bluuebunny/crossref_metadata_2025_split huggingface dataset:
B-cell-SpecificWwoxDeletion Promotes Plasmablastic Tumor Development and Pro-Inflammatory Signatures in a Myeloma Mouse Model
Authors: Hussain Tabish , Bramble Matthew D. , Liu Bin , Abba Martin C. , Chesi Marta , Aldaz C. Marcelo 
Published: February 2025

AbstractDeletions and translocations affectingWWOXaccompanied by loss of expression are frequently observed in B cell neoplasms and are linked to poor prognosis. Our previous research showed thatWwoxdeletion early in B cell development induces genomic instability, neoplastic transformation, and monoclonal gammopathies in mice. In this study, by crossingCd19 Wwoxknockout (KO) withVk*MYCmyeloma model mice, we generated a model with concurrentWwoxdeletion andMYCactivation, reproducing two common oncogenic alterations in B and plasma cell cancers. We observed thatVk*MYC:Wwox KOmice exhibited significantly reduced survival rates primarily due to the development of plasmablastic plasmacytomas and lymphomas. Transcriptome profiling from bone marrow derived Cd138+ plasma cells and plasmablastic tumors revealed enrichment of biofunctions related to tumorigenic phenotype and inflammation activation uponWwoxdeletion inVk*MYCmice.Wwox KOplasmablastic tumors displayed mutations affecting classical cancer genes, DNA damage response (DDR) genes, as well as overexpression of Aid/Apobec family members associated to hypermutation and DDR mutational signatures. These findings illustrate the significant pathobiological effects of B cell specificWwoxdeletion and support a relevant role for WWOX loss of function in B cell neoplastic progression towards more aggressive phenotypes.
MAJIQ-CLIN: A novel tool for the identification of Mendelian disease-causing variants from RNA-Seq data
Authors: Aicher Joseph K , Issakova Dina , Slaff Barry , Jewell San , Lahens Nicholas F , Grant Gregory R , Baralle Diana , Rosenfeld Jill A , Scott Daryl A ,   Undiagnosed Diseases Network, Bhoj Elizabeth J , Barash Yoseph 
Published: February 2025

AbstractThe current diagnostic rate for patients with suspected Mendelian genetic disorders is only 25 to 58%, even though whole exome sequencing (WES) is part of the standard of care. One reason for the low diagnostic rate is that traditional WES analysis methods struggle to detect RNA splicing aberrations. It is estimated that 15-50% of human pathogenic variants alter splicing, with numerous splice-altering variants being causal for known Mendelian disorders. Developing reliable diagnostic tools to detect, quantify, prioritize, and visualize RNA splicing aberrations from patient RNA sequencing is therefore crucial. We present MAJIQ-CLIN, a method to address this need to augment clinical diagnostic using RNA-Seq and compare it to existing tools. We include the first systematic evaluation of the accuracy of such tools using synthetic data across several aberration types and transcript inclusion levels; we also evaluate accuracy on several datasets of biologically validated solved test cases. We show that MAJIQ-CLIN compares favorably to existing tools in both accuracy and efficiency, then use MAJIQ-CLIN to investigate several unsolved patient cases from the Undiagnosed Diseases Network.
Clinically actionable hypertrophic cardiomyopathy genes in South Asian Indians
Authors: Rao Vinay J , Sairam Thiagarajan , Rathinavel Andiappan , Mohanan Kurukkanparampil Sreedharan , Ahamed Hisham , Shenthar Jayaprakash , Dhandapany Perundurai S 
Published: February 2025

