bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classification of Fetal State through the application of Machine Learning techniques on Cardiotocography records: Towards Real World Application
Authors: Ventura Dadario Andrew Maranhão , Espinoza Christian , Araújo Nogueira Wellington 
Published: June 2021

AbstractObjectiveAnticipating fetal risk is a major factor in reducing child and maternal mortality and suffering. In this context cardiotocography (CTG) is a low cost, well established procedure that has been around for decades, despite lacking consensus regarding its impact on outcomes.Machine learning emerged as an option for automatic classification of CTG records, as previous studies showed expert level results, but often came at the price of reduced generalization potential.With that in mind, the present study sought to improve statistical rigor of evaluation towards real world application.Materials and MethodsIn this study, a dataset of 2126 CTG recordings labeled as normal, suspect or pathological by the consensus of three expert obstetricians was used to create a baseline random forest model.This was followed by creating a lightgbm model tuned using gaussian process regression and post processed using cross validation ensembling.Performance was assessed using the area under the precision-recall curve (AUPRC) metric over 100 experiment executions, each using a testing set comprised of 30% of data stratified by the class label.ResultsThe best model was a cross validation ensemble of lightgbm models that yielded 95.82% AUPRC.ConclusionsThe model is shown to produce consistent expert level performance at a less than negligible cost. At an estimated 0.78 USD per million predictions the model can generate value in settings with CTG qualified personnel and all the more in their absence.
Inferring SARS-CoV-2 RNA shedding into wastewater relative to time of infection
Authors: Cavany Sean , Bivins Aaron , Wu Zhenyu , North Devin , Bibby Kyle , Perkins T. Alex 
Published: June 2021

AbstractSince the start of the COVID-19 pandemic, there has been interest in using wastewater monitoring as an approach for disease surveillance. A significant uncertainty that would improve interpretation of wastewater monitoring data is the intensity and timing with which individuals shed RNA from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) into wastewater. By combining wastewater and case surveillance data sets from a university campus during a period of heightened surveillance, we inferred that individual shedding of RNA into wastewater peaks on average six days (50% uncertainty interval (UI): 6 – 7; 95% UI: 4 – 8) following infection, and that wastewater measurements are highly overdispersed (negative binomial dispersion parameter,k= 0.39 (95% credible interval: 0.32 – 0.48)). This limits the utility of wastewater surveillance as a leading indicator of secular trends in SARS-CoV-2 transmission during an epidemic, and implies that it could be most useful as an early warning of rising transmission in areas where transmission is low or clinical testing is delayed or of limited capacity.
Transmission of SARS-CoV-2 associated with aircraft travel: a systematic review (Version 1)
Authors: Rosca EC , Heneghan C , Spencer EA , Brassey J , Plüddemann A , Onakpoya IJ , Evans D , Conly JM , Jefferson T 
Published: June 2021

