bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepSpot: Leveraging Spatial Context for Enhanced Spatial Transcriptomics Prediction from H&E Images
Authors: Nonchev Kalin , Dawo Sebastian , Silina Karina , Moch Holger , Andani Sonali ,   Tumor Profiler Consortium, Koelzer Viktor H , Rätsch Gunnar 
Published: February 2025

AbstractSpatial transcriptomics technology remains resource-intensive and unlikely to be routinely adopted for patient care soon. This hinders the development of novel precision medicine solutions and, more importantly, limits the translation of research findings to patient treatment. Here, we present DeepSpot, a deep-set neural network that leverages recent foundation models in pathology and spatial multi-level tissue context to effectively predict spatial transcriptomics from H&E images. DeepSpot substantially improved gene correlations across multiple datasets from patients with metastatic melanoma, kidney, lung, or colon cancers as compared to previous state-of-the-art. Using DeepSpot, we generated 1 792 TCGA spatial transcriptomics samples (37 million spots) of the melanoma and renal cell cancer cohorts. We anticipate this to be a valuable resource for biological discovery and a benchmark for evaluating spatial transcriptomics models. We hope that DeepSpot and this dataset will stimulate further advancements in computational spatial transcriptomics analysis.
Probing Large Language Model Hidden States for Adverse Drug Reaction Knowledge
Authors: Berkowitz Jacob , Weissenbacher Davy , Srinivasan Apoorva , Friedrich Nadine A. , Acitores Cortina Jose Miguel , Kivelson Sophia , Hernandez Graciela Gonzalez , Tatonetti Nicholas P. 
Published: February 2025

AbstractLarge language models (LLMs) integrate knowledge from diverse sources into a single set of internal weights. However, these representations are difficult to interpret, complicating our understanding of the models’ learning capabilities. Sparse autoencoders (SAEs) linearize LLM embeddings, creating monosemantic features that both provide insight into the model’s comprehension and simplify downstream machine learning tasks. These features are especially important in biomedical applications where explainability is critical. Here, we evaluate the use of Gemma Scope SAEs to identify how LLMs store known facts involving adverse drug reactions (ADRs). We transform hidden-state embeddings of drug names from Gemma2-9b-it into interpretable features and train a linear classifier on these features to classify ADR likelihood, evaluating against an established benchmark. These embeddings provide strong predictive performance, giving AUC-ROC of 0.957 for identifying acute kidney injury, 0.902 for acute liver injury, 0.954 for acute myocardial infarction, and 0.963 for gastrointestinal bleeds. Notably, there are no significant differences (p > 0.05) in performance between the simple linear classifiers built on SAE outputs and neural networks trained on the raw embeddings, suggesting that the information lost in reconstruction is minima. This finding suggests that SAE-derived representations retain the essential information from the LLM while reducing model complexity, paving the way for more transparent, compute-efficient strategies. We believe that this approach can help synthesize the biomedical knowledge our models learn in training and be used for downstream applications, such as expanding reference sets for pharmacovigilance.
Evaluating Online Educational Resources for Osteogenesis Imperfecta: Insights from Google, Bing, and ChatGPT3.5
Authors: Partin Victoria-Jessica C. , Hooberman-Pineiro Andrea M. , Pace April A. 
Published: February 2025

AbstractBackgroundOsteogenesis Imperfecta is a rare connective tissue disorder resulting in a diverse range of musculoskeletal deformities. Patient education is an important aspect of medical care, and the internet is a popular place in which patients seek information about their medical conditions. This is a cross-sectional study aimed to evaluate the quality of online patient educational websites about Osteogenesis Imperfecta.MethodsThe authors searched for patient education websites using Google and Bing. Twenty patient education websites about Osteogenesis Imperfecta were collected from each. Websites meeting inclusion criteria were evaluated using the DISCERN tool, JAMA Benchmark Criteria evaluation, and a Flesch-Kincaid Readability test. T-tests were used to compare search engine results. Websites were also collected from ChatGPT3.5, however, due to hallucinations and exclusion criteria, the sample size was too small to compare to the search engines.ResultsNo significant differences were found between search engines, and the quality of the websites was not high. Average overall ratings for DISCERN were mediocre, and for the JAMA criteria they were low. The average readability scores required 8-9 grade level reading comprehension, although there was variability within each search engine.ConclusionNeither search engine provided significantly better sources than the other, the overall quality of the websites was not high, and the low readability scores could make these difficult for patients with lower health literacy to understand
Burden of rodent-borne viruses in rodents and zoonotic risk in human in Cambodia: a descriptive and observational study
Authors: Guillebaud Julia , Nouhin Janin , Hul Vibol , Hoem Thavry , Yanneth Oudamdaniel , Sim Mala , Khun Limmey , Phalla Y , Ken Sreymom , Pum Leakhena , Lim Reaksa , Meng Channa , Chhel Kimtuo , Nuon Sithun , Hoem Sreyleak , Nguon Kunthy , Chan Malen , Ly Sowath , Karlsson Erik A. , Reynes Jean-Marc , Sakunthabhai Anavaj , Dussart Philippe , Duong Veasna 
Published: February 2025

