Epilepsy is increasingly recognized as a disorder not only of neuronal dysfunction but also of immune dysregulation within the central nervous system (CNS). Accumulating evidence points to a critical role for the immune microenvironment in shaping epileptogenesis—the process that underlies the development and progression of epilepsy. In this Review, we examine the spatial dynamics of neuroimmune interactions, highlighting how local inflammatory niches emerge and evolve across brain compartments such as the parenchyma and perivascular space. We describe how the spatial organization and activation of resident glial cells, alongside the infiltration of peripheral immune cells facilitated by blood–brain barrier (BBB) disruption, contribute to region-specific patterns of neuroinflammation. Critically, we emphasize that understanding “where” these neuroimmune interactions occur—their precise spatial organization within distinct cellular microenvironments—is as fundamental as identifying “what” immune cells are involved or “how” they function. Particular focus is given to the localized actions of immune mediators, including regulatory T cells and pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α, and their influence on neuronal excitability. We also discuss the spatiotemporal heterogeneity of immune signatures across different epilepsy syndromes, drawing from both experimental models and clinical observations. Finally, we explore emerging therapeutic strategies that target spatially defined immune responses and consider the potential of spatial biomarkers and advanced tissue-mapping technologies to refine disease classification and guide precision therapies. By positioning the spatial immune landscape as a central feature of epileptogenesis, we propose a framework for developing effective, potentially curative interventions for epilepsy.
Cardiovascular diseases are major causes of mortality globally. They often co-occur and are interrelated, leading to partial-order relationships among their onset times. However, these onset times are subject to informative censoring due to the occurrence of death, posing significant challenges for survival prediction. In this article, we propose a novel copula-based framework that learns dependence among multiple correlated marginal components through a pseudo-likelihood for estimation. We adopt nonparametric marginals, alleviating the reliance on marginal distribution assumptions typically required in conventional copula models, and estimate the association between the onsets of intermediate cardiovascular diseases and death by solving a concordance estimating equation. Under this framework, a renewable risk assessment method is developed for dynamic survival prediction, leveraging information on disease onset times and the maximum follow-up duration. Our proposed method yields estimators with well-established properties, and its flexibility and predictive effectiveness are demonstrated through extensive simulation studies. We apply the method to data from a heart disease study, demonstrating the benefits of incorporating the associations among various cardiovascular diseases and their synergistic effects on mortality for dynamic prediction of overall survival.
Early detection of highly pathogenic avian influenza (HPAI) and endemic poultry diseases is critical for global food security. While computer vision models excel at classifying diseases from fecal imaging, deploying these systems at scale is bottlenecked by farm data privacy concerns and institutional data silos. Furthermore, existing open-source agricultural datasets frequently suffer from severe, undocumented data contamination. In this paper, we introduce $\textbf{FecalFed}$, a privacy-preserving federated learning framework for poultry disease classification. We first curate and release $\texttt{poultry-fecal-fl}$, a rigorously deduplicated dataset of 8,770 unique images across four disease classes, revealing and eliminating a 46.89$\%$ duplication rate in popular public repositories. To simulate realistic agricultural environments, we evaluate FecalFed under highly heterogeneous, non-IID conditions (Dirichlet $α=0.5$). While isolated single-farm training collapses under this data heterogeneity, yielding only 64.86$\%$ accuracy, our federated approach recovers performance without centralizing sensitive data. Specifically, utilizing server-side adaptive optimization (FedAdam) with a Swin-Small architecture achieves 90.31$\%$ accuracy, closely approaching the centralized upper bound of 95.10\%. Furthermore, we demonstrate that an edge-optimized Swin-Tiny model maintains highly competitive performance at 89.74$\%$, establishing a highly efficient, privacy-first blueprint for on-farm avian disease monitoring.
There is growing evidence that not only the novel coronavirus disease (COVID‐19) but also the COVID‐19 vaccines can cause a variety of skin reactions. In this review article, we provide a brief overview on cutaneous findings that have been observed since the emerging mass COVID‐19 vaccination campaigns all over the world. Unspecific injection‐site reactions very early occurring after the vaccination are most frequent. Type I hypersensitivity reactions (e.g. urticaria, angio‐oedema and anaphylaxis) likely due to allergy to ingredients may rarely occur but can be severe. Type IV hypersensitivity reactions may be observed, including delayed large local skin lesions (“COVID arm”), inflammatory reactions in dermal filler or previous radiation sites or even old BCG scars, and more commonly morbilliform and erythema multiforme‐like rashes. Autoimmune‐mediated skin findings after COVID‐19 vaccination include leucocytoclastic vasculitis, lupus erythematosus and immune thrombocytopenia. Functional angiopathies (chilblain‐like lesions, erythromelalgia) may also be observed. Pityriasis rosea‐like rashes and reactivation of herpes zoster have also been reported after COVID‐19 vaccination. In conclusion, there are numerous cutaneous reaction patterns that may occur following COVID‐19 vaccination, whereby many of these skin findings are of immunological/autoimmunological nature. Importantly, molecular mimicry exists between SARS‐CoV‐2 (e.g. the spike‐protein sequences used to design the vaccines) and human components and may thus explain some COVID‐19 pathologies as well as adverse skin reactions to COVID‐19 vaccinations.
