bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thoracic Computed Tomographic Abnormalities in Patients with Spinal Tuberculosis: A Prospective Evaluation
Authors: Sahu Anil Kumar , Kumar Neeraj , Garg Ravindra Kumar , Malhotra Hardeep Singh , Malhotra Kiran Preet , Rizvi Imran , Jain Amita , Parihar Anit , Verma Rajesh , Sharma Praveen Kumar , Uniyal Ravi , Pandey Shweta 
Published: November 2024

AbstractBackgroundSpinal tuberculosis is the most common osteoarticular tuberculosis. Many published isolated reports indicated that many patients with spinal tuberculosis have lung abnormalities. In this study, we aimed to prospectively evaluate the prevalence and spectrum of lung abnormalities.MethodsWe enrolled consecutive newly diagnosed patients with spinal tuberculosis. They were subjected to spinal magnetic resonance imaging and computed tomography (CT) of the chest. They were followed for 6 months. The outcome was assessed by using the modified Rankin scale.ResultsOut of 66 patients, 22 (33.3%) patients had CT thorax abnormalities. The most frequent finding was lung fibrosis (13 patients). Six patients had consolidation. Miliary tuberculosis was demonstrated in one patient. Cavitation, atelectasis, emphysema, and traction bronchiectasis were noted in one patient each. Nine patients had pleural effusion. Pleural thickening and empyema were noted in two patients each. Empyema was noted in 2 patients. Hydropneumothorax was seen in one patient. One patient had endobronchial tuberculosis. Hilar and Mediastinal lymphadenopathy was noted in 6 patients. At inclusion, 59 (89.4%) patients had modified Rankin scale scores≥3. After 6 months, 58 (87.9%) patients showed improvement and achieved a score <3. In 4 patients, spinal surgery was needed. The remaining three patient’s disability statuses remained unaltered. CT chest abnormalities were not associated with improvement in disability.ConclusionOne-third of patients with spinal tuberculosis had tuberculous CT thorax abnormalities. The most frequent CT thorax finding was lung fibrosis. CT abnormalities did not affect the outcome.
Persistent Health and Cognitive Impairments Up to Four Years Post-COVID-19 in Young Students: The Impact of Virus Variants and Vaccination Timing
Authors: Latifi Ashkan , Flegr Jaroslav 
Published: November 2024

AbstractThe long-term consequences of COVID-19 infection are becoming increasingly evident in recent studies. This repeated cross-sectional study aimed to explore the long-term health and cognitive effects of COVID-19, focusing on how virus variants, vaccination, illness severity, and time since infection impact post-COVID-19 outcomes. We examined three cohorts of university students (N=584) and used non-parametric methods to assess correlations of various health and cognitive variables with SARS-CoV-2 infection, COVID-19 severity, vaccination status, time since infection, time since vaccination, and virus variants. Our results show that some health and cognitive impairments persist, with some even progressively worsening especially fatigue in women and memory in men, up to four years post-infection, with the Wuhan variant having the most significant long-term impact and the Omicron variant the least. Interestingly, the severity of the acute illness was not correlated with the variant of SARS-CoV-2. The analysis also revealed that individuals who contracted COVID-19 after vaccination had better health and cognitive outcomes compared to those infected before vaccination. The study also discusses some limitations inherent in cross-sectional studies, particularly those arising from the stronger tendency of individuals with poorer health, compared to healthier individuals, to avoid infection and prioritize vaccination. To mitigate potential bias, the study proposes focusing on factors such as illness severity and time since infection or vaccination when analyzing persistent symptoms.
Evaluation of Radiology Residents’ Reporting Skills Using Large Language Models: An Observational Study
Authors: Atsukawa Natsuko , Tatekawa Hiroyuki , Oura Tatsushi , Matsushita Shu , Horiuchi Daisuke , Takita Hirotaka , Mitsuyama Yasuhito , Omori Ayako , Shimono Taro , Miki Yukio , Ueda Daiju 
Published: November 2024