AbstractBackgroundAir travel may be associated with the spread of viruses via infected passengers and potentially through in-flight transmission. Given the novelty of the SARS-CoV-2 virus, transmission associated with air travel is based on what is known about the dynamics of transmission of other respiratory virus infections, especially those due to other coronaviruses and influenza. Our objective was to provide a rapid summary and evaluation of relevant data on the transmission of SARS-CoV-2 aboard aircraft, report important policy implications, and highlight research gaps requiring urgent attention.MethodsThis review is part of an Open Evidence Review on Transmission Dynamics of SARS-CoV-2. We searched LitCovid, medRxiv, Google Scholar, and the WHO Covid-19 database from 1 February 2020 to 27 January 2021 and included studies on the transmission of SARS-CoV-2 aboard aircraft. We assessed study quality based on five criteria and reported important findings.ResultsWe included 18 studies on in-flight transmission of SARS-CoV-2, representing 130 unique flights and two studies on wastewater from aircraft. The overall quality of reporting was low. Two wastewater studies reported PCR-positive SARS-CoV-2 samples, but with relatively high Cycle threshold values ranging from 36 to 40. The definition of an index case was very heterogeneous across the studies. The proportion of contacts traced ranged from 0.68% to 100%. In total, the authors successfully traced 2800/19729 passengers, 140/180 crew members, and 8/8 medical staff. Altogether, 273 index cases were reported, with 64 secondary cases. No secondary cases were reported in three studies, each investigating one flight. The secondary attack rate among the studies that followed up >80% of the passengers and crew (including data on 10 flights) varied between 0% and 8.2%. The included studies reported on the possibility of SARS-CoV-2 transmission from asymptomatic, pre-symptomatic, and symptomatic individuals. Viral cultures were performed in two studies, with 10 positive results reported. Genomic sequencing and phylogenetic analysis were performed in individuals from four flights, with the completeness of genomic similarity ranging from 81-100%.ConclusionCurrent evidence suggests that SARS-CoV-2 can be transmitted during aircraft travel, but the published data do not permit any conclusive assessment of the likelihood and extent. Furthermore, the quality of evidence from most published studies is low. The variation in study design and methodology restricts the comparison of findings across studies. Standardized guidelines for conducting and reporting future studies of transmission on aircrafts should be developed.
Virtual care and the impact of COVID-19 on nursing: A single centre evaluation
Authors: Hughes Luke , Petrella Anika , Phillips Natasha , Taylor Rachel M 
Published: June 2021

ABSTRACTAimsThe overall aim of this evaluation was to look at the impact of the changes in working practices during the pandemic on nurses. This secondary analysis provided an evaluation of virtual care and being able/required to work from home.DesignThis was secondary analysis of an evaluation using semi-structured interviews.MethodsConducted at a single National Health Service (NHS) university hospital in the United Kingdom between May-July 2020. Forty-eight operational leads and nurses participated in semi-structured interviews which were digitally recorded, transcribed verbatim and analysed using a framework analysis.ResultsTwo overarching themes emerged relating to the patient experience and nursing experience. There were both positive and negative elements associated with virtual care and remote working related to these themes. However, the majority of nurses found virtual clinics were useful when proper resources were provided, and managerial strategies were put in place to support them. Participants felt virtual care could benefit many but not all patient groups moving forward, and that flexibility around working from home would be desirable in the future.ConclusionVirtual care and remote working were implemented to accommodate the restrictions imposed because of the pandemic. The benefits of these changes to nurses and patients support these being business as usual. However, clear policies are needed to ensure nurses feel supported when working remotely and there are robust assessments in place to ensure virtual care is provided to patients who have access to the necessary technology.ImpactThis was a study of the move to virtual care and remote working during the COVID-19 pandemic. Telemedicine and flexible working were not common in the NHS prior to the pandemic but the current evaluation supports the role out of these as standard care with policies in place to ensure nurses and patients are appropriately supported.
Normal mean oral temperature is 98°F, not 98.2°F or 98.6°F
Authors: Kumar Nitin , Ronanki Kavya , Panda Prasan Kumar , Kapoor Mayank , Singh Yogesh , Bhadoriya Ajeet Singh 
Published: June 2021

