bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genome-wide scan of Flortaucipir PET levels findsJARID2associated with cerebral tau deposition
Authors: Gunasekaran Tamil Iniyan , Meena Devendra , Lee Annie J , Wu Siwei , Dumitrescu Logan , Sperling Reisa , Hohman Timothy J ,   Alzheimer’s Disease Neuroimaging Initiative and the A4 Study, Huang Jingxian , Dehghan Abbas , Tzoulaki Ioanna , Mayeux Richard , Vardarajan Badri 
Published: October 2024

AbstractBACKGROUNDGenetic research on Alzheimer’s disease (AD) has primarily focused on amyloid-β (Aβ) pathogenesis, with fewer studies exploring tau pathology. Elucidating the genetic basis of tau pathology could identify novel pathways in AD.METHODSWe conducted a genome-wide association study of tau standard uptake value ratios (SUVRs) from[18]F-flortaucipir positron emission tomography (PET) images to identify genetic variants underlying Tau pathology. Genetic data and tau-SUVRs from[18]F-flortaucipir PET images were acquired from the A4 (311 with preclinical AD) and ADNI (280 cognitively normal, 76 with mild cognitive impairment, and 19 AD patients) studies. Circulating plasma proteins in UK Biobank Pharma Proteomics Project (UKBPPP, N=54,129) were used to validate genetic findings. SNP genotypes were tested for association with Tau-SUVR levels adjusting for age, sex and population substructure variables. AD association of polygenic risk scores (PRS) of tau and amyloid-SUVRs were assessed. Causal effect of plasma protein levels on Tau pathology were tested using Mendelian randomization analyses.RESULTSGWAS of tau-SUVR revealed two significant loci: rs78636169 (P=5.76×10-10) inJARID2and rs7292124 (P=2.20×10-8) nearISX. Gene-based analysis of tau deposition highlightedAPOE(P=2.55×10-6),CTNNA3(P=2.86×10-6) andJARID2(P=1.23×10-4), a component of thePRC2multi-protein complex which regulates gene expression. Mendelian randomization analysis of available circulating plasma proteins in the UK Biobank Pharma Proteomics Project (UKBPPP) identified LRRFIP1, a protein that binds withPRC2multi-protein complex, as potentially causally linked to tau pathology. Genes associated with both amyloid and tau pathologies were enriched in endocytosis and signal transduction pathways. AD polygenic risk score (PRS) was associated with amyloid-SUVR but not with tau-SUVR. Amyloid-SUVR PRS had a notable association with AD clinical status, particularly in youngerAPOE-ε4 carriers, whereas tau-SUVR PRS showed a stronger association in older carriers.CONCLUSIONWe identified a novel potential therapeutic target,JARID2in thePRC2multi-protein complex, for tau pathology. Furthermore, gene pathway analysis clarified the distinct roles of Aβ and tau in AD progression, underscoring the complexity of genetic influences across different stages of the disease.
Comparison of methods for identifying the optimal treatment duration in trials for antibiotics
Authors: Dufault Suzanne M. , Aldana Brian H. , Phillips Patrick P.J. 
Published: October 2024

