bluuebunny/crossref_metadata_2025_split huggingface dataset:
SIEVE: One-stop differential expression, variability, and skewness analyses using RNA-Seq data
Authors: Li Hongxiang , Khang Tsung Fei 
Published: April 2024

AbstractMotivationRNA-Seq data analysis is commonly biased towards detecting differentially expressed genes and insufficiently conveys the complexity of gene expression changes between biological conditions. This bias arises because discrete models of RNA-Seq count data cannot fully characterize the mean, variance, and skewness of gene expression distribution using independent model parameters. A unified framework that simultaneously tests for differential expression, variability, and skewness is needed to realize the full potential of RNA-Seq data analysis in a systems biology context.ResultsWe present SIEVE, a statistical methodology that provides the desired unified framework. SIEVE embraces a compositional data analysis framework that transforms discrete RNA-Seq counts to a continuous form with a distribution that is well-fitted by a skew-normal distribution. Simulation results show that SIEVE controls the false discovery rate and probability of Type II error better than existing methods for differential expression analysis. Analysis of the Mayo RNA-Seq dataset for Alzheimer’s disease using SIEVE reveals that a gene set with significant expression difference in mean, standard deviation and skewness between the control and the Alzheimer’s disease group strongly predicts a subject’s disease state. Furthermore, functional enrichment analysis shows that relying solely on differentially expressed genes detects only a segment of a much broader spectrum of biological aspects associated with Alzheimer’s disease. The latter aspects can only be revealed using genes that show differential variability and skewness. Thus, SIEVE enables fresh perspectives for understanding the intricate changes in gene expression that occur in complex diseasesAvailabilityThe SIEVE R package and source codes are available athttps://github.com/Divo-Lee/SIEVE.
The recombination efficiency of the bacterial integron depends on the mechanical stability of the synaptic complex
Authors: Vorobevskaia Ekaterina , Loot Céline , Mazel Didier , Schlierf Michael 
Published: April 2024

AbstractThe predominant tool for adaptation in Gram-negative bacteria is a genetic system called integron. Under conditions of stress, it rearranges gene cassettes, ensuring their sampling through expression, to offer a solution for overcoming the initial stress. Integrons are a major actor of multiple antibiotic resistances, a recognized major global health threat. Cassettes are recombined by a unique recombination process involving a tyrosine recombinase – the IntI integrase – and folded single-stranded DNA hairpins – theattCsites which terminate each cassette. Four recombinases and twoattCsites form a macromolecular synaptic complex, which is key to the recombination process and the focus of our study. The bottom strand of allattCsites shows highest recombination efficiencyin vivothan the top one, however, the efficiency still varies several orders of magnitude and the underlying reason remains unclear. Here, we established an optical tweezers force-spectroscopy assay that allows us to probe the synaptic complex stability. We found for seven combinations ofattCsites great variability in the mechanical stability. Two protein variants also showed a strong influence on the mechanical stability. We then determined thein vivorecombination efficiencies of the differentattCsite combinations and protein variants and discovered a strong correlation between recombination efficiency and mechanical stability of the synaptic complex, indicating a regulatory mechanism from the DNA sequence to the macromolecular complex stability. Taking into account known forces during DNA metabolism, we suggest that the variation of thein vivorecombination efficiency is mediated strongly by the synaptic complex stability.
Repeatability of gait of children with spastic cerebral palsy in different walking conditions
Authors: Everaert Laure , Dewit Tijl , Huenaerts Catherine , Van Campenhout Anja , Labey Luc , Desloovere Kaat 
Published: April 2024