AbstractBackgroundRodent-borne viruses, including hantaviruses, arenaviruses, and rodent hepatitis virus (HEV-C), pose significant health threats to humans, causing severe diseases such as hepatitis, respiratory illness, and hemorrhagic fevers. In Cambodia, data on these viruses remain limited, and their burdens on human health are unknown. This study investigated the presences of these viruses in rodents and assessed potential human exposure across diverse environmental and socio-economic contexts in Cambodia.MethodsThe study was conducted in urban, semi-urban, and rural areas of Cambodia during the rainy (2020) and dry seasons (2022). Rodents were screened for arenavirus, hantavirus, and HEV-C using RT-PCR. Human serum samples from the same site were tested for IgG antibodies using ELISA. Factors associated with virus spillover into humans were analyzed.FindingsAmong 750 rodents, 9.7% carried at least one virus: 5.2% arenavirus, 3.3% hantavirus, and 1.9% HEV-C. Infection rates were highest in urban (14.5%), followed by semi-urban (11.9%) and rural (2.1%) interfaces. Arenavirus was more prevalent during the rainy season, while hantavirus and HEV-C remained consistent across seasons. Seroprevalence in human was 12.7% for arenavirus, 10.0% for hantavirus, and 24.2% for HEV. Higher arenavirus seroprevalence was associated with urban recidency and lower education level. Hantavirus seroprevalence was associated with urban residency, acute hepatitis history, and flood-prone living areas. HEV seroprevalence increased with urban residency, increasing age, and medical condition history.InterpretationOur findings highlighted the need for rodent control, improved market infrastructure, enhanced waste management, and public awareness on hygiene practices and zoonotic risks, especially in urban and high-risk areas.
Liquid-Dendrite Spiking Neural Network for Edge Devices: A 130 K-Parameter, 535 KB Model for Time-Domain Epileptic Seizure Detection
Authors: Herbozo Contreras Luis Fernando , Yu Leping , Huang Zhaojing , Nikpour Armin , Kavehei Omid 
Published: February 2025

AbstractEpilepsy is a significant global health issue, requiring dependable diagnostic tools like scalp encephalogram (scalp-EEG), sub-scalp EEG, and intracranial EEG (iEEG) for precise seizure detection and treatment. AI has emerged as a powerful tool in this domain, offering the potential for real-time, responsive monitoring. Traditional methods often rely on feature extraction techniques like Short-Time Fourier Transform (STFT), which can increase power consumption, making them less suitable for deployment on edge devices. While large models can improve accuracy without STFT, their size also limits their practicality for edge applications. This study introduces Liquid-Dendrite, a novel bio-inspired model for seizure detection, leveraging Liquid-Time Constant Spiking Neurons (LTC-SN) and dendrites spiking neurons (dSN) with heterogeneous time-constants. The model comprises two hidden layers with dendritic neurons and one layer of liquid-time constant networks. Our model achieves a memory efficacy of 535 KB with 130 K trainable parameters. The model was tested across the most noteworthy epilepsy datasets for scalp EEG (TUH and CHB-MIT) and iEEG (EPILEPSIAE). Our model demonstrated commendable performance, achieving AUROC scores of 83%, 96%, and 93%, respectively, outperforming some existing models in an energy and memory-efficient way. Moreover, we conducted a robustness test by blacking out EEG channels at the inference stage, where we showed the ability of our network to work with fewer channels. We could deploy our tiny model and perform inference at the edge of the Raspberry Pi 5 without the need for additional quantization. This highlights the potential of Neuro-Inspired AI for efficient, small-scale, and energy-embedded AI systems across different brain modalities.
The range-resident logistic model: a new framework to formalize the population-dynamics consequences of range residency
Authors: Menezes Rafael , Calabrese Justin M. , Fagan William F. , Prado Paulo Inácio , Martinez-Garcia Ricardo 
Published: February 2025