AbstractBackgroundLarge language models (LLMs) have the potential to objectively evaluate radiology resident reports; however, research on their use for feedback in radiology training and assessment of resident skill development remains limited.PurposeThis study aimed to assess the effectiveness of LLMs in revising radiology reports by comparing them with reports verified by board-certified radiologists and to analyze the progression of resident’s reporting skills over time.Materials and methodsTo identify the LLM that best aligned with human radiologists, 100 reports were randomly selected from a total of 7376 reports authored by nine first-year radiology residents. The reports were evaluated based on six criteria: (1) Addition of missing positive findings, (2) Deletion of findings, (3) Addition of negative findings, (4) Correction of the expression of findings, (5) Correction of the diagnosis, and (6) Proposal of additional examinations or treatments. Reports were segmented into four time-based terms, and 900 reports (450 CT and 450 MRI) were randomly chosen from the initial and final terms of the residents’ first year. The revised rates for each criterion were compared between the first and last terms using the Wilcoxon Signed-Rank test.ResultsAmong the LLMs tested, GPT-4o demonstrated the highest level of agreement with board-certified radiologists. Significant improvements were noted in Criteria 1–3 when comparing reports from the first and last terms (all P < 0.023) using GPT-4o. In contrast, no significant changes were observed for Criteria 4–6. Despite this, all criteria except for Criterion 6 showed progressive enhancement over time.ConclusionLLMs can effectively provide feedback on commonly corrected areas in radiology reports, enabling residents to objectively identify and improve their weaknesses and monitor their progress. Additionally, LLMs may help reduce the workload of radiologists’ mentors.
Application of Bayesian spatial modelling to uncover geographical disparities and improve antimicrobial resistant surveillance
Authors: Wozniak Teresa Maria , Young Alys , Conlan David , Shausan Aminath , Dyda Amalie , Sartorius Benn , Cespedes Marcela 
Published: November 2024

AbstractIntroductionDisease surveillance is an essential element of an effective response to antimicrobial resistance (AMR). Associations between AMR cases and area-level drivers such as remoteness and socio-economic disadvantage have been observed, but spatial associations when modelling routinely collected surveillance data that are often imperfect or missing have not been previously possible.AimWe aimed to use spatial modelling to adjust for area-level variables and to enhance AMR surveillance for missing or sparse data, in an effort to provide clinicians and policy makers with more actionable epidemiological information.MethodsWe used monthly antimicrobial susceptibility data for methicillin-resistantStaphylococcus aureus(MRSA) from a surveillance system in Australia. MRSA was assessed for the effects of age, sex, socio-economic and access to healthcare services indices by fitting Bayesian spatial models.ResultsWe analysed data for 77, 760 MRSA isolates between 2016 and 2022. We observed significant spatial heterogeneity in MRSA and found significant associations with age, sex and remoteness, but not socio-economic status. MRSA infections were highest in adult females aged 16-60 living in very remote regions and lowest in senior males aged 60+ years living in inner regional areas..ConclusionCurrent disease surveillance approaches for antimicrobial resistant infections have limited spatial comparability, are not timely, and at risk of sampling bias. Bayesian spatial models borrow information from neighbouring regions to adjust for unbalanced geographical information and can fill information gaps of current MRSA surveillance. Assessment of disease spatial variation is especially critical in settings which have diverse geography, dispersed populations or in regions with limited microbiological capacity.
A Flexible Framework for Local-Level Estimation of the Effective Reproductive Number in Geographic Regions with Sparse Data
Authors: Hossain Md Sakhawat , Goyal Ravi , Martin Natasha K , DeGruttola Victor , Chowdhury Mohammad Mihrab , McMahan Christopher , Rennert Lior 
Published: November 2024

