bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bibliometric Analysis of Perioperative Organ Injury: Insights into the State of Basic Science Research
Authors: Wang Jinlian , Li Hui , Ju Changqing , Liang Yafen , Eltzschig Holger , Liu Hongfang 
Published: September 2024

AbstractPerioperative organ injury represents a significant clinical challenge, leading to severe and often long-term complications for patients undergoing surgery. Despite its critical importance, the basic science underlying these injuries remains underexplored. This study employs bibliometric analysis to assess the research landscape of perioperative organ injury, focusing on key conditions such as myocardial infarction, ischemia-reperfusion injury, heart failure, acute respiratory distress syndrome (ARDS), perioperative lung injury, liver ischemia-reperfusion injury, ischemic acute kidney injury, and perioperative stroke. By analyzing publication trends, citation patterns, and research themes, this study aims to provide insights into the current state of basic science research in this field, highlighting gaps and opportunities for future investigations.
An Analysis of Transgender Crowdfunding in the Context of State-Led Anti-Transgender Legislation
Authors: Nagpal Meera , Wang Julia , Mahapatra Anjali , Chintapally Neha , Butler Christi 
Published: September 2024

AbstractThis study investigates the impact of anti-transgender legislation on the use of crowdfunding platforms by transgender and gender diverse (TGD) individuals in the United States. Using data from 349 GoFundMe campaigns created between March and June 2022, we analyzed the reasons for seeking funding and their potential correlation with state-level anti-transgender policies. Our findings reveal that 61% of campaigns originated from states with proposed or enacted anti-transgender legislation, with 19.5% coming from states posing significant barriers to healthcare services for TGD individuals. The most common reasons for crowdfunding included housing (44.7%), living expenses (35.5%), and gender-affirming surgeries (28.1%). Notably, campaigns from anti-transgender states showed a significant correlation with explicitly mentioning hostile state environments (Φ = 0.2843, p < 0.0001) and seeking relocation (Φ = 0.2040, p = 0.0001). These results highlight the profound impact of discriminatory legislation on TGD individuals, forcing many to seek alternative financial support for basic needs and essential medical care. This research underscores the urgent need for more inclusive healthcare policies and support systems for the TGD community.
Online46: online cognitive assessments in elderly cohorts - the British 1946 birth cohort case study
Authors: Cai Ziyuan , Giunchiglia Valentina , Street Rebecca , Giovane Martina Del , Lu Kirsty , Popham Maria , Wong Andrew , Murray-Smith Heidi , Richards Marcus , Crutch Sebastian , Hellyer Peter J. , Schott Jonathan M , Hampshire Adam 
Published: September 2024

ABSTRACTINTRODUCTIONOnline assessments are scalable and cost-effective for detecting cognitive changes, especially in elderly cohorts with limited mobility and higher vulnerability to neurological conditions. However, determining the uptake, adherence, and usability of these assessments in older adults, who may have less experience with mobile devices is crucial.METHODS1,776 members (aged 77) of the MRC National Survey of Health and Development (NSHD) were invited to complete 13 online cognitive tasks. Adherence was measured through task compliance, while uptake (consent, attempt, completion) was linked to health and sociodemographic factors. Usability was evaluated through qualitative feedback.RESULTSThis study’s consent (56.9%), attempt (80.5%), and completion (88.8%) rates are comparable to supervised NSHD sub-studies. Significant predictors of uptake included education, sex, handedness, cognitive scores, weight, smoking, alcohol consumption, and disease burden.DISCUSSIONWith key recommendations followed, online cognitive assessments are feasible, with good adherence, and usability in older adults.
Genomic Surveillance for Enhanced Healthcare Outbreak Detection and Control
Authors: Sundermann Alexander J. , Kumar Praveen , Griffith Marissa P. , Waggle Kady D. , Srinivasa Vatsala Rangachar , Raabe Nathan , Mills Emma G. , Coyle Hunter , Ereifej Deena , Creager Hannah M. , Ayres Ashley , Tyne Daria Van , Pless Lora Lee , Snyder Graham M. , Roberts Mark , Harrison Lee H. 
Published: September 2024