AbstractBackgroundPrimary hypertrophic cardiomyopathy (HCM) is predominantly a genetic disease causing left ventricular hypertrophy in the absence of other cardiac and systemic metabolic diseases. Currently, limited data exists on the prevalence of clinically actionable gene variants for primary HCM in South Asian Indians (SAI), that is necessary for minimizing disparities in interpreting ancestry-specific variants.ObjectivesThe ClinGen Hereditary Cardiovascular Disorders (HCVD) Gene Curation Expert Panel categorized HCM-causing genes into five categories according to their clinical relevance: definitive, strong, moderate, limited, and disputed. However, comprehensive studies examining this classification in SAI are lacking.MethodsWhole-exome sequencing was performed for 335 primary SAI-HCM patients, including all known cardiovascular genes and clinically actionable gene categories to determine their allele frequencies.ResultsSAI-HCM exomes revealed a total of 194 P/LP and VUS across 26 clinically actionable genes in 119 (35.52%) of the 335 cases. The SAI-HCM cohort exhibited significantly fewer variants in the 12 definitive category genes compared to other global HCM cohorts (17.33% vs. 41.21%, P = 0.0003). For the five strong/moderate genes, no significant difference was observed between the SAI-HCM cohort and other global HCM cohorts (2.59% vs. 2.49%, P = 1). Among the 21 limited and disputed genes,MYH6showed a significantly higher prevalence of variants in the SAI-HCM cohort than in the other global HCM cohorts (5.07% vs. 1.67%, P = 0.0408).ConclusionsThe clinically actionable gene variants in SAI-HCM cohort differed significantly from other global HCM cohorts, specificallyMYBPC3,MYH7, andMYH6.
Immune, metabolic, anatomical, and functional features of people after successful tuberculosis treatment: an exploratory analysis
Authors: Webber Tariq , Macdonald Candice , Tameris Michele , Tredoux Nicolette , Bierman Anandi , Gutschmidt Andrea , Tönsing Susanne , Hiemstra Andriëtte , Noor Firdows , Snyders Candice , Richardson Tracy , Fransman Bernadine , Allwood Brian , Hatherill Mark , Kleynhans Leanie , Loxton André G. , Walzl Gerhard , Chegou Novel , du Plessis Nelita , Shaw Jane A , Malherbe Stephanus T. 
Published: February 2025

ABSTRACTBackgroundWe explored the underlying mechanisms that may drive Post-tuberculosis (TB) lung disease, a multifactorial, heterogenous, and prevalent disease.MethodsExtensive clinical phenotyping through fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET)-computed tomography (CT) scans, pulmonary function testing, and symptom and quality of life questionnaires, was performed on a cohort of 48 adults who completed TB treatment within 6 months prior. Immunological characteristics of paired blood- and bronchoalveolar lavage fluid (BALF)-derived immune cells were assessed by multiplex bead-based immunoassay, ELISA and flow cytometry.ResultsThere was agreement between measures of inflammation on PET, the severity of anatomical abnormalities on CT, and pulmonary function testing. However, of these, only PET was associated with exercise tolerance and symptom scores. Measures of radiologic extent (total glycolytic activity and SUVmax on PET, and segments involved on CT) also correlated with proteins detected in blood that implicate Type 1 (IFN-γ, TNFα, IL-12) and Type 2 (IL-4, IL-33) responses, ongoing remodelling of lung tissue (MMPs), airways and vasculature (VEGF), as well as subsets of activated CD8+and CD4+T-cells.ConclusionThe radiologic extent of structural post-TB lung involvement is associated with a range of impaired lung function measures and immunological dysregulation. Our findings suggest that obstructive and restrictive lung pathology due to pulmonary tuberculosis do not occur in opposition but rather point towards a mixed pathology in most TB survivors.
Longitudinal associations between parental bonding and child preschool social-emotional problems: The unique and combined role of mothers and fathers
Authors: Hatakka Enni , Flykt Marjo , Rusanen Erja , Kylliäinen Anneli , Paavonen E. Juulia , Kiviruusu Olli 
Published: February 2025

