bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparedness toward COVID-19 pandemics and associated factors among health care workers in Dire Dawa Hospitals, Eastern Ethiopia: A facility-based cross-sectional study
Authors: Menberu Tameru , Mekonnen Robel , Manaye Yibekal , Kebede Mesfin , Solomon Yonatan , Sema Alekaw , Yazie Bekele , Ayalew Agumas Fentahun 
Published: November 2021

Introduction: Health care workers are at the frontline of the response against the COVID-19 outbreak. Poor preparedness and infection prevention practices among health care workers compound the hazard and occurrence of COVID-19 hospital transmission. Thus, the study aimed to assess preparedness toward COVID-19 pandemics and associated factors among health care workers in Hospitals of Eastern Ethiopia. Methods: Facility-based cross-sectional study was conducted from 20 June to July 10 2020. A simple random sampling technique was used to select 423 health care workers. Data were collected using a structured self-administered questionnaire and analyzed using SPSS Version 23. Bivariate and multivariable logistic regression was conducted to identify factors associated with the outcome variable, and statistical significance was declared at a p-value less than 0.05. Results: This study revealed that the proportion of health care workers’ preparedness toward the COVID-19 pandemic was 40.9% (95% CI: 36.2–45.9). Working in a public hospital (AOR = 2.7, 95% CI: 1.6–4.3), being unafraid of transmitting COVID-19 to patients (Adjusted odds ratio/AOR = 4.6, 95% CI: 2.2–10.0), feeling safe at the workplace (AOR = 3.3, 95% CI: 1.7–6.4)), satisfied with the infection control policy (AOR = 6.0, 95% CI: 2.3–15.0), and not feeling anxious about the likelihood of COVID-19 spread (AOR = 2.1, 95% CI: 1.3–3.4) were significantly associated with COVID-19 preparedness. Conclusion: The majority of the health care workers were not prepared for COVID-19 pandemics. Feeling safe at the workplace scared of transmitting COVID-19 to patients, satisfied with the infection control policy, and feeling anxious concerning the likelihood of COVID-19 were factors associated with health care workers’ preparedness to COVID-19. The current awareness creation training, including motivational and psychological preparation for all health care workers, is mandatory, regardless of their profession or working place.
Benefits of prescribing low-dose digoxin in atrial fibrillation
Authors: Rosca Ciprian Ilie , Kundnani Nilima Rajpal , Tudor Anca , Rosca Maria-Silvia , Nicoras Violeta-Ariana , Otiman Gabriela , Ciurariu Elena , Ionescu Alin , Stelian Morariu , Sharma Abhinav , Borza Claudia , Lighezan Daniel Florin 
Published: November 2021

Introduction The role of digoxin (cardiac glycoside) in controlling the heart rate (HR) for the treatment of atrial fibrillation (AF) patients has not been explored in depth. Methods To contribute to the limited data, our team conducted retrospective analysis of the clinical records of 1444 AF patients. We divided the AF patients into two groups, wherein group 1 patients were treated with beta-blockers (BB), low-dose digoxin, and an anticoagulant (vitamin K antagonist/factor-IIa inhibitor/factor-Xa inhibitor), and group 2 patients were treated with just BB and an anticoagulant. Our objectives were to compare the impact of combination therapy of BB and digoxin on the resting HR in patients with permanent AF and the patients’ quality of life (QOL) at periodic intervals. Results The findings of our study showed a better control of the resting HR rate (<110bpm) and an improved QOL among the group 1 patients when compared with group 2 patients. Conclusion Our findings are indicative of the favorable clinical outcomes that resulted from the addition of a low-dose of digoxin to the AF treatment regimen. However, larger studies/trials elucidating the outcomes of AF patients treated with the dual rate control therapy are required, to clarify the role of digoxin, guide the choice of agents, and standardize the AF treatment protocol.
Automated Risk Stratification of Hip Osteoarthritis Development in Patients With Femoroacetabular Impingement Using an Unsupervised Clustering Algorithm: A Study From the Rochester Epidemiology Project
Authors: Ko Sunho , Pareek Ayoosh , Jo Changwung , Han Hyuk-Soo , Lee Myung Chul , Krych Aaron J. , Ro Du Hyun 
Published: November 2021

