Integrin β3 (ITGB3) functions as a pivotal transmembrane receptor mediating bidirectional signalling between cells and the extracellular matrix within the tumour microenvironment (TME). Dysregulated ITGB3 expression activates downstream pathways such as FAK/PI3K-Akt/mTOR, orchestrating core malignant processes including invasion, metastasis, angiogenesis, immune evasion, and autophagy modulation. Beyond its mechanistic roles, ITGB3 serves as a valuable biomarker for early diagnosis and prognostic assessment. Therapeutic strategies targeting ITGB3 encompass small-molecule inhibitors, monoclonal antibodies, and emerging Traditional Chinese Medicine (TCM) formulations, which offer unique multi-component regulatory advantages. This review systematically elucidates the structure-function relationship of ITGB3, its multidimensional regulatory mechanisms in tumour progression, and current targeted intervention strategies. Ultimately, we aim to provide theoretical insights for establishing ITGB3-guided precision medicine and integrated treatment systems.
David F. Moreno, David F. Moreno, Ferran Nadeu
et al.
BackgroundMYD88 L265P is an early mutation in IgM monoclonal gammopathy of undetermined significance (MGUS) and asymptomatic Waldenström macroglobulinemia (WM). Given the high prevalence of the MYD88 mutation observed in epidemiological studies, its presence is not sufficient to drive disease progression. In fact, a recent risk model of progression reported that the impact of other laboratory biomarkers was superior to the MYD88 mutation’s presence. Due to the low incidence of these clinicopathological entities, there is a need for a better characterization of tumor and immune cells that can help to identify novel biomarkers. We hypothesize that the characterization of the risk groups in asymptomatic patients could improve the discovery of drivers of disease progressionMethodsWe characterized the genomic and immune landscape of the most recent prognostic risk categories in 19 IgM MGUS and 17 asymptomatic WM patients. We performed targeted next generation sequencing (NGS) on CD19+ cells from bone marrow samples at diagnosis using a panel of 54 lymphoma-driver genes. Whole bone marrow samples were also used to measure mRNA gene expression in tumor and immune cells using the PanCancer ImmuneProfiling panel on the nCounter platform (NanoString).ResultsWe observed that low-risk patients were only characterized by the presence of MYD88 L265P, while intermediate- and high-risk groups harbored additional mutations on CXCR4, KMT2D, ARID1A and EP300. Regarding the mRNA expression analyses, we found an increased proportion of myeloid cells in the low-risk group, with monocytes having a significant decrease in low versus high-risk patients. The high-risk group also upregulated genes involved in the activation of NF-κB and B-cell receptor (BCR) signaling, while low-risk patients upregulated genes associated with an alternative activation of B cells or a decrease of the BCR signaling, such as TOLLIP, CEACAM1 and CR1.ConclusionsBeyond the MYD88 mutation, we described novel molecular mechanisms associated with high-risk patients, as an effort moving towards easy-to use new biomarkers in IgM gammopathy.
Shruti Apte, Greicy K Bonifacio-Pereira, Sourav Ghosh
et al.
Opportunistic pathogens switch from a commensal to pathogenic state by sensing and responding to a variety of environmental cues, including temperature fluctuations. Minor temperature oscillations can alert the pathogen to a changing niche ecosystem, necessitating efficient sensing and rapid integration to trigger behavioral change. This is typically achieved through master regulators, dictating pleiotropic phenotypes. Here, we uncover a pivotal role of minor temperature shifts in transition of Streptococcus pneumoniae (SPN) from commensal to virulent lifestyles, mediated via an RNA thermosensing (RNAT) element within the untranslated region of the global regulator CiaR. By positively regulating the expression of the surface adhesin, Phosphorylcholine (PCho), in response to elevated temperature, CiaR potentiates pneumococcal infection. Engineering the RNAT structure to create translation restrictive or permissive versions allowed us to demonstrate how modulation in expression of CiaR could alter pneumococcal invasion capability, influencing infection outcomes. Moreover, intranasal administration of PCho mitigated SPN-induced bacteraemic pneumonia. Since a majority of opportunistic respiratory bacterial pathogens decorate their surface with PCho, this signaling arm could be exploited for anti-infective interventions.
