bluuebunny/crossref_metadata_2025_split huggingface dataset:
Willingness to Enroll in Social Health Insurance and associated factors among Household Heads in Obio/Akpor Local Government Area of Rivers State
Authors: Chinelo Kenechukwu Tabansi , Harry Tamunotonye , Apugo Uchechukwu 
Published: December 2022

ABSTRACTBackgroundSocial health insurance (SHI) schemes cover the healthcare needs of the informal sector workers, provide them with financial protection, minimize the equity gap, reduce Out-Of-Pocket (OOP) spending, and enhance utilization of the healthcare system.ObjectiveTo determine the willingness to enrol in SHI and its associated factors among household heads in Obio-Akpor Local Government Area (LGA) of Rivers state.MethodThis community-based cross-sectional study was carried out using the clusters and the EPI random walk sampling technique to obtain data from 205 selected household heads who were administered with an interviewer-administered questionnaire. The collected data was analysed using SPSS version 21.ResultAccording to the findings, majority of the respondents were males (58.5%), aged between 31 – 45 years (52.7%), married (57.1%), completed the tertiary level of education (65.4%), employed or self-employed (95.1%), from a family size of 3 – 4 (56.1%) and earned 50,000 to 100,000 Naira monthly (27.8%). Also, 63.0% are not enrolled in any health insurance scheme out of which 60.5% are not willing to enrol, with lack of trust in the management of the scheme, no believe in paying for sickness or have other means of meeting their healthcare needs identified as the major reasons. However, younger age, higher educational level, employment status, and earning >50,000 – 100,000 monthly had statistically (p<0.05) significant effect on the willingness of the respondents to enrol on SHIConclusionThis result showed a low level of enrolment in health insurance as well as willingness to enrol in Shi, hence, both insurance companies, government and even Non-Governmental Organisation should carry out strategic campaigns to dispel false rumours about SHI while outlining the merits.
The frequency of pathogenic variation in the All of Us cohort reveals ancestry-driven disparities
Authors: Venner Eric , Patterson Karynne , Kalra Divya , Wheeler Marsha M. , Chen Yi-Ju , Kalla Sara E. , Yuan Bo , Karnes Jason H. , Lee Breanna , Walker Kimberly , Smith Josh , Mcgee Sean , Radhakrishnan Aparna , Haddad Andrew , Wang Qiaoyan , Jarvik Gail , Toledo Diana , Musick Anjene , Gibbs Richard A. 
Published: December 2022

AbstractDisparities in the data that underlies clinical genomic interpretation is an acknowledged problem but there is a paucity of data demonstrating it. The National Institutes of Health’sAll of UsResearch Program aims to collect whole genome sequences, electronic health record (EHR) data, surveys and physical measurements for over a million participants of diverse ancestry and varied access to healthcare resources. We grouped participants by computed genetic ancestry and summarized the frequency of pathogenic variation within these groups. The European subgroup showed the highest rate of pathogenic variation (2.1%), with other ancestry groups ranging from 1.04% (East Asian) to 1.87% (‘Other’). Pathogenic variants were most frequently observed in genes related to Breast/Ovarian Cancer, Hypercholesterolemia or Hemochromatosis. Variant frequencies were consistent with gnomAD and some notable exceptions were resolved using gnomAD subsets. We additionally use this data to enrich sets of participants for specific genetic findings and to calculate penetrance. Differences in the frequency of pathogenic variants observed between ancestral groups generally indicate biases of ascertainment, but some may indicate differences in disease prevalence. These analyses are available on theAll of UsResearcher Workbench.
Cervical Cancer Screening Using Residual Learning
Authors: Abdulsamad Mohamed , Alshareef Esam Alsadiq , Ebrahim Fawzi 
Published: December 2022

