bluuebunny/crossref_metadata_2025_split huggingface dataset:
Total phenolic content and antioxidant and anticholinesterase activities of medicinal plants from the State’s Cocó Park (Fortaleza-CE, Brazil)
Authors: Morais Selene Maia de , Lopes Francisco Flávio da Silva , Fontenele Gilson Araújo , Silva Marcus Vinícius Ferreira da , Fernandes Victor Borges , Alves Daniela Ribeiro 
Published: April 2021

The State’s Cocó Park in the city of Fortaleza-CE present mainly a mangrove flora and include landscape and medicinal plants. The aim of this study is determining the total phenol content, antioxidant activity against the free radical DPPH and the inhibition of the enzyme acetylcholinesterase (AChE) in ethanolic extracts of 30 medicinal plants and thus assess which plants have potential against Alzheimer's Disease. The plants rich in phenolic compounds with amounts ranging from 297.46 ± 26.94 to 599.30 ± 17.08 mg GAE/g plant extract, which showed greater antioxidant activities (with IC50 against DPPH radical from 3.44 ± 0.16 to 3.73 ± 0.12 µg mL-1) and higher acetylcholinesterase inhibiting power (IC50 < 20 μg mL-1) were Anacardium occidentale, Ceiba pentandraLaguncularia racemosa, Mangifera indica, Myracrodrum urundeuva and Terminalia catappa. Then, these species and their constituents are recommended for more specific studies related to Alzheimer´s Disease.
Association of Increased Circulating Acetic Acid With Poor Survival in Pseudomonas aeruginosa Ventilator-Associated Pneumonia Patients
Authors: Qi Xiaoling , Zhang Li , Xu Jing , Tao Zheying , Wang Xiaoli , Qiu Yuzhen , Pan Tingting , Liu Zhaojun , Qu Hongping , Tan Ruoming , Liu Jialin 
Published: April 2021

BackgroundWe previously found that microbial disruption in Pseudomonas aeruginosa ventilator-associated pneumonia (PA-VAP) patients are long-lasting. Long-term microbial dysbiosis may lead to changes in metabolites. Short-chain fatty acids (SCFAs) are microbial fermentation products and show beneficial effects in patients with pneumonia. In this study, we aimed to explore the association between circulating SCFA levels and clinical outcomes in patients with PA-VAP.MethodsIn this study, we analyzed SCFAs in the serum of 49 patients with PA-VAP by gas chromatography-mass spectrometry analysis. Twenty of these patients died, and 29 survived. The correlation between serum SCFAs and patient survival and immune parameters was analyzed.ResultsWe developed a partial least squares discriminant analysis (PLS-DA) model to examine differential SCFAs in 49 patients with PA-VAP. Among the seven SCFAs, only acetic acid was increased in non-survivors (P = 0.031, VIP &gt; 1). Furthermore, high levels of acetic acid (&gt;1.96ug/ml) showed increased 90-day mortality compared to low levels of acetic acid (&lt;1.96ug/ml) in Kaplan-Meier survival analyses (P = 0.027). Increased acetic acid also correlated with reduced circulating lymphocyte and monocyte counts.ConclusionOur study showed that increased circulating acetic acid is associated with 90-day mortality in PA-VAP patients. The decrease in lymphocytes and monocytes might be affected by acetic acid and involved in the poor prognosis.
Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools
Authors: Saifee Murtaza , Wu Jian , Liu Yingna , Ma Ping , Patlidanon Jutima , Yu Yinxi , Ying Gui-Shuang , Han Ying 
Published: April 2021

Purpose: To introduce and validate hvf_extraction_script, an open-source software script for the automated extraction and structuring of metadata, value plot data, and percentile plot data from Humphrey visual field (HVF) report images.Methods: Validation was performed on 90 HVF reports over three different report layouts, including a total of 1,530 metadata fields, 15,536 value plot data points, and 10,210 percentile data points, between the computer script and four human extractors, compared against DICOM reference data. Computer extraction and human extraction were compared on extraction time as well as accuracy of extraction for metadata, value plot data, and percentile plot data.Results: Computer extraction required 4.9-8.9 s per report, compared to the 6.5-19 min required by human extractors, representing a more than 40-fold difference in extraction speed. Computer metadata extraction error rate varied from an aggregate 1.2-3.5%, compared to 0.2-9.2% for human metadata extraction across all layouts. Computer value data point extraction had an aggregate error rate of 0.9% for version 1, &lt;0.01% in version 2, and 0.15% in version 3, compared to 0.8-9.2% aggregate error rate for human extraction. Computer percentile data point extraction similarly had very low error rates, with no errors occurring in version 1 and 2, and 0.06% error rate in version 3, compared to 0.06-12.2% error rate for human extraction.Conclusions: This study introduces and validates hvf_extraction_script, an open-source tool for fast, accurate, automated data extraction of HVF reports to facilitate analysis of large-volume HVF datasets, and demonstrates the value of image processing tools in facilitating faster and cheaper large-volume data extraction in research settings.
Predictive Analytic Model for Diagnosis of Ectopic Pregnancy
Authors: Rueangket Ploywarong , Rittiluechai Kristsanamon 
Published: April 2021