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising 3,535 closed-ended multiple-choice questions and 337 open-ended real diagnosis cases, which encompasses all the 33 main categories and 465 subcategories of HPB diseases defined in the International Statistical Classification of Diseases, 10th Revision (ICD-10). The multiple-choice questions are curated from public datasets and synthesized data, and the clinical cases are collected from prestigious medical journals, case-sharing platforms, and collaborating hospitals. By evalauting commercial and open-source general and medical LLMs on our established benchmark, namely ClinBench-HBP, we find that while commercial LLMs perform competently on medical exam questions, they exhibit substantial performance degradation on HPB diagnosis tasks, especially on complex, inpatient clinical cases. Those medical LLMs also show limited generalizability to HPB diseases. Our results reveal the critical limitations of current LLMs in the domain of HPB diseases, underscoring the imperative need for future medical LLMs to handle real, complex clinical diagnostics rather than simple medical exam questions. The benchmark will be released at https://clinbench-hpb.github.io.
Md. Jalal Uddin Chowdhury, Zumana Islam Mou, Rezwana Afrin
et al.
A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it's too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we've mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle is used, which has 17,430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.
Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists frequently consult case reports and literature when confronted with unfamiliar findings. Following this line of reasoning, we introduce RADAR, Retrieval Augmented Diagnostic Reasoning Agents, an agentic system for rare disease detection in brain MRI. Our approach uses AI agents with access to external medical knowledge by embedding both case reports and literature using sentence transformers and indexing them with FAISS to enable efficient similarity search. The agent retrieves clinically relevant evidence to guide diagnostic decision making on unseen diseases, without the need of additional training. Designed as a model-agnostic reasoning module, RADAR can be seamlessly integrated with diverse large language models, consistently improving their rare pathology recognition and interpretability. On the NOVA dataset comprising 280 distinct rare diseases, RADAR achieves up to a 10.2% performance gain, with the strongest improvements observed for open source models such as DeepSeek. Beyond accuracy, the retrieved examples provide interpretable, literature grounded explanations, highlighting retrieval-augmented reasoning as a powerful paradigm for low-prevalence conditions in medical imaging.
In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease (AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and CNN-BLSTM models for binary classification using both multi-channel fixed-size and frame-based spectrograms. Our results showed that handwriting tasks and spectrogram channel combinations significantly impacted classification performance. The highest F1-score (89.8%) was achieved for AD vs. CTL, while PD vs. CTL reached 74.5%, and PD vs. PDM scored 77.97%. CNN consistently outperformed CNN-BLSTM. Different sliding window lengths were tested for constructing frame-based spectrograms. A 1-second window worked best for AD, longer windows improved PD classification, and window length had little effect on PD vs. PDM.
Umakanta Maharana, Sarthak Verma, Avarna Agarwal
et al.
Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and providing enhanced healthcare access for underprivileged communities. The early diagnosis of various diseases continues to be a significant challenge in healthcare, primarily due to the nonspecific nature of early symptoms, the shortage of expert medical practitioners, and the need for prolonged clinical evaluations, all of which can delay treatment and adversely affect patient outcomes. With impressive accuracy in prediction across a range of diseases, LLMs have the potential to revolutionize clinical pre-screening and decision-making for various medical conditions. In this work, we study the diagnostic capability of LLMs for Rheumatoid Arthritis (RA) with real world patients data. Patient data was collected alongside diagnoses from medical experts, and the performance of LLMs was evaluated in comparison to expert diagnoses for RA disease prediction. We notice an interesting pattern in disease diagnosis and find an unexpected \textit{misalignment between prediction and explanation}. We conduct a series of multi-round analyses using different LLM agents. The best-performing model accurately predicts rheumatoid arthritis (RA) diseases approximately 95\% of the time. However, when medical experts evaluated the reasoning generated by the model, they found that nearly 68\% of the reasoning was incorrect. This study highlights a clear misalignment between LLMs high prediction accuracy and its flawed reasoning, raising important questions about relying on LLM explanations in clinical settings. \textbf{LLMs provide incorrect reasoning to arrive at the correct answer for RA disease diagnosis.}
Vivian Cao, MS, Serena J. Lee, BS, Yun Bai, MS
et al.