AbstractCervical cancer is a leading cancer in the female population. This disease is considered dangerous as its slow and unpredicted growth. The prevention of such cancer can be mostly achieved by screening its transformation zones. The cervical pre-cancerous zones can be considered as three types: type 1, type 2, and type 3. Screening and analyzing these three stages can be crucial for preventing their transformation into cancer. Hence, it is essentially important to have an automated and intelligent system that can grade the cervical pre-cancerous colposcopy images into one of the three types. This can help in providing the right treatment and prevent cancer transformation. In this paper, we develop a residual learning-based model (ResNet-50) to be trained for classifying the type of a colposcopy cervical image into type 1, type 2, and type 3. Experimentally, the model was fine-tuned and evaluated on a public dataset of colposcopy cervical images and achieved promising results in cervical cancer screening of accuracy of 77% and F1-score of 79%.
Research waste from poor reporting of core methods and results and redundancy in studies of reporting guideline adherence: a meta-research review
Authors: Dal Santo Tiffany , Rice Danielle B. , Amiri Lara S.N. , Tasleem Amina , Li Kexin , Boruff Jill T. , Geoffroy Marie-Claude , Benedetti Andrea , Thombs Brett D. 
Published: December 2022

ABSTRACTObjectivesWe investigated meta-research studies that evaluated adherence to prominent reporting guidelines (CONSORT, PRISMA, STARD, STROBE) in health research studies to determine the proportion that (1) provided an explanation for how complex guideline items were rated for adherence and (2) provided results from individual studies reviewed in addition to aggregate results. We also examined the conclusions of each meta-research study to assess redundancy of findings across studies.DesignCross-sectional meta-research review.Data sourcesMEDLINE (Ovid) searched on July 5, 2022.Eligibility criteria for selecting studiesStudies in any language were eligible if they used any version of the CONSORT, PRISMA, STARD, or STROBE reporting guidelines or their extensions to evaluate reporting in at least 10 human health research studies. We excluded studies that modified a reporting guideline or its items or evaluated fewer than half of reporting guideline items. Main outcomes were (1) the proportion of meta-research studies that provided a coding explanation that could be used to replicate the study or verify its results and (2) the proportion that provided individual-level study results in the main text, supplemental materials, or via an internet link.ResultsOf 148 included meta-research studies, 14 (10%, 95% confidence interval [CI] 6% to 15%) provided a fully replicable coding explanation, and 49 (33%, 95% CI 26% to 41%) completely reported individual study results. Of 90 studies that classified reporting as adequate or inadequate in the study abstract, 6 (7%, 95% CI 3% to 14%) concluded that reporting was adequate but none of those 6 studies provided information on how items were coded or provided item-level results for included studies.ConclusionsMuch of published meta-research on reporting in health research is likely wasteful. Few studies report enough information for verification or replication, and almost all find that reporting in health research studies is suboptimal. These findings highlight the importance of shifting the focus from assessing reporting adequacy to developing, testing, and implementing strategies to improve reporting.FundingThere was no specific funding for this study.ProtocolPosted on the Open Science Framework June 29, 2022 (https://osf.io/gtm4z/).
Knowledge on tuberculosis, services of TB control programme and associated socio-demographic inequity among rural participants of Jaipur, Rajasthan, India: A cross sectional study
Authors: Anand P.K. , Dhikav V. , Rathore M. , Toteja G.S. , Singh Mradula , Singh Bhanwar Manohar , Meena C.R. , Jangid Anil , Devanda M.R. , Mathur M.L. 
Published: December 2022