Background: Studies evaluating the natural history of femoroacetabular impingement (FAI) are limited. Purpose: To stratify the risk of progression to osteoarthritis (OA) in patients with FAI using an unsupervised machine-learning algorithm, compare the characteristics of each subgroup, and validate the reproducibility of staging. Study Design: Cohort study (prognosis); Level of evidence, 2. Methods: A geographic database from the Rochester Epidemiology Project was used to identify patients with hip pain between 2000 and 2016. Medical charts were reviewed to obtain characteristic information, physical examination findings, and imaging details. The patient data were randomly split into 2 mutually exclusive sets: train set (70%) for model development and test set (30%) for validation. The data were transformed via Uniform Manifold Approximation and Projection and were clustered using Hierarchical Density-based Spatial Clustering of Applications with Noise. Results: The study included 1071 patients with a mean follow-up period of 24.7 ± 12.5 years. The patients were clustered into 5 subgroups based on train set results: patients in cluster 1 were in their early 20s (20.9 ± 9.6 years), female dominant (84%), with low body mass index (<19 ); patients in cluster 2 were in their early 20s (22.9 ± 6.7 years), female dominant (95%), and pincer-type FAI (100%) dominant; patients in cluster 3 were in their mid 20s (26.4 ± 9.7) and were mixed-type FAI dominant (92%); patients in cluster 4 were in their early 30s (32.7 ± 7.8), with high body mass index (≥29 ), and diabetes (17%); and patients in cluster 5 were in their early 30s (30.0 ± 9.1), with a higher percentage of males (43%) compared with the other clusters and with limited internal rotation (14%). Mean survival for clusters 1 to 5 was 17.9 ± 0.6, 18.7 ± 0.3, 17.1 ± 0.4, 15.0 ± 0.5, and 15.6 ± 0.5 years, respectively, in the train set. The survival difference was significant between clusters 1 and 4 ( P = .02), 2 and 4 ( P < .005), 2 and 5 ( P = .01), and 3 and 4 ( P < .005) in the train set and between clusters 2 and 5 ( P = .03) and 3 and 4 ( P = .01) in the test set. Cluster characteristics and prognosis was well reproduced in the test set. Conclusion: Using the clustering algorithm, it was possible to determine the prognosis for OA progression in patients with FAI in the presence of conflicting risk factors acting in combination.
Identification of cell states using super-enhancer RNA
Authors: Tu Yueh-Hua , Juan Hsueh-Fen , Huang Hsuan-Cheng 
Published: November 2021

Abstract
Background
A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states.

Results
Here, we defined super-enhancer RNAs as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process.

Conclusions
The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.
Decreasing trends in potentially inappropriate medications in older people: a nationwide repeated cross-sectional study
Authors: Drusch Solène , Le Tri Thien , Ankri Joël , Zureik Mahmoud , Herr Marie 
Published: November 2021

Abstract
Background
Potentially Inappropriate Medications (PIMs) and polypharmacy are widely used indicators of suboptimal prescribing for older people. The aim of this study was to describe the changes in the prevalence of PIMs and polypharmacy among people aged 75 years and over between 2011 and 2019 in France.

Methods
PIMs and polypharmacy were assessed among people aged 75 years and over every two years between 2011 and 2019 using the French health insurance data system. Sixteen PIM criteria from the 2015 Beers and STOPP lists were assessed. Polypharmacy (5 to 9 drugs) and hyper-polypharmacy (≥10 drugs) were defined based on the average number of drugs dispensed per quarter. The Annual Percent Change (APC) and 95%CI were assessed using linear regression models after standardization of the prevalence on age and sex.

Results
The study population included 5,777,645 individuals over 75 years old in 2011 and 6,328,155 in 2019. The prevalence of PIMs decreased from 49.6 to 39.6% over the study period (APC: − 1.19% [− 1.35;-1.04]). Of the sixteen indicators assessed, the prevalence of thirteen decreased between 2011 and 2019. Benzodiazepines were the most frequent PIMs (34.7% in 2011 to 26.9% in 2019), followed by anticholinergic drugs (12.1% in 2011 to 8.3% in 2019), oral non-steroidal anti-inflammatory drugs (11.4 to 7.8%), and PIMs related to antihypertensive drugs (7.4 to 6.0%). Overall, women and individuals aged 85 years and older were more likely to receive PIMs. The prevalence of hyper-polypharmacy decreased from 30.5 to 25.9% over the study period.

Conclusion
This study, which is the first to assess the change in prevalence of PIMs and polypharmacy over time from comprehensive health data in France, highlights that PIMs and hyper-polypharmacy declined between 2011 and 2019. However, PIMs remains frequent for older people and often involves benzodiazepines.
Exploring the benefits of participation in community-based running and walking events: a cross-sectional survey of parkrun participants
Authors: Quirk Helen , Bullas Alice , Haake Steve , Goyder Elizabeth , Graney Mike , Wellington Chrissie , Copeland Robert , Reece Lindsey , Stevinson Clare 
Published: November 2021

Abstract
Background
Whilst the benefits of physical activity for health and wellbeing are recognised, population levels of activity remain low. Significant inequalities exist, with socioeconomically disadvantaged populations being less physically active and less likely to participate in community events. We investigated the perceived benefits from participation in a weekly running/walking event called parkrun by those living in the most socioeconomically deprived areas and doing the least physical activity.

