bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficacy and Safety of Total Body Irradiation versus Chemotherapy Conditioning for Hematopoietic Stem Cell Transplant in Adult Acute Lymphoblastic Leukemia: A Systematic Review and Meta-Analysis
Authors: Ans Hasaan , Amjad Haiqah , Nazir Sara , Cheema Sara Sajjad , Mistry Dhruv , Fatima Tazeen , Khan Ahmed Yar , Imtiaz Muhammad Usama , Khan Fasih Mand , Khalid Saif , Rehman Mohammad Ebad Ur , Faisal Muhammad Salman 
Published: November 2024

AbstractBackgroundHematopoietic stem cell transplantation (HSCT) is a potentially curative option for adults with acute lymphoblastic leukemia (ALL) who have achieved remission. This systematic review and meta-analysis compare the efficacy of total body irradiation (TBI) versus chemotherapy (CHT) based regimens for conditioning in adult ALL patients being prepared for HSCT.MethodsA comprehensive literature search was conducted in MEDLINE, Embase, the Cochrane Library, and relevant trial registries from their inception to August 2024. The inclusion criteria encompassed all randomized controlled trials (RCTs) and cohort studies that compared TBI) with CHT as conditioning regimens prior to HSCT in adult patients with ALL. The primary outcomes assessed were overall survival (OS) and event-free survival (EFS). All statistical analyses were carried out using RevMan version 5.4, using a random-effects model.ResultsThis meta-analysis includes 20 cohort studies and one RCT. The relative risk (RR) for OS was 1.37 (95% CI: 1.20–1.57), while the RR for EFS was 1.28 (95% CI: 1.15–1.43), highlighting the superior efficacy of TBI-based regimens in this patient population. TBI was also superior in terms of relapse rate (RR 0.71). The two regimens were comparable in terms of non-relapse mortality, acute graft-versus-host disease (GVHD), and chronic GVHD.ConclusionWhen used for conditioning prior to HSCT, TBI-based conditioning regimens demonstrate superior OS, EFS, and relapse outcomes compared to CHT-based regimens.
Modeling dynamic disease-behavior feedbacks for improved epidemic prediction and response
Authors: Du Hongru , Zahn Matthew , Loo Sara , Alleman Tijs , Truelove Shaun , Patenaude Bryan , Gardner Lauren , Papageorge Nicholas , Hill Alison 
Published: November 2024

AbstractHuman behavior significantly influences infectious disease transmission, yet traditional models often overlook this factor, limiting predictions of disease and the associated socioeconomic impacts. We introduce a feedback-informed epidemiological model that integrates economic decision-making with infectious disease dynamics. Individuals weigh costs and benefits, then choose behaviors that influence their risk of infection and disease progression, thereby shaping population-level dynamics. Applying this model to a scenario based on the early COVID-19 pandemic, we examine decisions to abstain from work to mitigate infection risk. Our findings reveal that feedback between disease and behavior notably affect infection rates and overall welfare, especially when accounting for individual economic and health vulnerabilities, which are often in tension. We evaluate counterfactual policies, including labor restrictions and cash transfers, illustrating how our framework can simultaneously address epidemiological, economic, and equity-related questions. This flexible and extendable modeling framework offers a powerful tool for assessing infectious disease interventions.
Comparative Performance of Claude and GPT Models in Basic Radiological Imaging Tasks
Authors: Nguyen Cindy , Carrion D , Badawy MK 
Published: November 2024

BackgroundPublicly available artificial intelligence (AI) Visual Language Models (VLMs) are constantly improving. The advent of vision capabilities on these models could enhance workflows in radiology. Evaluating their performance in radiological image interpretation is vital to their potential integration into practice.AimThis study aims to evaluate the proficiency and consistency of the publicly available VLMs, Claude and GPT, across multiple iterations in basic image interpretation tasks.MethodSubsets from publicly available datasets, ROCOv2 and MURAv1.1, were used to evaluate 6 VLMs. A system prompt and image were inputted into each model thrice. The outputs were compared to the dataset captions to evaluate each model’s accuracy in recognising the modality, anatomy, and detecting fractures on radiographs. The consistency of the output across iterations was also analysed.ResultsEvaluation of the ROCOv2 dataset showed high accuracy in modality recognition, with some models achieving 100%. Anatomical recognition ranged between 61% and 85% accuracy across all models tested. On the MURAv1.1 dataset, Claude-3.5-Sonnet had the highest anatomical recognition with 57% accuracy, while GPT-4o had the best fracture detection with 62% accuracy. Claude-3.5-Sonnet was the most consistent model, with 83% and 92% consistency in anatomy and fracture detection, respectively.ConclusionGiven Claude and GPT’s current accuracy and reliability, integration of these models into clinical settings is not yet feasible. This study highlights the need for ongoing development and establishment of standardised testing techniques to ensure these models achieve reliable performance.
The lived experience of rejection sensitivity in ADHD - a qualitative exploration
Authors: Rowney-Smith Annabel , Sutton Beth , Quadt Lisa , Eccles Jessica A 
Published: November 2024