AbstractBackgroundOur research focuses on local level estimation of the effective reproductive number, which describes the transmissibility of an infectious disease and represents the average number of individuals one infectious person infects at a given time. The ability to accurately estimate the infectious disease reproductive number in geographically granular regions is critical for disaster planning and resource allocation. However, not all regions have sufficient infectious disease outcome data for estimation.MethodsWe propose a two-step approach that incorporates existingRtestimation procedures (EpiEstim, EpiFilter, and EpiNow2) using data from geographic regions with sufficient data (step 1) into a covariate-adjusted Bayesian Integrated Nested Laplace Approximation (INLA) spatial model to predictRtin regions with sparse or missing data (step 2). Our flexible framework effectively allows for implementing any existing estimation procedure forRtin regions with coarse or entirely missing data. We perform external validation to evaluate predictive performance.ResultsWe applied our method to estimateRtusing data from South Carolina (SC) counties and ZIP codes during the first COVID-19 wave (‘Wave 1’, June 16, 2020 – August 31, 2020) and the second wave (‘Wave 2’, December 16, 2020 – March 02, 2021). Among the three methods used in the first step, EpiNow2 yielded the highest accuracy ofRtprediction in the regions with entirely missing data. Median county-level percentage agreement (PA) was 90.9% (IQR: 89.9-92.0%) and 92.5% (IQR: 91.6-93.4%) for Wave 1 and 2, respectively. Median zip code-level PA was 95.2% (Interquartile Range, IQR: 94.4-95.7%) and 96.5% (IQR: 95.8-97.1%) for Wave 1 and 2, respectively. Using EpiEstim and EpiFilter yielded median PA ranging from 81.9%-90.0% and 87.2%-92.1% (respectively) across both waves and geographic granularities.ConclusionThese findings demonstrate that the proposed methodology is a useful tool for small-area estimation ofRt, as our flexible framework yields high prediction accuracy for regions with entirely missing data regardless of the (step 1) estimation procedure used.
Evaluating Synthetic Diffusion MRI Maps created with Diffusion Denoising Probabilistic Models
Authors: Chattopadhyay Tamoghna , Jagad Chirag , Kush Rudransh , Desai Vraj Dharmesh , Thomopoulos Sophia I. , Villalón-Reina Julio E. , Thompson Paul M. 
Published: November 2024

AbstractGenerative AI models, such as Stable Diffusion, DALL-E, and MidJourney, have recently gained widespread attention as they can generate high-quality synthetic images by learning the distribution of complex, high-dimensional image data. These models are now being adapted for medical and neuroimaging data, where AI-based tasks such as diagnostic classification and predictive modeling typically use deep learning methods, such as convolutional neural networks (CNNs) and vision transformers (ViTs), with interpretability enhancements. In our study, we trained latent diffusion models (LDM) and denoising diffusion probabilistic models (DDPM) specifically to generate synthetic diffusion tensor imaging (DTI) maps. We developed models that generate synthetic DTI maps of mean diffusivity by training on real 3D DTI scans, and evaluating realism and diversity of the synthetic data using maximum mean discrepancy (MMD) and multi-scale structural similarity index (MS-SSIM). We also assess the performance of a 3D CNN-based sex classifier, by training on combinations of real and synthetic DTIs, to check if performance improved when adding the synthetic scans during training, as a form of data augmentation. Our approach efficiently produces realistic and diverse synthetic data, potentially helping to create interpretable AI-driven maps for neuroscience research and clinical diagnostics.
Wild gut microbiome suppresses the opportunistic pathogenAeromonasin medaka under domesticated rearing conditions
Authors: Kawano Keisuke , Kawabe Kai , Sano Yuki , Hori Tomoyuki , Kihara Minoru , Kikuchi Yoshitomo , Itoh Hideomi 
Published: November 2024