AbstractIntroductionTuberculosis (TB) is one of the top 10 causes of deaths worldwide. India is still a highest TB burden country. There is scarcity of data on TB knowledge from Rajasthan state of India.Materials & MethodsCross-sectional community based study was carried out at Model Rural Health Research Unit, Jaipur, an unit of Department of Health Research, Ministry of Health & Family Welfare, Government of India.ResultsStudy reports the result from 1993 adult participants from 10 villages of 2 sub-districts of district Jaipur. About 88.9% of studied participants knew that TB is an infectious disease and it spreads from TB patient to healthy person in close contact. Only 22.3% of participants knew ‘DOTS is the name of treatment for TB’. While, only 58.9% knew ‘sputum is used for diagnosis of TB’ at health centres.Scheduled castes, scheduled tribesandbackward classessocial groups knew less than the mainstream ‘General’ social group. The observed difference was statistically significant (p<0.05). Logistic regression analysis estimated the relative contribution in knowledge status.DiscussioThe knowledge of study participants on transmission of tuberculosis was similar to the knowledge of population in country wide study. They poorly knew that sputum is used for diagnosing the tuberculosis disease; socio-demographic inequity exists in this knowledge too. People from older age groups, underprivileged social groups and minority need extra educational activities.
A spatio-temporal mapping and bayesian modelling of risk factors of pneumonia symptoms in under-five children in Nigeria
Authors: Atoloye K. A. , Lawal T. V. , Adebowale A. S. , Fagbamigbe A.F. 
Published: December 2022

AbstractBackgroundPneumonia remains a public health challenge in most parts of the world, with Nigeria having the highest number of pneumonia-related deaths. Understanding the geographical distribution, trends and risk factors associated with pneumonia symptoms will aid an appropriate intervention of pneumonia and subsequently reduce its burden in Nigeria.MethodThis cross-sectional study used data from the 2008, 2013 and 2018 Nigeria Demographic Health Survey. In each of the survey round, a multi-stage cluster sampling technique was used to select the eligible respondents who are women of reproductive age. The outcome variables are the presence of key symptoms of pneumonia: fever, cough, and short rapid breaths. Optimized hotspot analysis was used to identify states with a significantly high prevalence of pneumonia symptoms, MCMC mixed-effect models were fitted to each symptom.ResultsThe prevalence of cough was 12.1%, 10.1% and 16.9% in 2008, 2013 and 2018 respectively, 16.2%, 13.3%, and 25.7% for fever; and 41.7%, 42.5% and 6.5% for short rapid breaths respectively with variations across the states. The adjusted odds of having a cough among the children aged 6-11, 12-23, 24-35 and 36-47 months were 95% higher (adjusted odds ratio (aOR) =1.95, 95% Credible Interval (CrI): 1.77, 2.18), 92% higher (aOR=1.92, 95% CrI: 1.73, 2.12), 45% higher (aOR=1.45, 95% CrI: 1.31, 1.62) and 15% higher (aOR=1.15, 95% CrI: 1.03, 1.27) respectively, relative to ages 0-5 months. Similar patterns were noticed for fever and short rapid breaths. Mothers’ education was significant for cough and fever but not for short rapid breaths. Mothers’ age was significant only for short rapid breaths at higher odds. Other significantly associated factors with symptoms include residence type, housing quality, wealth index and region.ConclusionFever, cough, and short rapid Breaths are prevalent among under-five children in Nigeria. These symptoms are associated with different characteristics and varied across states in Nigeria. Therefore, it is pertinent that mothers improve on the available preventive and management strategies with the view to mitigating the consequences of pneumonia symptoms among under-five children in Nigeria.
Tumour-infiltrated cortex participates in large-scale cognitive circuits
Authors: Mandal Ayan S. , Assem Moataz , Romero-Garcia Rafael , Coelho Pedro , McDonald Alexa , Woodberry Emma , Morris Robert C. , Price Stephen J. , Duncan John , Santarius Thomas , Suckling John , Hart Michael G. , Erez Yaara 
Published: December 2022

