bluuebunny/crossref_metadata_2025_split huggingface dataset:
Risk Stratification for Chronic Kidney Disease After Liver Transplant for Metabolic Dysfunction-associated Steatohepatitis (MASH) Cirrhosis: Results From the NailMASH Consortium
Authors: Satapathy Sanjaya K. , Elwir Saleh , Brandman Danielle , Smith Coleman , Jiang Yu , Vanatta Jason , Ha Nghiem B. , Cheung Amanda C. , Bhat Mamatha , Patel Pratik , Siddiqui Mohammad S. , Rinella Mary E. , Watt Kymberly D. 
Published: October 2024

Background.
Chronic kidney disease (CKD) is a well-recognized complication in patients undergoing liver transplantation (LT), particularly those with metabolic dysfunction-associated steatohepatitis (MASH), a leading cause of cirrhosis in the modern era. This study sought to refine risk stratification for CKD events post-LT in cirrhosis patients with MASH by leveraging baseline renal function at transplant.

Methods.
A total of 717 MASH cirrhosis patients who had LT (1997–2017) at 7 US centers (NailMASH Consortium) were analyzed. Patients were categorized by estimated glomerular filtration rate (eGFR) at transplant: low (LGFR, eGFR ≤30 mL/min/1.73 m²), medium (MGFR, eGFR >30–≤60 mL/min/1.73 m²), and high (HGFR, eGFR >60 mL/min/1.73 m²). Time-related eGFR intercepts, slopes, and assessments of advanced-stage CKD (aCKD) events, defined as 2 eGFR levels <30 mL/min/1.73 m² separated by ≥90 d, were examined.

Results.
Post-LT, LGFR group showed increased eGFR, whereas the HGFR group experienced a decline. The 3-mo mark was identified as a “reset point,” signifying a new reference level, beyond which a different rate of decline was observed. After 3 mo, mean eGFRs of the LGFR group approached MGFRs, whereas the mean eGFR of the HGFR group continued to decrease but remained higher than other groups during a 60-mo follow-up. LGFR patients had significantly higher aCKD probability than MGFR and HGFR groups. Subanalysis at 3 mo post-LT revealed more aCKD events in the LGFR group compared with MGFR and HGFR groups (P < 0.0001).

Conclusions.
The study underscores renal impact of LT in MASH cirrhosis, indicating unique eGFR trajectories post-LT tied to baseline eGFR, with a reset point at 3 mo. Monitoring post-LT renal function, especially in those at aCKD risk, is crucial. Renal-sparing immunosuppression may help, regardless of baseline eGFR. Further studies are needed for interventions addressing renal dysfunction of patients with MASH post-LT.
DeepVul: A Multi-Task Transformer Model for Joint Prediction of Gene Essentiality and Drug Response
Authors: Jararweh Ala , Arredondo David , Macaulay Oladimeji , Dicome Mikaela , Tafoya Luis , Hu Yue , Virupakshappa Kushal , Boland Genevieve , Flaherty Keith , Sahu Avinash 
Published: October 2024

AbstractDespite their potential, current precision oncology approaches benefit only a small fraction of patients due to their limited focus on actionable genomic alterations. To expand its applicability, we propose DeepVul, a multi-task transformer-based model designed to predict gene essentiality and drug response from cancer transcriptome data. DeepVul aligns gene expressions, gene perturbations, and drug perturbations into a latent space, enabling simultaneous and accurate prediction of cancer cell vulnerabilities to numerous genes and drugs. Benchmarking against existing precision oncology approaches revealed that DeepVul not only matches but also complements oncogene-defined precision methods. Through interpretability analyses, DeepVul identifies underlying mechanisms of treatment response and resistance, as demonstrated with BRAF vulnerability prediction. By leveraging wholegenome transcriptome data, DeepVul enhances the clinical actionability of precision oncology, aiding in the identification of optimal treatments across a broader range of cancer patients. DeepVul is publicly available athttps://github.com/alaaj27/DeepVul.git.
IDH-mutant gliomas arise from glial progenitor cells harboring the initial driver mutation
Authors: Park Jung Won , Kwak Jiehoon , Kim Keon-Woo , Jung Saehoon , Nam Chang Hyun , Kim Hyun Jung , Lee Sang Mee , Park Ji-Hyung , Yoo Jihwan , Shim Jin-Kyoung , Kim Chungyeul , Ahn Sangjeong , Pusch Stefan , von Deimling Andreas , Chang Jong Hee , Kim Se Hoon , Ju Young Seok , Kang Seok-Gu , Lee Jeong Ho 
Published: October 2024

