bluuebunny/crossref_metadata_2025_split huggingface dataset:
A drug identification model developed using deep learning technologies: experience of a medical center in Taiwan
Authors: Ting Hsien-Wei , Chung Sheng-Luen , Chen Chih-Fang , Chiu Hsin-Yi , Hsieh Yow-Wen 
Published: April 2020

Abstract
Background
Issuing of correct prescriptions is a foundation of patient safety. Medication errors represent one of the most important problems in health care, with ‘look-alike and sound-alike’ (LASA) being the lead error. Existing solutions to prevent LASA still have their limitations. Deep learning techniques have revolutionized identification classifiers in many fields. In search of better image-based solutions for blister package identification problem, this study using a baseline deep learning drug identification (DLDI) aims to understand how identification confusion of look-alike images by human occurs through the cognitive counterpart of deep learning solutions and thereof to suggest further solutions to approach them.

Methods
We collected images of 250 types of blister-packaged drug from the Out-Patient Department (OPD) of a medical center for identification. The deep learning framework of You Only Look Once (YOLO) was adopted for implementation of the proposed deep learning. The commonly-used F1 score, defined by precision and recall for large numbers of identification tests, was used as the performance criterion. This study trained and compared the proposed models based on images of either the front-side or back-side of blister-packaged drugs.

Results
Our results showed that the total training time for the front-side model and back-side model was 5 h 34 min and 7 h 42 min, respectively. The F1 score of the back-side model (95.99%) was better than that of the front-side model (93.72%).

Conclusions
In conclusion, this study constructed a deep learning-based model for blister-packaged drug identification, with an accuracy greater than 90%. This model outperformed identification using conventional computer vision solutions, and could assist pharmacists in identifying drugs while preventing medication errors caused by look-alike blister packages. By integration into existing prescription systems in hospitals, the results of this study indicated that using this model, drugs dispensed could be verified in order to achieve automated prescription and dispensing.
Detection of bovine viral diarrhea virus genotype 1 in aerosol by a real time RT-PCR assay
Authors: Hou Peili , Xu Yaru , Wang Hongmei , He Hongbin 
Published: April 2020

Abstract
Background
As a pestivirus of the Flaviviridae family, bovine viral diarrhea virus (BVDV), has imposed a large burden on animal husbandry worldwide, and such virus can be transmitted mainly through direct contact with other infected animals and probably via aerosols. In the present study, we aimed to develop a real-time RT-PCR method for detection of BVDV-1 in aerosol samples.

Methods
A pair of primers specific for highly conserved regions of the BVDV-1 5′-UTR was designed. The standard curve and sensitivity of the developed assay were assessed based on 10-fold serial dilutions of RNA molecular standard. The specificity of the assay was evaluated with other pestiviruses and infectious bovine viruses. The clinical performance was examined by testing 169 aerosol samples.

Results
The results showed that a good linear relationship existed between the standard curve and the concentration of template. The lowest detection limit was 5.2 RNA molecules per reaction. This assay was specific for detection of BVDV-1, and no amplification was found for other pestiviruses such as classical swine fever virus (CSFV), border disease virus (BDV), and common infectious bovine viruses, including BVDV-2, infectious bovine rhinotracheitis virus (IBRV), bovine parainfluenza virus type 3 (BPIV-3), bovine respiratory syncytial virus (BRSV), bovine ephemeral fever virus (BEFV) and bovine coronavirus (BcoV). The assay was highly reproducible with low variation coefficient values (CVs) for intra-assay and inter-assay. A total of 169 aerosol samples collected from six dairy herds were tested using this method. The results showed that the positive detection rate of BVDV-1 was 17.2% (29/169), which was significantly higher compared with the conventional RT-PCR. Additionally, the positive samples (n = 29) detected by real-time RT-PCR were verified by BVDV RPA-LFD, and a concordance rate of 100% was obtained between them.