Objective: Ectopic pregnancy (EP) is a serious condition. Delayed diagnosis could lead to life-threatening outcomes. The study aimed to develop a diagnostic predictive model for EP to approach suspected cases with prompt intervention before the rupture occurred.Methods: A retrospective cross-sectional study enrolled 347 pregnant women presenting first-trimester complications (abdominal pain or vaginal bleeding) with diagnosis suspected of pregnancy of unknown location, who were eligible and underwent chart review. The data including clinical risk factors, signs and symptoms, serum human chorionic gonadotropin (hCG), and ultrasound findings were analyzed. The statistical predictive score was developed by performing logistic regression analysis. The testing data of 30 patients were performed to test the validation of predictive scoring.Results: From a total of 22 factors, logistic regression method–derived scoring model was based on five potent factors (history of pelvic inflammatory disease, current use of emergency pills, cervical motion tenderness, serum hCG ≥1,000 mIU/ml, and ultrasound finding of adnexal mass) using a cutoff score ≥3. This predictive index score was able to determine ectopic pregnancy with an accuracy of 77.8% [95% confidence interval (CI) = 73.1–82.1], specificity of 91.0% (95% CI = 62.1–72.0), sensitivity of 67.0% (95% CI = 88.0–94.0), and area under the curve of 0.906 (95% CI = 0.875–0.937). In the validation group, no patient with negative result of this score had an EP.Conclusion: Statistical predictive score was derived with high accuracy and applicable performance for EP diagnosis. This score could be used to support clinical decision making in routine practice for management of EP.
Frailty and Its Associated Risk Factors: First Phase Analysis of Multicentre Indonesia Longitudinal Aging Study
Authors: Setiati Siti , Soejono Czeresna Heriawan , Harimurti Kuntjoro , Dwimartutie Noto , Aryana I. G. P. Suka , Sunarti Sri , Budiningsih Fatichati , Mulyana Roza , Dwipa Lazuardhi , Sudarso Agus , Rensa Rensa , Istanti Rahmi , Azwar Muhammad Khifzhon , Marsigit Jessica 
Published: April 2021

Background: National long-term care development requires updated epidemiological data related to frailty. We aimed to find the prevalence of frailty and its associated factors among Indonesian elderly.Methods: We conducted first-phase cross-sectional analysis of Indonesia Longitudinal Aging Study (INALAS) data collected from community-dwelling outpatients aged 60 years and older without acute illness in nine geriatric service care centres. Descriptive, bivariate and multivariate analyses were conducted.Results: Among 908 elderly in this study, 15.10% were robust, 66.20% were pre-frail, and 18.70% were frail. Functional dependence was associated with frailty among Indonesian elderly (OR 5.97, 95% CI 4.04–8.80). Being depressed and at risk for malnutrition were also associated with frailty with OR 2.54, 95% CI 1.56–4.12, and OR 2.56, 95% CI 1.68–3.90, respectively. Prior history of fall (OR 1.77, 95% CI 1.16–2.72) and hospitalization (OR 1.46, 95% CI 0.97–2.20) in the previous 12 months were associated with frailty. There is also significant association between poly pharmacy and frailty (OR 2.42, 95% CI 1.50–3.91).Conclusion: Approximately one in five Indonesian community-dwelling elderly was frail. Frailty is associated with functional dependence, being at risk for malnutrition or being malnourished, depression, history of fall, history of hospitalization, and poly pharmacy. There may be bidirectional relationships between the risk factors and frailty. The development of long-term care in Indonesia should be considered, without forcing the elderly who need it.
Sleep Timing and Risk of Dementia Among the Chinese Elderly in an Urban Community: The Shanghai Aging Study
Authors: Li Xiantao , Ding Ding , Zhao Qianhua , Wu Wanqing , Xiao Zhenxu , Luo Jianfeng , Yaffe Kristine , Leng Yue 
Published: April 2021