AbstractObjectivesThree-dimensional gait analysis is the ‘gold standard’ for measurement and description of gait. Gait variability can arise from intrinsic and extrinsic factors and may vary between walking conditions. This study aimed to define the inter-trial and inter-session repeatability in gait analysis data of children with cerebral palsy (CP) who were walking in four conditions, namely barefoot or with ankle-foot orthosis (AFO), and overground or treadmill.DesignTest-retest repeatability study.SettingRehabilitation facility with a human motion analysis laboratory.ParticipantsTen ambulatory children with spastic CP (age=5-15years). In addition, two control datasets (N=56; N=18) of typically developing children were used as a reference.InterventionsNot applicable.Main Outcome MeasuresInter-trial and inter-session variability was measured using intra-class correlations (ICC’s) with accompanying confidence intervals, standard error of measurement (SEM), as well as the SEM expressed as percentage (%SEM) of the total joint-range-of-motion of typically developed children.ResultsOverall we found good to excellent ICC-values and favourable SEM-values for the inter-session Gait Profile Score (ICC=0.85-0.98, SEM=0.45°-0.91°) and Gait Variable Scores (ICC=0.85-0.99, SEM=0.22-1.11°) for the lower-limb joints. The %SEM was the highest for the ankle joint (%SEM=0.8%–3.0%). For the continuous waveform data, only in the ankle joint, differences were observed. Namely, smaller SEM-values for the AFO-condition (mean inter-trial=0.14°; mean inter-session=1.121°) in comparison to the barefoot-condition (mean inter-trial= 0.55°; mean inter-session=2.22°). For all the kinetic parameters, the treadmill conditions showed smaller SEM-values in comparison to the overground condition.ConclusionsAll conditions proved to be repeatable, showing good to excellent ICC-values. The ankle kinematics were more repeatable when the participants were walking with their AFOs in comparison to barefoot walking. Taking the total joint-range-of-motion into account, the knee joint showed the most repeatable motion, while ankle motions showed the lowest repeatability. For kinetics, treadmill conditions showed better repeatability than the overground conditions.
Temporo-spatial cellular atlas of the regenerating alveolar niche in idiopathic pulmonary fibrosis
Authors: Weeratunga Praveen , Hunter Bethany , Sergeant Martin , Bull Joshua , Clelland Colin , Denney Laura , Vuppusetty Chaitanya , Burgoyne Rachel , Woo Jeongmin , Hu Tian , Borthwick Lee , Shaw James , Antanaciuvete Agne , Filby Andrew , Byrne Helen , Fisher Andrew , Ho Ling-Pei 
Published: April 2024

AbstractHealthy repair of the alveoli requires alveolar stem cells to differentiate into cells designed for gas exchange. In chronic lung fibrotic disease like idiopathic pulmonary fibrosis (IPF), alveolar epithelial cells regenerate abnormally. The cause of this is unknown but its highly cellular, inflamed and structurally altered regenerating niche is likely to be relevant. Here, in unique sets of human lung tissues capturing advancing fibrosis, and with a 33-plex single cell imaging mass cytometry (IMC), we provide a high resolution and comprehensive temporo-spatial cell atlas of the regenerating alveolar niches. Using a suite of mathematical tools, we expose an organized immune network and identify CD206hialveolar macrophages as a central immune cell in the immune-alveolar epithelial interactome. A spatially-directed receptor-ligand analysis offers an in-silico mechanism by which these macrophages influenced alveolar regeneration. Our study unravels a complex cellular environment and identifies key interactions that influence alveolar regeneration in a fibrotic lung.
Simulated Misuse of Large Language Models and Clinical Credit Systems
Authors: Anibal James , Huth Hannah , Gunkel Jasmine , Gregurick Susan , Wood Bradford 
Published: April 2024