Methods
A cross-sectional online survey was emailed to 2,318,135 parkrun participants in the UK. Demographic and self-reported data was collected on life satisfaction, happiness, health status, physical activity, motives, and the perceived benefits of parkrun. Motivation, health status and benefits were compared for sub-groups defined by physical activity level at parkrun registration and residential Index of Multiple Deprivation.

Results
60,000 completed surveys were received (2.7% of those contacted). Respondents were more recently registered with parkrun (3.1 v. 3.5 years) than the parkrun population and had a higher frequency of parkrun participation (14.5 v. 3.7 parkruns per year). Those inactive at registration and from deprived areas reported lower happiness, lower life satisfaction and poorer health compared to the full sample. They were more likely to want to improve their physical health, rather than get fit or for competition. Of those reporting less than one bout of activity per week at registration, 88% (87% in the most deprived areas) increased their physical activity level and 52% (65% in the most deprived areas) reported improvements to overall health behaviours. When compared to the full sample, a greater proportion of previously inactive respondents from the most deprived areas reported improvements to fitness (92% v. 89%), physical health (90% v. 85%), happiness (84% v. 79%) and mental health (76% v. 69%).

Conclusion
The least active respondents from the most socioeconomically deprived areas reported increases to their activity levels and benefits to health and wellbeing since participating in parkrun. Whilst the challenge of identifying how community initiatives like parkrun can better engage with underrepresented populations remains, if this can be achieved they could have a critical public health role in addressing inequalities in benefits associated with recreational physical activity.
Survival analysis of factors affecting the timing of COVID-19 non-pharmaceutical interventions by U.S. universities
Authors: Cevasco Kevin E. , Roess Amira A. , North Hayley M. , Zeitoun Sheryne A. , Wofford Rachel N. , Matulis Graham A. , Gregory Abigail F. , Hassan Maha H. , Abdo Aya D. , von Fricken Michael E. 
Published: November 2021

Abstract
Background
During March of 2020 the Centers for Disease Control and Prevention (CDC) announced non-pharmaceutical intervention (NPI) guidance as the primary mitigation strategy against growing COVID-19 community spread due to the absence of a vaccine or effective treatment at that time. CDC guidance states that NPIs are most effective when instituted in an early, targeted, and layered fashion. NPIs are effective in slowing spread, and measures should be custom-tailored to each population. This study examines factors associated with implementation and timing of NPI interventions across large public and private U.S. universities at the onset of the COVID-19 pandemic.

Methods
NPI decisions of interest include when U.S. universities canceled international travel, shifted to online learning, moved faculty/staff to remote work, limited campus housing, and closed campus for all non-essential personnel. Cox proportional hazard analyses of retrospective data were conducted to assess the time to NPI events. Hazard ratios were calculated for university governance, campus setting, religious affiliation, health infrastructure, faculty diversity, and student demographics. The methods control for variance inflation factors, COVID case prevalence, and time varying covariates of spring break and states’ state of emergency (SOE) orders. This study captures NPI decisions at 575 U.S. universities during spring of 2020 which affected the movement of seven million students and two million employees.

Results
Universities located in districts represented by Democratic party congressional members reported earlier NPI implementation than Republican (Cox proportional hazard ratio (HR) range 0.61–0.80). University religious affiliation was not associated with the timing any of the NPI decisions. Universities with more diverse faculty showed an association with earlier NPI implementation (HR range 0.65–0.76). The existence of university-affiliated health infrastructure was not associated with NPI timing.