AbstractThe extent to which tumour-infiltrated brain tissue contributes to cognitive function remains unclear. While prior studies have suggested involvement of tumour-infiltrated tissue in local circuits associated with language and motor function, it is unknown whether such tissue participates in distributed networks important for higher-order cognitive abilities like executive function. In this study, we tested the hypothesis that cortical tissue infiltrated by diffuse low-grade gliomas participates in large-scale cognitive circuits using a unique combination of intracranial electrocorticography (ECoG) and resting-state functional magnetic resonance (fMRI) imaging in four patients. We observed significant task-related high gamma (70-250 Hz) power modulations in tumour-infiltrated cortex in response to increased cognitive effort, implying preserved functionality of neoplastic tissue for complex tasks. Strikingly, we found that tumour locations corresponding to task-responsive electrodes exhibited functional connectivity patterns that significantly co-localised with canonical brain networks implicated in executive function. Finally, we discovered that tumour regions with larger task-related high gamma power elevations tended to be more functionally connected to the dorsal attention network, further demonstrating the participation of tumour-infiltrated cortex in large-scale brain networks that support executive function in health. Overall, this study contributes convergent fMRI-ECoG evidence that tumour-infiltrated cortex participates in large-scale neurocognitive circuits, reflecting preserved functionality of neoplastic brain tissue relevant to clinical management.Significance statementGliomas interact with healthy neural circuits as they grow in the brain. Understanding these interactions is crucial for planning surgeries to remove gliomas without inducing long-term cognitive deficits. While prior studies have shown that glioma-infiltrated tissue can integrate within local functional circuits, it is unknown whether such tissue participates in large-scale whole-brain networks. Using electrocorticography, we show that glioma-infiltrated tissue responds significantly to tasks of increasing cognitive demand, reflecting its involvement in executive function processes. Using pre-operative functional neuroimaging, we found that tumour regions responsive to complex tasks were also functionally connected to large-scale networks implicated in executive function. These findings imply that gliomas participate within large-scale cognitive circuits, possibly reflecting preserved functionality relevant to clinical management.
Omada: Robust clustering of transcriptomes through multiple testing
Authors: Kariotis Sokratis , Fang Tan Pei , Lu Haiping , Rhodes Chris , Wilkins Martin , Lawrie Allan , Wang Dennis 
Published: December 2022

AbstractCohort studies increasingly collect biosamples for molecular profiling and are observing molecular heterogeneity. High throughput RNA sequencing is providing large datasets capable of reflecting disease mechanisms. Clustering approaches have produced a number of tools to help dissect complex heterogeneous datasets, however, selecting the appropriate method and parameters to perform exploratory clustering analysis of transcriptomic data requires deep understanding of machine learning and extensive computational experimentation. Tools that assist with such decisions without prior field knowledge are nonexistent. To address this we have developed Omada, a suite of tools aiming to automate these processes and make robust unsupervised clustering of transcriptomic data more accessible through automated machine learning based functions. The efficiency of each tool was tested with five datasets characterised by different expression signal strengths to capture a wide spectrum of RNA expression datasets. Our toolkit’s decisions reflected the real number of stable partitions in datasets where the subgroups are discernible. Within datasets with less clear biological distinctions, our tools either formed stable subgroups with different expression profiles and robust clinical associations or revealed signs of problematic data such as biased measurements.
A Multifaceted Framework To Assess tradeoffs in Interpretability, Explanatory and Predictive Performances Of Alternative Joint Species Distribution Models
Authors: Violet Clément , Boyé Aurélien , Chevalier Mathieu , Gauthier Olivier , Grall Jacques , Marzloff Martin P. 
Published: December 2022