AbstractBackground98.6°F is generally accepted as normal body temperature as defined by Wunderlich (1868) and later challenged by Mackowiak (1992) and Protsiv (2020) who concluded as 98.2°F based on cross-sectional studies. Hence, the normal body temperature at present needs quantification?MethodsA longitudinal study on the healthy population of Northen-India were followed-up over 1-year. Participants were advised for self-monitoring of oral temperature with a standard digital thermometer in either left or right sublingual pocket and record it in the thermometry diary. The study was considered complete if the participant had all the three phases of the study (i.e. non-febrile, febrile, and post-febrile phases) or completed the duration of the study.ResultsThe mean oral temperature of the participants (n=144) during the non-febrile and post-febrile phases (temperature readings=6543) were 98°F (SD, 0.61) and 98.01°F (SD, 0.60) respectively (P<0.001). The mean oral temperature during post-febrile phase was found to be 0.01°F higher than non-febrile phase. With the diurnal variability, the morning (AM), noon (AN), and afternoon (PM) mean temperatures were 97.91, 98.08, and 98.27°F (P<0.001) respectively during the non-febrile phase. Similar trends were observed in variability among men and women, and seasons.ConclusionsThe mean oral temperature is 98°F (SD, 0.61). The temperature is as low as 96.90F and as high as 99.10F. The temperature during post-febrile phase was found to be higher than the non-febrile phase temperature like PM over AN & AM, women over men, summer over other seasons in the non-febrile phase, spring over others in the post-febrile phase.
A landscape of synergistic drug combinations in non-small-cell lung cancer
Authors: Nair Nishanth Ulhas , Greninger Patricia , Friedman Adam , Amzallag Arnaud , Cortez Eliane , Sahu Avinash Das , Lee Joo Sang , Dastur Anahita , Egan Regina K. , Murchie Ellen , Crowther Giovanna Stein , McClanaghan Joseph , Boisvert Jessica , Damon Leah , Ho Jeffrey , Tam Angela , Garnett Mathew J , Engelman Jeffrey A. , Haber Daniel A. , Ruppin Eytan , Benes Cyril H. 
Published: June 2021

SummaryTargeted therapeutics have advanced cancer treatment, but single agent activity remains limited by de novo and acquired resistance. Combining targeted drugs is broadly seen as a way to improve treatment outcome, motivating the ongoing search for efficacious combinations. To identify synergistic targeted therapy combinations and study the impact of tumor heterogeneity on combination outcome, we systematically tested over 5,000 two drug combinations at multiple doses across a collection of 81 non-small cancer cell lines. Both known and novel synergistic combinations were identified. Strikingly, very few combinations yield synergy across the majority of cell line models. Importantly, synergism mainly arises due to sensitization of single agent resistant models, rather than further sensitize already sensitive cell lines, frequently via dual targeting of a single or two highly interconnected pathways. This drug combinations resource, the largest of its kind should help delineate new synergistic regimens by facilitating the understanding of drug synergism in cancer.
HLA Allele Imputation with Multitask Deep Convolutional Neural Network
Authors: Chi Calvin 
Published: June 2021

AbstractMotivationThe Human leukgocyte antigen (HLA) system is a highly polymorphic gene complex encoding the major histocompatibility complex proteins in humans. HLA alleles are of strong epidemiological interest for their large effect sizes in associations with autoimmune diseases, infectious diseases, severe drug reactions, and transplant medicine. Since HLA genotyping can be time-consuming and cost-prohibitive, methods to impute HLA alleles from SNP genotype data have been developed, including HLA Genotype Imputation with Attribute Bagging (HIBAG), HLA*IMP:02, and SNP2HLA. However, limitations of these imputation programs include imputation accuracy, computational runtime, and ability to impute HLA allele haplotypes.ResultsWe present a deep learning framework for HLA allele imputation using a multitask convolutional neural network (CNN) architecture. In this approach, we use phased SNP genotype data flanking ±250 kb from each HLA locus to simultaneously impute HLA allele haplotyes across loci HLA-A, -B, -C, -DQA1, -DQB1, -DPA1, -DPB1, and -DRB1. We start by tokenizing phased genotype sequences into k-mers that serve as input to the model. The CNN architecture starts with a shared embedding layer for learning low-dimensional representations of k-mers, shared convolutional layers for detecting genotype motifs, and branches off into separate densely-connected layers for imputing each HLA loci. We present evidence that the CNN used information from known tag SNPs to impute HLA alleles, and demonstrate the architecture is robust against a selection of hyperparameters. On the T1DGC dataset, our model achieved 97.6% imputation accuracy, which was superior to SNP2HLA’s performance and comparable to HIBAG’s performance. However, unlike HIBAG, our method can impute an entire HLA haplotype sequence instead of imputing one locus at a time. Additionally, by separating the training and inference steps, our imputation program provides user flexibility to reduce usage time.AvailabilityThe source code is available at https://github.com/CalvinTChi/HLA_imputationContactcalvin.chi@berkeley.edu
Development of a multiplex real-time PCR assay for BCG and validation in a clinical laboratory
Authors: Duffy Shannon C , Venkatesan Manigandan , Chothe Shubhada , Poojary Indira , Verghese Valsan Philip , Kapur Vivek , Behr Marcel A , Michael Joy S 
Published: June 2021