AbstractLarge language models (LLMs) have been proposed to support many healthcare tasks, including disease diagnostics and treatment personalization. While AI may be applied to assist or enhance the delivery of healthcare, there is also a risk of misuse. LLMs could be used to allocate resources via unfair, unjust, or inaccurate criteria. For example, a social credit system uses big data to assess “trustworthiness” in society, penalizing those who score poorly based on evaluation metrics defined only by a power structure (e.g., a corporate entity or governing body). Such a system may be amplified by powerful LLMs which can evaluate individuals based on multimodal data - financial transactions, internet activity, and other behavioral inputs. Healthcare data is perhaps the most sensitive information which can be collected and could potentially be used to violate civil liberty or other rights via a “clinical credit system”, which may include limiting access to care. The results of this study show that LLMs may be biased in favor of collective or systemic benefit over protecting individual rights, potentially enabling this type of future misuse. Moreover, experiments in this report simulate how clinical datasets might be exploited with current LLMs, demonstrating the urgency of addressing these ethical dangers. Finally, strategies are proposed to mitigate the risk of developing large AI models for healthcare.
Polygenic scores for autism are associated with neurite density in adults and children from the general population
Authors: Gu Yuanjun , Maria-Stauffer Eva , Bedford Saashi A. ,   APEX consortium,   iPSYCH-autism consortium, Romero-Garcia Rafael , Grove Jakob , Børglum Anders D. , Martin Hilary , Baron-Cohen Simon , Bethlehem Richard A.I. , Warrier Varun 
Published: April 2024

AbstractGenetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4,928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.
Effectiveness of the 2023-2024 Omicron XBB.1.5-containing mRNA COVID-19 vaccine (mRNA-1273.815) in preventing COVID-19-related hospitalizations and medical encounters among adults in the United States: An interim analysis
Authors: Kopel Hagit , Araujo Andre B. , Bogdanov Alina , Zeng Ni , Winer Isabelle , Winer-Jones Jessamine , Lu Tianyi , Marks Morgan A. , Bonafede Mac , Nguyen Van Hung , Martin David , Mansi James A. 
Published: April 2024

ABSTRACTBackground/ObjectivesCOVID-19 continues to pose a significant burden that impacts public health and the healthcare system as the SARS-CoV-2 virus continues to evolve. Regularly updated vaccines are anticipated to boost waning immunity and provide protection against circulating variants. This study evaluated vaccine effectiveness (VE) of mRNA-1273.815, a 2023-2024 Omicron XBB.1.5-containing mRNA COVID-19 vaccine, at preventing COVID-19-related hospitalizations and any medically attended COVID-19 in adults ≥18 years, overall, and by age and underlying medical conditions.MethodsThis retrospective cohort study used the Veradigm Network EHR linked to claims data to identify US adults ≥18 years of age who received the mRNA-1273.815 vaccine (exposed) matched 1:1 to individuals who did not receive a 2023-2024 updated COVID-19 vaccine (unexposed). Patients in the unexposed cohort were randomly matched to eligible mRNA-1273.815 recipients. Inverse probability of treatment weighting was used to adjust for differences between the two cohorts. The exposed cohort was vaccinated between September 12, 2023, and December 15, 2023, and individuals in both cohorts were followed up for COVID-19-related hospitalizations and medically attended COVID-19 until December 31, 2023. A Cox regression model was used to estimate the hazard ratio (HR). VE of the mRNA-1273.815 vaccine in preventing COVID-19-related hospitalizations and any medically attended COVID-19 was estimated as 100*(1-HR). Subgroup analyses were performed for adults ≥50, adults ≥65, and individuals with underlying medical conditions associated with severe COVID-19 outcomes.ResultsOverall, 859,335 matched pairs of mRNA-1273.815 recipients and unexposed adults were identified. The mean age was 63 years, and 80% of the study population was ≥50 years old. 61.5% of the mRNA-1273.815 cohort and 66.4% of the unexposed cohort had an underlying medical condition. Among the overall adult population (≥18 years), VE was 60.2% (53.4–66.0%) against COVID-19-related hospitalization and 33.1% (30.2%–35.9%) against medically attended COVID-19 over a median follow-up of 63 (IQR: 44–78) days. VE estimates by age and underlying medical conditions were similar.ConclusionsThese results demonstrate the significant protection provided by mRNA-1273.815 against COVID-19-related hospitalizations and any medically attended COVID-19 in adults 18 years and older, regardless of their vaccination history, and support CDC recommendations for vaccination with the 2023-2024 Omicron XBB.1.5-containing COVID-19 vaccine to prevent COVID-19-related outcomes, including hospitalizations.
Utilizing Large Language Models for Enhanced Clinical Trial Matching: A Study on Automation in Patient Screening
Authors: Beattie Jacob , Neufeld Sarah , Yang Daniel , Chukwuma Christian , Gul Ahmed , Desai Neil , Jiang Steve , Dohopolski Michael 
Published: April 2024