ABSTRACTBackgroundThe optimal duration of antibiotic treatmentmust strike a delicate balance: it must be long enough to achieve desirable efficacy yet short enough to prevent the development of toxicities, adverse events, and mitigate other arduous aspects related to patient burden. Historically, the approach used to determine duration of antibiotic treatment has been inefficient, severely impacting the refinement of therapeutics for tuberculosis where treatment duration, and its complications, can be extensive. Meeting the WHO’s End TB target of developing shorter, more tolerable TB therapeutics will require rigorously re-evaluating best practices in duration-ranging trial designs and statistical methodologies. Many of the challenges in duration-ranging have parallels and proposed solutions in the field of dose-ranging where the literature is substantially more established and where the traditions of qualitative, pairwise comparison studies have been replaced with model-based approaches. Such methods are more efficient and allow for interpolation between the doses observed. Research on efficient study designs and methods for duration-ranging, while similarly attempting to capture a monotonic response relationship, has only just accelerated in earnest over the last two decades.MethodsThis work examines the utility of cutting-edge dose-finding methods (such as MCP-Mod) for duration-ranging of TB treatments. We compare the operating characteristics of the adapted model-based duration-ranging methodologies against standard qualitative methods for the purposes of estimating optimal duration and describing the duration-response relationship, using a simulation study motivated by a Multi-Arm Multi-Stage Response Over Continuous Intervention (MAMS-ROCI) clinical trial design. We explore three specific targets: 1) power to detect a duration-response relationship, 2) ability to accurately reproduce the duration-response curve, and 3) ability to estimate the optimal duration within an acceptable margin of error.ResultsWe find that model-based methods outperform standard qualitative comparisons on every target examined, particularly when the sample size is constrained to that of a typical Phase II trial.ConclusionsWe conclude that the success of the next era in TB therapeutics duration evaluation trials, and antibiotics duration-ranging more broadly, will meaningfully rely on the ability to simultaneous pair innovative model-based statistical methods with re-imagined study designs such as MAMS-ROCI.
Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for ‘Complete Data’
Authors: Acitores Cortina Jose M. , Fatapour Yasaman , Zietz Michael , Brown Kathleen LaRow , Gisladottir Undina , Peter Danner , Bear Don’t Walk Oliver John , Kuchi Aditi , Srinivasan Apoorva , Liu Hongyu , Berkowitz Jacob , Tsang Kevin , Friedrich Nadine , Kievelson Sophia , Tatonetti Nicholas P. 
Published: October 2024

ABSTRACTObjectiveIntegrated clinical databases from national biobanks have advanced the capacity for disease research. Data quality and completeness filters are used when building clinical cohorts to address limitations of data missingness. However, these filters may unintentionally introduce systemic biases when they are correlated with race and ethnicity. In this study, we examined the race/ethnicity biases introduced by applying common filters to four clinical records databases.Materials and MethodsWe used 19 filters commonly used in electronic health records research on the availability of demographics, medication records, visit details, observation periods, and other data types. We evaluated the effect of applying these filters on self-reported race and ethnicity. This assessment was performed across four databases comprising approximately 12 million patients.ResultsApplying the observation period filter led to a substantial reduction in data availability across all races and ethnicities in all four datasets. However, among those examined, the availability of data in the white group remained consistently higher compared to other racial groups after applying each filter. Conversely, the Black/African American group was the most impacted by each filter on these three datasets, Cedars-Sinai dataset, UK-Biobank, and Columbia University Dataset.Discussion and ConclusionOur findings underscore the importance of using only necessary filters as they might disproportionally affect data availability of minoritized racial and ethnic populations. Researchers must consider these unintentional biases when performing data-driven research and explore techniques to minimize the impact of these filters, such as probabilistic methods or the use of machine learning and artificial intelligence.
The Development and Validation of the Global Post Trauma Symptom Scale-Uganda among Trauma-Affected Adults
Authors: Murphy Michalopoulos Lynn , Meinhart Melissa , Walton Erin , Robertson David , Thompson Autumn , Northrup Thomas , Kim Jong Sung , Conway Anne , Aggarwal Nikita 
Published: October 2024

AbstractThe purpose of this study was to adapt and validate the Global Post-Traumatic Stress Scale (GPTSS) among adult caregivers of youth living with HIV in Uganda. This is the first study to adapt and validate a non-western instrument measuring post-traumatic symptoms in Uganda, which is critical in the accurate assessment of caregiver trauma on psychosocial functioning. The study utilized qualitative (i.e., cognitive interviews) and quantitative (e.g., classical test and item response theory) methods to establish content, criterion, and construct validity. The results indicated that the GPTSS was a valid and reliable assessment tool for the population. Specifically, cognitive interview results (n=31) indicated that participants broadly understood items of the GPTSS, but revisions in wording were needed to capture the intended meaning of some items. A reduced version of the GPTSS, GPTSS-U, was created through item response theory analyses based on discrimination criteria and difficulty parameters. A positive relationship between higher GPTSS-U and the PHQ-9 scores indicated acceptable criterion validity with a moderate effect size (r = 0.547;p< 0.000). Results from the graded response model indicated high discrimination parameters (rangeb= -.88, 3.14). Test information function curve findings indicated that the GPTSS is most precise at moderate to severe levels of post-traumatic symptoms. Using nested linear regression models, we found incremental validity as the total scores on the GPTSS-U significantly predicted functional impairment (p< 0.05) beyond the impact of the Post Traumatic Stress Checklist-5. Overall findings indicate that post-traumatic symptom measures must be adapted and developed for cross-cultural use to maintain validity and contextual relevance.
No maternal health without mental health: suggested indicators to monitor perinatal mental health globally
Authors: Palestra Francesca , Moran Allisyn C. , Chowdhary Neerja , Salisbury Tatiana Taylor , Dua Tarun , McNab Shanon , Layton Elly , Kennedy Elissa C , Homer Caroline SE , Fisher Jane , Honickman Simone , Hill Zelee 
Published: October 2024