Background: Growing evidence has suggested a link between poor sleep quality and increased risk of dementia. However, little is known about the association between sleep timing, an important behavior marker of circadian rhythms, and dementia risk in older adults, and whether this is independent of sleep duration or quality.Methods: We included data from 1,051 community-dwelling older men and women (aged≥ 60y) without dementia from the Shanghai Aging Study. At baseline, participants reported sleep timing, duration, and quality using the Chinese version of the Pittsburgh Sleep Quality Index (CPSQI). Dementia diagnosis over the following 7.3 years was determined by neurologists using DSM-IV criteria. We used Cox proportional hazards models to examine the association between bedtime (before 9 p.m., after 11 p.m. vs. 9–11 p.m.), rise time (before 6 a.m., after 8 a.m. vs. 6–8 a.m.), and risk of dementia.Results: A total of 238 (22.8%), 675 (64.5%), and 133 (12.7%) participants reported going to bed before 9 p.m., between 9 and 11 p.m., and after 11 p.m., respectively, while 272 (26%), 626 (59.9%), and 148 (14.2%) reported getting up before 6 a.m., between 6 and 8 a.m., and after 8 a.m., respectively. Participants who reported going to bed earlier had a lower education level, were less likely to be smokers, more likely to have hypertension or diabetes, and had longer sleep duration but poorer sleep quality compared to those who reported a later bedtime. We found 47 incidents of dementia among 584 participants followed up over an average of 7.3 years. After adjustment for demographics, education, income, body mass index, depressive symptoms, smoking, alcohol use, physical activity, comorbidities, APOE4 genotype, and baseline MMSE, those with a bedtime of before 9 p.m. were two times more likely to develop dementia [hazard ratio (HR)=2.16 (95%CI: 1.06–4.40)], compared to those going to bed between 9 and 11 p.m. Later bedtime (i.e., after 11 p.m.) showed the opposite but had a non-significant association with dementia risk (HR=0.15, 95%CI: 0.02–1.29). We did not find an association for rise time and risk of dementia.Conclusion: Earlier sleep timing in older adults without dementia was associated with an increased risk of dementia. Future studies should examine the underlying mechanisms of this association and explore the usefulness of sleep timing as a preclinical marker for dementia.
The Efficacy of Combined Cisplatin and Nanoparticle Albumin-Bound Paclitaxel in a Stage IV Pancreatic Squamous Cell Carcinoma Patient With a Somatic BRCA2 Mutation: A Case Report
Authors: Huang Xiaozhun , Wang Chunling , Ma Teng , Huang Zhangkan , Zhou Houhong , Xu Lin , Zhang Renjie , Zhao Jianjun , Zhang Yefan , Huang Zhen , Shao Lin , Wang Yang , Yang Fan , Che Xu 
Published: April 2021

Pancreatic squamous cell carcinoma (SCC) is a rare primary pancreatic malignancy with a poor prognosis. The median overall survival (OS) for metastatic setting is only 4 months and the optimal management remains poorly defined. In the present study, we report a 52-year-old female patient with stage IV primary SCC of the pancreas harboring a deleteous BRCA2 somatic mutation. After 10 cycles of chemotherapy of cisplatin combined with nanoparticle albumin-bound paclitaxel, metastatic lesions in the liver and lymph nodes achieved radiographic complete responses and pancreatic lesion shrank from 5.7 to 1.5 cm in diameter. The patient subsequently underwent a posterior radical antegrade modular pancreatosplenectomy with R0 resection and residual liver lesions were also resected. After 3 months, a tumor relapsed in the liver. She was then treated with olaparib combined with pembrolizumab and achieved stable disease on the liver lesion. The patient eventually died from cerebral hemorrhage with a long OS of 21 months. Our case demonstrated a favorable clinical activity and survival advantage of the combined cisplatin and nanoparticle albumin-bound paclitaxel, which might serve as a therapeutic option for the patient with BRCA-mutant pancreatic SCC.
A Radiomics Nomogram for Preoperative Prediction of Early Recurrence of Small Hepatocellular Carcinoma After Surgical Resection or Radiofrequency Ablation
Authors: Wen Liting , Weng Shuping , Yan Chuan , Ye Rongping , Zhu Yuemin , Zhou Lili , Gao Lanmei , Li Yueming 
Published: April 2021