ABSTRACTBackgroundCurrent methods are insufficient alone for outbreak detection in hospitals. Real-time genomic surveillance using offers the potential to detect otherwise unidentified outbreaks. We initiated and evaluated the Enhanced Detection System for Healthcare-associated Transmission (EDS-HAT), a real-time genomic surveillance program for outbreak detection and mitigation.MethodsThis study was conducted at UPMC Presbyterian Hospital from November 2021 to October 2023. Whole genome sequencing (WGS) was performed weekly on healthcare-associated clinical bacterial isolates to identify otherwise undetected outbreaks. Interventions were implemented in real-time based on identified transmission. A clinical and economic impact analysis was conducted to estimate infections averted and net cost savings.ResultsThere were 3,921 bacterial isolates from patient healthcare-associated infections that underwent WGS, of which 476 (12.1%) clustered into 172 outbreaks (size range 2-16 patients). Of the outbreak isolates, 292 (61.3%) had an identified epidemiological link. Among the outbreaks with interventions, 95.6% showed no further transmission on the intervened transmission route. The impact analysis estimated that, over the two-year period, 62 infections were averted, with gross cost savings of $1,011,146, and net savings of $695,706, which translates to a 3.2-fold return on investment. Probabilistic sensitivity analysis showed EDS-HAT was cost-saving and more effective in 98% of simulations.ConclusionReal-time genomic surveillance enabled the rapid detection and control of outbreaks in our hospital and resulted in economic benefits and improvement in patient safety. This study demonstrates the feasibility and effectiveness of integrating genomic surveillance into routine infection prevention practice, offering a paradigm shift in healthcare outbreak detection and control.
Molecular Characterization of Pediatric Acute Lymphoblastic Leukemia via Integrative Transcriptomics: A Multicenter Study in Argentina
Authors: Ruiz María Sol , Abbate María Mercedes , Sosa Ezequiel , Avendaño Daniel , Mercado Ignacio Gomez , Lacreu María Laura , Riccheri María Cecilia , Schuttenberg Virginia , Aversa Luis , Vazquez Elba , Gueron Geraldine , Cotignola Javier 
Published: September 2024

AbstractPediatric acute lymphoblastic leukemia (ALL) is a highly heterogeneous disease at the molecular level, with over 20 subtypes of B-cell ALL (B-ALL) identified to date. These subtypes are critical for guiding risk-adapted therapy and precision medicine, as they are defined by specific chromosomal rearrangements, gene expression profiles, aneuploidies and point mutations. However, the ability to accurately classify these subtypes is limited by the technical and economic challenges faced by many centers, particularly in low- and middle-income countries. In this study, we employed transcriptome sequencing to perform molecular classification of B-ALL at diagnosis in pediatric patients enrolled in the multicentric ALLIC-GATLA-2010 clinical protocol in Argentina. Using a combination of bioinformatic tools, we successfully identified single nucleotide variants, fusion transcripts and gene expression profiles, achieving molecular classification in over 90% of patients. Our analysis also revealed high-risk molecular features and novel genetic alterations. These findings hold potential clinical value for improving risk stratification and identifying therapeutic targets, particularly for patients who remain unclassified by conventional diagnostic methods.
RaBIt: An Effective Visualization-Driven Tool for Power and Sample Size Estimation in Two-Stage General Randomized Basket Trial Designs
Authors: Chen Desmond Zeya , Patel Sahil S. , Xie Aoqi , Chen Jiayin , Castle David , Ma Clement 
Published: September 2024

AbstractBasket trial designs with interim analysis have gained significant attention due to their adaptability, flexibility, and scalability. In response to the need for user-friendly tools that enhance the real-world applicability of these designs, we developed a web-based interface aimed at facilitating two-stage basket trial designs. Built using R Shiny, the tool was rigorously validated for output consistency by comparing it to an established R pipeline. Additionally, user testing was conducted to ensure the interface is intuitive and easy to use. The result is a freely accessible tool that provides effective and convenient visualizations for general basket trial designs with interim analysis, available athttps://desmondzeyachen.shinyapps.io/AdaptiveTwoStageBasketTrialFeb14/. Future improvements may further expand the tool’s capabilities to accommodate the increasing complexity of trial designs needed by the research community.
AI-VOICE: A Method to Measure and Incorporate Patient Utilities Into AI-Informed Healthcare Workflows
Authors: Morse Keith E. , Higgins Michael C. , Qian Yichun , Callahan Alison , Shah Nigam H. 
Published: September 2024

