bluuebunny/crossref_metadata_2025_split huggingface dataset:
Notification and recordkeeping of occupational mesothelioma in India
Authors: Singh Raja , Frank Arthur L 
Published: February 2025

AbstractThis paper studies the number of occupational mesothelioma cases that have been reported as per law to the factories and mines regulators in India. Zero cases of mesothelioma, which is a notified/notifiable disease under the Mines Act 1952, and notified/notifiable under the broad category of occupational cancers under the Factories, Act 1948 have been notified between 2004 to 2024 (with the factory data compiled only for 8 years). This highlights many issues, including the lack of recordkeeping of occupational diseases when there are cases of mesothelioma being reported by hospitals under the National Cancer Registry Program and many being reported in the scientific literature from India. Though lack of data may not mean the lack of disease, these cases of mesothelioma may either point to a non-occupational exposure to asbestos as an aetiology for such cases, or fixing the recordkeeping and notification of cancers, including mesothelioma, as many occupational cases are reported in the scientific literature. This is especially true as India is one of the largest users and processors of imported asbestos, user of talcum powder and has other potential sources of exposure. With mesothelioma being a surrogate for asbestos exposure, this public health hazard needs focussed implementation of regulated safeguards both occupationally or through non occupational exposures.
Identification of DOK2 and PTPN11 as novel interactors of T cell specific adapter protein TSAd
Authors: Chan Hanna , Borowicz Pawel , Gilmour Brian C , Stensland Maria , Garcia-Loza Ivan , Phuyal Santosh , Marone Romina , Jeker Lukas T. , Nyman Tuula A , Spurkland Anne 
Published: February 2025

AbstractT cells play a crucial role in the adaptive immune system and depend on tightly regulated intracellular signalling pathways to respond in an appropriate manner. Adapter proteins have flexible and dynamic features, which allow them to regulate T cell signal transduction pathways. As adapter proteins are enzymatically inert and may play multiple roles in parallel, it has been a challenge to fully characterise their functions individually. One such protein in T cells, is T cell specific adapter protein (TSAd), which is upregulated following T cell receptor (TCR) stimulation and is believed to mediate Src family tyrosine kinase signalling. However, the functional role remains elusive, possibly due to limited insight into interactors that potentially bind TSAd. The only structurally well-defined feature within TSAd, is the Src homology 2 (SH2) domain. This conserved domain displays prototypic binding of phosphorylated tyrosines, which suggests that the adapter molecule is implicated in phosphotyrosine signalling pathways. Here, we used an unbiased approach to identify ligands of the TSAd SH2 domain, by using affinity-purification mass spectrometry (AP-MS). Several novel ligands, many of which are known to be implicated in negative regulation of T cell intracellular signalling, were identified. More specifically, we showed that TSAd binds DOK2 and PTPN11 and determined the tyrosines responsible for the TSAd SH2 domain-dependent interaction. Ablation of TSAd and DOK2 by CRISPR/Cas9 in Jurkat T cells resulted in altered tyrosine phosphorylation. Taken together, these findings provide new insight into the possible function of TSAd as a negative signalling node in T cells.
UK health and social care use during and after the pandemic: A qualitative study of the experiences of disabled people from minoritised ethnic groups
Authors: Rivas Carol , Anand Kusha , Ball Elizabeth , Begum Samina , Heera Neelam , Shahid Yesmin , Zuzer Lal Zainab , Moore Amanda P 
Published: February 2025

