bluuebunny/crossref_metadata_2025_split huggingface dataset:
Histopathological Profile of Cervical Biopsies in Northern Malawi: A Retrospective Study
Authors: Kaseka Paul Uchizi , Kayira Alfred , Chimbatata Chikondi Sharon , Chisale Master , Kamudumuli Pocha , Wu Tsung-Shu Joseph , Mbakaya BalwaniChingatichifwe , Sinyiza Frank Watson 
Published: January 2021

ABSTRACTObjectivesThis study was carried out to determine the histopathological profile of cervical biopsies in a public tertiary hospital in Mzuzu, northern region of Malawi.SettingA public tertiary hospital in Mzuzu, northern region of MalawiParticipantsThis was a retrospective study of all cervical biopsy specimen reports received in a public tertiary hospital in northern Malawi over a period of 5 years from July 2013-June 2018. Eleven reports which had missing demographic and clinical data or had inconclusive results were excluded. Demographic, clinical and histopathological data was obtained from original histology reports.ResultsA total of 500 cervical biopsy reports were reviewed during the study period. The mean age of the patients was 41.99±12.5. Age ranged from 15 to 80 years. Cervicitis accounted for 46.0% (n=162) of the total nonmalignant lesions seen, followed by cervical intraepithelial neoplasm (CIN), at 24.4% (n=86) and endocervical polyp, at 20.5% (n=72). Squamous cell carcinoma (SCC) accounted for 15.6% (n=78) of the total cervical biopsies studied and 85.7% of all total malignant lesions. All malignant tumours had HIV.ConclusionOur study shows that cervicitis and squamous cell carcinoma were most common among nonmalignant and malignant cervical biopsies respectively. Since the frequency of cervical cancer is high, there is need to have well detailed national policies to be put in place to increase detection of pre-invasive lesions in order to reduce the prevalence of cervical cancer.Strengths and limitation of this study StrengthsThis paper has shownThe need for well detailed national policies to be put in place to increase detection of pre-invasive lesions, which in turn will decrease the frequency of cervical cancer in the country.The importance of intensifying cervical cancer screening programmes among women and provision of long term ART to the HIV infected which may offer an opportunity for appropriate interventions to reduce morbidity, mortality and reduce complications among these women.LimitationsThis study used available programme health facility data and histopathological reports on cervical cancer which has its own limitations, such as incompleteness and bias in the sense that information is obtained only from people who came to the facility and underwent biopsy, leaving out those that did not seek medical care and or were not biopsied and therefore cannot be generalized to the general population.The study is a single-hospital-based review and as such inadequate to draw conclusions, but it does shed some light on pathological pattern of cervical cancer in Malawi.
Identical trends of SARS-Cov-2 transmission and retail and transit mobility during non-lockdown periods
Authors: Cazelles Bernard , Comiskey Catherine , Van Yen Benjamin Nguyen , Champagne Clara , Roche Benjamin 
Published: January 2021

AbstractRecent literature strongly supports the idea that mobility reduction and social distancing play a crucial role in transmission of SARS-Cov-2 infections. It was shown during the first wave that mobility restrictions reduce significantly infection transmission. Here we document the reverse relationship by showing, between the first two Covid-19 waves, a high positive correlation between the trends of SARS-Cov-2 transmission and mobility. These two trends oscillate simultaneously and increased mobility following lockdown relaxation has a significant positive relationship with increased transmission. From a public health perspective, these results highlight the importance of following the evolution of mobility when relaxing mitigation measures to anticipate the future evolution of the spread of the SARS-Cov-2.
Race and ethnicity do not impact eligibility for remdesivir- a single-center experience
Authors: Pischel Lauren , Walelo Makeda , Benson Jemma , Osborn Rebecca , Schrier Rachel , Tuan Jessica , Barakat Lydia , Ogbuagu Onyema 
Published: January 2021