AbstractDiscovering the cell-of-origin harboring the initial driver mutation provides a fundamental basis for understanding tumor evolution and development of new treatments. For isocitrate dehydrogenase (IDH)-mutant gliomas – the most common malignant primary brain tumors in adults under 50 – the cell-of-origin remains poorly understood. Here, using patient brain tissues and genome-edited mice, we identified glial progenitor cells (GPCs), including oligodendrocyte progenitor cells (OPCs), as the glioma-originating cell type harboring the IDH mutation as the initial driver mutation. We conducted comprehensive deep sequencing, including droplet digital PCR and deep panel and amplicon sequencing to 128 tissues from 62 patients (29 IDH-mutant gliomas and 33 IDH-negative controls) comprising tumors, normal cortex or normal subventricular zone (SVZ), and blood. Surprisingly, low-level IDH mutation was found in the normal cortex away from the tumor in 38.5% (10 of 26) of IDH-mutant glioma patients, whereas no IDH mutation was detected in the normal SVZ. Furthermore, by analyzing cell-type–specific mutations, the direction of clonal evolution, the single-cell transcriptome from patient brains and novel mouse model of IDH-mutant glioma arising from mutation-carrying OPCs, we determined that GPCs, including OPCs, harboring the initial driver mutation are responsible for the development and evolution of IDH-mutant gliomas. In summary, our results demonstrate that GPCs containing the IDH mutation are the cells-of-origin harboring the initial driver mutation in IDH-mutant gliomas.
Bitter Taste Receptor Agonists Induce Apoptosis in Papillary Thyroid Cancer
Authors: Wei Kimberly , Hill Brianna L. , Miller Zoey A. , Mueller Arielle , Thompson Joel C. , Lee Robert J. , Carey Ryan M. 
Published: October 2024

AbstractBackgroundPapillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with a 20% recurrence rate. Bitter taste receptors (T2Rs) and their genes (TAS2Rs) may regulate survival in solid tumors. This study examined T2R expression and function in PTC cells.MethodsThree PTC cell lines (MDA-T32, MDA-T68, MDA-T85) were analyzed for expression using RT-qPCR and immunofluorescence. Live cell imaging measured calcium responses to six bitter agonists. Viability and apoptosis effects were assessed using crystal violet and caspase 3/7 activation assays. Genome analysis of survival was conducted.ResultsTAS2R14was consistently highly expressed in all cell lines. Five bitter agonists produced significant calcium responses across all cell lines. All bitter agonists significantly decreased viability and induced apoptosis. HigherTAS2R14expression correlated with better progression-free survival in patients (p<0.05).ConclusionsT2R activation by bitter agonists induces apoptosis and higherTAS2Rexpression is associated with survival, suggesting potential therapeutic relevance in thyroid cancer management.
Moving towards more holistic machine learning-based approaches for classification problems in animal studies
Authors: Christensen Charlotte , Ferreira André C. , Cherono Wismer , Maximiadi Maria , Nyaguthii Brendah , Ogino Mina , Herrera C Daniel , Farine Damien R. 
Published: October 2024