AbstractThe COVID-19 pandemic acted as a “super-catalyst” in accelerating the United Kingdom transition to remote health and social care, while exposing and deepening existing inequities. The 18-month mixed-methods longitudinal CICADA study explored this from the perspective of disabled individuals from minoritised ethnic groups (including undocumented migrants and asylum seekers) during and after the pandemic. Since our aim was to inform improvements, we used an asset and strengths-based approach underpinned by the embodiment model of disability and intersectional theory. Here we report on findings from 271 semi-structured interviews with disabled individuals from minoritised ethnic groups, follow-on workshops with interviewees in April and September 2022, and 4 key informant interviews.Findings revealed widespread dissatisfaction. Many found the shift to remote care inaccessible and disempowering. Challenges included difficulties in securing appointments, disrupted patient-clinician relationships, little regard for holistic care or comorbidities, and systemic exclusions due to intersecting and discriminating factors of language, accent, disability and complex needs, digital precarity, and undocumented status. Often, service management of expectations would have improved experiences. The increased burden from health and social care support-seeking counter-intuitively worsened once pandemic restrictions eased and led many to give up; others bypassed general practice or raised complaints, or relied on self-management, traditional remedies or informal support networks. Many resorted to costly and unaffordable private care, often specifically from within their communities, and often having never registered with the National Health Service. There is an urgent need for a more inclusive, tailored approach to health and social care that considers intersecting disadvantages of race, disability, citizenship and socioeconomic status, facilitates community connections and empowerment, and provides support for self-care, alternative care and education. By foregrounding community voices, this research offers valuable insights for policymakers and providers aiming to address disparities and improve health and social care outcomes for marginalized populations.
AI-Driven Early Detection of Severe Influenza in Jiangsu, China: A Deep Learning Model Validated Through The Design of Multi-Center Clinical Trials and Prospective Real-World Deployment
Authors: Chen Yifei , Bo Yan 
Published: February 2025

AbstractInfluenza causes about 650,000 deaths worldwide each year, and the high mortality rate of severe cases is closely related to subjective bias in clinical assessment and inconsistent diagnostic and treatment standards. To this end, this study developed and validated a deep learning-based model for early diagnosis of severe influenza that optimises risk stratification by integrating clinical data from multiple sources. The study included 87 tertiary general hospitals in Jiangsu Province, China, and used a five-stage validation framework (model development, external validation, multi-reader study, randomised controlled trial, and prospective validation) to analyse electronic health record data covering demographic, symptomatic, laboratory indicators, and imaging features between 2019 and 2025. Expected results showed that the model-assisted diagnosis had a significantly higher AUC value of 0.18 (95% CI: 0.14-0.22) and a 32% lower rate of misdiagnosis compared to traditional clinical assessment, and performed consistently in elderly and chronically ill patients and in hospitals in resource-limited areas (subgroups with AUCs of >0.82 in all cases). The expectation of the study will be realised that the model can effectively improve the early recognition of severe influenza by dynamically integrating multidimensional information, especially for scenarios where healthcare resources are unevenly distributed. The implementation of this study followed strict ethical norms (JD-LK-2019-106-01, The Second Affiliated Hospital of Soochow University; 2024-10-02, The Affiliated Hospital of Yangzhou University), and the de-identified data were managed through an encrypted platform (osf.io/ayj75) and planned to open-source the model code in order to promote clinical translation and cross-region collaboration, and to provide a scalable influenza precision prevention and control decision support tools.Trial:ChiCTR2000028883Registration DOI:https://doi.org/10.17605/OSF.IO/SC93Y
A metagenomic approach to One Health surveillance of antimicrobial resistance in a UK veterinary centre
Authors: Elton Linzy , Mateos Alonso Dupuy , Frosini Siân Marie , Jepson Rosanne , Rofael Sylvia , McHugh Timothy D , Wey Emmanuel Q 
Published: February 2025