AbstractMycobacterium bovisbacille Calmette-Guérin (BCG) is a live attenuated vaccine which can result in local or disseminated infection, most commonly in immunocompromised individuals. Differentiation of BCG from other members of theMycobacterium tuberculosiscomplex (MTBC) is required to diagnose BCG disease, which requires specific management. Current methods for BCG diagnosis are based on mycobacterial culture and conventional PCR; the former is time-consuming and the latter often unavailable. Further, there are reports that certain BCG strains may be associated with a higher rate of adverse events. This study describes the development of a two-step multiplex real-time PCR assay which uses single nucleotide polymorphisms to detect BCG and identify early or late BCG strains. The assay has a limit of detection of 1 pg BCG boiled lysate DNA and was shown to detect BCG in both pure cultures and experimentally infected tissue. Performance was assessed on 19 suspected BCG clinical isolates at Christian Medical College in Vellore, India taken from January 2018 to August 2020. Of these 19 isolates, 10 were identified as BCG (6 early and 4 late strains) and 9 were identified as other MTBC members. Taken together, the results demonstrate the ability of this assay to identify and characterize BCG disease from cultures and infected tissue. The capacity to identify BCG may improve patient management and the ability to discriminate between BCG strains may enable BCG vaccine pharmacovigilance.
Direct comparison of venipuncture serum draws versus whole blood finger-stick specimens by anti-COVID-19 IgG/IgM rapid lateral flow immunoassay and ELISA
Authors: Baig Irfan , Tagwerker Christian , Brunson Eric J. , Mundo Kristine , Dutra-Smith Davan , Carias-Marines Mary Jane , de Zoysa Ranulu Samanthi , Smith David J. 
Published: June 2021

AbstractDuring the COVID-19 pandemic, manufacturers have developed several diagnostic test kits that include lateral flow immunoassays (LFIA) also known as rapid cassette testing. Rapid cassette testing provides qualitative test results indicating the presence or absence of IgG and IgM antibodies to determine COVID-19 (SARS-CoV-2) infection among individuals. Venipuncture blood draws have been the traditional and widely proposed sample collection method but is costly and not applicable to point-of-care testing (POC) and in remote settings. Whole blood finger-stick blood collections traditionally used by diabetics for glucose level testing is an ideal scenario, but raises concerns regarding the outcome of test results in regards to specificity and sensitivity. In this study we directly compare simultaneous collections of venipuncture serum (SST) blood draws and whole blood finger-sticks (n = 75) to detect human Anti-COVID-19 IgG and IgM antibodies using an EUA-approved lateral flow immunoassay, showing equal to enhanced performance characteristics for this specimen type.
The Prevention and Control of Cancer by Metformin in Patients with Type 2 Diabetes: A Systematic Mapping Review
Authors: Mitchell Albania , Price Michelle , Cipriano Gabriela 
Published: June 2021