Background: Autoantibodies to type I interferons have been identified in association with a variety of inflammatory and autoimmune diseases. Type I interferons have demonstrated inhibitory effects on mast cell proliferation and degranulation. Systemic mastocytosis (SM) is a disease characterized by increased mast cell burden and mediator release. Whether autoantibodies to type I interferon are present in the sera of patients with SM, and if so, whether they correlate with characteristics of disease, is unknown. Objective: The purpose of this study was to determine whether autoantibodies to type I interferons are observed in the sera of patients with SM, and if so, whether they correlate with biomarkers of disease severity. Methods: We analyzed sera from 89 patients with SM for concentrations of autoantibodies to type I interferon by using a multiplex particle-based assay and signal neutralization capacity by using a STAT1 activity assay and then compared these measurements with those in a database of information on 1284 healthy controls. Results: Our cohort was predominantly female (57.3%), with a median age of 56 years. Of the cohort members, 13 produced autoantibodies to IFN-β, 3 to IFN-ω, and 0 to IFN-α. None of the 13 sera demonstrated signal neutralization. Neither autoantibody concentration nor signaling inhibition measurements correlated with tryptase concentrations or D816V allele burden. Conclusion: Although a small subpopulation of patients with SM have autoantibodies to type I interferons, there was no correlation between autoantibody production and signaling inhibition. These data are consistent with the conclusion that autoantibodies to type I interferon do not play a significant role in the pathogenesis or severity of SM.
BackgroundThe rising prevalence of herpes simplex type 2 (HSV-2) infection poses a growing global public health challenge. A comprehensive understanding of its epidemiology and burden disparities in China is crucial for informing targeted and effective intervention strategies in the future.MethodsWe followed Cochrane and PRISMA guidelines for a systematic review and included publications published in Chinese and English bibliographic systems until March 31st, 2024. We synthesized HSV-2 seroprevalence data across different population types. We used random-effects models for meta-analyses and conducted meta-regression to assess the association between population characteristics and seroprevalence.ResultsOverall, 23,999 articles were identified, and 402 publications (1,203,362 participants) that reported the overall seroprevalence rates (858 stratified measures) were included. Pooled HSV-2 seroprevalence among the general population (lower risk) was 7.7% (95% CI: 6.8-8.7%). Compared to the general population, there is a higher risk of HSV-2 prevalence among intermediate-risk populations (14.8%, 95% CI: 11.0-19.1%), and key populations (31.7%, 95% CI: 27.4-36.1%). Female sexual workers (FSWs) have the highest HSV-2 risk (ARR:1.69, 95% CI: 1.61-1.78). We found northeastern regions had a higher HSV-2 seroprevalence than other regions (17.0%, 95% CI: 4.3-35.6%, ARR: 1.38, 95% CI: 1.26-1.50, Northern China as the reference group). This highlighted the disparity by population risk levels and regions. We also found lower HSV-2 prevalence estimates in publications in Chinese bibliographic databases than those in English databases among key populations (such as MSM and HIV-discordant populations).ConclusionThere is a gradient increase in HSV-2 prevalence risk stratification. We also identified region, population, and age disparities and heterogeneities by publication language in the HSV-2 burden. This study provides guidance for future HSV-2 prevention to eliminate disparities of HSV-2 infection and reduce overall HSV-2 burden.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=408108, identifier CRD42023408108.