AbstractBackgroundThe gut microbiome plays a crucial role in the metabolic health and pathogen resistance of various host animals. It is also well established that external environmental factors can influence the gut microbiome, leading to differences in its composition. However, the functional implications of these differences remain poorly understood. This study examined the gut microbiome of medaka (Oryzias latipesspecies complex) by comparing domesticated and wild populations, with the aim of gaining insights into the functional significance of their specific characteristics, particularly those of the wild-type microbiome.ResultsFor the comparative analysis of the gut microbiome, 48 domesticated and 122 wild medaka were collected from multiple laboratories, pet stores and streams across Japan. The results showed that wild medaka exhibited significantly higher gut microbiome diversity, with a broader range of bacterial members. In contrast, the gut microbiome of domesticated medaka harbored lower microbial diversity and was consistently dominated byAeromonas, a typical opportunistic pathogen in fish. Additionally, 88.6% ofAeromonasisolates from domesticated medaka exhibited haemolytic activity. Moreover, a domesticated rearing experiment with wild populations showed no proliferation or dominance ofAeromonasin their gut, as observed in domesticated medaka. A further rearing experiment revealed that pre-exposing antibiotic-treated medaka to sediments from their natural habitats preventedAeromonascolonisation, even when reared under domesticated conditions.ConclusionsThese findings suggest that the habitat-derived wild gut microbiome can inhibitAeromonasproliferation in domesticated fish, highlighting its potential to mitigate opportunistic diseases in aquaculture.
Proteostasis dysregulation in p.A53T-α-Synuclein iPSC-derived astrocytes exacerbates neurodegeneration in a Parkinson’s disease model with Lewy-like pathology
Authors: Paschou Christina , Apokotou Olympia , Kollias Anastasios , Charmpi Konstantina , Dede Sofia , Samiotaki Martina , Palese Francesca , Dimoula Konstantina , Emmanouilidou Evangelia , Taoufik Era , Zurzolo Chiara , Matsas Rebecca , Papastefanaki Florentia 
Published: November 2024

AbstractAlpha-Synuclein (αSyn) plays a central role in Parkinson’s disease (PD) and the p.A53T mutation causes an early-onset familial form of PD with severe manifestations. The pathological effects of the p.A53T-αSyn mutation have been extensively investigated in neurons, yet the consequences on astrocytes and astrocytic contribution to PD pathology are understudied. Here, we differentiated induced pluripotent stem cells from PD patients carrying the p.A53T-αSyn mutation to astrocytes, which uncovered cell-intrinsic phenotypes, including calcium dyshomeostasis and accumulation of protein aggregates. Proteomic profiling and functional analyses revealed perturbed protein catabolic processes, involving the proteasome and autophagy, associated with lysosomal malfunction. Dopamine neurons co-cultured with p.A53T-αSyn astrocytes displayed exacerbated neurodegeneration with hallmark Lewy-like pathologies, reversed by control astrocytes at least due to their ability to resolve neuronal αSyn aggregates by endocytic clearance. Our findings underscore a critical impact of p.A53T-αSyn on astrocytic protein quality control mechanisms, positioning astrocytes as important contributors to PD neuropathology.HighlightsiPSC-derived astrocytes from PD patients with the p.A53T-αSyn mutation display cell-autonomous pathological phenotypes and toxic αSyn accumulationProteome and functional analyses reveal failure of major proteostasis mechanisms in p.A53T-αSyn astrocytes, largely related to lysosomal malfunctionInherent Lewy-like pathological features are identified in p.A53T-αSyn astrocyte-neuron co-culturesp.A53T-αSyn astrocytes induce PD-relevant neuropathology to healthy neuronsControl but not p.A53T-αSyn astrocytes alleviate neuropathology in co-cultured neurons
Deciphering gene regulatory programs underlying functionally divergent naïve T cell subsets
Authors: Zhu Hongya , Jiang Ya , McNairn Adrian J. , Fogarty Elizabeth A. , Tabilas Cybelle , Patel Ravi K. , Chobirko Jason D. , Munn Paul R. , Smith Norah L. , Grenier Jennifer K. , Rudd Brian D. , Grimson Andrew 
Published: November 2024

AbstractNaïve CD8+ T cells are a heterogeneous population, with different subsets possessing distinct functions and kinetics upon activation. However, the gene regulatory circuits differentiating these naïve subsets are not well studied. In this work, we analyzed a large collection of public and newly generated RNA-seq and ATAC-seq profiles of different subsets of naïve CD8+ T cells, revealing significant differences in the gene regulatory landscapes between subsets. We leveraged these data by employing a network inference algorithm, Inferelator, to identify the transcriptional regulatory circuits active in each subset. The predicted transcriptional network of the naïve CD8+ T cell pool was validated by multiple orthogonal approaches, including CUT&Tag and Micro-C. Interestingly, our network analysis revealed a novel role forEomesin promoting effector cell differentiation in specific cell subsets. Moreover, we uncovered multiple novel regulators across a variety of subsets and discovered several modules of genes that were co-regulated by shared sets of transcription factors in distinct subsets. Collectively, our data defines the gene regulatory programs differentiating naïve CD8+ T cells and facilitates the identification of novel transcription factors that may alter the propensity of naïve CD8+ T cells to become effector or memory cells after infection.
Human cerebellum and ventral tegmental area interact during extinction of learned fear
Authors: Nio Enzo , Pereira Patrick Pais , Diekmann Nicolas , Petrenko Mykola , Doubliez Alice , Ernst Thomas M. , Batsikadze Giorgi , Maderwald Stefan , Deuschl Cornelius , Üngör Metin , Cheng Sen , Merz Christian J. , Quick Harald H. , Timmann Dagmar 
Published: November 2024