Conclusions
Taken together, we developed a real-time RT-PCR assay for quantitative analysis of BVDV-1 in aerosol samples, and our finding provided valuable insights into the risk on aerosol transmission of BVDV-1.
CPEB2-activated PDGFRα mRNA translation contributes to myofibroblast proliferation and pulmonary alveologenesis
Authors: Lai Yen-Ting , Chao Hsu-Wen , Lai Alan Chuan-Ying , Lin Shu-Hui , Chang Ya-Jen , Huang Yi-Shuian 
Published: April 2020

AbstractBackgroundAlveologenesis is the final stage of lung development to form air-exchanging units between alveoli and blood vessels. Genetic susceptibility or hyperoxic stress to perturb this complicated process can cause abnormal enlargement of alveoli and lead to bronchopulmonary dysplasia (BPD)-associated emphysema. Platelet-derived growth factor receptor α (PDGFRα) signaling is crucial for alveolar myofibroblast (MYF) proliferation and its deficiency is associated with risk of BPD, but posttranscriptional mechanisms regulating PDGFRα synthesis during lung development remain largely unexplored. Cytoplasmic polyadenylation element-binding protein 2 (CPEB2) is a sequence-specific RNA-binding protein and translational regulator. Because CPEB2-knockout (KO) mice showed emphysematous phenotypes, we investigated how CPEB2-controlled translation affects pulmonary development and function.MethodsRespiratory and pulmonary functions were measured by whole-body and invasive plethysmography. Histological staining and immunohistochemistry were used to analyze morphology, proliferation, apoptosis and cell densities from postnatal to adult lungs. Western blotting, RNA-immunoprecipitation, reporter assay, primary MYF culture and ectopic expression rescue were performed to demonstrate the role of CPEB2 in PDGFRα mRNA translation and MYF proliferation.ResultsAdult CPEB2-KO mice showed emphysema-like dysfunction. The alveolar structure in CPEB2-deficient lungs appeared normal at birth but became simplified through the alveolar stage of lung development. In CPEB2-null mice, we found reduced proliferation of MYF progenitors during alveolarization, abnormal deposition of elastin and failure of alveolar septum formation, thereby leading to enlarged pulmonary alveoli. We identified that CPEB2 promoted PDGFRα mRNA translation in MYF progenitors and this positive regulation could be disrupted by H2O2, a hyperoxia-mimetic treatment. Moreover, decreased proliferating ability in KO MYFs due to insufficient PDGFRα expression was rescued by ectopic expression of CPEB2, suggesting an important role of CPEB2 in upregulating PDGFRα signaling for pulmonary alveologenesis.ConclusionsCPEB2-controlled translation, in part through promoting PDGFRα expression, is indispensable for lung development and function. Since defective pulmonary PDGFR signaling is a key feature of human BPD, CPEB2 may be a risk factor for BPD.
The influence of chlorine in indoor swimming pools on the composition of breathing phase of professional swimmers
Authors: Swinarew Andrzej S. , Stanula Arkadiusz J. , Gabor Jadwiga , Raif Paweł , Paluch Jarosław , Karpiński Jakub , Kubik Klaudia , Okła Hubert , Ostrowski Andrzej , Tkacz Ewaryst , Skoczyński Szymon , Waśkiewicz Zbigniew , Rosemann Thomas , Nikolaidis Pantelis T. , Knechtle Beat 
Published: April 2020

AbstractObjectivesSwimming is one of the most popular forms of physical activity. Pool water is cleaned with chlorine, which - in combination with compounds contained in water - could form chloramines and trichloromethane in the swimmer’s lungs. The aim of the present study was to examine the effect of swimming training in an indoor pool on the composition of swimmers’ respiratory phase metabolomics, and develop a system to provide basic information about its impact on the swimmer’s airway mucosa metabolism, which could help to assess the risk of secondary respiratory tract diseases i.e. sport results, condition, and health including lung acute and chronic diseases).DesignA group of competitive swimmers participated in the study and samples of their respiratory phase before training, immediately after training, and 2 h after training were assessed.MethodsSixteen male national and international-level competitive swimmers participated in this study. Respiratory phase analysis of the indoor swimming pool swimmers was performed. Gas chromatography combined with mass spectrometry (GCMS) was used in the measurements. All collected data were transferred to numerical analysis for trends of tracking and mapping. The breathing phase was collected on special porous material and analyzed using GCMS headspace.ResultsThe obtained samples of exhaled air were composed of significantly different metabolomics when compared before, during and after exercise training. This suggests that exposition to indoor chlorine causes changes in the airway mucosa.ConclusionThis phenomenon may be explained by occurrence of a chlorine-initiated bio-reaction in the swimmers’ lungs. The obtained results indicate that chromatographic exhaled gas analysis is a sensitive method of pulmonary metabolomic changes assessment. Presented analysis of swimmers exhaled air indicates, that indoor swimming may be responsible for airway irritation caused by volatile chlorine compounds and their influence on lung metabolism.
Ethylene glycol and glycolic acid production from xylonic acid by Enterobacter cloacae
Authors: Zhang Zhongxi , Yang Yang , Wang Yike , Gu Jinjie , Lu Xiyang , Liao Xianyan , Shi Jiping , Kim Chul Ho , Lye Gary , Baganz Frank , Hao Jian 
Published: April 2020