BackgroundPatients with small hepatocellular carcinoma (HCC) (≤3 cm) still have a poor prognosis. The purpose of this study was to develop a radiomics nomogram to preoperatively predict early recurrence (ER) (≤2 years) of small HCC.MethodsThe study population included 111 patients with small HCC who underwent surgical resection (SR) or radiofrequency ablation (RFA) between September 2015 and September 2018 and were followed for at least 2 years. Radiomic features were extracted from the entire tumor by using the MaZda software. The least absolute shrinkage and selection operator (LASS0) method was applied for feature selection, and radiomics signature construction. A rad-score was then calculated. Multivariable logistic regression analysis was used to establish a prediction model including independent clinical risk factors, radiologic features and rad-score, which was ultimately presented as a radiomics nomogram. The predictive ability of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve and internal validation was performed via bootstrap resampling and 5-fold cross-validation method.ResultsA total of 53 (53/111, 47.7%) patients had confirmed ER according to the final clinical outcomes. In univariate logistic regression analysis, cirrhosis and hepatitis B infection (P=0.015 and 0.083, respectively), hepatobiliary phase hypointensity (P=0.089), Child-Pugh score (P=0.083), the preoperative platelet count (P=0.003), and rad-score (P&lt;0.001) were correlated with ER. However, after multivariate logistic regression analysis, only the preoperative platelet count and rad-score were included as predictors in the final model. The area under ROC curve (AUC) of the radiomics nomogram to predict ER of small HCC was 0.981 (95% CI: 0.957, 1.00), while the AUC verified by bootstrap is 0.980 (95% CI: 0.962, 1.00), indicating the goodness-of-fit of the final model.ConclusionsThe radiomics nomogram containing the clinical risk factors and rad-score can be used as a quantitative tool to preoperatively predict individual probability of ER of small HCC.
Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis
Authors: Marcinkevics Ricards , Reis Wolfertstetter Patricia , Wellmann Sven , Knorr Christian , Vogt Julia E. 
Published: April 2021

Background:Given the absence of consolidated and standardized international guidelines for managing pediatric appendicitis and the few strictly data-driven studies in this specific, we investigated the use of machine learning (ML) classifiers for predicting the diagnosis, management and severity of appendicitis in children.Materials and Methods:Predictive models were developed and validated on a dataset acquired from 430 children and adolescents aged 0-18 years, based on a range of information encompassing history, clinical examination, laboratory parameters, and abdominal ultrasonography. Logistic regression, random forests, and gradient boosting machines were used for predicting the three target variables.Results:A random forest classifier achieved areas under the precision-recall curve of 0.94, 0.92, and 0.70, respectively, for the diagnosis, management, and severity of appendicitis. We identified smaller subsets of 6, 17, and 18 predictors for each of targets that sufficed to achieve the same performance as the model based on the full set of 38 variables. We used these findings to develop the user-friendly online Appendicitis Prediction Tool for children with suspected appendicitis.Discussion:This pilot study considered the most extensive set of predictor and target variables to date and is the first to simultaneously predict all three targets in children: diagnosis, management, and severity. Moreover, this study presents the first ML model for appendicitis that was deployed as an open access easy-to-use online tool.Conclusion:ML algorithms help to overcome the diagnostic and management challenges posed by appendicitis in children and pave the way toward a more personalized approach to medical decision-making. Further validation studies are needed to develop a finished clinical decision support system.
Qing-Yi Decoction in the Treatment of Acute Pancreatitis: An Integrated Approach Based on Chemical Profile, Network Pharmacology, Molecular Docking and Experimental Evaluation
Authors: Wei Tian-Fu , Zhao Liang , Huang Peng , Hu Feng-Lin , Jiao Ju-Ying , Xiang Kai-Lai , Wang Zhi-Zhou , Qu Jia-Lin , Shang Dong 
Published: April 2021

Background: Qing-Yi Decoction (QYD) is a classic precompounded prescription with satisfactory clinical efficacy on acute pancreatitis (AP). However, the chemical profile and overall molecular mechanism of QYD in treating AP have not been clarified.Methods: In the present study, a rapid, simple, sensitive and reliable ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS)-based chemical profile was first established. An integration strategy of network pharmacology analysis and molecular docking based identified ingredients was further performed to screen out the potential targets and pathways involved in the treatment of QYD on AP. Finally, SD rats with acute pancreatitis were constructed to verify the predicted results through a western blot experiment.Results: A total of 110 compounds, including flavonoids, phenolic acids, alkaloids, monoterpenes, iridoids, triterpenes, phenylethanoid glycosides, anthraquinones and other miscellaneous compounds were identified, respectively. Eleven important components, 47 key targets and 15 related pathways based on network pharmacology analysis were obtained. Molecular docking simulation indicated that ERK1/2, c-Fos and p65 might play an essential role in QYD against AP. Finally, the western blot experiments showed that QYD could up-regulate the expression level of ERK1/2 and c-Fos, while down-regulate the expression level of p65.Conclusion: This study predicted and validated that QYD may treat AP by inhibiting inflammation and promoting apoptosis, which provides directions for further experimental studies.
Population Pharmacokinetic Model of Iohexol in Dogs to Estimate Glomerular Filtration Rate and Optimize Sampling Time
Authors: Baklouti Sarah , Concordet Didier , Borromeo Vitaliano , Pocar Paola , Scarpa Paola , Cagnardi Petra 
Published: April 2021