AbstractJoint Species Distribution Models(jSDM) are increasingly used to explain and predict biodiversity patterns. By accounting for species co-occurrence patterns and potentially including species-specific information,jSDMs capture the processes that shape ecological communities. Yet, factors like missing covariates or omitting ecologically-important species may alter the interpretability and effectiveness ofjSDMs. Additionally, while the specific formulation of ajSDMdirectly affects its performances, the effects of choices related to model structure, such as inclusion, or not of phylogeny or trait information, are not well-explored.Here, we developed a multifaceted framework to comprehensively assess performances of alternativejSDMformulations at both species and community levels. We applied this framework to four alternative models fitted on presence/absence and abundance data of a polychaete assemblage sampled in two coastal habitats over 500 km and 8 years. Relative to a benchmarkjSDMonly capturing the effects of abiotic predictors and residual co-occurrence patterns, we explored the performance of alternative formulations that also included species phylogeny, traits, or some additional 179 non-target species, which were sampled alongside the species of interest. For both presence/absence and abundance data, explanatory power was good for all models but their interpretability and predictive power varied. Relative to the benchmark model, predictive errors on species abundances decreased by 95% or 53%, when including non-target species, or phylogeny, respectively. These differences across models relate to changes in both species-environment relationships and residual co-occurrence patterns. While considering trait data did not improve explanatory or predictive power, it facilitated interpretation of trait-mediated species response to environmental gradients.This study demonstrates trade-offs injSDMformulation for explaining or predicting species data, highlighting the importance of using a comprehensive framework to compare models.Furthermore, our study provides some guidance for model selection tailored to specific objectives and available data.
Deep learning-based predictive identification of functional subpopulations of hematopoietic stem cells and multipotent progenitors
Authors: Wang Shen , Han Jianzhong , Huang Jingru , Shi Yuheng , Zhou Yuyuan , Kim Dongwook , Islam Md Khayrul , Zhou Jane , Ostrovsky Olga , Li Chenchen , Lian Zhaorui , Liu Yaling , Huang Jian 
Published: December 2022

AbstractHematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) are crucial for maintaining lifelong hematopoiesis. Developing methods to distinguish stem cells from other progenitors and evaluate stem cell functions has been a central task in stem cell research. Deep learning has been demonstrated as a powerful tool in cell image analysis and classification. In this study, we explored the possibility of using deep learning to differentiate HSCs and MPPs based on their light microscopy (DIC) images. After extensive training and validation with large image data sets, we successfully develop a three-class classifier (we named it the LSM model) that reliably differentiate long-term HSCs (LT-HSCs), short-term HSCs (ST-HSCs), and MPPs. Importantly, we demonstrated that our LSM model achieved its differentiating capability by learning the intrinsic morphological features from cell images. Furthermore, we showed that the performance of our LSM model was not affected by how these cells were identified and isolated, i.e., sorted by surface markers or intracellular GFP markers. Prospective identification of HSCs and MPPs in Evi1GFPtransgenic mice by LSM model suggested that the cells with the highest GFP expression were LT-HSCs, and this prediction was substantiated later by a long-term competitive reconstitution assay. Moreover, based on DIC image data sets, we also successfully built another two-class classifier that can effectively distinguish aged HSCs from young HSCs, which both express the same surface markers but are functionally different. This finding is of particular interest since it may provide a novel quick and efficient approach, obviating the need for a time-consuming transplantation experiment, to evaluate the functional states of HSCs. Together, our study provides evidence for the first time that HSCs and MPPs can be differentiated by deep learning based on cell morphology. This novel and robust deep learning-based platform will provide a basis for the future development of a new generation stem cell identification and separation system. It may also provide new insight into molecular mechanisms underlying the self-renewal feature of stem cells.
Greengenes2 enables a shared data universe for microbiome studies
Authors: McDonald Daniel , Jiang Yueyu , Balaban Metin , Cantrell Kalen , Zhu Qiyun , Gonzalez Antonio , Morton James T. , Nicolaou Giorgia , Parks Donovan H. , Karst Søren , Albertsen Mads , Hugenholtz Philip , DeSantis Todd , Song Se Jin , Bartko Andrew , Havulinna Aki S. , Jousilahti Pekka , Cheng Susan , Inouye Mike , Niiranen Teemu , Jain Mohit , Salomaa Veikko , Lahti Leo , Mirarab Siavash , Knight Rob 
Published: December 2022

Abstract16S rRNA and shotgun metagenomics studies typically yield different results, usually attributed to biases in PCR amplification of 16S rRNA genes. Here, we introduce Greengenes2 and show that differences in reference phylogeny are more important. By inserting sequences into a whole-genome phylogeny, we show that 16S rRNA and shotgun metagenomic data generated from the same samples agree in principal coordinates space, taxonomy, and in phenotype effect size when analyzed with the same tree.