from: http://www.icmr.nic.in/ 6 Imran M Najmi AK Rashid MF Tabrez S Shah MA Clinical research regulation in India-history, development, initiatives, challenges and controversies: Still long way to go J Pharm Bioallied Sci 2013 5 2 9 23559817 7 Drugs and Cosmetics Act Last accessed on 2017 Feb 20 Available from: https://www.rgcb.res.in/uploads/2014/07/Schedule-Y.pdf 8 Indian Council of Medical Research, New Delhi Last accessed on 2017 Feb 20 Available from: http://www.icmr.nic.in/ethical_guidelines.pdf 9 Central Drugs Standard Control Organization Last accessed on 2017 Feb 20 Available from: http://www.cdsco.nic.in/html/GCP1.html 10 Bhave A Menon S Regulatory environment for clinical research: Recent past and expected future Perspect Clin Res 2017 8 11 6 28194332 11 Last accessed on 2017 Feb 25 Available from: https://www.rgcb.res.in/uploads/2014/07/Schedule-Y.pdf 12 Last accessed on 2017 Feb 25 Available from: https://www.indiankanoon.org/doc/50800010/ 13 Government of India. Ministry of Health and Family Welfare Last accessed on 2017 Feb 26 Available from: http://www.mohfw.nic.in/index1.php?lang=1&level=5&sublinkid=4481&lid=2641 14 G.S.R 313 Last accessed on 2017 Feb 26 Available from: http://www.cdsco.nic.in/writereaddata/GSR%20313%20(E)%20dated%2016_03_2016.pdf 15 Registration of Ethics Committees. GSR72E 2013 2 08 Last accessed on 2017 Feb 25 Available from: http://www.cdsco.nic.in/writereaddata/G.S.R%2072(E)%20dated%2008.02.2013.pdf 16 Clinical Trials Registry – India Which Clinical Trials are Required to Be Registered? Last accessed on 2017 Feb 21 Available from: http://www.ctri.nic.in/Clinicaltrials/faq.php 17 Satyanarayana K Sharma A Parikh P Vijayan VK Sahu DK Nayak BK Statement on publishing clinical trials in Indian biomedical journals J Postgrad Med 2008 54 78 9 18480516 18 Office Order. Audio-visual Recording of Informed Consent of Process 2013 11 19 Last accessed on 2017 Feb 26 Available from: http://www.cdsco.nic.in/writereaddata/Office%20Order%20dated%2019.11.2013.pdf 19 ICH Harmonized Tripartite Guideline Last accessed on 2017 Feb 27 Available from: http://www.cdsco.nic.in/writereaddata/GSR%20313%20(E)%20dated%2016_03_2016.pdf 20 G.S.R 292 (E). Ministry of Health and Family Welfare Last accessed on 2017 Feb 26 Available from: http://www.cdsco.nic.in/writereaddata/1Draft%20Rules%20on%20compensation.pdf 21 Government of India. Ministry of Health and Family Welfare Last accessed on 2017 Feb 20 Available from: http://www.cdsco.nic.in/writereaddata/ORDER%20and%20Formula%20to%20Determine%20the%20quantum%20of%20compensation%20in%20the%20 cases%20of%20Clinical%20Trial%20related%20serious%20Adverse%20Events (SAEs)%20of%20Injury%20other%20than%20Death.pdf 22 G.S.R 983 (E). Ministry of Health and Family Welfare Last accessed on 2017 Feb 28 Available from: http://www.cdsco.nic.in/writereaddata/grsoct17983.pdf 23 Indian Council of Medical Research, New Delhi Last accessed on 2017 Feb 22 Available from: http://www.icmr.nic.in/hmsc_document/hmsc_project2/introduction.pdf 24 Country Cooperation Strategy at a Glance, World Health Organization Last accessed on 2017 Feb 20 Available from: http://www.who.int/countryfocus/cooperation_strategy/ccsbrief_ind_en.pdf 25 Boutayeb A The double burden of communicable and non-communicable diseases in developing countries Trans R Soc Trop Med Hyg 2006 100 191 9 16274715 26 Chaturvedi M Gogtay NJ Thatte UM Do clinical trials conducted in India match its healthcare needs? An audit of the clinical trials registry of India [CTRI] Perspect Clin Res 2017 In press 27 Muthuswamy V Status of ethical review and challenges in India Indian Pediatr 2005 42 1189 90 16424554

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==== Front Indian J AnaesthIndian J AnaesthIJAIndian Journal of Anaesthesia0019-50490976-2817Medknow Publications & Media Pvt Ltd India IJA-61-20010.4103/ija.IJA_313_16Review ArticlePost-caesarean analgesia: What is new? Kerai Sukhyanti Saxena Kirti Nath 1Taneja Bharti 1Department of Anaesthesiology, Rajiv Gandhi Super Speciality Hospital, New Delhi, India1 Department of Anaesthesiology and Intensive Care, Maulana Azad Medical College and Associated Hospitals, New Delhi, IndiaAddress for correspondence: Dr. Sukhyanti Kerai, B 3/59, Upper Ground Floor, Paschim Vihar, New Delhi, India. E-mail: drsukhi25@gmail.com3 2017 61 3 200 214 Copyright: © 2017 Indian Journal of Anaesthesia2017This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.Adequate post-operative analgesia after caesarean section (CS) is vital as it impacts the distinct surgical recovery requirements of the parturient. Although newer analgesic modalities and drugs for post-caesarean analgesia have been introduced over the recent years, review of the literature suggests suggests