AbstractAttention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental condition, commonly associated with differences in attention, and/or hyperactivity and impulsiveness. A lesser known, but highly impactful characteristic of ADHD is emotional dysregulation, which causes difficulties in emotional expression and identification. An aspect of emotional dysregulation which remains relatively unexplored in ADHD, is rejection sensitivity. In people who experience rejection sensitivity, perceived rejection and/or criticism can evoke extreme dysphoria. To investigate the lived experience of rejection sensitivity in ADHD individuals, five undergraduates participated in focus-group interviews. The subsequent thematic analysis revealed three key themes which encapsulated the experience;withdrawal,masking, andbodily sensations. The participants explained how rejection sensitivity elicited unpleasant bodily sensations, anxiety, and misery, and how, in turn, they used masking to camouflage these feelings. They then discussed how the use of masking had caused them to become dissociated from themselves and to withdraw from others, which often resulted in loneliness. It was apparent that rejection sensitivity significantly impacts mental wellbeing, eliciting feelings such as anxiousness, despair, and embarrassment. In turn, social function is significantly impaired, alongside career opportunities, and daily life. Our findings indicate a need for deeper understanding of ADHD traits and emotional dysregulation, which may in turn lessen the presence of rejection sensitivity in ADHD individuals.
PanEcho: Complete AI-enabled echocardiography interpretation with multi-task deep learning
Authors: Holste Gregory , Oikonomou Evangelos K. , Wang Zhangyang , Khera Rohan 
Published: November 2024

ABSTRACTEchocardiography is a mainstay of cardiovascular care offering non-invasive, low-cost, increasingly portable technology to characterize cardiac structure and function1. Artificial intelligence (AI) has shown promise in automating aspects of medical image interpretation2,3, but its applications in echocardiography have been limited to single views and isolated pathologies4–7. To bridge this gap, we presentPanEcho, a view-agnostic, multi-task deep learning model capable of simultaneously performing 39 diagnostic inference tasks from multi-view echocardiography.PanEchowas trained on >1 million echocardiographic videos with broad external validation across an internal temporally distinct and two external geographically distinct sets. It achieved a median area under the receiver operating characteristic curve (AUC) of 0.91 across 18 diverse classification tasks and normalized mean absolute error (MAE) of 0.13 across 21 measurement tasks spanning chamber size and function, vascular dimensions, and valvular assessment.PanEchoaccurately estimates left ventricular (LV) ejection fraction (MAE: 4.4% internal; 5.5% external) and detects moderate or greater LV dilation (AUC: 0.95 internal; 0.98 external) and systolic dysfunction (AUC: 0.98 internal; 0.94 external), severe aortic stenosis (AUC: 0.99), among others.PanEchois a uniquely view-agnostic, multi-task, open-source model that enables state-of-the-art echocardiographic interpretation across complete and limited studies, serving as an efficient echocardiographic foundation model.
Current applications of artificial intelligence for Fuchs endothelial corneal dystrophy: a systematic review
Authors: Liu Siyin , Kandakji Lynn , Stupnicki Aleksander , Sumodhee Dayyanah , Leucci Marcello , Hau Scott , Balal Shafi , Okonkwo Arthur , Moghul Ismail , Allan Bruce , Gore Dan , Muthusamy Kirithika , Hardcastle Alison , Davidson Alice E , Liskova Petra , Pontikos Nikolas 
Published: November 2024