AbstractMachine-learning (ML) is revolutionizing field and laboratory studies of animals. However, a challenge when deploying ML for classification tasks is ensuring the models are reliable. Currently, we evaluate models using performance metrics (e.g., precision, recall, F1), but these can overlook the ultimate aim, which is not the outputs themselves (e.g. detected species or individual identities, or behaviour) but their incorporation for hypothesis testing. As improving performance metrics has diminishing returns, particularly when data are inherently noisy (as human-labelled, animal-based data often are), researchers are faced with the conundrum of investing more time in maximising metrics versus doing the actual research. This raises the question: how much noise can we accept in ML models? Here, we start by describing an under-reported factor that can cause metrics to underestimate model performance. Specifically, ambiguity between categories or mistakes in labelling validation data produces hard ceilings that limit performance metrics. This likely widespread issue means that many models could be performing better than their metrics suggest. Next, we argue and show that imperfect models (e.g. low F1 scores) can still be useable. Using a case study on ML-identified behaviour from vulturine guineafowl accelerometer data, we first propose a simulation framework to evaluate robustness of hypothesis testing using models that make classification errors. Second, we show how to determine the utility of a model by supplementing existing performance metrics with ‘biological validations’. This involves applying ML models to unlabelled data and using the models’ outputs to test hypotheses for which we can anticipate the outcome. Together, we show that effects sizes and expected biological patterns can be detected even when performance metrics are relatively low (e.g., F1: 60-70%). In doing so, we provide a roadmap for validation approaches of ML classification models tailored to research in animal behaviour, and other fields with noisy, biological data.HighlightsEvaluating machine learning (ML) models must go beyond performance metricsMislabels in validation data leads to underestimation of model’s performanceUnderestimated metrics can cause research delays despite models being usefulWe propose simulations and biological validations to evaluate model performanceModels with low standard metrics can still be powerful for hypothesis testing
The combined effect of viral infection and temperature on the gene response of melon and zucchini plants with different levels of temperature tolerance
Authors: De Moya-Ruiz C. , Rabadán M.P. , Gómez P. 
Published: October 2024

AbstractBiotic and abiotic environmental factors shape plant responses. As such the interplay between viral infection and heat-stress can trigger specific physiological and metabolic plant responses that lead to gene-specific changes in defense and development. However, although plant gene expression patterns have been thoroughly studied under a single stress, the extent to which the combination of both stressors could modulate common or exclusive signaling pathways remains unclear. In this study, we examined the effects of watermelon mosaic virus infection and diurnal temperature variations (20/14 °C, 26/20 °C, and 32/24 °C) on the gene responses of two plant species (melon and zucchini), each with high- and low-temperature tolerance, using a differential 3’mRNA-seq approach. The WMV load was much greater in zucchini than in melon plants, and was also dependent on the temperature conditions and tolerance of each plant species. Our comparative RNA-seq analysis revealed that the percentage of differentially expressed genes (DEGs) was higher in the thermo-susceptible plants of both species under the combination of WMV infection and low temperatures (20 °C). Among these significantly regulated genes, between 37 % and 45 % were related to biotic and/or abiotic stress. Furthermore, we found that 30 GO terms were involved in the response to both combined stress from low temperatures and 23 GO terms for high temperatures, which were exclusive to the thermotolerant varieties. Together, these findings allowed the identification of two unique orthologous genes linked to temperature and virus infection in melon and zucchini plants. Understanding the effects of biotic and abiotic factors on plant responses is essential for unraveling the complexity of plant-pathogen-environment interactions and developing strategies to enhance plant resilience and productivity under changing climatic conditions.
Exact Continuous Spiking Rate Inference
Authors: Stern Merav 
Published: October 2024

AbstractMany cognitive functions involve multiple brain areas simultaneously processing, distributing, and sharing information. The wide-field imaging technique can record such brain-wide activity since it offers a unique combination of simultaneous recordings from a wide field of view at a high rate. This unique combination is achieved by using a single-photon camera, which is set to capture, from a large area, neural activity-driven light emission generated by calcium indicators. Adequately analyzing this captured data requires inferring the underlying neural activity from the recorded fluorescence, a challenging mathematical problem. The challenge arises from the calcium indicator dynamics, which distort the neural dynamics as it transforms the neural activity into light emission and from the presence of noise. The wide-field setting adds a distinctive challenge. Its wide field of view recordings at a high rate constrains the spatial resolution to be limited. As a result, each fluorescence trace captured by each camera’s pixel originates from many neuron activities and not a single neuron as typical of other recording techniques. No previous, rigorously studied analytic solution exists for inferring neural activity from recorded fluorescence in the wide-field setting. Here, we phrase the inference problem arising from wide-field recordings as an optimization problem and solve it exactly. To ensure the robustness of our solution and provide a solid foundation for its application, we rigorously verify it using real data. We further suggest a novel approach for the optimization problem parameter-tuning. Beyond recovering the neural dynamics, our inference will allow future studies to retrieve accurate, correlation-based analyses of brain-wide activity.
Longitudinal Microbiome-based Interpretable Machine Learning for Identification of Time-Varying Biomarkers in Early Prediction of Disease Outcomes
Authors: Dai Yifan , Qian Yunzhi , Qu Yixiang , Guan Wyliena , Xie Jialiu , Wang Duan , Butler Catherine , Dashper Stuart , Carroll Ian , Divaris Kimon , Liu Yufeng , Wu Di 
Published: October 2024

