bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic Profiles in Pharmacogenes Indicate Personalized Drug Therapy for COVID-19
Authors: Wang Lei-Yun , Cui Jia-Jia , OuYang Qian-Ying , Zhan Yan , Wang Yi-Min , Xu Xiang-Yang , Guo Cheng-Xian , Yin Ji-Ye 
Published: March 2020

AbstractBackgroundThe coronavirus disease 2019 (COVID-19) has become a global pandemic currently. Many drugs showed potential for COVID-19 therapy. However, genetic factors which can lead to different drug efficiency and toxicity among populations are still undisclosed in COVID-19 therapy.MethodsWe selected 67 potential drugs for COVID-19 therapy (DCTs) from clinical guideline and clinical trials databases. 313 pharmaco-genes related to these therapeutic drugs were included. Variation information in 125,748 exomes were collected for racial differences analyses. The expression level of pharmaco-genes in single cell resolution was evaluated from single-cell RNA sequencing (scRNA-seq) data of 17 healthy adults.ResultsPharmacogenes, including CYP3A4, ABCB1, SLCO1B1, ALB, CYP3A5, were involved in the process of more than multi DCTs. 224 potential drug-drug interactions (DDIs) of DCTs were predicted, while 112 of them have been reported. Racial discrepancy of common nonsynonymous mutations was found in pharmacogenes including: VDR, ITPA, G6PD, CYP3A4 and ABCB1 which related to DCTs including ribavirin, α-interferon, chloroquine and lopinavir. Moreover, ACE2, the target of 2019-nCoV, was only found in parts of lung cells, which makes drugs like chloroquine that prevent virus binding to ACE2 more specific than other targeted drugs such as camostat mesylate.ConclusionsAt least 17 drugs for COVID-19 therapy with predictable pharmacogenes should be carefully utilized in risk races which are consisted of more risk allele carriers. At least 29 drugs with potential of DDIs are reported to be affected by other DDIs, they should be replaced by similar drugs without interaction if it is possible. Drugs which specifically targeted to infected cells with ACE2 such as chloroquine are preferred in COVID-19 therapy.
What is required to prevent a second major outbreak of the novel coronavirus SARS-CoV-2 upon lifting the metropolitan-wide quarantine of Wuhan city, China
Authors: Zhang Lei , Shen Mingwang , Ma Xiaomeng , Su Shu , Gong Wenfeng , Wang Jing , Tao Yusha , Zou Zhuoru , Zhao Rui , Lau Joseph , Li Wei , Liu Feng , Ye Kai , Wang Youfa , Zhuang Guihua , Fairley Christopher K 
Published: March 2020

BackgroundThe Chinese government implemented a metropolitan-wide quarantine of Wuhan city on 23rd January 2020 to curb the epidemic of the coronavirus COVID-19. Lifting of this quarantine is imminent. We modelled the effects of two key health interventions on the epidemic when the quarantine is lifted.MethodWe constructed a compartmental dynamic model to forecast the trend of the COVID-19 epidemic at different quarantine lifting dates and investigated the impact of different rates of public contact and facial mask usage on the epidemic.ResultsWe estimated that at the end of the epidemic, a total of 65,572 (46,156-95,264) individuals would be infected by the virus, among which 16,144 (14,422-23,447, 24.6%) would be infected through public contacts, 45,795 (32,390-66,395, 69.7%) through household contact, 3,633 (2,344-5,865, 5.5%) through hospital contacts (including 783 (553-1,134) non-COVID-19 patients and 2,850 (1,801-4,981) medical staff members). A total of 3,262 (1,592-6,470) would die of COVID-19 related pneumonia in Wuhan. For an early lifting date (21st March), facial mask needed to be sustained at a relatively high rate (≥85%) if public contacts were to recover to 100% of the pre-quarantine level. In contrast, lifting the quarantine on 18th April allowed public person-to-person contact adjusted back to the pre-quarantine level with a substantially lower level of facial mask usage (75%). However, a low facial mask usage (<50%) combined with an increased public contact (>100%) would always lead a significant second outbreak in most quarantine lifting scenarios. Lifting the quarantine on 25th April would ensure a smooth decline of the epidemics regardless of the combinations of public contact rates and facial mask usage.ConclusionThe prevention of a second epidemic is viable after the metropolitan-wide quarantine is lifted but requires a sustaining high facial mask usage and a low public contact rate.
Improved deep learning model for differentiating novel coronavirus pneumonia and influenza pneumonia
Authors: Zhou Min , Chen Yong , Yang Dexiang , Xu Yanping , Yao Weiwu , Huang Jingwen , Jin Xiaoyan , Pan Zilai , Tan Jingwen , Wang Lan , Xia Yihan , Zou Longkuan , Xu Xin , Wei Jingqi , Guan Mingxin , Feng Jianxing , Zhang Huan , Qu Jieming 
Published: March 2020

