bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cell-mediated Immunity to Guide Primary Prophylaxis for CMV Infection in Organ Transplant Recipients: A Multicenter Single-arm Prospective Study
Authors: Solera Javier T. , Ferreira Victor H. , Cervera Carlos , Hosseini-Moghaddam Seyed M. , Gill John , Shalhoub Sarah , Zaltzman Jeff , Kumar Deepali , Humar Atul 
Published: August 2024

Background.
There are few interventional studies using CMV cell-mediated immunity (CMI) to guide antiviral prophylaxis. We assessed the Quantiferon-CMV (QTF-CMV) assay to guide CMV prophylaxis duration in high-risk organ transplant recipients.

Methods.
A single-arm, multicenter, prospective interventional study including high-risk kidney, pancreas, liver, and heart transplant recipients who were either donor CMV-seropositive, recipient-seronegative (D+/R–) or recipient-seropositive with antithymocyte globulin (R+/ATG) induction. CMI testing was performed using the QTF-CMV assay at months 3, 4, 5, and 6 posttransplant. Prophylaxis was discontinued for a positive CMI but continued for a negative result up to a maximum of 6 mo. The primary endpoint was CMV viremia ≥1000 IU/mL up to 1 y posttransplant.

Results.
One hundred eight patients were included, comprising kidney (n = 89), kidney-pancreas (n = 7), liver (n = 10), and heart (n = 2) transplants. Eighty-nine patients (82.4%) completed the study protocol (n = 39 D+/R– and n = 50 R+/ATG). In the D+/R– group, only 1 of 39 patients (2.6%) had a positive QTF-CMV result. In the R+/ATG group, 33 of 50 patients (66%) had a positive QTF-CMV result before 6 mo, allowing for early discontinuation of prophylaxis (28 at month 3, 4 at month 4, and 1 at month 5). During the follow-up, CMV viremia ≥1000 IU/mL occurred in only 4 of 33 patients (12.1%) who discontinued prophylaxis early compared with 6 of 17 patients (35.3%) with negative QTF-CMV results and continued prophylaxis (hazard ratio 0.31; 95% confidence interval, 0.09-1.09; P = 0.07). No R+ patient developed CMV disease.

Conclusions.
QTF-CMV-guided prophylaxis appears useful in R+ patients who may benefit from a tailored duration of prophylaxis. This strategy does not appear to be useful in D+/R– patients.
A Single Center Observational Study on 25-hydroxyvitamin D levels in Meniscus Injury Patients
Authors: Hu Shuaishuai , Zhang Duzheng , Cong Ruijun 
Published: August 2024

AbstractThere is limited research on the association between 25-hydroxyvitamin D levels and meniscus injury. This study investigated serum 25-hydroxyvitamin D (25-OHD) in meniscus injury patients and its association with other bioinorganic chemistry of micronutrients, and immune cells numbers from January 2023 to December 2023. A total of 198 participants were recruited between the age of 18 and 89 years. Participants with renal or liver failure, or any other chronic conditions, who were taking medications that might affect the metabolism of vitamin D, were not included in the study. In this study, we categorized serum 25(OH)D concentrations as follows: ≤30ngm/dl was categorized as insufficient, and >30ngm/dl was deemed sufficient. Among the 198 participants, 82% (n=162) were serum 25(OH)D deficient, while only 18% (n=36) participants were serum 25(OH)D sufficient. No significant difference observed in vitamin D deficiency among difference age, BMI, sex, blood pressure, inflammatory cell numbers, and other vitamins compared to the vitamin D sufficient group. Moreover, the serum 25(OH)D concentrations were negatively related to the severity of meniscus injury based on MRI examination. In conclusion, severe vitamin D deficiency is more common in patients with meniscus injury and may play a significant role in their prognosis.
Noninvasive electrocardiographic imaging assessment of intraventricular synchrony for conduction system pacing device: a novel algorythm to assess intraventricular synchrony
Authors: Eltsov Ivan , Del Monte Alvise , Pannone Luigi , Overeinder Ingrid , Della Rocca Domenico , Scacciavillani Roberto , Verbrugge Frederik H , Zeng Qingguo , Bala Gezim , Paparella Andrea Maria , Talevi Giacomo , Stroker Erwin , Sieira Juan , Gharaviri Ali , Sarkozy Andrea , Chierchia Gian-Battista , La Meir Mark , de Asmundis Carlo , Almorad Alexandre 
Published: August 2024