ObjectiveThis study aims to assess the efficacy of pharmacological interventions in mitigating graft injury in transplant patients with antibody-mediated rejection (AMR) through a network meta-analysis (NMA).MethodsA search was conducted on databases such as Cochrane Library, PubMed, EmBase, and Web of Science for randomized controlled trials (RCTs) on pharmacological interventions for alleviating graft injury following AMR. The search was performed for publications up to April 12, 2024. Two reviewers conducted independent reviews of the literature, extracted data, and assessed the risk of bias (ROB) in the included studies using the ROB assessment tool recommended by the Cochrane Handbook for Systematic Reviews of Interventions 5.1.0. A Bayesian NMA was conducted using R 4.4.0, RStudio software, and the GeMTC package to assess the outcomes in estimated glomerular filtration rate (eGFR), mean fluorescence intensity (MFI), g-score, and infection under pharmacological treatments.ResultsA total of 8 RCTs involving 215 patients and 6 different pharmacological treatments were included in this NMA. The results indicated that the increase in eGFR by eculizumab (SUCRA score: 81) appeared to be more promising. The decrease in MFI by bortezomib (SUCRA score: 72.3), rituximab (SUCRA score: 68.2), and clazakizumab (SUCRA score: 67.1) demonstrated better efficacy. The decrease in g-score by eculizumab (SUCRA score: 74.3), clazakizumab (SUCRA score: 72.2), and C1INH (SUCRA score: 63.6) appeared to have more likelihood. For infection reduction, clazakizumab (SUCRA score: 83.5) and bortezomib (SUCRA score: 66.8) might be better choices.ConclusionThe results of this study indicate that eculizumab has the potential to enhance eGFR and reduce g-score. Bortezomib demonstrates superior efficacy in reducing MFI. Clazakizumab appears to be more effective in reducing infections.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024546483.
Michele N. Maughan, Jenna D. Gadberry, Caitlin E. Sharpes
et al.
Since the advent of the Universal Detector Calibrant (UDC) by scientists at Florida International University in 2013, this tool has gone largely unrecognized and under-utilized by canine scent detection practitioners. The UDC is a chemical that enables reliability testing of biological and instrumental detectors. Training a biological detector, such as a scent detection canine, to respond to a safe, non-target, and uncommon compound has significant advantages. For example, if used prior to a search, the UDC provides the handler with the ability to confirm the detection dog is ready to work without placing target odor on site (i.e., a positive control), thereby increasing handler confidence in their canine and providing documentation of credibility that can withstand legal scrutiny. This review describes the UDC, summarizes its role in canine detection science, and addresses applications for UDC within scent detection canine development, training, and testing.
Asish Bera, Debotosh Bhattacharjee, Ondrej Krejcar
Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. A GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs. The best classification performances are: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40X: 95.50%, and BreakHis 100X: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, PND-Net achieves improved performances using five-fold cross validation.
Ruiying Wang, Lorena V. N. Oliveira, Diana Lourenco
et al.
AbstractVaccination with glucan particles (GP) containing the Cryptococcus neoformans chitin deacetylases Cda1 and Cda2 protect mice against experimental cryptococcosis. Here, immunological correlates of vaccine-mediated protection were explored. Studies comparing knockout and wild-type mice demonstrated CD4+ T cells are crucial, while B cells and CD8+ T cells are dispensable. Protection was abolished following CD4+ T cell depletion during either vaccination or infection but was retained if CD4+ T cells were only partially depleted. Vaccination elicited systemic and durable antigen-specific immune responses in peripheral blood mononuclear cells (PBMCs), spleens, and lungs. Following vaccination and fungal challenge, robust T-helper (Th) 1 and Th17 responses were observed in the lungs. Protection was abrogated in mice congenitally deficient in interferon (IFN) γ, IFNγ receptor, interleukin (IL)-1β, IL-6, or IL-23. Thus, CD4+ T cells and specific proinflammatory cytokines are required for GP-vaccine-mediated protection. Importantly, retention of protection in the setting of partial CD4+ T depletion suggests a pathway for vaccinating at-risk immunocompromised individuals.
Marcos C. Vieira, Anna-Karin E. Palm, Christopher T. Stamper
et al.