AbstractBackgroundPatients are important participants in their medical care, yet artificial intelligence (AI) models are used to guide care with minimal patient input. This limitation is made partially worse due to a paucity of rigorous methods to measure and incorporate patient values of the tradeoffs inherent in AI applications.This paper presents AI-VOICE (Values-Oriented Implementation and Context Evaluation), a novel method to collect patient values, or utilities, of the downstream consequences stemming from an AI model’s use to guide care. The results are then used to select the model’s risk threshold, offering a mechanism by which an algorithm can concretely reflect patient values.MethodsThe entity being evaluated by AI-VOICE is anAI-informed workflow, which is composed of the patient’s health state, an action triggered by the AI model, and the benefits and harms accrued as a consequence of that action. The utilities of these workflows are measured through a survey-based, standard gamble experiment. These utilities define a patient-specific ratio of the cost of an inaccurate prediction versus the benefits of an accurate one. This ratio is mapped to the receiver-operator-characteristic curve to identify the risk threshold that reflects the patient’s values.The survey instrument is made freely available to researchers through a web-based application.ResultsA demonstration of AI-VOICE is provided using a hypothetical sepsis prediction algorithm.ConclusionAI-VOICE offers an accessible, quantitative method to incorporate patient values into AI-informed healthcare workflows.
Retrieval-Augmented Generation for Extracting CHA₂DS₂-VASc Risk Factors from Unstructured Clinical Notes in Patients with Atrial Fibrillation
Authors: Adejumo Philip , Thangaraj Phyllis , Shankar Sumukh Vasisht , Dhingra Lovedeep Singh , Aminorroaya Arya , Khera Rohan 
Published: September 2024

AbstractBackgroundAssessment of stroke risk in patients with atrial fibrillation (AF) is crucial for guiding anticoagulation therapy. CHA₂DS₂-VASc is a widely used score for defining this risk, but current assessments rely on manual calculation by clinicians or approximations from structured EHR data elements. Unstructured clinical notes contain rich information that could enhance risk assessment. We developed and validated a Retrieval-Augmented Generation (RAG) approach to extract CHA₂DS₂-VASc risk factors from unstructured notes in patients with AF.MethodsWe employed a RAG architecture paired with the large language model, Llama3.1, to extract features relevant to CHA₂DS₂-VASc scores from unstructured notes. The model was deployed on a random set of 1,000 clinical notes (934 AF patients) from Yale New Haven Health System (YNHHS). To establish a gold standard, 2 clinicians manually reviewed and labeled CHA₂DS₂-VASc risk factors in a random subset of 200 notes. The CHA₂DS₂-VASc scores were calculated for each patient using structured data alone and by incorporating risk factors identified with RAG. We assessed performance across risk factors using macro-averaged area under the receiver operating characteristic (AUROC). For external validation, we utilized 100 manually labeled clinical notes from the MIMIC-IV database.ResultsThe RAG model demonstrated robust performance in extracting risk factors from clinical notes. In the 1000 clinical notes, RAG identified several risk factors more frequently than structured elements, including hypertension (82.4% vs 26.2%), stroke/TIA (62.9% vs 45.5%), vascular disease (83.4% vs 56.6%), and diabetes (84.1% vs 47.2%). In the 200 expert-annotated notes, the RAG approach achieved high performance for various risk factors, with AUROCs ranging from 0.96 to 0.98 for hypertension, diabetes, and age ≥75 years. Incorporating risk factors identified by RAG increased CHA₂DS₂-VASc scores compared with using structured data alone.ConclusionAn LLM-optimized RAG can accurately extract CHA₂DS₂-VASc risk factors from unstructured clinical notes in AF patients. This approach can enable computable risk assessment and guide appropriate anticoagulation therapy.
LD-informed deep learning for Alzheimer’s gene loci detection using WGS data
Authors: Jo Taeho , Bice Paula , Nho Kwangsik , Saykin Andrew J. ,   the Alzheimer’s Disease Sequencing Project
Published: September 2024