Structured AbstractBackgroundLeft Bundle branch area pacing has become the procedure of choice for various indications including atrioventricular block and considered to be a physiologic modality of pacing compared to RV apex pacing.ObjectivesThe purpose of this study was to assess ventricular activation and synchrony in patients with LBBAP device using ECG imaging (ECGI).Methods25 consecutive patients underwent an LBBAP device implantation have been included in the study. ECG and ECGI analysis have been performed the day after implantation. Native and paced QRS, LVAT, RVAT and V1AD were calculated using ECG. TVACT, LVACT, LVACTi, RVACT, RVACTi and IVDS were calculated based on ECGI. All patients have been followed up for 12 months.ResultsAll patients were divided in 2 groups (wide and narrow QRS) based on intrinsic ECG and then based on paced ECG QRS.For initially narrow QRS group, activation time and synchrony during pacing was comparable to native. In wide QRS group these parameters were significantly improved.At paced rhythm analysis, classic ECG LBBAP parameters (paced QRS and LVAT) were not sufficient to properly evaluate the ventricular activation for paced rhythm. Discordance between ECG and ECGI analysys was identified in 25 patients. Two additional 12 lead ECG parameters predicting the ECGI measurements were found - V1AD and dRVAT. Follow up showed stable values of ejection fraction, paced QRS and pacing parameters.ConclusionsECG imaging can bring a significant value into assessing the efficacy of new pacing modalities and provide more data for precise determination of implantation outcomes, including detailed activation assessment and comparison to intrinsic conduction. Key ECGI values confirming proper ventricular activation have been defined and correlated with 12 lead ECG parameters to predict ventricle activation from ECG only.
Using Meta-Transformers for Multimodal Clinical Decision Support and Evidence-Based Medicine
Authors: Mohammed Sabah , Fiaidhi Jinan , Martinez Abel Serracin 
Published: August 2024

AbstractThe advancements in computer vision and natural language processing are keys to thriving modern healthcare systems and its applications. Nonetheless, they have been researched and used as separate technical entities without integrating their predictive knowledge discovery when they are combined. Such integration will benefit every clinical/medical problem as they are inherently multimodal - they involve several distinct forms of data, such as images and text. However, the recent advancements in machine learning have brought these fields closer using the notion of meta-transformers. At the core of this synergy is building models that can process and relate information from multiple modalities where the raw input data from various modalities are mapped into a shared token space, allowing an encoder to extract high-level semantic features of the input data. Nerveless, the task of automatically identifying arguments in a clinical/medical text and finding their multimodal relationships remains challenging as it does not rely only on relevancy measures (e.g. how close that text to other modalities like an image) but also on the evidence supporting that relevancy. Relevancy based on evidence is a normal practice in medicine as every practice is an evidence-based. In this article we are experimenting with meta-transformers that can benefit evidence based predictions. In this article, we are experimenting with variety of fine tuned medical meta-transformers like PubmedCLIP, CLIPMD, BiomedCLIP-PubMedBERT and BioCLIP to see which one provide evidence-based relevant multimodal information. Our experimentation uses the TTi-Eval open-source platform to accommodate multimodal data embeddings. This platform simplifies the integration and evaluation of different meta-transformers models but also to variety of datasets for testing and fine tuning. Additionally, we are conducting experiments to test how relevant any multimodal prediction to the published medical literature especially those that are published by PubMed. Our experimentations revealed that the BiomedCLIP-PubMedBERT model provide more reliable evidence-based relevance compared to other models based on randomized samples from the ROCO V2 dataset or other multimodal datasets like MedCat. In this next stage of this research we are extending the use of the winning evidence-based multimodal learning model by adding components that enable medical practitioner to use this model to predict answers to clinical questions based on sound medical questioning protocol like PICO and based on standardized medical terminologies like UMLS.
A Scalable Framework for Benchmarking Embedding Models for Semantic Medical Tasks
Authors: Soffer Shelly , Glicksberg Benjamin S , Kovatch Patricia , Efros Orly , Freeman Robert , Charney Alexander W , Nadkarni Girish N , Klang Eyal 
Published: August 2024