AbstractPerinatal mental health refers to the psychological wellbeing of women during pregnancy and up to one year postpartum. Perinatal mental health conditions as depression significantly affect maternal, newborn health, and child development worldwide. However, the absence of standardized indicators limits effective monitoring and evaluation. This paper introduces a framework with indicators for global perinatal mental health monitoring.The framework development involved a scoping review, expert consultations, and stakeholder surveys. A global expert group, guided by evidence and the World Health Organization (WHO), identified an initial set of indicators. Two rounds of online surveys were conducted, allowing stakeholders to rank these indicators and suggest additional ones. Indicators were chosen based on their validity, reliability, relevance, feasibility, and potential impact for advocacy purposes on perinatal mental health. The WHO expert working group finalized the set of indicators.Six perinatal mental health indicators were identified for future testing. These include three primary indicators: policy presence, screening coverage, and prevalence of perinatal mental health conditions. Three secondary indicators cover mental health expenditure, healthcare professional training, and provision of care.Challenges to implementing standardized monitoring include resource limitations and data collection methods. Strengthening health workers and facility capacity to measure, report, and interpret perinatal mental health data, including the use of screening tools, is crucial. The integration of these indicators into existing systems, such as health information management systems (HIMS) and national surveys, will be key.Monitoring perinatal mental health is essential for improving maternal and newborn outcomes. The proposed framework offers a means to enhance global monitoring and guide policy interventions. Global stakeholders are encouraged to integrate perinatal mental health into their policy, program, and monitoring agendas.
Unveiling the Origins and Genetic Makeup of the ‘Forgotten People’: A Study of the Sarmatian-Period Population in the Carpathian Basin
Authors: Schütz Oszkár , Maróti Zoltán , Tihanyi Balázs , Kiss Attila P. , Nyerki Emil , Gînguță Alexandra , Kiss Petra , Varga Gergely I. B. , Kovács Bence , Maár Kitti , Kovacsóczy Bernadett Ny. , Lukács Nikoletta , Major István , Marcsik Antónia , Patyi Eszter , Szigeti Anna , Tóth Zoltán , Walter Dorottya , Wilhelm Gábor , Andrási Réka Cs. , Bernert Zsolt , Kis Luca , Oța Liana , Pálfi György , Pintye Gábor , Pópity Dániel , Simalcsik Angela , Soficaru Andrei Dorian , Spekker Olga , Varga Sándor , Neparáczki Endre , Török Tibor 
Published: October 2024

SummaryThe nomadic Sarmatians dominated the Pontic Steppe from 3rd century BCE and the Great Hungarian Plain from 50 CE until the Huns’ 4th-century expansion. In this study, we present the first large-scale genetic analysis of 156 genomes from 1st- to 5th-century Hungary and the Carpathian foothills. Our findings reveal minor East Asian ancestry in the Carpathian Basin (CB) Sarmatians, distinguishing them from other regional populations. Using F4-statistics, qpAdm, and IBD analysis, we show that CB Sarmatians descended from Steppe Sarmatians originating in the Ural and Kazakhstan regions, with Romanian Sarmatians serving as a genetic bridge between the two groups. We also identify two previously unknown migration waves during the Sarmatian era and a notable continuity of the Sarmatian population into the Hunnic period, despite a smaller influx of Asian-origin individuals. These results shed new light on Sarmatian migrations and the genetic history of a key population neighbouring the Roman Empire.
Insights into Heart Failure Metabolite Markers through Explainable Machine Learning
Authors: Baron Cantin , Mehanna Pamela , Daneault Caroline , Hausermann Leslie , Busseuil David , Tardif Jean-Claude , Dupuis Jocelyn , Des Rosiers Christine , Ruiz Matthieu , Hussin Julie 
Published: October 2024