AbstractPurposeFuchs Endothelial Corneal Dystrophy (FECD) is a common, age-related cause of visual impairment. This systematic review synthesizes evidence from the literature on Artificial Intelligence (AI) models developed for the diagnosis and management of FECD.MethodsWe conducted a systematic literature search in MEDLINE, PubMed, Web of Science, and Scopus from January 1, 2000, to June 31, 2024. Full-text studies utilizing AI for various clinical contexts of FECD management were included. Data extraction covered model development, predicted outcomes, validation, and model performance metrics. We graded included studies using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. This review adheres to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations.ResultsNineteen studies were analyzed. Primary AI algorithms applied in FECD diagnosis and management included neural network architectures specialized for computer vision, utilized on confocal or specular microscopy images, or anterior segment optical coherence tomography images. AI was employed in diverse clinical contexts, such as assessing corneal endothelium and edema, and predicting post-corneal transplantation graft detachment and survival. Despite many studies reporting promising model performance, a notable limitation was that only 3 studies performed external validation. Bias introduced by patient selection processes and experimental designs was evident in the included studies.ConclusionsDespite the potential of AI algorithms to enhance FECD diagnosis and prognostication, further work is required to evaluate their real-world applicability and clinical utility.Translational RelevanceThis review offers critical insights for researchers, clinicians, and policymakers, aiding their understanding of existing AI research in FECD management and guiding future health service strategies.
Neuromorphic Imaging Cytometry on Human Blood Cells
Authors: Zhang Ziyao , Yang Haoxiang , Li Jiayin , Chong Shin Wei , Eshraghian Jason K. , Yong Ken-Tye , Vigolo Daniele , McGuire Helen M. , Kavehei Omid 
Published: November 2024

AbstractImage-enhanced cytometry and sorting are powerful technologies that provide single-cell resolution and, where possible, cell actuation based on spatial and fluorescence characterisation. With the emergence of deep learning (DL), numerous cytometry-related works incorporate DL to assist their research in handling data-intensive and repetitive workloads. The rich spatial information provided by single-cell images has exceptional use with DL models to classify cells, detect rare cell events, disclose irregularity and achieve higher sample purity than a conventional feature-gating strategy. One of the significant challenges in these image-enable technologies is the constrained throughput owing to the data-expensive image acquisition and balancing between speed and resolution. This work introduces a novel paradigm by adopting a bio-inspired neuromorphic photosensor to capture fast-moving cell events. It facilitates a data-efficient, fluorescence-sensitive, fast inference approach to establish a foundation for neuromorphic-enabled cytometry/sorting applications. We have also curated the first neuromorphic-encoded cell dataset, including human blood cells (red blood cells, neutrophils, lymphocytes, thrombocytes), endothelial cells and polystyrene-based microparticles. To evaluate the data quality and potential of DL-based gating, we have directly trained a hybrid classification model based on this dataset, accomplishing a promising performance of 97% accuracy and F1 score with a significant reduction in memory usage and power consumption. Combining neuromorphic imaging and DL holds substantial potential to develop into a next-generation AI-assisted cytometry and sorting application.
Fast marching to branching morphologies
Authors: Kriuchechnikova Anna , Levdik Tatiana , Brazhe Alexey 
Published: November 2024

AbstractMany living systems self-organize into tree-like hierarchical branching patterns. How the function of these systems is shaped by their morphology, and whether it reflects optimization of any resource or cost draws a sustained interest. Applications in computational biology and biomimetics require tools to generate branching morphologies across a wide range of feature space. Here we propose a simple framework to build branching structures as converging gradient-descent paths in travel-time maps, computed with fast-marching algorithm over a stochastic speed field, with a structure- dependent feedback on local speed and path targets. We demonstrate the effects of speed field profile, feedback properties and seed density and sampling order on the resulting morphologies and their transport properties. The main utility of the framework is in its simplicity and the ability to generate realistic standalone structures in 2D in 3D, as well as tiling networks resembling astrocyte syncytium in the brain.
Connectivity Map and perturbation-based sensitivity analysis identifies MEK inhibitors as senolytics in human lung fibroblasts
Authors: Nayeri Rad Amirhossein , Sperger Simon , Marsh Leigh M. , Hoetzenecker Konrad , Lämmermann Ingo , Grillari Johannes 
Published: November 2024