AbstractResearch is currently limited on the impact of maternal and especially paternal postpartum bonding on child social-emotional development. This study is part of the nationally representative CHILD-SLEEP -sample (n = 710 families), where postpartum bonding was assessed with Postpartum Bonding Questionnaire (PBQ) at the child age of eight months, and child social-emotional problems were assessed with the Five to Fifteen (FTF) and the Strengths and Difficulties Questionnaire (SDQ) at child age of five years. Both maternal and paternal postpartum bonding difficulties at eight months were associated with child externalizing, internalizing and peer problems at five years. When parental depressive symptoms were controlled, the association remained significant only with internalizing problems. When investigating joint maternal and paternal bonding, we found both cumulative and mother-driven effects with child internalizing problems. The results emphasize the role of early parental bonding for later child mental health, highlighting the importance of early parental support.
Sentiment analysis of employees and COVID-19 vaccine hesitancy at workplace
Authors: Akpan Ikpe Justice , Warner Teai , Peters Mbuotidem 
Published: February 2025

AbstractVaccination is a potent means to combat the spread of infectious disease epidemics or pandemics, such as the COVID-19 pandemic. However, getting sufficient people to accept the vaccine and achieve herd immunity remains a significant challenge. This study evaluates preschool workers’ sentiments and challenges during in-person schooling amidst the COVID-19 pandemic and attitudes toward COVID-19 vaccination through a survey. The study surveyed preschool workers as part of a consulting project. Workers’ sentiments are analyzed using Azure Machine Learning (AML) data analytic method, statistical analysis, and theoretical evaluation. The results show that no association exists between employee job category (teachers versus support staff) and COVID-19 vaccine hesitancy. However, fewer teaching staff were hesitant to take the COVID-19 vaccine than the support staff (46% < 50%), but the difference was not significant [Chi-Square (χ2) = 0.009; p>0.05]. The overall sentiments of all preschool workers showed 46%, indicating low neutrality, implying hesitancy toward the COVID-19 vaccine. Preschool workers who felt optimistic about the vaccine’s potency cited scientific reasons; those with neutral sentiments needed further information and assurances about the side effects, indicating that appropriate health education can sway more people positively towards accepting the vaccine; people with negative sentiments displayed distrust, fear, personal beliefs, and misinformation. The results also highlight the need to educate all workers on vaccine potency despite the levels of education. Regardless of academic qualifications, any uninformed person can fall prey to misinformation and conspiracy theories. Proper health education helps people make informed decisions.
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre
Authors: Chowdhury Tasfia Noor , Mou Sanjida Afrin , Rahman Kazi Naimur 
Published: February 2025

ABSTRACTPatient length of stay (LoS) is a critical metric for evaluating the efficacy of hospital management. The primary objectives encompass to improve efficiency and reduce costs while enhancing patient outcomes and hospital capacity within the patient journey. By seamlessly merging data-driven techniques with simulation methodologies, the study proposes an all-encompassing framework for the optimization of patient flow. Using a comprehensive dataset of 2.3 million de-identified patient records, we analyzed demographics, diagnoses, treatments, services, costs, and charges with machine learning models (Decision Tree, Logistic Regression, Random Forest, Adaboost, LightGBM) and Python tools (Spark, AWS clusters, dimensionality reduction). Our model predicts patient length of stay (LoS) upon admission using supervised learning algorithms. This hybrid approach enables the identification of key factors influencing LoS, offering a robust framework for hospitals to streamline patient flow and resource utilization. The research focuses on patient flow corroborates the efficacy of the approach, illustrating decreased patient length of stay within a real healthcare environment. The findings underscore the potential of hybrid data-driven models in transforming hospital management practices. This innovative methodology provides generally flexible decision-making, training, and patient flow enhancement; such a system could have huge implications for healthcare administration and overall satisfaction with healthcare.
Discovery and optimization of a guanylhydrazone-based small molecule to replace bFGF for cell culture applications
Authors: Feofanov Mikhail , Daubner Gerrit Martin , Saltalamacchia Andrea , Köhler Karsten , Schulz Christine , Henry Clare Elizabeth , Ziegler Michael Josef , Benabderrahmane Mohammed , Hiault Florence Andrée Suzanne , Decker Tim-Michael , Shen Mei-Chun , Pahl Jürgen , Lambertz Sophie , Noori Hamid R. 
Published: February 2025