AbstractBackgroundChest CT had high sensitivity in diagnosing novel coronavirus pneumonia (NCP) at early stage, giving it an advantage over nucleic acid detection in time of crisis. Deep learning was reported to discover intricate structures from clinical images and achieve expert-level performance in medical image analysis. To develop and validate an integrated deep learning framework on chest CT images for auto-detection of NCP, particularly focusing on differentiating NCP from influenza pneumonia (IP).Methods35 confirmed NCP cases were consecutively enrolled as training set from 1138 suspected patients in three NCP designated hospitals together with 361 confirmed viral pneumonia patients from center one including 156 IP patients, from May, 2015 to February, 2020. The external validation set enrolled 57 NCP patients and 50 IP patients from eight centers.Results96.6% of NCP lesions were larger than 1 cm and 76.8% were with intensity below –500 Hu, indicating less consolidation than IP lesions which had nodules ranging 5-10 mm. The classification schemes accurately distinguished NCP and IP lesions with area under the receiver operating characteristic curve (AUC) above 0.93. The Trinary scheme was more device-independent and consistent with specialists than the Plain scheme, which achieved a F1 score of 0.847, higher than the Plain scheme (0.774), specialists (0.785) and residents (0.644).ConclusionsOur study potentially provides an accurate early diagnosis tool on chest CT for NCP with high transferability, and shows high efficiency in differentiating NCP and IP, helping to reduce misdiagnosis and contain the pandemic transmission.
Effects of hypertension, diabetes and coronary heart disease on COVID-19 diseases severity: a systematic review and meta-analysis
Authors: Chen Yingyu , Gong Xiao , Wang Lexun , Guo Jiao 
Published: March 2020

SummaryBackgroundCOVID-19 patients with chronic diseases such as hypertension, diabetes and coronary heart diseases is more likely to worsen, but with mixed results for COVID-19 severity. This meta-analysis is to analyze the correlation between hypertension, diabetes, coronary heart disease and COVID-19 disease severity.MethodsAvailable data from PubMed, Web of Science, China National Knowledge Infrastructure Database, WanFang Database and VIP Database, were analyzed using a fixed effects model meta-analysis to derive overall odds ratios (OR) with 95% CIs. Funnel plots and Begg’s were used to assess publication bias.FindingsOf 182 articles found following our initial search, we assessed 34 full-text articles, of which 9 articles with 1936 COVID-19 patients met all selection criteria for our meta-analysis. No significant heterogeneity between studies. There were significant correlations between COVID-19 severity and hypertension [OR=2.3 [95% CI (1.76, 3.00), P<0.01], diabetes [OR=2.67, 95% CI (1.91, 3.74), P<0.01], coronary heart disease [OR=2.85 [95% CI (1.68, 4.84), P<0.01]. Most of the studies in the funnel plot are on the upper part and few on the base part, and are roughly symmetrical left and right. Begg’s test: hypertension (Z=-0.1, P=1.0), diabetes (Z=0.73, P=0.466), coronary heart disease (Z=0.38, P=0.707), all found no publication bias.InterpretationHypertension, diabetes, and coronary heart disease can affect the severity of COVID-19. It may be related to the imbalance of angiotensin-converting enzyme 2 (ACE2) and the cytokine storm induced by Glucolipid metabolic disorders (GLMD).FundingNational Natural Science Foundation of China (No. 81830113, 81530102); Major basic and applied basic research projects of Guangdong Province of China (No. 2019B030302005); National key R & D plan “Research on modernization of traditional Chinese medicine” (No. 2018YFC1704200) and Natural Science Foundation of Guangdong Province (No. 2018A030313391)
A Machine Learning Model Reveals Older Age and Delayed Hospitalization as Predictors of Mortality in Patients with COVID-19
Authors: Sarkar Jit , Chakrabarti Partha 
Published: March 2020