AbstractAs the Coronavirus-2019 (COVID-19) pandemic continues, multiple therapies are rapidly being tested for efficacy in clinical trials. Clinical trials should be racially and ethnically representative of the population that will eventually benefit from these medications. There are multiple potential barriers to racial and ethnic minority enrollment in clinical trials, one of which could be that inclusion and exclusion criteria select for certain racial or ethnic groups disproportionately. In this observational cohort study at a single health care system, we examined if there were differences in eligibility for treatment with remdesivir based on clinical trial criteria for racial and ethnic minorities compared to non-Hispanic Whites. 201 electronic medical record charts were reviewed manually. Self-identified Whites were older than other racial or ethnic groups. At the time of presentation, Black, Latinx, and White participants met inclusion criteria for remdesivir at similar rates (72%, 80%, and 73% respectively), exclusion criteria at similar rates (43%, 38% and 49% for Black, Latinx and White participants respectively). In this study, there was no difference in eligibility for remdesivir based on race or ethnicity alone.
Use of Diagnosis Codes to Find Blood Transfusion Adverse Events in Electronic Health Records
Authors: Bright Roselie A. , Bright-Ponte Susan J. , Palmer Lee Anne , Rankin Summer K. , Blok Sergey 
Published: January 2021

ABSTRACTBackgroundElectronic health records (EHRs) and big data tools offer the opportunity for surveillance of adverse events (patient harm associated with medical care). We chose the case of transfusion adverse events (TAEs) and potential TAEs (PTAEs) because 1.) real dates were obscured in the study data, and 2.) there was emerging recognition of new types during the study data period.ObjectiveWe aimed to use the structured data in electronic health records (EHRs) to find TAEs and PTAEs among adults.MethodsWe used 49,331 adult admissions involving critical care at a major teaching hospital, 2001-2012, in the MIMIC-III EHRs database. We formed a T (defined as packed red blood cells, platelets, or plasma) group of 21,443 admissions vs. 25,468 comparison (C) admissions. The ICD-9-CM diagnosis codes were compared for T vs. C, described, and tested with statistical tools.ResultsTAEs such as transfusion associated circulatory overload (TACO; 12 T cases; rate ratio (RR) 15.61; 95% CI 2.49 to 98) were found. There were also PTAEs similar to TAEs, such as fluid overload disorder (361 T admissions; RR 2.24; 95% CI 1.88 to 2.65), similar to TACO. Some diagnoses could have been sequelae of TAEs, including nontraumatic compartment syndrome of abdomen (52 T cases; RR 6.76; 95% CI 3.40 to 14.9) possibly being a consequence of TACO.ConclusionsSurveillance for diagnosis codes that could be TAE sequelae or unrecognized TAE might be useful supplements to existing medical product adverse event programs.
Detection of SARS-CoV-2 in the air from hospitals and closed rooms occupied by COVID-19 patients
Authors: Moharir Shivranjani C , Chandra T. Sharath , Goel Arushi , Thakur Bhuwaneshwar , Bhalla Gurpreet Singh , Kumar Dinesh , Naruka Digvijay Singh , Kumar Ashwani , Tuli Amit , Suravaram Swathi , Bingi Thrilok Chander , Srinivas M , Mesipogu Rajarao , Reddy Krishna , Khosla Sanjeev , Tallapaka Karthik Bharadwaj , Mishra Rakesh K 
Published: January 2021

AbstractTo understand air transmission characteristics of SARS-CoV-2 and risks for health care personnel and visitors to hospitals, we analyzed air samples collected from various enclosures in hospitals at Hyderabad and Mohali and performed closed room experiments with COVID-19 positive individuals. We collected 64 air samples from COVID and non-COVID areas of various hospitals and 17 samples from closed rooms occupied by COVID patients. 4 samples from COVID care areas were positive for SARS-CoV-2 with no obvious predilection towards ICU/non-ICU areas in the hospital samples. In the closed room experiments, where one or more COVID-19 patients spent a short duration of time, one sample - collected immediately after the departure of three symptomatic patients from the room - was positive. Our results indicate that the chance of picking up SARS-CoV-2 in the air is directly related to a number of COVID positive cases in the room, their symptomatic status, and the duration of exposure and that the demarcation of hospital areas into COVID and non-COVID areas is a successful strategy to prevent cross infections. In neutral environmental conditions, the virus does not seem to spread farther away from the patients, especially if they are asymptomatic, giving an objective evidence for the effectiveness of physical distancing in curbing the spread of the epidemic.
Unsupervised Discovery of Risk Profiles on Negative and Positive COVID-19 Hospitalized Patients
Authors: Nezhadmoghadam Fahimeh , Tamez-Peña Jose 
Published: January 2021