AbstractIndividual movement is critical in shaping population dynamics. However, theoretical frame-works linking these two processes often make unrealistic assumptions about how animals move or, when attempting to incorporate realistic movement patterns, must rely on numerical simulations. To address this gap, we introduce the range-resident logistic model, which is an easy-to-simulate and mathematically tractable extension of the spatial logistic model that incorporates empirically supported range-resident movement. Our framework unifies previous non-spatial and (sessile) spatial formulations of the logistic model as limiting cases, recovering these two limits when home ranges are infinitely large and vanishingly small, respectively. Interpolating between these two regimes, the long-term population size depends nonlinearly on home-range size and spatial distribution. Neglecting range residency can hence lead to significantly under- or overestimating population carrying capacity. To better understand these results, we also introduce a novel crowding index that depends on movement parameters and can be estimated from tracking data. This index captures the influence of spatial structure on population size, and serves as a robust predictor of abundance. The range-resident logistic model is thus a unifying framework bridging movement and population ecology, and provides new insights into how different patterns of individual space usage shape intraspecific interactions and regulate population growth.
The Polymorphisms, Solvent Accessibility and Conservatism of Hepatitis C Virus Nonstructural 5B Protein
Authors: Lian Dejun 
Published: February 2025

ABSTRACTThe polymorphisms of protein or protein family, that is, the divergences of amino acid and nucleotide sequences have provided much useful information on the divergent evolution of proteins. In this paper, we analyzed the polymorphisms of enzyme NS5B of HCV for which sequence variation among most isolates have been characterized and protein structures of the catalytic domain form of this enzyme are also known. For this protein, we found that solvent accessibility of residues in the protein structure is a strong predictor of whether or not an amino acid will be polymorphic and the residue variability. Apart from polymorphism, we found conservatism at every level among site is universal for this protein. We also found that purifying selection at different levels was strong in the forming of the polymorphisms and conservatism of this protein.
Dentate nucleus deep brain stimulation for spinocerebellar ataxia: results of a six-month follow-up
Authors: Zhao Liang , Qiu Chang , Dong Wenwen , Luo Bei , Sun Jian , Yan Jiuqi , Wei Xiang , Lu Guanghan , Liu Jingxuan , Zhang Wenbin 
Published: February 2025

AbstractSpinocerebellar ataxia (SCA) is a genetically heterogeneous neurodegenerative disorder lacking effective treatments currently. While noninvasive cerebellum neuromodulation showed positive results, invasive techniques like deep brain stimulation (DBS) have not been fully assessed for SCA patients. This study evaluated the treatment outcomes of DBS targeting the cerebellar dentate nucleus (DN) in six SCA patients (SCA1, SCA3, SCA12) over a six-month follow-up. Bilateral DN-DBS leads were precisely implanted using a neurosurgical robot. The stimulation parameters, including current, frequency, and pulse width, were programmed iteratively at predefined intervals, and the improvements in ataxia symptoms were evaluated based on the Scale for the Assessment and Rating of Ataxia (SARA) and the International Cooperative Ataxia Rating Scale (ICARS). Current results showed that current amplitude was fundamental to ensuring therapeutic efficacy, while frequency differentially alleviated tremor with high-frequency stimulation and gait disorder at low frequencies. At the six-month follow-up, the SARA and ICARS scores decreased by 43% (8.17 ± 2.58 vs. 14.33 ± 1.51,p=0.014) and 51% (18.67 ± 7.50 vs. 38.17 ± 8.13,p=0.013) respectively compared with baseline. Our study demonstrated the promising therapeutic benefits of DN-DBS for SCA patients and provided preliminary experience for individualized parameter programming.
Human genetics suggests differing causal pathways fromHMGCRinhibition to coronary artery disease and type 2 diabetes
Authors: Hwang Seongwon , Karhunen Ville , Patel Ashish , Lockhart Sam , Carter Paul , Whittaker John , Burgess Stephen 
Published: February 2025