AbstractBackgroundClinical trial matching, essential for advancing medical research, involves detailed screening of potential participants to ensure alignment with specific trial requirements. Research staff face challenges due to the high volume of eligible patients and the complexity of varying eligibility criteria. The traditional manual process, both time-consuming and error-prone, often leads to missed opportunities. Utilizing Artificial Intelligence (AI) and Natural Language Processing (NLP) can significantly enhance the accuracy and efficiency of this process through automated patient screening against established criteria.MethodsUtilizing data from the National NLP Clinical Challenges (n2c2) 2018 Challenge, we utilized 202 longitudinal patient records. These records were annotated by medical professionals and evaluated against 13 selection criteria encompassing various health assessments. Our approach involved embedding medical documents into a vector database to determine relevant document sections, then using a large language model (GPT-3.5 Turbo and GPT-4 OpenAI API) in tandem with structured and chain-of-thought prompting techniques for systematic document assessment against the criteria. Misclassified criteria were also examined to identify classification challenges.ResultsThis study achieved an accuracy of 0.81, sensitivity of 0.80, specificity of 0.82, and a micro F1 score of 0.79 using GPT-3.5 Turbo, and an accuracy of 0.87, sensitivity of 0.85, specificity of 0.89, and micro F1 score of 0.86 using GPT-4 Turbo. Notably, some criteria in the ground truth appeared mislabeled, an issue we couldn’t explore further due to insufficient label generation guidelines on the website.ConclusionOur findings underscore the significant potential of AI and NLP technologies, including large language models, in the clinical trial matching process. The study demonstrated strong capabilities in identifying eligible patients and minimizing false inclusions. Such automated systems promise to greatly alleviate the workload of research staff and improve clinical trial enrollment, thus accelerating the process and enhancing the overall feasibility of clinical research.
Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis
Authors: Li Frank , Zhang Xuan , Comellas Alejandro P. , Hoffman Eric A. , Graham Michael M. , Lin Ching-Long 
Published: April 2024

ABSTRACTBackgroundThe objective of this study is to understand chronic obstructive pulmonary disease (COPD) phenotypes and their progressions by quantifying heterogeneities of lung ventilation from the single photon emission computed tomography (SPECT) images and establishing associations with the quantitative computed tomography (qCT) imaging-based clusters and variables.MethodsEight COPD patients completed a longitudinal study of three visits with intervals of about a year. CT scans of these subjects at residual volume, functional residual capacity, and total lung capacity were taken for all visits. The functional and structural qCT-based variables were derived, and the subjects were classified into the qCT-based clusters. In addition, the SPECT variables were derived to quantify the heterogeneity of lung ventilation. The correlations between the key qCT-based variables and SPECT-based variables were examined.ResultsThe SPECT-based coefficient of variation (CVTotal), a measure of ventilation heterogeneity, showed strong correlations (|r| ≥ 0.7) with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung on cross-sectional data. As for the two-year changes, the SPECT-based maximum tracer concentration (TCmax), a measure of hot spots, exhibited strong negative correlations with fSAD%Total, Emph%Total, average airway diameter in the left upper lobe, and airflow distribution in the middle and lower lobes.ConclusionSmall airway disease is highly associated with the heterogeneity of ventilation in COPD lungs. TCmaxis a more sensitive functional biomarker for COPD progression than CVTotal. Besides fSAD%Totaland Emph%Total, segmental airways narrowing and imbalanced ventilation between upper and lower lobes may contribute to the development of hot spots over time.
Impact of COVID-19 on diabetes mellitus outcomes and care in sub-Saharan Africa: A scoping review
Authors: Sseguya Wenceslaus , Bahendeka Silver , MacLennan Sara , Mody Nimesh , Guntupalli Aravinda Meera 
Published: April 2024