AbstractINTRODUCTIONThe exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large-scale high throughput sequencing data. This study presents Deep-Block, a multi-stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer’s disease (AD). The framework employs a three-stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.METHODSThe Deep-Block was applied to a large-scale whole genome sequencing (WGS) dataset from the Alzheimer’s Disease Sequencing Project (ADSP), comprising 7,416 non-Hispanic white participants (3,150 cognitively normal older adults (CN), 4,266 AD).RESULTS30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer’s disease. Subsequently, the Deep-Block identified novel SNPs within the top 1,500 LD blocks and confirmed previously known variants, includingAPOErs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross-validated against established AD-associated loci from the European Alzheimer’s and Dementia Biobank (EADB) and the GWAS catalog.DISCUSSIONThe Deep-Block framework effectively processes large-scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework’s findings are supported by tissue-specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep-Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large-scale sequencing studies.
Analysis of facial expressions recorded from patients during psychiatric interviews
Authors: Mineur L. , Heide M. , Eickhoff S. , Avram M. , Franzen L. , Buschmann F. , Schröpfer F. , Rogg H.V. , Andreou C. , Brügge N.S. , Handels H. , Borgwardt S. , Korda A. 
Published: September 2024

AbstractMental health research increasingly focuses on the relationship between psychiatric symptoms and observable manifestations of the face and body1. In recent studies2,3, psychiatric patients have shown distinct patterns in movement, posture and facial expressions, suggesting these elements could enhance clinical diagnostics.The analysis of the facial expressions is grounded on the Facial Action Coding System (FACS)4. FACS provides a systematic method for categorizing facial expressions based on specific muscle movements, enabling detailed analysis of emotional and communicative behaviors. This method combined with recent advancements in Artificial Intelligence (AI) has shown promising results for the detection of the patient mental state.We analyze video data from patients with various psychiatric symptoms, using open-source Python toolboxes for facial expression and body movement analysis. These toolboxes facilitate face detection, facial landmark detection, emotion detection and motion recognition. Specifically, we aim to explore the connection between these physical expressions and established diagnostic tools, like symptom severity scores, and finally enhance psychiatric diagnostics by integrating AI-driven analysis of video data.By providing a more objective and detailed understanding of psychiatric symptoms, this study could lead to earlier detection and more personalized treatment approaches, ultimately improving patient outcomes. The findings will contribute to the development of innovative diagnostic tools that are both efficient and accurate, addressing a critical need in mental health care.
Returning Individual-Level Urgent or Emergent Research Results to Participants: The Project Baseline Health Study Experience
Authors: Pagidipati Neha , Heidenfelder Brooke , Kwee Lydia Coulter , Rodriguez Fatima , Chatterjee Ranee , Parikh Kishan S. , Khouri Michel G. , Stiller Jennifer , Eckstrand Julie , Marcom P. Kelly , Mettu Priyatham S. , Jaffe Glenn J. , Shashidhar Sumana , Swope Susan , Spielman Susan , Fraulo Elizabeth , Newby L. Kristin , Douglas Pamela , Wong Charlene , Green Robert , Shore Scarlet , Mega Jessica , Hernandez Adrian , Campbell Paul , Mahaffey Kenneth W. , Shah Svati H. 
Published: September 2024