AbstractRecently, the elimination of the disease-associated accumulation of senescent cells using senolytics has been shown to exert health benefits in animal studies. However, due to the heterogeneity of cell senescence and its unrecognized master regulators, drug development faces a complexity that must be handled. Bioinformatic elucidation of genes and pathways involved in senolysis and prediction of senolytic activity of compounds can cut costs and facilitate faster achievements in the field. In the present investigation, after obtaining the consensus gene signature of senescent fibroblasts of lung origin and deriving its anti-apoptotic module, we utilized Connectivity Map (CMap) alongside small molecule and genetic perturbation sensitivity data in cancer cell lines to identify drugs and genetic interventions that might induce apoptosis or sensitize senescent cells to apoptosis. Through bioinformatic evaluations, we speculate that activation of early stages of autophagy which contributes to the formation of autophagosomes, concurrent with the activation of waste protein concealment system by the mean of p62 and chaperoning system alongside an increase in JUNB gene expression can secure the survival of the senescent cells even when homeostasis of different cellular processes is disrupted. Moreover, our bioinformatic evaluation proposed selumetinib, a MEK inhibitor, as a senolytic against senescent lung fibroblasts. The senolytic activity of a variety of MEK inhibitors in senescent lung fibroblasts was confirmed using human lung fibroblasts in vitro.
ANTIMICROBIAL SUSCEPTIBILITY PROFILE OFEdwardsiella tardaFROM FARMED FISH IN ILORIN METROPOLIS, KWARA STATE NIGERIA
Authors: Olabisi F.O. , Adeshina I. 
Published: November 2024

ABSTRACTThis study determined the susceptibility profile and multiple antibiotic resistance (MAR) index ofEdwardsiella tardaobtained from farmed fish in Ilorin Metropolis, Kwara State, Nigeria. 30 isolates were obtained from 30 catfish samples from the major fish market in Ilorin Metropolis. It was observed that the Isolates showed resistance to tetracycline (100%), ampicillin (100%), cefotaxime (100%) and ciprofloxacin (97%).Edwardsiella tardaisolates were entirely susceptible to Gentamycin (100%) while also showing varying levels of susceptibility to Enfloxacin (77%), Norfloxacin (67%), Sulphamethoxazole (50%), Composulphonamides (23%) and Chloramphenicol (10%). The isolates showed an intermediate range of susceptibility to Chloramphenicol (90%), Composulphonamides (77%), Sulphamethoxazole (50%) and Ciprofloxacin (3%). Analysis of the MAR index of isolates revealed that 97% of the isolates had a MAR index of 0.4, with only 3% of the isolates showing a multiple antibiotic resistance index of 0.3, which is an indication that farmed fishes in the Ilorin metropolis had a high risk of exposure to antibiotics.
Wnt activation in mature dermal adipocytes leads to lipodystrophy and skin fibrosis via ATGL-dependent lipolysis
Authors: Ma Qiannan , Segal Ella X. , Reynolds Claire , Madhavan Suneeti R. , Wyetzner Rachel H. , Gregory Megan , Atit Radhika P. 
Published: November 2024