AbstractIntroductionThere are currently no standardised guidelines for genomic surveillance of One Health (OH) antimicrobial resistance (AMR). This project aimed to utilise metagenomics to identify AMR genes present in a companion animal hospital and compare these with phenotypic results from bacterial isolates from clinical specimens from the same veterinary hospital.MethodsSamples were collected from sites around a primary companion animal veterinary hospital in North London. Metagenomic DNA was sequenced using Oxford Nanopore Technologies (ONT) MinION. The sequencing data were analysed for AMR genes, plasmids and clinically relevant pathogen species. These data were compared to phenotypic speciation and antibiotic susceptibility tests (ASTs) of bacteria isolates from patients.ResultsThe most common resistance genes identified wereaph(n=101 times genes were isolated across 48 metagenomic samples),sul(84),blaCARB(63),tet(58) andblaTEM(46). In clinical isolates, a high proportion of phenotypic resistance to the β-lactams was identified. Rooms with the greatest mean number of resistance genes identified per swab site were the medical preparation room, dog ward and surgical preparation room. Twenty-four Gram-positive and four enterobacterial plasmids were identified. Sequencing reads matched with 14/22 (64%) of the phenotypically isolated bacterial species.DiscussionMetagenomics identified AMR genes, plasmids and species of relevance to human and animal medicine. Communal animal-handling areas harboured more AMR genes than areas animals did not frequent. When considering infection prevention and control (IPC) measures, adherence to, and frequency of, cleaning schedules, alongside potentially more comprehensive disinfection of animal-handling areas may reduce the number of potentially harmful bacteria present.PubMed “veterinary” or “companion” AND “AMR” or “resistan*” NOT (Review[Publication Type]) (2018-2023)“veterinary” or “companion” AND “AMR” or “resist*” AND “sequencing” or “metagenomic*” “veterinary” or “companion” AND “AMR” or “resistan*” NOT (Review[Publication Type]) (2018-2023)https://www.iscaid.org/clinical-practice
Scanning Electron Microscopy Study of Bacterial Growth in Mycelial Extracellular Matrices
Authors: Browner Davin , Adamatzky Andrew 
Published: February 2025

AbstractFungi and bacteria are found living in a wide variety of environments and their interactions are important in many processes including soil health, human and animal physiology and in biotechnological applications. The specificity of interaction between these microorganisms in co-culture is difficult to establish. For example, differentiation between trivial processes as a result of stochastic mixing compared with mutualistic or antagonistic interactions. Here, we investigate a single morphological feature of co-cultures of planktonic bacterial growth within biofilm-forming liquid cultures of mycelium. Namely, the attachment of bacterial co-habitants of speciesBacillus subtilisto fungal hyphae of speciesHericium erinaceus. The bacteria-in-mycelial-biofilm method was developed and utilised to allow for attachment of bacteria to hyphae via containment within extracellular polymeric substances (EPS) and the overall extracellular matrix (ECM) of the mycelium. Attachment structures appear to result from the hyphal surface as a result of production of EPS. The mean biofilm area across T1-3 was 3.90 (µm2) ± 0.72 (µm2) and the mean percentage coverage was 18.33 (%) ± 5.52 (%). The bacterial biofilm components could not be ruled out as co-contributing to formation of attachment structures due to the structures being present connecting individual bacterium as well as to hyphae.
Identification of polyphosphate-binding proteins inE. coliuncovers targets involved in translation control and ribosome biogenesis
Authors: Baijal Kanchi , Kore Brianna , Abramchuk Iryna , Denoncourt Alix , Han Shauna , Simms Abby , Dagenais Amy , Long Abagail R. , Rudner Adam D. , Lavallée-Adam Mathieu , Gray Michael J. , Downey Michael 
Published: February 2025

ABSTRACTIn many bacteria, polyphosphate kinase (PPK) enzymes use ATP to synthesize polyphosphate (polyP) in response to cellular stress. These chains of inorganic phosphates are joined by high-energy bonds and can reach hundreds of residues in length. PolyP plays diverse functions in helping bacteria adjust to changing environmental conditions. However, the molecular mechanisms underlying these functions are poorly understood. In eukaryotic cells, polyacidic serine- and lysine-rich (PASK) motifs of proteins can mediate binding to polyP chains. Whereas PASK motifs are relatively common in yeast and human cells, we report that these sequences are rare in bacteria commonly used for polyP research. Thus, to identify novel polyP-binding proteins inEscherichia coli,we carried out an untargeted screen and identified 7 novel targets with links to translation control and ribosome biogenesis. For two targets, the GTPase activating protein YihI and the ribonuclease Rnr, we mapped the regions of polyP interaction to non-PASK sequences and identified lysine residues critical for binding. We found that deletion ofrnrsuppressed the slow growth phenotype of Δppkmutants grown on minimal media. Conversely,ppkdeletion resulted in decreased Rnr protein expression. These phenotypes were dependent on the polyP binding region of Rnr but independent of polyP binding itself, suggesting a complex interplay between PPK and Rnr function inE. coli.Overall, our work provides new insights into the scope of polyP binding proteins and extends the connections between polyP and the regulation of protein translation inE. coli.
Donor transcription suppresses D-loops incisand promotes genome stability
Authors: Djeghmoum Yasmina , Piazza Aurèle 
Published: February 2025