AbstractInformation generated from longitudinally-sampled microbial data has the potential to illuminate important aspects of development and progression for many human conditions and diseases. Identifying microbial biomarkers and their time-varying effects can not only advance our understanding of pathogenetic mechanisms, but also facilitate early diagnosis and guide optimal timing of interventions. However, longitudinal predictive modeling of highly noisy and dynamic microbial data (e.g., metagenomics) poses analytical challenges. To overcome these challenges, we introduce a robust and interpretable machine-learning-based longitudinal microbiome analysis framework, LP-Micro, that encompasses: (i) longitudinal microbial feature screening via a polynomial group lasso, (ii) disease outcome prediction implemented via machine learning methods (e.g., XGBoost, deep neural networks), and (iii) interpretable association testing between time points, microbial features, and disease outcomes via permutation feature importance. We demonstrate in simulations that LP-Micro can not only identify incident disease-related microbiome taxa but also offers improved prediction accuracy compared to existing approaches. Applications of LP-Micro in two longitudinal microbiome studies with clinical outcomes of childhood dental disease and weight loss following bariatric surgery yield consistently high prediction accuracy. The identified critical early predictive time points are informative and aligned with clinical expectations.
Nanoscale curvature of the plasma membrane regulates mechanoadaptation through nuclear deformation and rupture
Authors: Francis Emmet A. , Sarikhani Einollah , Patel Vrund , Meganathan Dhivya Pushpa , Jahed Zeinab , Rangamani Padmini 
Published: October 2024

AbstractNuclear translocation of the transcription regulatory proteins YAP and TAZ is a critical readout of cellular mechanotransduction. Recent experiments have demonstrated that cells on substrates with well-defined nanotopographies exhibit an altered mechanical and signaling response when compared to those on flat substrates, demonstrating mechanoadaptation to geometric constraints. Specifically, such cells show lower rates of focal adhesion formation, resulting in lower amounts of YAP/TAZ nuclear translocation. In this study, we investigate how the crosstalk between substrate nanotopography and mechanotransduction affects cytoskeletal activity and the nuclear transport of YAP/TAZ. We develop a biophysical model that incorporates plasma membrane (PM) curvature-dependent inhibition of integrin-mediated signaling, PM curvature-sensitive actin assembly, and stretch-induced opening of nuclear pore complexes (NPCs) upon indentation of the nuclear envelope (NE) by nanopillars. Our model predicts lower levels of cytoskeletal activation on nanopillar substrates, consistent with experiments. We demonstrate that this effect can be partially compensated for by increasing the indentation of the NE, leading to local cytoskeletal accumulation and enhanced YAP/TAZ transport through stretched NPCs. Nuclear deformation and cytoskeletal arrangement in our model agree well with experimental fluorescence images and electron micrographs of cells on nanopillar substrates. We then use our model to predict the effects of NE rupture on YAP/TAZ nuclear abundance, showing that if nuclear entry is favored over export through these rupture-induced pores, YAP/TAZ accumulates in the nucleus. We confirm this prediction experimentally, showing that nuclear YAP/TAZ increases in cells with ruptured NEs.
Social contact patterns and their impact on the transmission of respiratory pathogens in rural China
Authors: Liang Yuxia , Zhang Juanjuan , You Qian , Wang Qianli , Yang Xiaohong , Zhong Guangjie , Dong Kaige , Zhao Zeyao , Liu Nuolan , Yan Xuemei , Lu Wanying , Peng Cheng , Zhou Jiaxin , Lin Jiqun , Litvinova Maria , Jit Mark , Ajelli Marco , Yu Hongjie 
Published: October 2024