AbstractCOVID-19 is a viral disease that affects people in different ways: Most people will develop mild symptoms; others will require hospitalization, and a few others will die. Hence identifying risk factors is vital to assist physicians in the treatment decision. The objective of this paper is to determine whether unsupervised analysis of risk factors of positive and negative COVID-19 subjects may be useful for the discovery of a small set of reliable and clinically relevant risk-profiles. We selected 13367 positive and 19958 negative hospitalized patients from the Mexican Open Registry. Registry patients were described by 13 risk factors, three different outcomes, and COVID-19 test results. Hence, the dataset could be described by 6144 different risk-profiles per age group. To discover the most common risk-profiles, we propose the use of unsupervised learning. The data was split into discovery (70%) and validation (30%) sets. The discovery set was analyzed using the partition around medoids (PAM) method and robust consensus clustering was used to estimate the stable set of risk-profiles. We validated the reliability of the PAM models by predicting the risk-profile of the validation set subjects. The clinical relevance of the risk-profiles was evaluated on the validation set by characterizing the prevalence of the three patient outcomes: pneumonia diagnosis, ICU, or death. The analysis discovered six positives and five negative COVID-19 risk-profiles with strong statistical differences among them. Henceforth PAM clustering with consensus mapping is a viable method for unsupervised risk-profile discovery among subjects with critical respiratory health issues.
Cost-utility of two minimally-invasive surgical techniques for operable oropharyngeal cancer: Transoral robotic surgery versus transoral laser microsurgery
Authors: Parimbelli Enea , Soldati Federico , Duchoud Lorry , Armas Gian Luca , de Almeida John R. , Broglie Martina , Quaglini Silvana , Simon Christian 
Published: January 2021

AbstractImportanceTransoral robotic surgery (TORS) and transoral laser micro-surgery (TLM) are two different but competing minimally invasive techniques to surgically remove operable oropharyngeal squamous cell cancers (OPSCC). As of now, no comparative analysis as to the cost-utility of these techniques exists.ObjectiveRecent population-level data suggest for TORS and TLM equivalent tumor control, but different total costs, need for adjuvant chemoradiation, and learning curves. Therefore, the objective of this study was to compare TORS and TLM from the cost-utility (C/U) point of view using a decision-analytical model from a Swiss hospital perspective.DesignOur decision-analytical model combines decision trees and a Markov model to compare TORS and TLM strategies. Model parameters were quantified using available literature, original cost data from two Swiss university tertiary referral centers, and utilities elicited directly from a Swiss population sample using standard gamble. C/U and sensitivity analyses were used to generate results and gauge model robustness.SettingSwiss hospital perspectiveInterventionCost-utility analysisMain outcome measureComparative cost-utility data from TLM and TORSResultsIn the base case analysis TLM dominates TORS. This advantage remains robust, even if the costs for TORS would reduce by up to 25%. TORS begins to dominate TLM, if less than 59,7% patients require adjuvant treatment (pTorsAlone>0.407), whereby in an interval between 55%-62% (pTorsAlone 0.38-0.45) cost effectiveness of TORS is sensitive to the prescription of adjuvant CRT. Also, exceeding 29% of TLM patients requiring a re-operation for inadequate margins renders TORS more cost-effective.ConclusionTLM is more cost-effective than TORS. However, this advantage is sensitive to various parameters i.e. the number of re-operations and adjuvant treatment.Key pointsQuestionCompare cost-utility of TORS versus TLMFindingsIn the base case analysis TLM dominates TORS, even if the costs for TORS would reduce by up to 25%. TORS begins to dominate TLM, if less than 59,7% patients require adjuvant treatment, whereby in an interval between 55%-62% cost effectiveness of TORS is sensitive to the prescription of adjuvant CRT. Exceeding 29% of TLM patients requiring a re-operation for inadequate margins renders TORS more cost-effective.MeaningTLM is more cost-effective than TORS. However, this advantage is sensitive to the number of re-operations and adjuvant treatment
Imprecise assessment of mask use may obscure associations with SARS-CoV-2 positivity
Authors: Bérubé Sophie , Clipman Steven J. , Mehta Shruti H. , Solomon Sunil S. , Wesolowski Amy 
Published: January 2021