SummaryD-loops are DNA joint molecule intermediates central to DNA break repair by homologous recombination (HR). Priority rules between recombination and transcription at the donor locus have not been investigated. Here, using a controlled break induction system and physical detection of D-loops inS. cerevisiae, we show that donor transcription by RNA polymerase II acutely suppresses D-loops incis, in an orientation-dependent manner. This inhibition does not rely on endogenous transcription factors, the RNA product, RNA:DNA hybrids, or previously characterized D-loop disruption factors. Transcription can be the major D-loop suppression pathway and inhibits the formation of repeat-mediated genome rearrangements. Transcription is therefore a negative regulator of HR at the D-loop level that promotes genome stability. These findings reveal the functional prioritization between two universal DNA-dependent processes.HighlightsDonor transcription suppresses D-loops acutely and incisDonor transcription is independent from conservedtransD-loop disruption pathwaysTranscription directionality modulates D-loop suppressionTranscription locally enforces HR fidelity
Identification of a multi-omics factor predictive of long COVID in the IMPACC study
Authors: Gabernet Gisela , Maciuch Jessica , Gygi Jeremy P. , Moore John F. , Hoch Annmarie , Syphurs Caitlin , Chu Tianyi , Jayavelu Naresh Doni , Corry David B. , Kheradmand Farrah , Baden Lindsey R. , Sekaly Rafick-Pierre , McComsey Grace A. , Haddad Elias K. , Cairns Charles B. , Rouphael Nadine , Fernandez-Sesma Ana , Simon Viviana , Metcalf Jordan P. , Agudelo Higuita Nelson I , Hough Catherine L. , Messer William B. , Davis Mark M. , Nadeau Kari C. , Pulendran Bali , Kraft Monica , Bime Chris , Reed Elaine F. , Schaenman Joanna , Erle David J. , Calfee Carolyn S. , Atkinson Mark A. , Brackenridge Scott C. , Melamed Esther , Shaw Albert C. , Hafler David A. , Ozonoff Al , Bosinger Steven E. , Eckalbar Walter , Maecker Holden T. , Kim-Schulze Seunghee , Steen Hanno , Krammer Florian , Westendorf Kerstin , Network IMPACC , Peters Bjoern , Fourati Slim , Altman Matthew C. , Levy Ofer , Smolen Kinga K. , Montgomery Ruth R. , Diray-Arce Joann , Kleinstein Steven H. , Guan Leying , Ehrlich Lauren I. R. 
Published: February 2025

SUMMARYFollowing SARS-CoV-2 infection, ∼10-35% of COVID-19 patients experience long COVID (LC), in which often debilitating symptoms persist for at least three months. Elucidating the biologic underpinnings of LC could identify therapeutic opportunities. We utilized machine learning methods on biologic analytes and patient reported outcome surveys provided over 12 months after hospital discharge from >500 hospitalized COVID-19 patients in the IMPACC cohort to identify a multi-omics “recovery factor”. IMPACC participants who experienced LC had lower recovery factor scores compared to participants without LC. Biologic characterization revealed increased levels of plasma proteins associated with inflammation, elevated transcriptional signatures of heme metabolism, and decreased androgenic steroids in LC patients. The recovery factor was also associated with altered circulating immune cell frequencies. Notably, recovery factor scores were predictive of LC occurrence in patients as early as hospital admission, irrespective of acute disease severity. Thus, the recovery factor identifies patients at risk of LC early after SARS-CoV-2 infection and reveals LC biomarkers and potential treatment targets.
Passive sensing with psyche: utilizing data from wearable technology to predict emotion states
Authors: Karim Helmet T. , Matin Ajmail , Goel Mayank 
Published: February 2025