Abstract
Background
Biological routes for ethylene glycol production have been developed in recent years by constructing the synthesis pathways in different microorganisms. However, no microorganisms have been reported yet to produce ethylene glycol naturally.

Results
Xylonic acid utilizing microorganisms were screened from natural environments, and an Enterobacter cloacae strain was isolated. The major metabolites of this strain were ethylene glycol and glycolic acid. However, the metabolites were switched to 2,3-butanediol, acetoin or acetic acid when this strain was cultured with other carbon sources. The metabolic pathway of ethylene glycol synthesis from xylonic acid in this bacterium was identified. Xylonic acid was converted to 2-dehydro-3-deoxy-d-pentonate catalyzed by d-xylonic acid dehydratase. 2-Dehydro-3-deoxy-d-pentonate was converted to form pyruvate and glycolaldehyde, and this reaction was catalyzed by an aldolase. d-Xylonic acid dehydratase and 2-dehydro-3-deoxy-d-pentonate aldolase were encoded by yjhG and yjhH, respectively. The two genes are part of the same operon and are located adjacent on the chromosome. Besides yjhG and yjhH, this operon contains four other genes. However, individually inactivation of these four genes had no effect on either ethylene glycol or glycolic acid production; both formed from glycolaldehyde. YqhD exhibits ethylene glycol dehydrogenase activity in vitro. However, a low level of ethylene glycol was still synthesized by E. cloacae ΔyqhD. Fermentation parameters for ethylene glycol and glycolic acid production by the E. cloacae strain were optimized, and aerobic cultivation at neutral pH were found to be optimal. In fed batch culture, 34 g/L of ethylene glycol and 13 g/L of glycolic acid were produced in 46 h, with a total conversion ratio of 0.99 mol/mol xylonic acid.

Conclusions
A novel route of xylose biorefinery via xylonic acid as an intermediate has been established.
Tenascin-C predicts poor outcomes for patients with colorectal cancer and drives cancer stemness via Hedgehog signaling pathway
Authors: Yang Zhaoting , Zhang Chengye , Feng Ying , Quan Mingji , Cui Yan , Xuan Yanhua 
Published: April 2020

Abstract
Background
Tenascin-C (TNC) is an extracellular matrix protein that is widely expressed in the stromal fibroblasts of various cancers. However, the roles of TNC in colorectal cancer (CRC) cells remain unclear.

Methods
The expression of TNC, cancer stem cell-like (CSC) and cell cycle markers, and Hedgehog (HH) signaling pathway genes were assessed in 100 paraffin embedded clinical CRC patient tissues using immunohistochemistry. The interaction between TNC and CSC marker or HH related genes in CRC cells were detected by immunofluorescence. Cell cycle distribution was measured by flow cytometry. Migration and invasion were evaluated by transwell assays. The expressions of TNC, CSC marker, and HH related proteins were analyzed by western blot.

Results
TNC expression was markedly upregulated in CRC tissues, and was associated with worse clinical outcomes. TNC overexpression was positively associated with CSC marker LSD1, cell cycle markers CDK4 and p16, and HH signaling pathway related genes SMO and GLI1 in clinical CRC tissue samples. TNC silencing downregulated the expression of the CSC marker LSD1, and the proliferation, migration, and invasion of CRC cells. Interestingly, the GLI1 inhibitor GANT61 strongly inhibited the expression of TNC in CRC cells.