AbstractBackgroundStatins lower low-density lipoprotein cholesterol (LDL-C) and reduce the risk of coronary artery disease (CAD). However, they also increase the risk of type 2 diabetes (T2D).MethodsWe consider genetic variants in the region of theHMGCRgene, which encodes the target of statins, and their associations with downstream consequences of statins. We use various statistical methods to identify causal pathways influencing CAD and T2D, and investigate whether these are the same or different for the two diseases.ResultsColocalization analyses indicated that LDL-C and body mass index (BMI) have distinct genetic predictors in this gene region, suggesting that they do not lie on the same causal pathway. Multivariable Mendelian randomization analyses restricted to variants in theHMGCRgene region revealed LDL-C and BMI as causal risk factors for CAD, and BMI as a causal risk factor for T2D, but not LDL-C. A Bayesian model averaging method prioritized BMI as the most likely causal risk factor for T2D, and LDL-C as the second most likely causal risk factor for CAD (behind ubiquinone). Colocalization analyses provided consistent evidence of LDL-C colocalizing with CAD, and BMI colocalizing with T2D; evidence was inconsistent for colocalization of LDL-C with T2D, and BMI with CAD.ConclusionsOur analyses suggest cardiovascular and metabolic consequences of statin usage are on different causal pathways, and hence could be influenced separately by targeted interventions. More broadly, our analysis workflow offers potential insights to identify pathway-specific causal risk factors that could provide possible repositioning or refinement opportunities for existing drug targets.Key messagesWe performed colocalization and cis-multivariable Mendelian randomization using genetic association data for variants in theHMGCRgene region to investigate causal pathways influencing coronary artery disease (CAD) and type 2 diabetes (T2D)Our analyses suggest that the impact of HMGCR inhibition on CAD risk is mediated by both low-density lipoprotein cholesterol (LDL-C) and body mass index (BMI), whereas for T2D, risk was mediated via BMI but not LDL-C.Our results suggest the possibility that targeted treatments could be developed to inhibit HMGCR in a more specific way that lowers CAD risk without increasing T2D risk.
Osteoporosis Prevalence in Cardiovascular Kidney Metabolic Syndrome: Implications for Mortality
Authors: Du Xi , Yang Qianyu , Duan Zhiqiang , Gu Hui , Zhang Xue , Liu Hao , Xiao Mao , Li Zhuoxing , Zhao Ming , Xiao Xiang 
Published: February 2025

AbstractObjectiveThis study aims to investigate the prevalence of osteoporosis (OS) in patients with Cardiovascular Kidney Metabolic (CKM) Syndrome and its relationship with the risk of mortality in CKM patients.MethodsPatients diagnosed with CKM were included from the National Health and Nutrition Examination Survey database between 2009 and 2018. The primary endpoint was all-cause mortality, while the secondary endpoint was cardiovascular mortality. The relationship between OS and the prognosis of all-cause mortality and cardiovascular mortality in CKM patients was assessed using Kaplan-Meier survival curves and multivariate Cox regression model.ResultsA total of 602 Non-CKM (stage 0) and 6129 CKM (stages 1, 2, 3, and 4) patients were included. The prevalence of OS in CKM patients at stages 0, 1, 2, 3, and 4 was 3.49%, 2.25%, 4.97%, 13.52%, and 8.39%, respectively. The multivariate Cox regression analysis revealed that compared to the normal bone mineral density group, CKM patients in the osteopenia group had a 55% increased risk of all-cause mortality (HR=1.55; 95% CI, 1.22-1.98,P= 0.001) and a 120% increase in the risk of cardiovascular mortality (HR=2.20; 95% CI, 1.39-3.46,P= 0.001); In the OS group, CKM patients exhibited a 199% increased risk of all-cause mortality (HR = 2.99; 95% CI, 1.86-4.82,P= 0.001) and a 2.14-fold increase in the risk of cardiovascular mortality (HR=3.14; 95% CI, 1.09-9.02,P= 0.03).ConclusionOsteopenia and OS are more prevalent in patients with CKM and are associated with an increased risk of all-cause and cardiovascular mortality.
Performance of DeepSeek-R1 in Ophthalmology: An Evaluation of Clinical Decision-Making and Cost-Effectiveness
Authors: Mikhail David , Farah Andrew , Milad Jason , Nassrallah Wissam , Mihalache Andrew , Milad Daniel , Antaki Fares , Balas Michael , Popovic Marko M. , Feo Alessandro , Muni Rajeev H. , Keane Pearse A. , Duval Renaud 
Published: February 2025