that we are far from achieving the goals of optimum post-operative analgesia. We conducted a systematic review of recent advances in modalities for post-caesarean analgesia. After systematic search and quality assessment of studies, we included a total of 51 randomised controlled trials that evaluated the role of opioids, transversus abdominis plane (TAP) block, wound infiltration/infusion, ketamine, gabapentin and ilioinguinal-iliohypogastric nerve block (II-IH NB) for post-caesarean analgesia. Administration of opioids still remains the gold standard for post-operative analgesia, but the associated troublesome side effects have led to the mandatory incorporation of non-opioid analgesics in post-CS analgesia regime. Among the non-opioid techniques, TAP block is the most investigated modality of the last decade. The analgesic efficacy of TAP block as a part of multimodal analgesia is established in post-CS cases where intrathecal morphine is not employed and in CS under general anaesthesia. Among non-steroidal anti-inflammatory drugs, COX-I inhibitors and intravenous paracetamol are found to be useful in post-operative analgesic regimen. The perioperative use of ketamine is found useful only in CS done under spinal anaesthesia; no benefit is seen where general anaesthesia is employed. Wound infiltration with local anaesthetics, systemic gabapentin and II-IH NB need further trials to assess their efficacy. Key words Anaesthesiacaesarean sectionpost-caesarean analgesia ==== Body INTRODUCTION Pain is ranked highest among undesirable clinical outcomes associated with caesarean section (CS).[1] Adequate post-operative analgesia in the obstetric patients is crucial as they have different surgical recovery needs which include breastfeeding and care of the newborn; these can be impaired if analgesia is unsatisfactory. The ideal post-CS analgesic regime should be efficacious without impacting the ability of mother to take care of the neonate and with minimal drug transfer through breast milk. However, observational data from developing as well as developed countries have shown that we are far from achieving these goals. In developing countries, limited availability of drugs, equipment and expertise are the major issues in providing adequate post-CS analgesia.[2] In the past 5 years, there has been a surge in studies describing newer post-operative analgesic modalities. Some of these modalities require less expertise and reduce consumption of opioids in post-operative period. LITERATURE SEARCH This systematic review examined the recent advances in modalities for post-operative analgesia after CS. We searched US National Library of Medicine database, Cochrane Central Register of Controlled Trials, EMBASE and CINAHL for randomised controlled trials (RCTs) that evaluated various analgesic modalities after CS. The terms post-operative analgesia, CS, post-caesarean analgesia were searched. The search was performed without any limits or language restrictions. The last search was performed on 15 October 2016. It revealed a total of 738 results. The RCTs published before 2010, review articles, retrospective studies, case reports and letter to the editor were excluded. After this, a total of 102 RCTs on various types of analgesic were available [Figure 1]. Figure 1 Flow diagram of review RESULTS The various analgesic modalities identified were transversus abdominis plane (TAP) block, local anaesthetic wound infiltration, non-steroidal anti-inflammatory drugs (NSAIDs) and acetaminophen, ilioinguinal-iliohypogastric nerve blocks (II-IH NBs), intrathecal additives, epidural analgesia, ketamine and gabapentin. The quality of the selected RCTs was assessed by two independent reviewers using the Jadad scale. Based on consensus of all authors, the studies scoring >3 on the Jadad scale were selected for data collection and further review. A standardised data collection form was used for outcome data extraction. Data recorded were trial characteristics including sample number, anaesthesia technique, post-operative regimen employed and outcome measures such as post-operative opioid consumption, pain scores and side effects. Based on this data, we describe the utility of intrathecal and epidural opioids, TAP block, II-IH NB and wound infiltration, ketamine, NSAIDs, acetaminophen and gabapentin for post-CS analgesia. INTRATHECAL AND EPIDURAL OPIOIDS In the present review, we found 13 RCTs on various intrathecal opioids used for post-caesarean analgesia; eight trials were excluded after evaluating them. The remaining five RCTs [Table 1] were taken up for review; out of these two trials evaluated the different doses of intrathecal morphine (ITM) while three studied lipophilic opioids fentanyl and sufentanil. Table 1 Opioids administered through various routes for post-caesarean analgesia The efficacy of ITM for post-CS pain control is well established, but the optimal dose is still debated. Previous investigators reported 100 µg ITM to be equivalent to higher doses both in terms of analgesia and side effects.