Conclusions
TNC may drive tumor progression and is involved in CSC properties via the HH signaling pathway. TNC has potential value in the evaluation of poor prognosis in CRC.
Tools to accelerate falciparum malaria elimination in Cambodia: a meeting report
Authors: Lek Dysoley , Callery James J. , Nguon Chea , Debackere Mark , Sovannaroth Siv , Tripura Rupam , Wojnarski Marius , Piola Patrice , Khean Soy Ty , Manion Kylie , Nguon Sokomar , Kunkel Amber , Vernaeve Lieven , Peto Thomas J. , Dantzer Emily , Davoeung Chan , Etienne William , Dondorp Arjen M. , Tuseo Luciano , von Seidlein Lorenz , Guintran Jean-Olivier 
Published: April 2020

AbstractCambodia targets malaria elimination by 2025. Rapid elimination will depend on successfully identifying and clearing malaria foci linked to forests. Expanding and maintaining universal access to early diagnosis and effective treatment remains the key to malaria control and ultimately malaria elimination in the Greater Mekong Subregion (GMS) in the foreseeable future. Mass Drug Administration (MDA) holds some promise in the rapid reduction of Plasmodium falciparum infections, but requires considerable investment of resources and time to mobilize the target communities. Furthermore, the most practical drug regimen for MDA in the GMS—three rounds of DHA/piperaquine—has lost some of its efficacy. Mass screening and treatment benefits asymptomatic P. falciparum carriers by clearing chronic infections, but in its current form holds little promise for malaria elimination. Hopes that “highly sensitive” diagnostic tests would provide substantial advances in screen and treat programmes have been shown to be misplaced. To reduce the burden on P. falciparum and Plasmodium vivax infections in people working in forested areas novel approaches to the use of malaria prophylaxis in forest workers should be explored. During an October 2019 workshop in Phnom Penh researchers and policymakers reviewed evidence of acceptability, feasibility and effectiveness of interventions to target malaria foci and interrupt P. falciparum transmission and discussed operational requirements and conditions for programmatic implementation.
Correlates of uptake of optimal doses of sulfadoxine-pyrimethamine for prevention of malaria during pregnancy in East-Central Uganda
Authors: Martin Mbonye K. , Venantius Kirwana B. , Patricia Ndugga , Bernard Kikaire , Keith Baleeta , Allen Kabagenyi , Godfrey Asiimwe , Rogers Twesigye , Damazo Kadengye T. , Dathan Byonanebye M. 
Published: April 2020

Abstract
Background
In 2012, the World Health Organization recommended that pregnant women in malaria-endemic countries complete at least three (optimal) doses of intermittent preventive treatment (IPTp) using sulfadoxine-pyrimethamine (SP) to prevent malaria and related adverse events during pregnancy. Uganda adopted this recommendation, but uptake remains low in East-Central and information to explain this low uptake remains scanty. This analysis determined correlates of uptake of optimal doses of IPTp-SP in East-Central Uganda.

Methods
This was a secondary analysis of the 2016 Uganda Demographic Health Survey data on 579 women (15–49 years) who attended at least one antenatal care (ANC) visit and had a live birth within 2 years preceding the survey. Uptake of IPTp-SP was defined as optimal if a woman received at least three doses; partial if they received 1–2 doses or none if they received no dose. Multivariate analysis using multinomial logistic regression was used to determine correlates of IPTp-SP uptake.

Results
Overall, 22.3% of women received optimal doses of IPTp-SP, 48.2% partial and 29.5% none. Attending ANC at a lower-level health centre relative to a hospital was associated with reduced likelihood of receiving optimal doses of IPTp-SP. Belonging to other religious faiths relative to Catholic, belonging to a household in the middle relative to poorest wealth index, and age 30 and above years relative to 25–29 years were associated with higher likelihood of receiving optimal doses of IPTp-SP.