AbstractDepression and anxiety are some of the most common mental health disorders in the world contributing to significant morbidity and mortality. Past treatments have focused primarily on treating depression and anxiety. However, there is an urgent need to detect chronic stress states and potentially intervene using just-in-time personalized interventions. Modern technology has revolutionized our ability to passively measure various biological and physiological signals. In our daily lives, we generate significant amounts of electronic data from our phones, wearable technology, watches, and even computers and cars. In this analysis, we focus on using wearable data from FitBit to passively predict daily mood states (e.g., sad/tense/anxious vs. happy). We use daily FitBit data from 38 participants and ~1200 days of data to predict mood states (e.g., sad/tense/anxious vs. happy) on a day-to-day basis using an elastic net regression machine learning algorithm. We were able to accurately predict these states using a cross-validated machine learning algorithm and identified features predictive of each of the mood states. In this proof-of-concept analysis, we show that predicting daily mood states is feasible and may help to not only detect daily mood states but also improve passive awareness and deliver just-in-time interventions.
Single-nucleus analysis of thoracic perivascular adipose tissue reveals critical changes in cell composition, communication, and gene regulatory networks induced by a high fat hypertensive diet
Authors: Terrian Leah , Thompson Janice M. , Bowman Derek E. , Panda Vishal , Contreras G. Andres , Rockwell Cheryl , Sather Lisa , Fink Gregory D. , Lauver D. Adam , Nault Rance , Watts Stephanie W. , Bhattacharya Sudin 
Published: February 2025

ABSTRACTCardiovascular disease (CVD) is the leading cause of death worldwide, with hypertension being its primary causal factor. Most blood vessels are surrounded by perivascular adipose tissue (PVAT), which regulates blood vessel tone through the secretion of vasoactive factors. PVAT is recognized as a key mediator of vascular function and dysfunction in CVD, although the underlying mechanisms remain poorly understood. To investigate PVAT’s mechanistic role in hypertension, we performed single nucleus RNA-Sequencing analysis of thoracic aortic PVAT from Dahl SS rats fed a high-fat, hypertensive diet. Computational analysis revealed extensive diet-induced changes in cell-type composition, cell-type specific gene expression, cell-cell communication pathways, and intracellular gene regulatory networks within PVAT. Furthermore, we identified key transcription factors mediating these networks and demonstrated through virtual knock-out experiments that these factors could serve as potential therapeutic targets for preventing or reversing PVAT’s hypertensive state.
TrackRefiner: A tool for refinement of bacillus cell tracking data
Authors: Ahmadi Atiyeh , Dostmohammadi Alireza , McLean Rhyan , Ingalls Brian 
Published: February 2025

AbstractMotivationSingle-cell resolution time-lapse microscopy of bacterial populations is a powerful tool for assessing cellular behavior and interaction dynamics. Realizing the full potential of this approach requires accurate image analysis: segmentation of individual cell objects, tracking of persistent cells from frame to frame, and connecting of mother cells to daughters when division events occur. In particular, accurate tracking is needed to produce longitudinal datasets for analysis of interactions and features that develop through time. Tracking is challenging when populations are densely packed or when cells undergo significant motion between frames. The leading software packages struggle to provide accurate data in such cases.ResultTo address this problem, we present TrackRefiner, a tool for refinement of bacillus cell tracking data. This package was specifically designed to refine the tracking outputs of CellProfiler, a commonly used image processing tool. TrackRefiner is built with a modular and publicly accessible structure, making it adaptable for integration with other image processing software. To assess the package’s performance, we manually determined ground truth tracking results for eight datasets from four research groups, comprising a total of 159,349 object links. This curated dataset, the first of its kind, serves as a valuable benchmark for assessing the performance of bacillus cell tracking algorithms. For timelapses with frequent imaging, TrackRefiner consistently achieved, with one exception, over 98% detection accuracy and corrected 57-100% of tracking errors. Accuracy was reduced for images sampled at lower frequency.Availability and implementationFor easy access, TrackRefiner has been published on PyPI and Anaconda. Source code and user manuals can be accessed via Github and OSF. The manually curated benchmark dataset is also posted at these sites.