[3] A further lower dose of 50 µg with 100 µg ITM was evaluated in two studies and found to be equally efficacious.[45] These results showed that there is no direct relationship between the dose of ITM and the quality of analgesia. Similar results were demonstrated in previous studies by other investigators.[36] The incidence of pruritus after ITM was found to increase linearly with increasing dose while other side effects such as urinary retention, nausea and vomiting were found to bear no relation with either the use or the dose of ITM. Neither of the two studies reported respiratory depression or sedation which may be attributed to the small sample size of the studies. Lipophilic opioids such as fentanyl and sufentanil given intrathecally, when compared to ITM, were found to provide only early post-operative analgesia.[7] The comparison of intrathecal fentanyl and sufentanil showed that sufentanil provides longer post-operative analgesia without increased incidence of side effects.[89] We found one RCT each on epidural morphine and lipophilic opioids [Table 1]. Traditionally, the dose of epidural morphine used for post-CS analgesia is 2–3 mg.[10] Recently an RCT on epidural morphine compared traditional dose of 3 mg to a lower dose of 1.5 mg. The authors found the 1.5 mg of epidural morphine to be equally efficacious and associated with lower incidence of nausea and pruritus.[11] Vora et al. studied a combination of epidural lipophilic opioid with a small amount of hydrophilic opioid and observed the additional benefit of immediate onset as compared to morphine alone.[12] PATIENT CONTROLLED EPIDURAL ANALGESIA Patient-controlled epidural analgesia (PCEA) has shown effective post-CS analgesia in three studies using opioids with local anaesthetic (LA) agents [Table 1]. Satisfactory post-operative analgesia has been reported with dilute concentrations of ropivacaine 0.025%–0.15% and 0.15% levobupivacaine.[13] Combining a lipophilic opioid like fentanyl or sufentanil to ropivacaine has an LA sparing effect with lower incidence of motor blockade in parturients.[14] However, with the addition of fentanyl to levobupivacaine, greater dizziness and pruritus and less paraesthesia is reported than with PCEA levobupivacaine alone.[15] Pure levobupivacaine could be an alternative regimen for the parturient who is concerned about opioid-related adverse events. CURRENT STATUS OF ORAL OPIOIDS Traditionally, oral opioids are employed as a step down analgesics for post-operative analgesia when the severity of pain decreases. Recently, there has been growing interest in their use as the primary post-caesarean analgesic method on the first post-operative day itself. The potential benefits of this approach include higher maternal acceptability, ease of administration and avoidance of complications associated with parenteral or neuraxial opioids. Oral oxycodone is the preferred opioid for such an approach, as it has a higher and more predictable oral bioavailability than morphine.[16] Oral oxycodone-based post-operative oral regime has been found equianalgesic to ITM after CS.[17] For oral methadone and tramadol; we could not find any study scoring >3 on Jaded scale in the present review. REGIONAL NERVE INFILTRATION TECHNIQUE: TRANSVERSUS ABDOMINIS PLANE BLOCK A total of 14 studies [Table 2] employing TAP block for post-CS analgesia were identified.[1819202122232425262728293031] Most of the investigators used ultrasound-guided TAP block; only in two studies TAP block was performed by anatomical landmark technique.[2021] Table 2 Studies using transversus abdominis plane block for post-caesarean analgesia There are three studies where TAP block was compared to control for post-CS analgesia and all three demonstrated block to be effective.[182122] In two of these studies, CS was performed under general anaesthesia while in the third study spinal anaesthesia was administered. However, when compared to ITM, TAP block was reported to be ineffectual in three studies.[192023] There was also no advantage of supplementing ITM with TAP block as concluded in two trials.[2931] The researchers also studied various other approaches to increase the efficacy of TAP block in comparison to ITM. These included adding clonidine or fentanyl and increasing the dose of LA, but all of them failed to show any benefit.[242730] When TAP block was compared as a part of multimodal analgesia comprising epidural morphine, less consumption of patient-controlled analgesia (PCA) morphine was noted.