ABSTRACTObjectiveThe recent pandemic of novel coronavirus disease 2019 (COVID-19) is increasingly causing severe acute respiratory syndrome (SARS) and significant mortality. We aim here to identify the risk factors associated with mortality of coronavirus infected persons using a supervised machine learning approach.Research Design and MethodsClinical data of 1085 cases of COVID-19 from 13th January to 28th February, 2020 was obtained from Kaggle, an online community of Data scientists. 430 cases were selected for the final analysis. Random Forest classification algorithm was implemented on the dataset to identify the important predictors and their effects on mortality.ResultsThe Area under the ROC curve obtained during model validation on the test dataset was 0.97. Age was the most important variable in predicting mortality followed by the time gap between symptom onset and hospitalization.ConclusionsPatients aged beyond 62 years are at higher risk of fatality whereas hospitalization within 2 days of the onset of symptoms could reduce mortality in COVID-19 patients.
Personalized prediction of rehabilitation outcomes in multiple sclerosis: a proof-of-concept using clinical data, digital health metrics, and machine learning
Authors: Kanzler Christoph M. , Lamers Ilse , Feys Peter , Gassert Roger , Lambercy Olivier 
Published: March 2020

AbstractBackgroundA personalized prediction of upper limb neurorehabilitation outcomes in persons with multiple sclerosis (pwMS) promises to optimize the allocation of therapy and to stratify individuals for resource-demanding clinical trials. Previous research identified predictors on a population level through linear models and clinical data, including conventional assessments describing sensorimotor impairments. The objective of this work was to explore the feasibility of providing an individualized and more accurate prediction of rehabilitation outcomes in pwMS by leveraging non-linear machine learning models, clinical data, and digital health metrics characterizing sensorimotor impairments.MethodsClinical data and digital health metrics were recorded from eleven pwMS undergoing neurorehabilitation. Machine learning models were trained on data recorded pre-intervention. The dependent variables indicated whether a considerable improvement on the activity level was observed across the intervention or not (binary classification), as defined by the Action Research Arm Test (ARAT), Box and Block Test (BBT), or Nine Hole Peg Test (NHPT).ResultsIn a cross-validation, considerable improvements in ARAT or BBT could be accurately predicted (94% balanced accuracy) by only relying on patient master data. Considerable improvements in NHPT could be accurately predicted (89% balanced accuracy), but required knowledge about sensorimotor impairments. Assessing these with digital health metrics instead of conventional scales allowed increasing the balanced accuracy by +17% . Non-linear machine-learning models improved the predictive accuracy for the NHPT by +25% compared to linear models.ConclusionsThis work demonstrates the feasibility of a personalized prediction of upper limb neurorehabilitation outcomes in pwMS using multi-modal data collected before neurorehabilitation and machine learning. Information from digital health metrics about sensorimotor impairment was necessary to predict changes in dexterous hand control, thereby underlining their potential to provide a more sensitive and fine-grained assessment than conventional scales. Non-linear models outperformed ones, suggesting that the commonly assumed linearity of neurorehabilitation is oversimplified.clinicaltrials.govregistration number:NCT02688231
The effects of exposure to predators on personality and plasticity
Authors: Bucklaew Amy , Dochtermann Ned 
Published: March 2020

AbstractPast experiences are known to affect average behavior but effects on “animal personality”, and plasticity are less well studied. To determine whether experience with predators influences these aspects, we compared the behavior ofGryllodes sigillatusbefore and after exposure to live predators. We found that emergence from shelter and distance moved during open-field trials (activity) changed after exposure, with individuals becoming less likely to emerge from shelters but more active when deprived of shelter. We also found that plasticity in activity increased after exposure to predators and some indications that differences among individuals (i.e. “personality”) in emergence from shelter and the amount of an arena investigated increased after exposure. Our results demonstrate that experience with predators affects not only the average behavior of individuals but also how individuals differ from each other—and their own prior behavior—even when all individuals have the same experiences.
Early prediction of high risk gestational diabetes mellitus via machine learning models
Authors: Wu Yan-Ting , Zhang Chen-Jie , Mol Ben Willem , Li Cheng , Chen Lei , Wang Yu , Sheng Jian-Zhong , Fan Jian-Xia , Shi Yi , Huang He-Feng 
Published: March 2020