AbstractBackgroundThe COVID-19 pandemic impacted diabetes mellitus clinical outcomes and chronic care globally. However, little is known about its impact in low-resource settings such as sub-Saharan Africa. Hence, to address this, we systematically conducted a scoping review to explore the COVID-19 impact on diabetes outcomes and care in countries of sub-Saharan Africa.MethodsWe applied our search strategy to PubMed, Web of Science, CINAHL, African Index Medicus, Google Scholar, Cochrane Library, Scopus, Science Direct, ERIC and Embase to obtain relevant articles published from January 2020 to March 2023. Two independent reviewers were involved in the screening of retrieved articles. Data from eligible articles were extracted from quantitative, qualitative and mixed methods studies. Numerical data were summarised using descriptive statistics, while a thematic framework was used to categorise and identify themes for qualitative data.ResultsWe found 42 of the retrieved 360 articles eligible, mainly from South Africa, Ethiopia and Ghana (73.4%). COVID-19 increased the risk of death (OR 1.30 – 9.0, 95% CI), hospitalisation (OR 3.30 – 3.73: 95% CI), and severity (OR: 1.30-4.05, 95% CI) in persons with diabetes mellitus. COVID-19 also increased the risk of developing diabetes mellitus in hospitalised cases. The pandemic, on the other hand, was associated with disruptions in patient self-management routine and diabetes mellitus care service delivery. Three major themes emerged, namely, (i) patient-related health management challenges, (ii) diabetes mellitus care service delivery challenges, and (iii) reorganisation of diabetes mellitus care delivery.ConclusionCOVID-19 increased mortality and morbidity among people living with diabetes mellitus. In addition, the COVID-19 pandemic worsened diabetes mellitus care management. Sub-Saharan African countries should, therefore, institute appropriate policy considerations for persons with diabetes mellitus during widespread emergencies.
Modeling the interplay between disease spread, behaviors, and disease perception with a data-driven approach
Authors: De Gaetano Alessandro , Barrat Alain , Paolotti Daniela 
Published: April 2024

AbstractIndividuals’ perceptions of disease influence their adherence to preventive measures, shaping the dynamics of disease spread. Despite extensive research on the interaction between disease spread, human behaviors, and interventions, few models have incorporated real-world behavioral data on disease perception, limiting their applicability. This study novelly integrates disease perception, represented by perceived severity, as a critical determinant of behavioral change into a data-driven compartmental model to assess its impact on disease spread. Using survey data, we explore scenarios involving a competition between a COVID-19 wave and a vaccination campaign, where individuals’ behaviors vary based on their perceived severity of the disease. Results demonstrate that behavioral heterogeneities influenced by perceived severity affect epidemic dynamics, with high heterogeneity yielding contrasting effects. Longer adherence to protective measures by groups with high perceived severity provides greater protection to vulnerable individuals, while premature relaxation of behaviors by low perceived severity groups facilitates virus spread. Epidemiological curves reveal that differences in behavior among groups can eliminate a second infection peak, resulting in a higher first peak and overall more severe outcomes. The specific modeling approach for how perceived severity modulates behavior parameters does not strongly impact the model’s outcomes. Sensitivity analyses confirm the robustness of our findings, emphasizing the consistent impact of behavioral heterogeneities across various scenarios. Our study underscores the importance of integrating risk perception into infectious disease transmission models and highlights the necessity of extensive data collection to enhance model accuracy and relevance.