AbstractIntroductionSocial contact patterns significantly influence the transmission dynamics of respiratory pathogens. Previous surveys have quantified human social contact patterns, yielding heterogeneous results across different locations. However, significant gaps remain in understanding social contact patterns in rural areas of China.MethodsWe conducted a pioneering study to quantify social contact patterns in Anhua County, Hunan Province, China, from June to October 2021, when there were minimal coronavirus disease-related restrictions in the area. Additionally, we simulated the epidemics under different assumptions regarding the relative transmission risks of various contact types (e.g., indoor versus outdoor, and physical versus non-physical).ResultsParticipants reported an average of 12.0 contacts per day (95% confidence interval: 11.3–12.6), with a significantly higher number of indoor contacts compared to outdoor contacts. The number of contacts was associated with various socio-demographic characteristics, including age, education level, income, household size, and travel patterns. Contact patterns were assortative by age and varied based on the type of contact (e.g., physical versus non-physical). The reproduction number, daily incidence, and infection attack rate of simulated epidemics were remarkably stable.DiscussionWe found many intergenerational households and contacts that pose challenges in preventing and controlling infections among the elderly in rural China. Our study also underscores the importance of integrating various types of contact pattern data into epidemiological models and provides guidance to public health authorities and other major stakeholders in preparing and responding to infectious disease threats in rural China.
Whole-genome re-sequencing of the Baikal seal and other phocid seals for a glimpse into their genetic diversity, demographic history, and phylogeny
Authors: Nebenführ Marcel , Arnason Ulfur , Janke Axel 
Published: October 2024

AbstractBackgroundThe iconic Baikal seal (Pusa sibirica), the smallest true seal, is a freshwater seal that is endemic to Lake Baikal where it became landlocked some million years ago. It is a rather abundant species of least concern, despite the limited habitat. Until recently, research on its genetic diversity has only been done on mitochondrial genes, restriction fragment analyses, and microsatellites, before its reference genome has been published.FindingsHere we report the genome sequences of six Baikal seals, and one individual of the Caspian seal, ringed seal, and the harbor seal, re-sequenced from Illumina paired-end short read data. Heterozygosity calculations of the six newly sequenced individuals are like the previously reported genomes. In addition, the novel genome data of the other species contributed to a more complete phocid seal phylogeny based on whole-genome data.ConclusionsDespite the long isolation of the land-locked Baikal seal population, the genetic diversity of this species is in the same range as other seal species. The Baikal seal appears to form a single, diverse population. However, targeted genome studies are needed to explore the genomic diversity throughout their distribution.
Global, Regional, and National Burdens of Ischemic Stroke Attributed to High Body-mass Index from 1990 to 2021
Authors: Zhang Yuan , Ren Xuesong , Zhao Xiaofeng , Meng Lina , Lu Hai , Zhang Chunhong 
Published: October 2024

AbstractBackgroundIschemic stroke (IS) is a significant contributor to the global disease burden, with overweight/obesity recognized as one of the primary risk factors. This study aims to evaluate the distribution and changes in the global burden of IS attributable to high body-mass index (BMI) from 1990 to 2021, providing a foundation for the development of targeted prevention and treatment strategies.MethodsBased on the modeling strategies of the 2021 global burden of disease (GBD) dataset, the burden of IS attributable to high BMI was estimated. The assessment covered the global disease burden and its distribution by region, country, development level, age group, and gender, as well as temporal trends. Age-standardized rates, along with the estimated annual percentage change (EAPC) in these rates, were utilized to describe this burden and its evolving trends. Locally estimated scatterplot smoothing (LOESS) regression was employed to analyze the trends of disease burden in relation to changes in socio-demographic index (SDI).ResultsFrom 1990 to 2021, although the age-standardized death rate and disability-adjusted life years (DALY) rate for IS attributable to high BMI have declined globally, the absolute number of deaths and DALYs nearly doubled, reaching 172,390.65 (95% UI, 347,912.75 to 25,064.57) and 4,439,186.31 (95% UI, 8,647,485.11 to 649,030.21) respectively. Meanwhile, significant disparities in disease burden existed across different regions, genders, and age groups. In high-middle SDI regions, the burden of IS caused by high BMI significantly exceeded that in other regions, while in low-income areas, this burden was rapidly increasing. Additionally, the increase in burden among men was significantly greater than that among women, and the previously concentrated burden in the elderly is now rapidly shifting toward younger groups.ConclusionsThe overweight and obese populations continue to present significant challenges in managing the burden of IS. In light of future population growth and aging trends, more effective stroke prevention strategies and practical solutions are urgently needed, particularly in low- and middle-income countries. Additionally, given the increasing disease burden among men and younger populations, it is essential to establish targeted preventive measures to address the specific needs of different groups and improve overall health outcomes.