AbstractAimsGestational diabetes mellitus (GDM) is a pregnancy-specific disorder that can usually be diagnosed after 24 gestational weeks. So far, there is no accurate method to predict GDM in early pregnancy.MethodsWe collected data extracted from the hospital’s electronic medical record system included 73 features in the first trimester. We also recorded the occurrence of GDM, diagnosed at 24-28 weeks of pregnancy. We conducted a feature selection method to select a panel of most discriminative features. We then developed advanced machine learning models, using Deep Neural Network (DNN), Support Vector Machine (SVM), K-Nearest Neighboring (KNN), and Logistic Regression (LR), based on these features.ResultsWe studied 16,819 women (2,696 GDM) and 14,992 women (1,837 GDM) for the training and validation group. DNN, SVM, KNN, and LR models based on the 73-feature set demonstrated the best discriminative power with corresponding area under the curve (AUC) values of 0.92 (95%CI 0.91, 0.93), 0.82 (95%CI 0.81, 0.83), 0.63 (95%CI 0.62, 0.64), and 0.85 (95%CI 0.84, 0.85), respectively. The 7-feature (selected from the 73-feature set) DNN, SVM, KNN, and LR models had the best discriminative power with corresponding AUCs of 0.84 (95%CI 0.83, 0.84), 0.69 (95%CI 0.68, 0.70), 0.68 (95%CI 0.67, 0.69), and 0.84 (95% CI 0.83, 0.85), respectively. The 7-feature LR model had the best Hosmer-Lemeshow test outcome. Notably, the AUCs of the existing prediction models did not exceed 0.75.ConclusionsOur feature selection and machine learning models showed superior predictive power in early GDM detection than previous methods; these improved models will better serve clinical practices in preventing GDM.Research in Context sectionEvidence before this studyA hysteretic diagnosis of GDM in the 3rd trimester is too late to prevent exposure of the embryos or fetuses to an intrauterine hyperglycemia environment during early pregnancy.Prediction models for gestational diabetes are not uncommon in previous literature reports, but laboratory indicators are rarely involved in predictive indicators.The penetration of AI into the medical field makes us want to introduce it into GDM predictive models.What is the key question?Whether the GDM prediction model established by machine learning has the ability to surpass the traditional LR model?Added value of this studyUsing machine learning to select features is an effective method.DNN prediction model have effective discrimination power for predicting GDM in early pregnancy, but it cannot completely replace LR. KNN and SVM are even worse than LR in this study.Implications of all the available evidenceThe biggest significance of our research is not only to build a prediction model that surpasses previous ones, but also to demonstrate the advantages and disadvantages of different machine learning methods through a practical case.
Evaluation of the auxiliary diagnosis value of antibodies assays for the detection of novel coronavirus (SARS-Cov-2)
Authors: Yong Gao , Yi Yuan , Tuantuan Li , Xiaowu Wang , Xiuyong Li , Ang Li , Mingfeng Han 
Published: March 2020

AbstractBackgroundThe spread of an novel coronavirus (SARS-CoV-2, previously named 2019-nCoV) has already taken on pandemic proportions, affecting over 100 countries in a matter of weeks. Elucidating the diagnostic value of different methods, especially the auxiliary diagnosis value of antibodies assays for SARS-CoV-2 infection is helpful for improving the sensitivities of pathogenic-diagnosis, providing timely treatment, and differentiating the infected cases from the healthy, thus preventing further epidemics.MethodsMedical records from 38 patients with confirmed SARS-CoV-2 infection in the Second People’s Hospital of Fuyang from January 22, 2020 to February 28, 2020 were collected and retrospectively analyzed. Specimens including throat swabs, sputum and serum were collected during the hospitalization period, viral RNAs and serum IgM-IgG antibodies to SARS-CoV-2 were measured respectively. The detectability of different methods as well as the auxiliary diagnosis value of antibodies test for SARS-CoV-2 infection were analyzed.ResultsAmong 38 patients, the total seropositive rate for IgM and IgG was 50.0% and 92.1%, respectively. Two patients remained seronegative throughout the course of illness. In the early phase of illness, the RNA test for sputum specimens possessed the highest detectability(92.3%), followed by the the RNA test for throat swabs (69.2%), and the antibodies assays presented lower positive rates(IgM, 23.0%, IgG, 53.8%). While, the sensitivity of antibodies assays overtook that of RNA test since day 8 after onset (IgM, 50.0%; IgG, 87.5%). Of note, the positive rate of throat swabs was only 13.0% for cases in later phase(≥15 d.a.o), and the sensitivities of IgM and IgG rose to 52.2% and 91.3%, respectively. Combined use of antibodies assay and qRT-PCR at the same time was able to improve the sensitivities of pathogenic-diagnosis, especially for the throat swabs group at the later stage of illness. Moreover, most of these cases with undetectable viral RNA in throat swabs specimens at the early stage of illness were able to be IgM/IgG seropositive after 7 days.ConclusionsThe antibodies detection against SARS-CoV-2 offers vital clinical information for physicians, and could be used as an effective supplementary indicator for suspected cases of negative viral nucleic acid detection or in conjunction with nucleic acid detection in the diagnosis of suspected cases.
Clinical and Paraclinical Characteristics of COVID-19 patients: A systematic review and meta-analysis
Authors: Heydari Keyvan , Rismantab Sahar , Shamshirian Amir , Lotfi Parisa , Shadmehri Nima , Houshmand Pouya , Zahedi Mohammad , Shamshirian Danial , Bathaeian Sahar , Alizadeh-Navaei Reza 
Published: March 2020