AbstractUnderstanding molecular traits through metabolomics offers an avenue to tailor cardiovascular prevention, diagnosis and treatment strategies more effectively. This study focuses on the application of machine learning (ML) and explainable artificial intelligence (XAI) algorithms to detect discriminant molecular signatures in heart failure (HF). In this study, we aim to uncover metabolites with significant predictive value by analyzing targeted metabolomics data through ML models and XAI methodologies. After robust quality control procedures, we analyzed 55 metabolites from 124 plasma samples, including 53 HF patients and 71 controls, comparing Logistic Regression (Logit) models with Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGB), all achieving high accuracy in predicting group labels: 84.20% (σ=5.46), 85.73% (σ=6.25), and 84.80% (σ=7.84), respectively. Permutation-based variable importance and Local Interpretable Model-agnostic Explanations (LIME) were used for group-level and individual-level explainability, respectively, complemented by H-Friedman statistics for variable interactions, yielding reliable, explainable insights of the ML models. Metabolites well-known for their association with heart failure, such as glucose and cholesterol, but also more recently described association such C18:1 carnitine, were reaffirmed in our analysis. The novel discovery of lignoceric acid (C24:0 fatty acid) as a critical discriminator, was confirmed in a replication cohort, underscoring its potential as a metabolite marker. Furthermore, our study highlights the utility of 2-way variable interaction analysis in unveiling a network of metabolite interactions essential for accurate disease prediction. The results demonstrate our approach’s efficacy in identifying key metabolites and their interactions, illustrating the power of ML and XAI in advancing personalized healthcare solutions.SeeGraphical Abstract
Coupled Solvent Dynamics and Protein Dynamics Help Drive Functional Differences in Exon-19 Deletion Mutants in the Epidermal Growth Factor Receptor (EGFR/ErbB1/HER1) Kinase Domain
Authors: Patil Keshav , Ashtekar Kumar D. , Tsutsui Yuko , Dhabal Debdas , Suresh Krishna , Lemmon Mark A. , Radhakrishnan Ravi 
Published: October 2024

AbstractDeletions in Exon-19 of the epidermal growth factor receptor (EGFR) play a pivotal role in the pathogenesis of non-small cell lung cancer (NSCLC), influencing patient response to tyrosine kinase inhibitors (TKIs). Although these mutations are known to affect treatment efficacy, the precise molecular mechanisms have been unclear. Building upon recent insights from the study [DOI: 10.1038/s41467-022-34398-z], which identified two distinct mutation profiles associated with differential drug sensitivity and clinical outcomes, our research delves into the molecular dynamics that drive these variances.We employed molecular dynamics simulations, enhanced sampling methods, and machine learning to classify Exon-19 deletion mutations into two profiles based on their conformational dynamics. Profile 1 mutations display only localized motions in key subdomains in their fluctuations about the equilibrium state, and a high affinity for ATP and consequent resistance to TKIs, while profile 2 mutations show reduced ATP binding affinity due to delocalized motion characterized by an increased flexibility between the N– and C-lobes of the EGFR kinase domain. This structural flexibility perturbs the ATP binding site, leading to decreased affinity and, heightened sensitivity to TKIs.Our use of the INDUS technique has shed light on the collective solvent dynamics, further elucidating the coupling between long timescale solvent fluctuations and protein conformational dynamics, that likely contributes to the observations in HDX-MS studies. Our free energy analysis, covering timescales relevant to both HDX-MS and ligand interaction, provides a deeper understanding of the relationship between protein and solvent dynamics and their collective impact on drug efficacy in NSCLC with EGFR Exon-19 deletions.
Gene encoding CC-NBS-LRR protein on rye chromosome 1RS confers wheat stripe rust resistance
Authors: Wang Chunhui , Chang Yanan , Wang Mian , Wang Jing , Liu Chang , Fan Chaolan , Yi Congyang , Zhou Chen , Yuan Jing , Yang Wuyun , Liu Dengcai , Wang Tao , Liu Yang , Ye Xingguo , Han Fangpu 
Published: October 2024