Conclusion
University NPI implementation was largely driven by local COVID-19 epidemiology, culture and political concerns. The timing of university NPI decisions varied by regional politics, faculty demographics, university governance, campus setting, and foreign student prevalence adjusting for COVID-19 state case prevalence and spring break timing. Religious affiliation and presence of university health infrastructure were not associated with timing.
Polyphenol-rich extract of Ocimum gratissimum leaves prevented toxic effects of cyclophosphamide on the kidney function of Wistar rats
Authors: Alabi Quadri K. , Akomolafe Rufus O. , Omole Joseph G. , Aturamu Ayodeji , Ige Mokolade S. , Kayode Oyindasola O. , Kajewole-Alabi Deborah 
Published: November 2021

AbstractBackgroundCyclophosphamide (CP) is one of the potent and low cost chemotherapy used in clinical setting against a variety of tumors. However, its association with nephrotoxicity limits its therapeutic use.Ocimum gratissimumleaf is a medicinal plant with numerous pharmacological and therapeutic efficacies, such as antioxidant, anti-inflammation, and anti-apoptotic properties.MethodsThe present study was designed to evaluate the protective effect ofOcimum gratissimum(OG) against CP-induced kidney dysfunction in rats. Rats were pre-treated with 400 mg/kg b.w. of leave extract ofOcimum gratissimum(Ocimum G.) for 4 days and then 50 mg/kg b.w. of CP was co-administered from day 5 to day 7 along withOcimum G.Markers of renal function and oxidative stress, food and water intake, electrolytes, aldosterone, leukocytes infiltration, inflammation and histopathological alteration were evaluated.ResultsObvious renal inflammation and kidney injuries were observed in CP treated groups. However, administration of leave extract ofOcimum G.prevented oxidative stress, kidney injuries, attenuated inflammation, increased aldosterone production and reduced sodium ion and water loss in rats. The plasma creatinine, urea and urine albumin concentration were normalized after the administration ofOcimum G.extract in rats treated with CP.Ocimum G.also decreased the plasma concentrations of Interleukin-(IL)-6, C-reactive protein and activity of myeloperoxidase and malondialdehyde in CP treated rats.ConclusionOcimum G.prevented kidney injury and enhanced renal function via inhibiting inflammation and oxidant-induced CP toxicity. The efficacy ofOcimum G.is related to the presence of various phytochemicals in the plant.
Low-cost peer-taught virtual research workshops for medical students in Pakistan: a creative, scalable, and sustainable solution for student research
Authors: Ukrani Ronika Devi , Shaikh Ayesha Niaz , Martins Russell Seth , Fatima Syeda Sadia , Naseem Hamna Amir , Baig Mishall Ahmed 
Published: November 2021

AbstractBackgroundPakistan has not been a major contributor to medical research, mainly because of the lack of learning opportunities to medical students. With the increase in online learning systems during COVID-19, research related skills can be taught to medical students via low-cost peer taught virtual research workshops.Aim of the StudyTo assess the effectiveness of a comprehensive low-cost peer-taught virtual research workshops amongst medical students in Pakistan.MethodsThis quasi-experimental study assessed the effectiveness of five virtual research workshops (RWs) in improving core research skills. RWs for medical students from across Pakistan were conducted over Zoom by medical students (peer-teachers) at the Aga Khan University, Pakistan, with minimal associated costs. The content of the workshops included types of research, ethical approval and research protocols, data collection and analysis, manuscript writing, and improving networking skills for research. Improvement was assessed via pre-and post-quizzes for each RW, self-efficacy scores across 16 domains, and feedback forms. Minimum criteria for completion of the RW series was attending at least 4/5 RWs and filling the post-RW series feedback form. A 6-month post-RW series follow-up survey was also emailed to the participants.ResultsFour hundred medical students from 36 (/117; 30.8%) different medical colleges in Pakistan were enrolled in the RWs. However, only 307/400 (76.75%) medical students met the minimum requirement for completion of the RW series. 56.4% of the participants belonged to the pre-clinical years while the rest were currently to clinical years. The cohort demonstrated significant improvement in pre-and post-quiz scores for all 5 RWs (p <  0.001) with the greatest improvement in Data Collection and Analysis (+ 34.65%), and in self-efficacy scores across all domains (p <  0.001). 166/307 (54.1%) participants responded to the 6 months post-RWs follow-up survey. Compared to pre-RWs, Research involvement increased from 40.4 to 62.8% (p <  0.001) while proportion of participants with peer-reviewed publications increased from 8.4 to 15.8% (p = 0.043).ConclusionVirtual RWs allow for a wide outreach while effectively improving research-related knowledge and skills, with minimal associated costs. In lower-middle-income countries, virtual RWs are a creative and cost-effective use of web-based technologies to facilitate medical students to contribute to the local and global healthcare research community.
Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
Authors: Wang Suhuai , Li Jingjie , Sun Lin , Cai Jianing , Wang Shihui , Zeng Linwen , Sun Shaoqing 
Published: November 2021

Abstract
Background
Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI).

Methods
A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git). The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set.

Results
Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI.

Conclusions
We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research.
Trial registration Clinical Trial Registry No.: ChiCTR2100041960.