[28] In two other studies, TAP block was observed to be equally effective in comparison to wound infiltration for analgesia.[2526] TAP block as a part of multimodal analgesia after CS is useful in reducing opioid consumption and their side effects only in parturient receiving general anaesthesia or in situ ations where ITM is not used. The probable explanation for this may be that since ITM already provides effective analgesia for somatic and visceral afferents, post-operative analgesia is not improved by adding TAP block to ITM. Further, larger studies with adequate power and evaluation of lower dose of ITM with TAP block need to be evaluated. ILIOINGUINAL-ILIOHYPOGASTRIC NERVE BLOCK There are five studies on II-IH NB for post-CS analgesia [Table 3].[3233343536] In three studies,[323334] the block was performed by the blind bilateral multilevel II-IH NB technique described by Bell et al.[37] In one trial[35] a single injection as described by Huffnagle et al.[38] was used and, in another, an ultrasound-guided block[36] was performed. In all these trials, except the one using ultrasound for nerve blockade, II-IH NB was seen to be effective for post-CS analgesia. The authors attribute the negative result to use of ITM for spinal anaesthesia which they considered 'standard of care'. However, Wolfson et al. found the landmark-based II-IH NB in addition to ITM to be effective in enhancing analgesia after CS.[34] The ultrasound-guided block is of moderate level difficulty and needs accurate visualisation of nerves. The drug is placed by out of plane approach till the nerves are seen to be completely surrounded by the drug. Further studies involving ultrasound-guided II-IH NBs are warranted to establish its analgesic efficacy. Table 3 Studies on ilioinguinal-iliohypogastric nerve block for post-caesarean analgesia WOUND INFILTRATION/CONTINUOUS WOUND INFUSION WITH LOCAL ANAESTHETICS There are two studies[3940] of single shot wound infiltration and four RCTs of continuous wound infusion[41424344] for post-caesarean analgesia [Table 4]. The drug used for infusion/infiltration is LA in six while in one study tramadol infiltration was compared to LA.[40] Table 4 Studies using wound infiltration technique for post-caesarean analgesia The trials comparing LA for continuous wound infusion have conflicting results. Most trials found continuous wound infusion to be less effective as compared to placebo[41] or ITM[42] or epidural levobupivacaine[40] whereas one found it to be equally effective to epidural morphine in post-operative analgesic requirement.[43] Rackleboom et al. demonstrated that placement of catheter for continuous wound infusion between transversalis fascia and peritoneum is more efficacious for analgesia as compared to placement above the fascia.[44] However, in the present review, the location of catheter is not the only determinant influencing analgesic efficacy as in only 1 out of 3 trials where the catheter was placed sub fascially reported a positive outcome. The other factors responsible could be the dose of LA used or continuous versus the intermittent boluses of LA through catheter. High concentration–low volume administration of ropivacaine (0.5%, 50 ml) is found to be more effective than low concentration and high volume (0.2%, 125 ml) for direct wound infiltration.[39] Further large-sized studies are required to establish the role of continuous wound infusion with LA in post-caesarean analgesia. KETAMINE FOR POST-CAESAREAN PAIN Ketamine is a non-competitive antagonist of the N-methyl-D-aspartate (NMDA) receptor that inhibits central sensitisation and has a pre-emptive analgesic effect to relieve post-operative pain. In particular, even when ketamine is administered in sub-anaesthetic low doses, it suppresses facilitation of pain related to (NMDA) receptors. A total of eight studies involving evaluation of analgesic efficacy of administration of intravenous ketamine in parturient undergoing CS were identified [Table 5].[4546474849505152] Most of the trials (6 out of 8) used spinal anaesthesia for CS. The timing and the dose of ketamine administered were observed to be variable. In two studies where general anaesthesia was used, ketamine was given before induction of anaesthesia[5052] whereas in three trials, involving spinal anaesthesia ketamine was administered immediately after subarachnoid block[444748] while in three it was given after delivery of the baby.