AbstractThe key elements for fear extinction learning are unexpected omissions of expected aversive events, which are considered to be rewarding. Given its reception of reward information, we tested the hypothesis that the cerebellum contributes to reward prediction error processing driving extinction learning via its connections with the ventral tegmental area (VTA). Forty-three young and healthy participants performed a three-day fear conditioning paradigm in a 7T MR scanner. The cerebellum and VTA were active during unexpected omissions of aversive unconditioned stimuli, particularly during initial extinction trials. Increased functional connectivity was observed between the cerebellum and VTA, indicating that the cerebellum could positively modulate VTA activity, which in turn might facilitate dopaminergic signaling during fear extinction learning. These results imply that an interaction between the cerebellum and VTA should be incorporated into the existing model of the fear extinction network.
Induction of pluripotent oncogenic stem cells from mouse fibroblasts
Authors: Shabrish Snehal , Devi Naorem Leimarembi , Mohanty Shubham , Kumar Rohit , Lopes Relestina , Akhter Nabila , Khare Naveen Kumar , Raghuram Gorantla V , Jadhav Vishalkumar , Shinde Sushma , Chandrani Pratik , Mittra Indraneel 
Published: November 2024

SummaryNatural biological agents that can transform normal somatic cells into cancer stem cells, have not been identified. We earlier reported that cell free chromatin particles (cfChPs) that circulate in blood of cancer patients can horizontally transfer themselves to healthy cells to induce dsDNA breaks and inflammation. Here we show that a single cell clone D5 developed from NIH3T3 mouse fibroblast cells treated with cfChPs isolated from sera of cancer patients exhibited upregulation of stemness related transcription factors and surface markers, and the ability to form spheroids in appropriate culture medium. Transcriptome analysis revealed upregulation of cancer related pathways including invasion, metastasis and stemness. Subcutaneous inoculation into SCID mice resulted in development of malignant tumors which expressed all three germline markers. Our results suggest the cfChPs that circulate in blood of cancer patients are oncogenic and can transform susceptible somatic cells into cancer stem cells with the potential to promote metastasis.Graphical Abstract
HumanTestisDB: A Comprehensive Atlas of Testicular Transcriptomics and Cellular Interactions
Authors: Wang Mengjie , Li Laihua , Cheng Qing , Zhang Hao , Liu Zhaode , Cui Yiqiang , Sha Jiahao , Yuan Yan 
Published: November 2024

AbstractAdvances in single-cell technology have enabled the detailed mapping of testicular cell transcriptomes, which is essential for understanding spermatogenesis. However, the fragmented nature of age-specific data from various literature sources has hindered comprehensive analysis. To overcome this, the Human Testis Database (HumanTestisDB) was developed, consolidating multiple human testicular sequencing datasets to address this limitation. Through extensive investigation, 38 unique cell types were identified, providing a detailed perspective on cellular variety. Furthermore, the database systematically categorizes samples into eight developmental stages, offering a structured framework to comprehend the temporal dynamics of testicular development. Each stage features comprehensive maps of cell-cell interactions, elucidating the complex communication network inside the testicular microenvironment at particular developmental stages. Moreover, by facilitating comparisons of interactions among various cell types at different stages, the database permits examining alterations that transpire during critical transitions in spermatogenesis. HumanTestisDB, available athttps://shalab.njmu.edu.cn/humantestisdb, offers vital insights into testicular transcriptomics and interactions, serving as an essential resource for advancing research in reproductive biology.