SUMMARYReplacing growth factors with a synthetic alternative molecule is an attractive opportunity to increase consistency, scalability and cost-effectiveness of cell-based products. Herein, we describe the discovery of a chemical class of FGFR1 agonists mimicking the action of basic fibroblast growth factor (bFGF), an essential component of cell-culture media. The guanylhydrazone-based molecule, TCB-32, was identified via structure-based virtual screening on the orthosteric binding site of FGFR1. It was shown to significantly increase cell proliferation by activating the FGFR1 signalling pathway like bFGF and exhibited enhanced thermostability over bFGF by containing activity over the course of several days. After extensive structure-activity relationship studies it was possible to increase potency and efficacy leading to three highly potent agonists. This finding has the potential to remove current bottlenecks in large-scale cell production as required for applications like cultivated meat or cell therapy.GRAPHICAL ABSTRACTHIGHLIGHTSDiscovery of a highly potent small molecule to replace bFGF in serum-free mediumActivation of proliferation through the FGFR1-signalling pathwayEnhanced thermal stability over bFGFSAR led to three small molecules with decreased EC50, named TCB-494, TCB-541 and TCB-621
BlueBerry: Closed-loop wireless optogenetic manipulation in freely moving animals
Authors: Nourizonoz Ali , Girard Benoit , Guyoton Maëlle , Galiñanes Gregorio , Thurnherr Raphael , Pellat Sebastien , Bellone Camilla , Huber Daniel 
Published: February 2025

AbstractOptogenetics is a powerful tool for neuroscientists, enabling manipulations of neuronal activity to explore their effects on behavior. Recent advances in wireless technology enabled the development of novel remotely controlled optogenetic devices, thereby promising to facilitate the study of more complex behaviors, including social interactions. Yet, studying such behaviors in real time and automated manner in large scale naturalistic settings or with multiple animals remains challenging. In addition, many published devices can only be reproduced in labs having specific engineering skills and fabrication facilities and are thus not accessible for the larger neuroscience community. Here we introduce BlueBerry, an open-source, multi-channel optogenetic device that can be assembled with off the shelf components. The lightweight (1.4 g) design and robust long-range communication makes Blueberry an ideal tool for behavior-triggered optogenetic studies with multiple animals interacting freely in a large-scale setting. We show how BlueBerry can be used to guide decision making during spatial navigation and demonstrate for the first time that social dynamics can be rapidly altered in groups of mice by triggering specific social behavior with closed-loop optogenetics.
A Bayesian decision-making model of implicit motor learning from internal and external errors
Authors: Kim Hyosub E. , Chua Romeo , Hu Davin 
Published: February 2025

AbstractA key challenge for the sensorimotor system is deciding which errors to learn from and which to ignore. Recent work has shown that humans are remarkably precise in parsing movement errors into internally- and externally-generated components for this purpose: Participants automatically ignore internally-generated reaching errors caused by motor noise, yet implicitly adapt to size-matched externally-generated errors caused by visuomotor rotations (Ranjan & Smith 2018, 2022). Following replication of these results with 16 neurotypical adults, we formalized our understanding of this behavior with a novel Bayesian decision-making model. The Parsing of Internal and External Causes of Error (PIECE) model frames adaptation as a process of causal inference regarding the source of error, with the magnitude of motor corrections reflecting a combination of state estimation and the observer’s degree-of-belief that their movement was externally perturbed. Thus, PIECE presents a challenge to a class of computational models that frames adaptation as a process of re-aligning the perceived hand position with the movement goal. We show that only PIECE can capture the precise parsing of internal versus external errors observed. Combined, this work provides a normative explanation of how the nervous system discounts intrinsic motor noise and adapts to perturbations, keeping movements finely-calibrated.
InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records
Authors: Kauffman Justin , Holmes Emma , Vaid Akhil , Charney Alexander W , Kovatch Patricia , Lampert Joshua , Sakhuja Ankit , Zitnik Marinka , Glicksberg Benjamin S , Hofer Ira , Nadkarni Girish N 
Published: February 2025

ABSTRACTElectronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic clinical decisions but are often unsuited for advanced machine learning (ML)–based patient-specific analyses. ML models and clinical heuristics learn generalizable relationships from predefined factors, yet many patients may not benefit if those factors are missing in the EHR or differ—however subtly—from typical training populations. Clinical heuristics are limited to low complexity, often linear, relationships and patterns between clinical variables. ML approaches in EHRs significantly expand pattern sophistication but require large, labeled datasets, which are often unattainable especially in low prevalence diseases and are limited by sources of random and non-random variation in EHRs. Deep learning (DL), in contrast with ML and clinical heuristics, learns features without predefinition but requires even greater label access for predictions. While DL can construct unsupervised EHR representations, the patterns and characteristics of less prevalent examples are poorly resolved, and downstream clinical applications still require labels. We present Inf-EHR, a framework to automatically compute clinical likelihoods from whole EHRs of patients from diverse clinical settings without need of large volumes of labeled training data. We apply deep geometric learning to EHRs through a novel procedure that converts whole EHRs to temporal graphs. These graphs naturally capture phenotypic temporal dynamics leading to unbiased representations. Using only a few labeled examples, InfEHR computes and automatically revises likelihoods leading to highly performant inferences especially in low prevalence diseases which are often the most clinically ambiguous. To demonstrate utility, we use EHRs from the Mount Sinai Health System and The University of California, Irvine Medical Center and test its performance compared to physician-provided clinical heuristics across two diseases with no clinical or epidemiological overlap: a rare disease (neonatal culture-negative sepsis) with prevalence of 2% in neonates, and a more common disease (adult post-operative acute kidney injury) with prevalence of 22%. We show that Inf-EHR is superior to existing clinical heuristics both for culture-negative sepsis (sensitivity: 0.65 vs .041, specificity: 0.99 vs.0.98) and post-operative acute kidney injury (sensitivity: 0.72 vs 0.20, specificity: 0.91 vs 0.97). We present the first application of geometric deep learning in EHRs that can be used in real world clinical settings at scale, for improving phenotype identification and resolving clinical uncertainty.
Stakeholder Networks and Systems for Health Equity: Accessibility of Newly Commissioned District Hospitals in Ghana
Authors: Frimpong Felix , Peprah Charles , Owusu-Addo Ebenezer 
Published: February 2025

AbstractEquitable access to healthcare necessitates the establishment of resilient stakeholder networks and comprehensive systemic integration to address the multifaceted needs of heterogeneous populations effectively. This research critically assesses the significance of stakeholder engagement in the planning and the provision of newly commissioned district hospitals in Ghana’s Ashanti Region, examining the extent to which their contributions influenced dimensions of accessibility and the uptake of services.A convergent parallel mixed-method design was employed, incorporating qualitative methods (indepth interviews) with stakeholders such as healthcare providers, community leaders, and local officials, and quantitative data was collected from healthcare seekers. The study applied an accessibility framework—availability, affordability, accommodation, geographical access, and acceptability—to assess the impact of systemic and stakeholder-driven initiatives.Stakeholder networks played a crucial role in the strategic planning and provision of the newly commissioned district hospital services. The collaborative decision-making process facilitated the key dimensions of access to healthcare to be prioritized. Nevertheless, the issue of affordability surfaced as a systemic limitation that critically impacted service utilization, particularly among economically disadvantaged populations. The research findings indicated that demographic groups characterized by female gender, advanced age, and higher educational attainment derived greater benefits from the stakeholder-oriented planning methodologies, whereas rural populations and those belonging to socio-economically disadvantaged backgrounds encountered substantial obstacles.The findings underscore the critical role of stakeholder networks in health promotion. Their engagement ensured alignment between systemic goals and community needs, fostering service uptake and reducing disparities. The study emphasizes the importance of leveraging multi-level connections between policy, health systems, and community networks to achieve Universal Health Coverage and Sustainable Development Goal 3