AbstractObjectiveAccumulation of extracellular matrix (ECM) and dermal adipocyte lipodystrophy occurs during skin fibrosis, which compromises the skin’s flexibility and function. We recently showed that sustained Wnt activation in dermal progenitor cells leads to fibrotic ECM thickening in the dermis and lipodystrophy of dermal white adipose tissue (DWAT). The aim of this study was to test if Wnt/β-catenin signaling in mature dermal adipocytes directly leads to lipodystrophy and impact skin fibrosis.MethodsWe developed a genetically lineage traceable, DWAT specific, inducible and reversible mouse model of Wnt activation (Adipo-β-catistab) in the dorsal skin. We analyzed the DWAT lipid droplet size, cell identity, and affect on ECM accumulation and remodeling in skin fibrosis. Adipocyte triglyceride lipase (ATGL) is the key rate limiting enzyme of the lipolysis pathway, which is a biological process of breaking down triglyceride stores in adipocytes. TheAtglgene was conditionally deleted in mature dermal adipocytes to test the requirement of the lipolysis pathway in the Wnt-induced lipodystrophy (Adipo-β-catistab;Atglfl/fl).ResultsHere, we utilize mouse genetic models with lineage tracing to show that Wnt activation in mature dermal adipocytes is sufficient to induce adipocyte lipodystrophy and fibrotic collagen remodeling. Upon withdrawal of adipocyte-restricted Wnt activation, lipodystrophy and fibrosis were reversed. Mechanistically, we find that Wnt activation stimulates Adipose Triglyceride Lipase (ATGL)-mediated lipolysis pathway. We foundAtglin dermal adipocytes is functionally required for Wnt-induced lipodystrophy in the DWAT and fibrotic remodeling.ConclusionCollectively, this study demonstrates that Wnt activation in dermal adipocytes promotes lipolysis and may be a novel therapeutic avenue for preventing and reversing lipodystrophy and skin fibrosis.
AI-Powered cellular morphometric biomarkers discovered in needle biopsy of prostatic cancer predict neoadjuvant androgen deprivation therapy response and prognosis: an international multicenter retrospective study
Authors: Yan Hong , Mao April W. , Li Dan , Pérez-Baena Manuel Jesús , Jiménez-Navas Alejandro , Wang Dawei , Hong Ryan , Cai Weidong , Pérez-Losada Jesus , Jen Kuang-Yu , Wang Sen , Peng Shan , Barcellos-Hoff Mary Helen , Mao Jian-Hua , Fu Yao , Iczkowski Kenneth A. , Gulati Shuchi , Chang Hang 
Published: November 2024

AbstractIt is imperative to identify patients with prostate cancer (PCa) who will benefit from androgen receptor signaling inhibitors that can impact quality of life upon prolonged use. Using our extensively-validated artificial-intelligence technique: cellular morphometric biomarker via machine learning (CMB-ML), we identified 13 CMBs from whole slide images of needle biopsies from the trial specimens (NCT02430480, n=37) that accurately predicted response to neoadjuvant androgen deprivation therapy (NADT) (AUC: 0.980). Notably, 13-CMB model stratified PCa patients into responder and non-responder groups after NADT treatment in an independent hospital cohort (n=122) that significantly associated with pathologic complete response (p=0.0005), biochemical-recurrence-free survival (p=0.024) and mTOR signaling pathway (p=0.03), suggesting potentially more clinical benefit from mTOR inhibitors in non-responder group. Additionally, genetic and genomic analysis revealed interplay between genetic variants and CMBs on NADT resistance, and provided molecular annotations for CMBs. Overall, prospective clinical implementation of 13-CMB model could assist precision care of PCa patients.SignificanceWe describe a highly accurate CMB model to predict the therapeutic benefit in prostate cancer patients and uncover the complex interplay between genetic variants and CMBs on NADT resistance. Our model relies only on widely available needle biopsy specimens and provides a robust and cost-effective solution for clinical implementation.
Heterogeneity of NOTCH3 activation mechanisms uncovers therapeutic potential for targeted therapies for CADASIL
Authors: Hosseini-Alghaderi Samira , Baron Martin 
Published: November 2024

AbstractNOTCH3 is a developmental signalling receptor that regulates vascular smooth muscle cell (VSMC) proliferation, maintenance and phenotypic plasticity. Its misregulation is linked to both age-associated and inherited forms of small vessel disease and vascular dementia. CADASIL is a dominantly inherited condition, linked to recurrent stroke and vascular dementia, which is caused by mutations in NOTCH3. The mutations result in the accumulation of the extracellular domain (ECD) of NOTCH3 which has a toxic gain of function effect. Misregulated signalling may also play a role. ECD detachment is an obligatory step in NOTCH3 activation, but the mechanisms of CADASIL mutant NOTCH3 activation are not understood. Some CADASIL mutants affect ligand-induced activation and so ligand interactions are not a common underlying requirement for disease progression. Here we show that both wild type and CADASIL mutant NOTCH3 proteins are endocytosed as a full-length protein and then undergo dissociation and independent trafficking of the ECD and ICD in the endosome. We further show heterogeneity of activation mechanisms between different CADASIL mutants defined by dependency or not on metalloproteases. Understanding the variety of mechanisms by which NOTCH3 signalling and ECD shedding occur will inform new targeted approaches to treatments of small vessel disease. Tuning NOTCH3 activity through modulation of the endocytic pathway may offer better tolerated approaches than direct targeting of NOTCH3 signalling, important when considering the balance of risk and benefit for treating long term, chronic, progressive diseases, like vascular dementia.