AbstractMasks are effective measures to prevent the transmission of SARS-CoV-2, however, lack of a national mandate coupled with poor adherence has led to suboptimal levels of transmission reduction. Although data has suggested that mask adherence is high, few studies have captured details on how mask wearing changes with activities and how these behaviors are associated with SARS-CoV-2 positivity. We recruited an online sample of 3,058 respondents from three US states (MD, FL, IL; n∼1000/state) between September 16 - October 15, 2020. The median age of the sample was 47; 53% were female, 56% were white and 22% were working outside the home. Seventy three percent of the sample reported always wearing a mask indoors and outdoors based on local guidelines, however, 78% of participants who reported always wearing a mask reported taking their mask off when outside the home. While overall masking according to guidelines was not significantly associated with SARS-CoV-2 positivity, sometimes, often or always removing a mask during activities were significantly associated with SARS-CoV-2 positivity (adjusted odds ratio for always vs never removing mask: 9.92; 95% CI: 1.16 – 85.1). These findings suggest that masks were most effective when worn without removal reflecting the need for consistent use.
De novogermline mutation in the Dual Specificity Phosphatase 10 gene accelerates autoimmune diabetes in Non-Obese Diabetic (NOD) mice
Authors: Foray Anne-Perrine , Candon Sophie , Hildebrand Sara , Marquet Cindy , Valette Fabrice , Pecquet Coralie , Lemoine Sebastien , Langa-Vives Francina , Dumas Michael , Hu Peipei , Santamaria Père , Lyon Stephen , Scott Lindsay , Bu Chun Hui , Wang Tao , Xu Darui , Moresco Eva Marie Y. , Scazzocchio Claudio , Bach Jean-François , Beutler Bruce , Chatenoud Lucienne 
Published: January 2021

AbstractHere we report the isolation by selective breeding of two sublines of Non-Obese Diabetic (NOD) mice exhibiting a significant difference in the incidence of autoimmune type 1 diabetes (T1D). Whole genome sequencing of the NOD/NckH(high T1D incidence) and NOD/NckL(low T1D incidence) revealed the presence of a limited number of variants specific to each subline. Treating the age of T1D onset as a quantitative trait and using automated meiotic mapping (AMM), enhanced susceptibility in the NOD/NckHsubline was unambiguously attributed to a recessive allele ofDusp10which encodes a dual specificity phosphatase. The causative effect of the mutation was verified with a high level of confidence by targetingDusp10with CRISPR/Cas9 in NOD/NckLmice: in these animals a higher incidence of diabetes was observed. Expression of wild-typeDusp10correlated with higher levels of surface PD-L1 in the islets of NOD/NckLmice.
Implementing Building-Level SARS-CoV-2 Wastewater Surveillance on a University Campus
Authors: Gibas Cynthia , Lambirth Kevin , Mittal Neha , Juel Md Ariful Islam , Barua Visva Bharati , Brazell Lauren Roppolo , Hinton Keshawn , Lontai Jordan , Stark Nicholas , Young Isaiah , Quach Cristine , Russ Morgan , Kauer Jacob , Nicolosi Bridgette , Akella Srinivas , Tang Wenwu , Chen Don , Schlueter Jessica , Munir Mariya 
Published: January 2021

AbstractThe COVID-19 pandemic has been a source of ongoing challenges and presents an increased risk of illness in group environments, including jails, long term care facilities, schools, and, of course, residential college campuses. Early reports that the SARS-CoV-2 virus was detectable in wastewater in advance of confirmed cases sparked widespread interest in wastewater based epidemiology (WBE) as a tool for mitigation of COVID-19 outbreaks. One hypothesis was that wastewater surveillance might provide a cost-effective alternative to other more expensive approaches such as pooled and random testing of groups. In this paper, we report the outcomes of a wastewater surveillance pilot program at the University of North Carolina at Charlotte, a large urban university with a substantial population of students living in on-campus dormitories. Surveillance was conducted at the building level on a thrice-weekly schedule throughout the university’s fall residential semester. In multiple cases, wastewater surveillance enabled identification of asymptomatic COVID-19 cases that were not detected by other components of the campus monitoring program, which also included in-house contact tracing, symptomatic testing, scheduled testing of student athletes, and daily symptom reporting. In the context of all cluster events reported to the University community during the fall semester, wastewater-based testing events resulted in identification of smaller clusters than were reported in other types of cluster events. Wastewater surveillance was able to detect single asymptomatic individuals in dorms with total resident populations of 150-200. While the strategy described was developed for COVID-19, it is likely to be applicable to mitigation of future pandemics in universities and other group-living environments.
Stages of COVID-19 pandemic and paths to herd immunity by vaccination: dynamical model comparing Austria, Luxembourg and Sweden
Authors: Kemp Françoise , Proverbio Daniele , Aalto Atte , Mombaerts Laurent , d’Hérouël Aymeric Fouquier , Husch Andreas , Ley Christophe , Gonçalves Jorge , Skupin Alexander , Magni Stefano 
Published: January 2021