ABSTRACTBackgroundReturning results to research participants is increasingly recognized as an ethical mandate, yet little is known about best practices to optimally communicate urgent or emergent results.MethodsThe Project Baseline Health Study (PBHS) was a prospective observational cohort study of 2,502 participants enrolled from 2017-2019 and followed through 2023. The cohort represented a broad spectrum of health and disease, enriched to include participants with an elevated risk for breast or ovarian cancer, lung cancer, and/or atherosclerotic cardiovascular disease (60% of participants). Urgent or emergent results were returned during or after the baseline visit from vital signs; clinical laboratory testing; and ocular, cardiovascular, and pulmonary imaging.ResultsAmong 2,002 participants in this analysis, 39.7% had at least one urgent or emergent finding returned, representing a total of 1,159 results returned over 3 years. The most commonly returned results were eye findings (n=246), pulmonary nodules (n=159), abnormal stress echocardiograms (n=123), abnormal rest electrocardiograms (bradycardia) (n=74), and lung parenchyma findings (n=55). Participants with urgent or emergent incidental findings were older (mean [SD] 58.0 [16.2] years vs. 48.0 [16.6] years) with a greater burden of cardiovascular, metabolic, or cancer comorbidities than those without urgent or emergent incidental findings.ConclusionsThis report from the PBHS study is one of the first to describe a process to systematically return urgent or emergent results to research participants. This process led to the successful return of clinically important results to participants but also required significant time and effort from study clinicians and staff.ClinicalTrials.govIdentifier:NCT03154346
Tissue of Origin Characterization of Cell Free DNA in Seminal Plasma: Implications for New Liquid Biopsies
Authors: Huang Stephanie , Hart James C. , Smith James F , Bench Shellie , Yepes Laura Rivas , Griscom Bailey , Clark-Langone Kim M. 
Published: September 2024

AbstractLiquid biopsies are becoming increasingly used for the detection and monitoring of disease states. While cell free DNA (cfDNA) in blood and urine have been well studied, much less is known about the composition of cfDNA in seminal fluid. We sought to characterize cfDNA in seminal fluid through tissue of origin studies using methylation analysis in men aged 21-60 yrs. We confirmed the observations of others that seminal fluid contains an abundance of cfDNA that is both nucleosomal and >1 kb. However, here we demonstrate for the first time that the high molecular weight (HMW) DNA harbors a lower sperm signal and higher somatic cell signal compared to the nucleosomal fraction. Prostate, granulocytes and kidney showed a mean predicted increased contribution of 6.2%, 4.9% and 2.9%, respectively in the HMW fraction. While sperm was the predominant signal in most men without vasectomies, the proportion of predicted prostate contribution reached as high as 26.5% in the HMW fraction. In other subjects without vasectomies, granulocyte cfDNA made up most of the signal. We also observed subject-specific cfDNA size distribution patterns that were reproducible over time, irrespective of abstinence times. These results suggest that seminal fluid is a rich source of cfDNA from various somatic cell types, and enriching for the HMW fraction would yield even higher sensitivity for somatic cfDNA detection. Considering these novel findings, it appears that seminal fluid may be able to serve as liquid biopsy for the detection and monitoring of prostate cancer, benign prostate hyperplasia, prostatitis and infertility.
Transformers in Skin Lesion Classification and Diagnosis: A Systematic Review
Authors: Adebiyi Abdulmateen , Abdalnabi Nader , Simoes Eduardo J. , Becevic Mirna , Smith Emily Hoffman , Rao Praveen 
Published: September 2024

AbstractSkin lesion classification is a critical task in dermatology, aiding in the early diagnosis and treatment of skin cancer. In recent years, transformer-based models, originally developed for Natural Language Processing (NLP) tasks, have shown promising results in many classification tasks specifically the image classification domains. This systematic review aims to provide a comprehensive overview of the current state of research on the application of transformers in skin lesion classification. Over the period 2017-2023, this systematic review investigated the application of transformer-based models in skin lesion classification, focusing on 57 articles retrieved from prominent databases which are PubMed, Scopus, and Medline. The inclusion criteria encompass studies centering on transformer-based models for skin lesion classification, utilization of diverse datasets (dermoscopic images, clinical images, or histopathological images), publication in peer-reviewed journals or conferences, and availability in English. Conversely, exclusion criteria filter out studies not directly related to skin lesion classification, research applying algorithms other than transformer-based models, non-academic articles lacking empirical data, papers without full-text access, and those not in English. Our findings underscore the adaptability of transformers to diverse skin lesion datasets, the utilization of pre-trained models, and the integration of various mechanisms to enhance feature extraction.