Antibodies result from the competition of B cell lineages evolving under selection for improved antigen recognition, a process known as affinity maturation. High-affinity antibodies to pathogens such as HIV, influenza, and SARS-CoV-2 are frequently reported to arise from B cells whose receptors, the precursors to antibodies, are encoded by particular immunoglobulin alleles. This raises the possibility that the presence of particular germline alleles in the B cell repertoire is a major determinant of the quality of the antibody response. Alternatively, initial differences in germline alleles’ propensities to form high-affinity receptors might be overcome by chance events during affinity maturation. We first investigate these scenarios in simulations: when germline-encoded fitness differences are large relative to the rate and effect size variation of somatic mutations, the same germline alleles persistently dominate the response of different individuals. In contrast, if germline-encoded advantages can be easily overcome by subsequent mutations, allele usage becomes increasingly divergent over time, a pattern we then observe in mice experimentally infected with influenza virus. We investigated whether affinity maturation might nonetheless strongly select for particular amino acid motifs across diverse genetic backgrounds, but we found no evidence of convergence to similar CDR3 sequences or amino acid substitutions. These results suggest that although germline-encoded specificities can lead to similar immune responses between individuals, diverse evolutionary routes to high affinity limit the genetic predictability of responses to infection and vaccination.
Shanaz A. Ghandhi, Shad R. Morton, Igor Shuryak
et al.
Abstract Background Non-human primates, such as Rhesus macaques, are a powerful model for studies of the cellular and physiological effects of radiation, development of radiation biodosimetry, and for understanding the impact of radiation on human health. Here, we study the effects of 4 Gy total body irradiation (TBI) at the molecular level out to 28 days and at the cytogenetic level out to 56 days after exposure. We combine the global transcriptomic and proteomic responses in peripheral whole blood to assess the impact of acute TBI exposure at extended times post irradiation. Results The overall mRNA response in the first week reflects a strong inflammatory reaction, infection response with neutrophil and platelet activation. At 1 week, cell cycle arrest and re-entry processes were enriched among mRNA changes, oncogene-induced senescence and MAPK signaling among the proteome changes. Influenza life cycle and infection pathways initiated earlier in mRNA and are reflected among the proteomic changes during the first week. Transcription factor proteins SRC, TGFβ and NFATC2 were immediately induced at 1 day after irradiation with increased transcriptional activity as predicted by mRNA changes persisting up to 1 week. Cell counts revealed a mild / moderate hematopoietic acute radiation syndrome (H-ARS) reaction to irradiation with expected lymphopenia, neutropenia and thrombocytopenia that resolved within 30 days. Measurements of micronuclei per binucleated cell levels in cytokinesis-blocked T-lymphocytes remained high in the range 0.27–0.33 up to 28 days and declined to 0.1 by day 56. Conclusions Overall, we show that the TBI 4 Gy dose in NHPs induces many cellular changes that persist up to 1 month after exposure, consistent with damage, death, and repopulation of blood cells.
Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain
et al.
Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. Current approaches frequently consider drug-target or drug-disease interactions individually, ignoring the interdependencies among all three entities. Within human metabolic systems, drugs interact with protein targets in cells, influencing target activities and subsequently impacting biological pathways to promote healthy functions and treat diseases. Moving beyond binary relationships and exploring tighter triple relationships is essential to understanding drugs' mechanism of action (MoAs). Moreover, identifying the heterogeneity of drugs, targets, and diseases, along with their distinct characteristics, is critical to model these complex interactions appropriately. To address these challenges, we effectively model the interconnectedness of all entities in a heterogeneous graph and develop a novel Heterogeneous Graph Triplet Attention Network (\texttt{HeTriNet}). \texttt{HeTriNet} introduces a novel triplet attention mechanism within this heterogeneous graph structure. Beyond pairwise attention as the importance of an entity for the other one, we define triplet attention to model the importance of pairs for entities in the drug-target-disease triplet prediction problem. Experimental results on real-world datasets show that \texttt{HeTriNet} outperforms several baselines, demonstrating its remarkable proficiency in uncovering novel drug-target-disease relationships.