Monitoring iohexol plasma clearance is considered a useful, reliable, and sensitive tool to establish glomerular filtration rate (GFR) and early stages of kidney disease in both humans and veterinary medicine. The assessment of GFR based on iohexol plasma clearance needs repeated blood sampling over hours, which is not easily attainable in a clinical setting. The study aimed to build a population pharmacokinetic (Pop PK) model to estimate iohexol plasma clearance in a population of dogs and based on this model, to indicate the best sampling times that enable a precise clearance estimation using a low number of samples. A Pop PK model was developed based on 5 iohexol plasma samples taken from 5 to 180 minutes (min) after an intravenous iohexol nominal dose of 64.7 mg/kg from 49 client-owned dogs of different breeds, sexes, ages, body weights, and clinical conditions (healthy or presenting chronic kidney disease CKD). The design of the best sampling times could contain either 1 or 2 or 3 sampling times. These were discretized with a step of 30 min between 30 and 180 min. A two-compartment Pop PK model best fitted the data; creatinine and kidney status were the covariates included in the model to explain a part of clearance variability. When 1 sample was available, 90 or 120 min were the best sampling times to assess clearance for healthy dogs with a low creatinine value. Whereas for dogs with CKD and medium creatinine value, the best sampling time was 150 or 180 min, for CKD dogs with a high creatinine value, it was 180 min. If 2 or 3 samples were available, several sampling times were possible. The method to define the best sampling times could be used with other Pop PK models as long as it is representative of the patient population and once the model is built, the use of individualized sampling times for each patient allows to precisely estimate the GFR.
Analysis of Genetic and Non-genetic Predictors of Levodopa Induced Dyskinesia in Parkinson’s Disease
Authors: Tirozzi Alfonsina , Modugno Nicola , Palomba Nicole Piera , Ferese Rosangela , Lombardi Alessia , Olivola Enrica , Gialluisi Alessandro , Esposito Teresa 
Published: April 2021

Background: Levodopa (L-Dopa), representing the therapeutic gold standard for the treatment of Parkinson disease (PD), is associated with side effects like L-Dopa induced dyskinesia (LID). Although several non-genetic and genetic factors have been investigated for association with LID risk, contrasting results were reported and its genetic basis remain largely unexplored.Methods: In an Italian PD cohort (N = 460), we first performed stepwise multivariable Cox Proportional Hazard regressions modeling LID risk as a function of gender, PD familiarity, clinical subtype, weight, age-at-onset (AAO) and years-of-disease (YOD), L-Dopa dosage, severity scores, and scales assessing motor (UPDRS-III), cognitive (MoCA), and non-motor symptoms (NMS). Then we enriched the resulting model testing two variants—rs356219 and D4S3481—increasing the expression of the SNCA gene, previously suggested as a potential mechanism of LID onset. To account for more complex (non-linear) relations of these variables with LID risk, we built a survival random forest (SRF) algorithm including all the covariates mentioned above.Results: Among tested variables (N = 460 case-complete, 211 LID events; total follow-up 31,361 person-months, median 61 months), disease duration showed significant association (p &lt; 0.005), with 6 (3–8)% decrease of LID risk per additional YOD. Other nominally significant associations were observed for gender—with women showing a 39 (5–82)% higher risk of LID—and AAO, with 2 (0.3–3)% decrease of risk for each year increase of PD onset. The SRF algorithm confirmed YOD as the most prominent feature influencing LID risk, with a variable importance of about 8% in the model. In genetic models, no statistically significant effects on incident LID risk was observed.Conclusions: This evidence supports a protective effect of late PD onset and gender (men) against LID risk and suggests a new independent protective factor, YOD. Moreover, it underlines the importance of personalized therapeutic protocols for PD patients in the future.