ABSTRACTText embeddings convert textual information into numerical representations, enabling machines to perform semantic tasks like information retrieval. Despite its potential, the application of text embeddings in healthcare is underexplored in part due to a lack of benchmarking studies using biomedical data. This study provides a flexible framework for benchmarking embedding models to identify those most effective for healthcare-related semantic tasks. We selected thirty embedding models from the multilingual text embedding benchmarks (MTEB) Hugging Face resource, of various parameter sizes and architectures. Models were tested with real-world semantic retrieval medical tasks on (1) PubMed abstracts, (2) synthetic Electronic Health Records (EHRs) generated by the Llama-3-70b model, (3) real-world patient data from the Mount Sinai Health System, and the (4) MIMIC IV database. Tasks were split into ‘Short Tasks’, involving brief text pair interactions such as triage notes and chief complaints, and ‘Long Tasks’, which required processing extended documentation such as progress notes and history & physical notes. We assessed models by correlating their performance with data integrity levels, ranging from 0% (fully mismatched pairs) to 100% (perfectly matched pairs), using Spearman correlation. Additionally, we examined correlations between the average Spearman scores across tasks and two MTEB leaderboard benchmarks: the overall recorded average and the average Semantic Textual Similarity (STS) score. We evaluated 30 embedding models across seven clinical tasks (each involving 2,000 text pairs), across five levels of data integrity, totaling 2.1 million comparisons. Some models performed consistently well, while models based on Mistral-7b excelled in long-context tasks. ‘NV-Embed-v1,’ despite being top performer in short tasks, did not perform as well in long tasks. Our average task performance score (ATPS) correlated better with the MTEB STS score (0.73) than with MTEB average score (0.67). The suggested framework is flexible, scalable and resistant to the risk of models’ overfitting on published benchmarks. Adopting this method can improve embedding technologies in healthcare.
Integrated multi-model analysis of intestinal inflammation exposes key molecular features of preclinical and clinical IBD
Authors: González-Acera Miguel , Patankar Jay V. , Erkert Lena , Cineus Roodline , Gámez-Belmonte Reyes , Leupold Tamara , Bubeck Marvin , Bao Li-Li , Dinkel Martin , Wang Ru , Limberger Heidi , Stolzer Iris , Gerlach Katharina , Mascia Fabrizio , Koop Kristina , Plattner Christina , Sturm Gregor , Weigmann Benno , Günther Claudia , Wirtz Stefan , Hildner Kai , Kühl Anja A. , Siegmund Britta , Atreya Raja ,   The IBDome Consortium, Hegazy Ahmed N. , Trajanoski Zlatko , Neurath Markus F. , Becker Christoph 
Published: August 2024