AbstractBackgroundWorldwide more than 72 million people have been infected and 1.6 million died with SARS-CoV-2 by 15th December 2020. Non-pharmaceutical interventions which decrease social interaction have been implemented to reduce the spread of SARS-CoV-2 and to mitigate stress on healthcare systems and prevent deaths. The pandemic has been tackled with disparate strategies by distinct countries resulting in different epidemic dynamics. However, with vaccines now becoming available, the current urgent open question is how the interplay between vaccination strategies and social interaction will shape the pandemic in the next months.MethodsTo address this question, we developed an extended Susceptible-Exposed-Infectious-Removed (SEIR) model including social interaction, undetected cases and the progression of patients trough hospitals, intensive care units (ICUs) and death. We calibrated our model to data of Luxem-bourg, Austria and Sweden, until 15th December 2020. We incorporated the effect of vaccination to investigate under which conditions herd immunity would be achievable in 2021.ResultsThe model reveals that Sweden has the highest fraction of undetected cases, Luxembourg displays the highest fraction of infected population, and all three countries are far from herd immunity as of December 2020. The model quantifies the level of social interactions, and allows to assess the level which would keep Reff (t) below 1. In December 2020, this level is around 1/3 of what it was before the pandemic for all the three countries. The model allows to estimate the vaccination rate needed for herd immunity and shows that 2700 vaccinations/day are needed in Luxembourg to reach it by mid of April and 45,000 for Austria and Sweden. The model estimates that vaccinating the whole country’s population within 1 year could lead to herd immunity by July in Luxembourg and by August in Austria and Sweden.ConclusionThe model allows to shed light on the dynamics of the epidemics in different waves and countries. Our results emphasize that vaccination will help considerably but not immediately and therefore social measures will remain important for several months before they can be fully alleviated.
Statistical and machine learning models for classification of human wear and delivery days in accelerometry data
Authors: Moore Ryan , Archer Kristin R. , Choi Leena 
Published: January 2021

AbstractPurposeAccelerometers are increasingly utilized in healthcare research to assess human activity. Accelerometry data are often collected by mailing accelerometers to participants, who wear the accelerometers to collect data on their activity. The devices are then mailed back to the laboratory for analysis. We develop models to classify days in accelerometry data as activity from actual human wear or the delivery process. These models can be used to automate the cleaning of accelerometry datasets that are adulterated with activity from delivery.MethodsFor the classification of delivery days in accelerometry data, we developed statistical and machine learning models in a supervised learning context using a large human activity and delivery labeled accelerometry dataset. We extracted several features, which were included to develop random forest, logistic regression, mixed effects regression, and multilayer perceptron models, while convolutional neural network, recurrent neural network, and hybrid convolutional recurrent neural network models were developed without feature extraction. Model performances were assessed using Monte Carlo cross-validation.ResultsWe found that a hybrid convolutional recurrent neural network performed best in the classification task with an F1 score of 0.960 but simpler models such as logistic regression and random forest also had excellent performance with F1 scores of 0.951 and 0.957, respectively.ConclusionThe models developed in this study can be used to classify days in accelerometry data as either human or delivery activity. An analyst can weigh the larger computational cost and greater performance of the convolutional recurrent neural network against the faster but slightly less powerful random forest or logistic regression. The best performing models for classification of delivery data are publicly available on the open source R package,PhysicalActivity.