ABSTRACTPurposeTo compare the performance and cost-effectiveness of DeepSeek-R1 with OpenAI o1 in diagnosing and managing ophthalmology clinical cases.Study DesignCross-sectional evaluation.MethodsA total of 300 clinical cases spanning 10 different ophthalmology subspecialties were collected from StatPearls. Each case presented a multiple-choice question regarding the diagnosis or management of the clinical case. DeepSeek-R1 was accessed through its public chat-based interface, while OpenAI o1 was queried via an Application Program Interface (API) with a standardized temperature setting of 0.3. Both models were prompted using the Plan-and-Solve+ (PS+) prompt engineering method, instructing them to answer multiple choice questions for each case. Performance was calculated as the proportion of correctly answered multiple choice questions. McNemar’s test was employed to compare the two models’ performance on paired data. Inter-model agreement for correct diagnoses was evaluated via Cohen’s kappa. A token-based cost analysis was performed to estimate the comparative expenditures of running each model at scale, accounting for both input prompts and model-generated output.ResultsDeepSeek-R1 and OpenAI o1 both achieved an identical overall performance of 82.0% (n=246/300; 95% CI: 77.3-85.9). Subspeciality-specific analysis revealed numerical variation in performance, though none of these comparisons reached statistical significance (p>0.05).Agreement in performance between the models was moderate overall (κ=0.503, p<0.001), with substantial agreement in Refractive Management/Intervention (κ=0.698, p<0.001) and moderate agreement in Retina/Vitreous (κ=0.561, p<0.001) and Ocular Pathology/Oncology (κ=0.495, p<0.01) cases. Cost analysis indicated an approximately 15-fold reduction in per-query, token-related expenses when using DeepSeek-R1 compared with OpenAI o1 for the same workload.ConclusionsDeepSeek-R1 demonstrates robust diagnostic reasoning and management decision-making capabilities, performing comparably to OpenAI o1 across a range of ophthalmic subspecialty cases, while also offering a substantial reduction in usage costs. These findings highlight the feasibility of utilizing open-weight, reinforcement learning-augmented LLMs as an accessible, cost-effective alternative to proprietary models.
Depressive symptoms and physical activity mediate the adverse effect of pain on functional independence in patients with arthritis: Evidence from the Canadian Longitudinal Study on Aging
Authors: Goubran Miriam , van Allen Zachary M. , Bilodeau Martin , Boisgontier Matthieu P. 
Published: February 2025

ABSTRACTObjectiveArthritis is a chronic condition affecting hundreds of millions of people worldwide, often leading to pain and functional limitations. This study aimed to investigate the direct and indirect effects of pain on functional independence in individuals with arthritis. Depressive symptoms and physical activity were examined as potential mediators of this relationship.MethodsA total of 6972 participants with arthritis (4930 with osteoarthritis and 694 with rheumatoid arthritis) were included from the Canadian Longitudinal Study on Aging. Multiple linear regression models, generalized linear models, and bootstrapping were used to assess the relationships between baseline pain, depressive symptoms, physical activity, and functional status at follow-up.ResultsBaseline pain was positively associated with depressive symptoms (b = 0.356 [95% CI: 0.310 to 0.402]) and negatively associated with physical activity (b = −0.083 [95% CI: −0.125 to −0.042]). Functional status at follow-up was significantly predicted by baseline pain (OR = 1.834 [95% CI: 1.306 to 2.610]), depressive symptoms (OR = 1.431 [95% CI: 1.205 to 1.709]), and physical activity (OR = 0.550 [95% CI: 0.440 to 0.683]). Mediation analysis showed that 30.4% of the effect of pain on functional status was mediated by the total indirect effect, with contributions from depressive symptoms (19.8%), physical activity (8.9%), and the serial mediation pathway (1.7%).ConclusionsPain at baseline was associated with a higher likelihood of functional dependence in basic and instrumental activities of daily living after a mean follow-up period of 6.3 years, with depressive symptoms and lower physical activity acting as partial mediators. These findings highlight the importance of managing pain, mental health, and physical activity in patients with arthritis to maintain functional independence.ImpactThese findings support the importance for intervention to target both mental health and exercise to mitigate functional decline resulting from the long-term effects of pain in patients with arthritis.