AbstractBackgroundInflammatory bowel disease (IBD) is a chronic inflammatory condition of the intestine with a complex and multifaceted pathogenesis. While various animal models exist to study specific disease mechanisms relevant to human IBD, a comprehensive comparative framework linking these to IBD pathophysiology is lacking.ObjectiveIn our study, we aimed at providing a framework that delineates common and unique features encountered in 13 widely used mouse models comparing them with human IBD to identify translatable pathways in model-cohort pairs. Another aim of our study was to provide an explorable resource for looking up gene and pathway level changes in mouse models assisting in hypothesis testing and minimizing animal burden abiding by the 3R principals.DesignWe employed comparative transcriptomic analyses with curated anda prioristatistical correlative methods between mouse models versus established as well as own patient datasets at both bulk and single cell levels.ResultsWe identify IBD-related pathways, ontologies, and cellular processes that are translatable between mouse models and patient cohorts. Moreover, we identify, known and novel IBD-associated subcellular mechanisms and how they are recapitulated in specific mouse models.ConclusionOur findings provide a valuable resource for selecting the most appropriate experimental paradigm to model unique features of IBD pathomechanisms, allowing analysis at the tissue, cellular, and subcellular levels.What is already known on this topicPreclinical modelling of IBD is key to the discovery of pathomechanisms and the evaluation of therapeutic approaches. However, individual models do not recapitulate the complexity of the disease and comprehensive studies comparing modelling paradigms with human IBD are lacking.What this study addsOur study provides a comparative analysis of thirteen commonly used intestinal inflammation models, identifying core-conserved pathways between mouse models and IBD patient cohorts. In addition, our study shows how specific pathways involved in IBD are recapitulated in specific mouse models and introduces a web tool to analyse the models.How this study might affect research, practice or policyBy identifying conserved and discrepant pathways between specific mouse models and IBD patient cohorts, our analysis platform provides an invaluable resource for translational IBD research.
Autoencoders with shared and specific embeddings for multi-omics data integration
Authors: Wang Chao , O’Connell Michael J. 
Published: August 2024

AbstractMotivationIn cancer research, different levels of high-dimensional data are often collected for the same subjects. Effective integration of these data by considering the shared and specific information from each data source can help us better understand different types of cancer.ResultsIn this study we propose a novel autoencoder (AE) structure with explicitly defined orthogonal loss between the shared and specific embeddings to integrate different data sources. We compare our model with previously proposed AE structures based on simulated data and real cancer data from The Cancer Genome Atlas. Using simulations with different proportions of differentially expressed genes, we compare the performance of AE methods for subsequent classification tasks. We also compare the model performance with a commonly used dimension reduction method, joint and individual variance explained. In terms of reconstruction loss, our proposed AE models with orthogonal constraints have a slightly better reconstruction loss. All AE models achieve higher classification accuracy than the original features, demonstrating the usefulness of the embeddings extracted by the model. Particularly, we show that the proposed models have consistently high classification accuracy on both training and testing sets. In comparison, the recently proposed MOCSS model that imposes an orthogonality penalty in the post-processing step has lower classification accuracy that is on par with JIVE.Availability and ImplementatioThe relevant datasets and models developed in this study are available in the following GitHub repository:https://github.com/wangc90/AE_Data_Integration.
Single-cell and spatial transcriptomics of vulvar lichen sclerosus reveal multi-compartmental alterations in gene expression and signaling cross-talk
Authors: Sun Peng , Kraus Christina N. , Zhao Wei , Xu Jiahui , Suh Susie , Nguyen Quy , Jia Yunlong , Nair Arjun , Oakes Melanie , Tinoco Roberto , Shiu Jessica , Sun Bryan , Elsensohn Ashley , Atwood Scott X. , Nie Qing , Dai Xing 
Published: August 2024