ABSTRACTObjectiveMetformin is commonly used as a first line therapy for type 2 diabetes; however, existing evidence suggests an influence in oncology. The objective of this systematic mapping review was to describe current literature regarding metformin and its role in preventing and /or controlling cancer in patients with type 2 diabetes.MethodWe searched PubMed, Cochrane Library, and ClinicalTrials.gov in February 2018 and April 2019 to identify research studies, systematic reviews and meta-analyses. Of the 318 citations identified, 156 publications were included in this analysis.ResultsThe most common cancer types researched were colorectal, liver, prostate, lung and breast with the United States contributing the most to this data. Author teams averaged six members and most studies were funded. Only 68% of the articles were available open access. Ovarian and esophageal cancers were amongst the least studied, but the most costly for care.
P2X4 Purinergic Receptors as a Therapeutic Target in Aggressive Prostate Cancer
Authors: Maynard Janielle P. , Lu Jiayun , Vidal Igor , Hicks Jessica , Mummert Luke , Ali Tamirat , Kempski Ryan , Carter Ayanna M. , Sosa Rebecca , Peiffer Lauren B. , Joshu Corinne E. , Lotan Tamara L. , De Marzo Angelo M. , Sfanos Karen S. 
Published: June 2021

AbstractProstate cancer (PCa) remains a leading cause of cancer-related deaths in American men and treatment options for metastatic PCa are limited. There is a critical need to identify new mechanisms that contribute to PCa progression, that distinguish benign from lethal disease, and that have potential for therapeutic targeting. P2X4 belongs to the P2 purinergic receptor family that is commonly upregulated in cancer and is associated with poorer outcomes. Herein, we report that the P2X4 purinergic receptor is overexpressed in PCa, associated with PCa metastasis, and a driver of tumor development in vivo. We observed P2X4 protein expression primarily in epithelial cells of the prostate, a subset of CD66+ neutrophils, and most CD68+ macrophages. Our analysis of tissue microarrays representing 491 PCa cases demonstrated significantly elevated P2X4 expression in cancer compared to benign tissue spots, in prostatic intraepithelial neoplasia, in cancer from White compared to Black men, and in PCa with ERG positivity or with PTEN loss. High P2X4 expression in benign tissues was likewise associated with the development of metastasis after radical prostatectomy. Treatment with P2X4-specific agonist CTP increased transwell migration and invasion of PC3, DU145, and CWR22Rv1 PCa cells. P2X4 antagonist 5-BDBD treatment resulted in a dose-dependent decrease in viability of PC3, DU145, LNCaP, CWR22Rv1, TRAMP-C2, Myc-CaP, BMPC1, and BMPC2 cells and decreased DU145 cell migration and invasion. Knockdown of P2X4 attenuated growth, migration, and invasion of PCa cells. Finally, knockdown of P2X4 in Myc-CaP cells resulted in significantly attenuated subcutaneous allograft growth in FVB/NJ mice. Collectively, these data strongly support a role for the P2X4 purinergic receptor in PCa aggressiveness and identifies P2X4 as a candidate for therapeutic targeting.
Computer-Assisted Mitotic Count Using a Deep Learning-based Algorithm Improves Inter-Observer Reproducibility and Accuracy in canine cutaneous mast cell tumors
Authors: Bertram Christof A. , Aubreville Marc , Donovan Taryn A. , Bartel Alexander , Wilm Frauke , Marzahl Christian , Assenmacher Charles-Antoine , Becker Kathrin , Bennett Mark , Corner Sarah , Cossic Brieuc , Denk Daniela , Dettwiler Martina , Gonzalez Beatriz Garcia , Gurtner Corinne , Haverkamp Ann-Kathrin , Heier Annabelle , Lehmbecker Annika , Merz Sophie , Noland Erica L. , Plog Stephanie , Schmidt Anja , Sebastian Franziska , Sledge Dodd G. , Smedley Rebecca C. , Tecilla Marco , Thaiwong Tuddow , Fuchs-Baumgartinger Andrea , Meuten Don J. , Breininger Katharina , Kiupel Matti , Maier Andreas , Klopfleisch Robert 
Published: June 2021

AbstractThe mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intra-observer discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying/classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, the computer-assisted review by pathologists may ensure reliability. In the present study we have compared partial (MC-ROI preselection) and full (additional visualization of MF candidate proposal and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MF and improving classification against imposters. The inter-observer consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study prove that computer assistance may lead to a more reproducible and accurate MCs in ccMCTs.