[464951] Continuous infusion of ketamine was employed in two studies[4749] and in the remaining trials, bolus doses of ketamine varying from 1 to 0.15 mg/kg were used. Table 5 Studies using perioperative low-dose ketamine for post-caesarean analgesia The reported outcomes were variable in these studies; two studies reported a negative outcome,[5051] two found ketamine to be effective in decreasing consumption of analgesics at 2 h post-operatively[4752] whereas five studies reported a positive outcome. These inconsistent results may be related to differences in anaesthesia technique, the dose and technique of ketamine administered and the post-operative analgesic regime used in trials. A recent meta-analysis observed the efficacy of perioperative ketamine in studies where it was administered during regional anaesthesia but not in the studies where the CSs were performed under general anaesthesia.[53] Another important consideration is the plasma volume expansion during pregnancy which can result in insufficient plasma ketamine levels.[54] Therefore, using a higher dose of ketamine or maintaining continuous infusion for longer time might result in adequate plasma levels. However, the psychomimetic side effects of ketamine may preclude this strategy as it impairs the ability of mother to care for newborn in the immediate post-operative period. NON-STEROIDAL ANTI-INFLAMMATORY DRUGS AND ACETAMINOPHEN/PARACETAMOL NSAIDs and acetaminophen are commonly added to a post-caesarean analgesic regimen along with opioid medications to improve post-caesarean pain and reduce opioid requirements. There are two studies investigating role of oral/intravenous acetaminophen either alone or in combination with an NSAIDs as a part of multimodal post-operative analgesic regime.[5556] Both acetaminophen and diclofenac, when used as a part of post-operative multimodal analgesia, resulted in reduced post-operative analgesic consumption. The combination of diclofenac-tramadol resulted in lower post-operative pain scores compared to diclofenac-acetaminophen.[55] Akhavanakbari et al. showed that diclofenac and indomethacin suppositories resulted in less rescue analgesic consumption compared to acetaminophen.[56] These results are consistent with previous studies. There are three studies using COX-2 inhibitors for post-CS analgesia.[575859] One study evaluated single dose of oral celecoxib 200 mg added to PCEA and compared it to analgesia provided by PCEA only.[57] There was no difference in the total drug consumption in either group. In a study by Wong et al., intravenous parecoxib was found to be as effective as ketorolac for post-CS analgesia.[58] It resulted in 22% PCA morphine reduction in the first post-operative day. Paech et al. compared the combination of oral celecoxib and intravenous parecoxib to paracetamol for post-CS analgesia.[59] They found that the combination of COX-2 inhibitors to be more effective in reducing analgesic requirement. Celecoxib and parecoxib provide a safe profile for both surgical patients and breastfeeding mothers. GABAPENTIN FOR ACUTE AND/OR CHRONIC PAIN FOLLOWING CAESAREAN SECTION Gabapentin is an anticonvulsant drug with significant analgesic properties. It binds to presynaptic voltage-gated calcium channels in the dorsal root ganglia of the spinal cord and prevents release of excitatory neurotransmitter. It is an established analgesic in chronic and neuropathic pain conditions. The pre-operative use of oral gabapentin has been shown to decrease acute pain after various surgical procedures. There are only two studies in literature exploring the role of gabapentin in post-CS analgesia, and both have conflicting results.[6061] While one study concluded significant improvement in pain score and maternal satisfaction in first 48 h postoperatively with a single dose of 600 mg, the other study failed to show any beneficial effect of gabapentin. Thus, a definitive conclusion about the use of gabapentin cannot be drawn at this stage, and further studies are warranted to evaluate the effect of gabapentin on acute and/or chronic pain after caesarean section. CONCLUSION From the present review, it is evident that multimodal analgesia including paracetamol, NSAIDs and oral opioids such as oxycodone should be given to all patients unless there are specific contraindications to any of these. Intraoperative interventions which should be considered are first, intrathecal or epidural morphine if regional anaesthesia has been used; and second, TAP block for CS under general anaesthesia. When ITM is included in post-CS analgesic regime, a dose of 50–75 µg balances desirable analgesia with fewer side effects. In future, the possibility of a further lower dose of epidural morphine and role of oral oxycodone as a primary post-operative analgesic regime may be explored. Further studies are needed to define the role of gabapentin, wound infiltration techniques and II-IH NB for post-CS analgesia. Financial support and sponsorship Nil. 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