ABSTRACTVulvar diseases are a critical yet often neglected area of women’s health, profoundly affecting patients’ quality of life and frequently resulting in long-term physical and psychological challenges. Lichen sclerosus (LS) is a chronic inflammatory skin disorder that predominantly affects the vulva, leading to severe itching, pain, scarring, and an increased risk of malignancy. Despite its profound impact on affected individuals, the molecular pathogenesis of vulvar LS (VLS) is not well understood, hindering the development of FDA-approved therapies. Here, we utilize single-cell and spatial transcriptomics to analyze lesional and non-lesional skin from VLS patients, as well as healthy control vulvar skin. Our findings demonstrate histologic, cellular, and molecular heterogeneities within VLS, yet highlight unifying molecular changes across keratinocytes, fibroblasts, immune cells, and melanocytes in lesional skin. They reveal cellular stress and damage in fibroblasts and keratinocytes, enhanced T cell activation and cytotoxicity, aberrant cell-cell signaling, and increased activation of the IFN, JAK/STAT, and p53 pathways in specific cell types. Using both monolayer and organotypic culture models, we also demonstrate that knockdown of select genes, which are downregulated in VLS lesional keratinocytes, partially recapitulates VLS-like stress-associated changes. Collectively, these data provide novel insights into the pathogenesis of VLS, identifying potential biomarkers and therapeutic targets for future research.
Cell-Free DNA GWAS Reveals Importance of p.Arg206Cys in DNASE1L3 for Non-Invasive Testing
Authors: Linthorst Jasper , Nivard Michel , Sistermans Erik A. 
Published: August 2024

SUMMARYProperties of cell-free DNA (cfDNA) are intensely studied for their potential as non-invasive biomarkers. We explored the effect of common genetic variants on concentration and fragmentation properties of cfDNA using a GWAS based on low-coverage whole genome sequencing data of 140.000 Dutch Non-Invasive Prenatal Tests (NIPT). Our GWAS detects many genome-wide significant loci, functional enrichments for Phagocytes, Liver, Adipose tissue, Macrophages and genetic correlations with autoimmune and cardiovascular disease. A common (7%) missense variant inDNASE1L3(p.Arg206Cys), strongly affects all cfDNA properties. It increases the size of fragments, lowers cfDNA concentrations, affects the distribution of cleave-site motifs and increases the fraction of circulating fetal DNA during pregnancy. For the application of NIPT, and potentially other cfDNA-based tests, this variant has direct clinical consequences as it increases the odds of inconclusive results and impairs the sensitivity of NIPT by causing predictors to overestimate the fetal fraction.HIGHLIGHTSCommon variants affect properties of plasma cell-free DNAp.Arg206Cys inDNASE1L3strongly affects the size of cell-free DNA fragmentsFragmentomics-based fetal fraction predictors are affected by p.Arg206CysGenetics behind cfDNA overlaps with autoimmune and cardiovascular diseases
Discriminatory experiences and depression among Chinese lesbian, gay, and bisexual individuals in the United States: A moderated mediation modeling analysis
Authors: Xie Hui , Lin Binx Yezhe , Jiang Xinyi , Deng Wisteria 
Published: August 2024

AbstractChinese individuals in the U.S. have suffered substantial discrimination during the COVID19 pandemic, a stressor exacerbated by multiple minority identities (e.g., sexual and gender minority). This study aimed to explore the mechanisms of discriminatory experiences, specifically how it interacted with stressors like internalized homophobia and protective factors such as resilience, to affect depressive symptoms among Chinese individuals with multiple minority identities. Between 2022-2023, 272 Chinese LGB individuals residing in the U.S. (Mean[age]=28.36; SD=5.01) was surveyed anonymously using the Everyday discrimination Scale, the Internalized Homophobia Scale, the Conner-Davidson Resilience scale, and the Center for Epidemiologic Studies Depression Scale. Moderation and mediation analyses were conducted using STATA 17 and JASP 0.18.3. Replicating the positive association between discriminatory experience and depressive symptoms, we found that discrimination explained depressive symptoms directly and through increasing internalized homophobia. As a protective factor, resilience moderated the internalized homophobia-depressive symptoms link, but not the discrimination-depressive symptoms link. For Chinese LGB individuals with higher levels of resilience, the positive impact of internalized homophobia on depressive symptoms was weaker compared to those with lower resilience. Our findings further the understanding of the mediating and moderating mechanisms between discriminatory experiences and depressive symptoms among individuals with multiple minority identities. Continued research and intervention development on promoting resilience and other protective factors tailored to Chinese LGB individuals in the U.S. are crucial for improving mental health.