Conclusions
In East-Central Uganda, uptake of optimal doses of IPTp-SP is very low. Improving institutional delivery and household wealth, involving religious leaders in programmes to improve uptake of IPTp-SP, and strengthening IPTp-SP activities at lower level health centers may improve uptake of IPTp-SP in the East-Central Uganda.
Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features
Authors: Calil Bruno Coelho , da Cunha Danilo Vieira , Vieira Marcus Fraga , de Oliveira Andrade Adriano , Furtado Daniel Antônio , Bellomo Junior Douglas Peres , Pereira Adriano Alves 
Published: April 2020

Abstract
Background
Temporomandibular disorders (TMDs) are pathological conditions affecting the temporomandibular joint and/or masticatory muscles. The current diagnosis of TMDs is complex and multi-factorial, including questionnaires, medical testing and the use of diagnostic methods, such as computed tomography and magnetic resonance imaging. The evaluation, like the mandibular range of motion, needs the experience of the professional in the field and as such, there is a probability of human error when diagnosing TMD. The aim of this study is therefore to develop a method with infrared cameras, using the maximum range of motion of the jaw and four types of classifiers to help professionals to classify the pathologies of the temporomandibular joint (TMJ) and related muscles in a quantitative way, thus helping to diagnose and follow up on TMD.

Methods
Forty individuals were evaluated and diagnosed using the diagnostic criteria for temporomandibular disorders (DC/TMD) scale, and divided into three groups: 20 healthy individuals (control group CG), 10 individuals with myopathies (MG), 10 individuals with arthropathies (AG). A quantitative assessment was carried out by motion capture. The TMJ movement was captured with camera tracking markers mounted on the face and jaw of each individual. Data was exported and analyzed using a custom-made software. The data was used to identify and place each participant into one of three classes using the K-nearest neighbor (KNN), Random Forest, Naïve Bayes and Support Vector Machine algorithms.

Results
Significant precision and accuracy (over 90%) was reached by KNN when classifying the three groups. The other methods tested presented lower values of sensitivity and specificity.

Conclusion
The quantitative TMD classification method proposed herein has significant precision and accuracy over the DC/TMD standards. However, this should not be used as a standalone tool but as an auxiliary method for diagnostic TMDs.
Microaneurysms detection in color fundus images using machine learning based on directional local contrast
Authors: Long Shengchun , Chen Jiali , Hu Ante , Liu Haipeng , Chen Zhiqing , Zheng Dingchang 
Published: April 2020

Abstract
Background
As one of the major
complications of diabetes, diabetic retinopathy (DR) is a leading
cause of visual impairment and blindness due to delayed diagnosis
and intervention. Microaneurysms appear as the earliest symptom of
DR. Accurate and reliable detection of microaneurysms in color
fundus images has great importance for DR screening.

Methods
A microaneurysms' detection method
using machine learning based on directional local contrast (DLC) is
proposed for the early diagnosis of DR. First, blood vessels were
enhanced and segmented using improved enhancement function based on
analyzing eigenvalues of Hessian matrix. Next, with blood vessels
excluded, microaneurysm candidate regions were obtained using shape
characteristics and connected components analysis. After image
segmented to patches, the features of each microaneurysm candidate
patch were extracted, and each candidate patch was classified into
microaneurysm or non-microaneurysm. The main contributions of our
study are (1) making use of directional local contrast in
microaneurysms' detection for the first time, which does make sense
for better microaneurysms' classification. (2) Applying three
different machine learning techniques for classification and
comparing their performance for microaneurysms' detection. The
proposed algorithm was trained and tested on e-ophtha MA database,
and further tested on another independent DIARETDB1 database.
Results of microaneurysms' detection on the two databases were
evaluated on lesion level and compared with existing algorithms.

Results
The proposed method has achieved better performance compared with existing algorithms on accuracy and computation time. On e-ophtha MA and DIARETDB1 databases, the area under curve (AUC) of receiver operating characteristic (ROC) curve was 0.87 and 0.86, respectively. The free-response ROC (FROC) score on the two databases was 0.374 and 0.210, respectively. The computation time per image with resolution of 2544×1969, 1400×960 and 1500×1152 is 29 s, 3 s and 2.6 s, respectively.

Conclusions
The proposed method
using machine learning based on directional local contrast of image
patches can effectively detect microaneurysms in color fundus images
and provide an effective scientific basis for early clinical DR
diagnosis.