AbstractStripe rust, a globally widespread disease, stands as one of the most significant threats to wheat cultivation. The 1BL/1RS translocation, renowned for its robust resistance to both rust and powdery mildew, has historically played an important role in wheat breeding and production. The gene for resistance to stripe rust on the 1RS is known asYr9and plays an important role in the production of wheat, but over the course of long-term breeding had lost its resistance due to the evolution of stripe rust towards greater and greater virulence. In this paper, we cloned the stripe rust resistance gene,Yr9, from triticale by genetic mapping approach. TheYr9encodes a typical nucleotide-binding leucine-rich repeat (NLR) protein. Both transgenic and overexpression ofYr9in highly stripe rust susceptible wheat varieties conferred complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34. In addition, theYr9allele in the 1BL/1RS translocation line also showed the same level of resistance to stripe rust. Both two alleles loses resistance when deployed in the field or inoculated with mixed physiological races collected from the field. Our findings provide valuable insights for breeders to strategically incorporate disease resistance genes and provides a foundation for further understanding how pathogenic bacteria might evolve to evade recognition via NLR type proteins.SignificanceThe 1BL/1RS translocation between wheat and rye is the most successful case of exogenous gene application in plant genetic improvement and has been used in wheat breeding for over 50 years. Here we report the cloning of a stripe rust resistance geneYr9located on rye chromosome 1RS using a triticale population. TheYr9encodes a coiled-coil nucleotide-binding site leucine-rich repeat (CC-NBS-LRR) protein that show complete resistance to the stripe rust races CYR17 and partial resistance to the stripe rust races CYR32, CYR33, and CYR34, albeit demonstrating susceptibility under field conditions. Our findings positionYr9as an ideal candidate gene to study the mechanism of inactivation of disease resistance genes as a result of pathogen evolution.
Microbiome Data Integration via Shared Dictionary Learning
Authors: Yuan Bo , Wang Shulei 
Published: October 2024

AbstractData integration is a powerful tool for facilitating a comprehensive and generalizable understanding of microbial communities and their association with outcomes of interest. However, integrating data sets from different studies remains a challenging problem because of severe batch effects, unobserved confounding variables, and high heterogeneity across data sets. We propose a new data integration method called MetaDICT, which initially estimates the batch effects by weighting methods in causal inference literature and then refines the estimation via a novel shared dictionary learning. Compared with existing methods, MetaDICT can better avoid the overcorrection of batch effects and preserve biological variation when there exist unobserved confounding variables or data sets are highly heterogeneous across studies. Furthermore, MetaDICT can generate comparable embedding at both taxa and sample levels that can be used to unravel the hidden structure of the integrated data and improve the integrative analysis. Applications to synthetic and real microbiome data sets demonstrate the robustness and effectiveness of MetaDICT in integrative analysis. Using MetaDICT, we characterize microbial interaction, identify generalizable microbial signatures, and enhance the accuracy of disease prediction in an integrative analysis of colorectal cancer metagenomics studies.
Reducing Surgeons’ Routine Task Burden, Enhancing Surgical Decision Making, and Ensuring Patient Safety Using SurgeryLLM
Authors: Ong Chin Siang , Obey Nicholas T. , Zheng Yanan , Cohan Arman , Schneider Eric B. 
Published: October 2024

AbstractSurgeryLLM, a large language model framework using Retrieval Augmented Generation demonstrably incorporated domain-specific knowledge from current evidence-based surgical guidelines when presented with patient-specific data. The successful incorporation of guideline-based information represents a substantial step toward enabling greater surgeon efficiency, improving patient safety, and optimizing surgical outcomes.