IntroductionRecently, a new strain of coronaviruses, which originated from Wuhan City, Hubei Province, China has been identified. According to the high prevalence of new coronavirus, further investigation on the clinical and paraclinical features of this disease seems essential. Hence, we carried out this systematic review and meta-analysis to figure out the unknown features.MethodsThis study was performed using databases of Web of Science, Scopus and PubMed. We considered English cross-sectional and case-series papers which reported clinical, radiological, and laboratory characteristics of patients with COVID-19. We used STATA v.11 and random effect model for data analysis.ResultsIn the present meta-analysis, 32 papers including 49504 COVID-19 patients were studied. The most common clinical symptoms were fever (84%), cough (65%) and fatigue (42%), respectively. The most common radiological and paraclinical features were bilateral pneumonia (61%), ground-glass opacity (50%), thrombocytopenia (36%) and lymphocytopenia (34%). The study also showed that the frequency of comorbidities and early symptoms was higher in critically severe patients. Moreover, we found the overall mortality rate of three percent.ConclusionAccording to that there are many cases without Computed Tomography Scan findings or clear clinical symptoms, it is recommended to use other confirming methods such RNA sequencing in order to identification of suspicious undiagnosed patients. Moreover, while there is no access to clinical and paraclinical facilities in in public places such as airports and border crossings, it is recommended to consider factors such as fever, cough, sputum and fatigue.
Extrapolation of Infection Data for the CoVid-19 Virus and Estimate of the Pandemic Time Scale
Authors: Langel Walter 
Published: March 2020

AbstractPredictions about the further development of the Corona pandemic are widely diverging. Here, a simple yet powerful algorithm is introduced for extrapolating infection rate and number of total infections from available data. The calculation predicts that under present conditions the infection rate in Germany will culminate in a few weeks and decrease to low values by mid-June 2020. Total number of infections will reach several 100000 though. A refinement of the calculation is presented in the supplemental material and shows that the lock down in Germany has reduced the total number of infections from a target value of 338 000 to 184 000, corresponding to a decrease of about 45%.
Understand research hotspots surrounding COVID-19 and other coronavirus infections using topic modeling
Authors: Dong Mengying , Cao Xiaojun , Liang Mingbiao , Li Lijuan , Liu Guangjian , Liang Huiying 
Published: March 2020

AbstractBackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a virus that causes severe respiratory illness in humans, which results in global outbreak of novel coronavirus disease (COVID-19) currently. This study aimed to evaluate the characteristics of publications involving coronaviruses as well as COVID-19 by using topic modeling.MethodsWe extracted all abstracts and retained the most informative words from the COVID-19 Open Research Dataset, which contains 35,092 pieces of coronavirus related literature published up to March 20, 2020. Using Latent Dirichlet Allocation modeling, we trained a topic model from the corpus, analyzed the semantic relationships between topics and compared the topic distribution between COVID-19 and other CoV infections.ResultsEight topics emerged overall: clinical characterization, pathogenesis research, therapeutics research, epidemiological study, virus transmission, vaccines research, virus diagnostics, and viral genomics. It was observed that current COVID-19 research puts more emphasis on clinical characterization, epidemiological study, and virus transmission. In contrast, topics about diagnostics, therapeutics, vaccines, genomics and pathogenesis only account for less than 10% or even 4% of all the COVID-19 publications, much lower than those of other CoV infections.ConclusionsThese results identified knowledge gaps in the area of COVID-19 and offered directions for future research.