Abstract Background National US data on the burden and risks for hepatitis C virus (HCV) infection in people with human immunodeficiency virus (HIV) during the direct-acting antiviral (DAA) era are limited. These data are important to understand current progress and guide future efforts toward HCV microelimination. Methods We evaluated (1) HCV prevalence (2011–2013, 2014–2017, 2018–2022) using a serial cross-sectional design and (2) correlates for HCV viremia (2018–2022) in adult people with HIV (PWH) within the Centers for AIDS Research Network of Integrated Clinic Systems (CNICS) cohort using multivariable adjusted relative risk regression. The most recent data from each time period were used for calculations and models. Results In the CNICS cohort, HCV viremia prevalence was 8.7% in 2011–2013, 10.5% in 2014–2017, and 4.8% in 2018–2022. Disparities in prevalence across demographic groups defined by age, gender, and race/ethnicity were smaller in 2018–2022 than earlier time periods. In relative risk regression, female gender, detectable HIV RNA, higher proportion of missed visits (last 18 months), higher FIB-4 score, higher depressive symptom severity, and current use of methamphetamine and illicit opioids were associated with HCV viremia in 2018–2022. Conclusions The prevalence of HCV viremia during the DAA era in this US-based national cohort of PWH improved over time and across demographic subgroups but remains higher than those without HIV. Our findings highlight the continued importance of prioritizing HCV care in all PWH, especially in certain key, less-reached groups. Proactive, comprehensive efforts to care engagement, substance use, mental health, and other social determinants will be crucial to improve reach, prevention, and treatment to achieve HCV elimination goals.
Zachary C. Lorson, William M. McFadden, Grace Neilsen
et al.
Abstract Small molecule-protein interactions underpin many biological functions and play an integral role in the treatment and prevention of several human diseases. These interactions can be key to understanding the mechanism of action of these compounds. Previous methods of determining protein-protein or protein-antibody interactions have been well established; however, the use of BLI in antiviral discovery is a promising and relatively new avenue. The high-throughput nature of this method in tandem with its pM sensitivity allows for quick and seamless identification of hit compounds. Here we discuss ways to overcome common pitfalls that can occur while using BLI such as nonspecific binding (NSB) and ligand drift while offering possible solutions. Characterizing small molecule-protein interactions is not trivial and optimizing the experimental conditions is imperative. To address this gap in knowledge, we present optimized BLI protocols for the study of three cases of protein-small molecule interactions: PF74 or Lenacapavir (LEN) with HIV-1 capsid protein (CA), and Nirmatrelvir (NIR) with SARS-CoV-2 Mpro. LEN and NIR are of particular interest because they are clinically relevant, and PF74, a well-studied control, was the first compound reported to target the LEN binding site. We demonstrate that BLI can be a powerful and effective tool in calculating the binding affinities between a protein and small molecule. These newly designed methods enabled calculation of K D values, the affinity between ligand and analyte, ranging from the micro to the sub-nanomolar range for CA binding events and confirmed the covalent interaction between NIR and Mpro. These protocols will facilitate efficient testing of new antivirals or derivatives in a high- throughput format. Summary Bio-Layer Interferometry (BLI) is a multifunctional technology that is used to determine valuable information on real-time kinetics including association and dissociation. Optimizing experimental conditions to acquire data about protein-ligand interactions can be challenging. We provide three example methods of collecting binding data that characterize how viral proteins interact with antivirals.
Akihiro Shimba, Akihiro Shimba, Satoru Munakata
et al.
The (pro)renin receptor (PRR) is a multifunctional transmembrane protein that enhances β-catenin/TCF1 signaling and V-ATPase-mediated lysosomal acidification. Emerging evidence indicates that it may also regulate potential roles in regulating T cell development, survival, and immune responses. Here, we demonstrated that PRR promotes the maturation and survival of T cells within the thymus. In particular, PRR-deficient mice exhibited a significant reduction in iNKT cells in the thymus and periphery. PRR promoted the energy synthesis process in mitochondria, as evidenced by increased mitochondrial amount and membrane potential. This phenomenon was accompanied by an increase in TCF1 expression and lysosomal acidification. Furthermore, PRR enhanced the survival of naive T and iNKT cells in the periphery, while simultaneously suppressing inflammatory cytokine-producing T cells, thereby preventing colitis. In contrast, PRR enhanced resistance against tumor growth by increasing the number of tumor-infiltrating Th1 and iNKT cells, which in turn promoted NK cell recruitment. This study indicates that PRR is critical for supporting T cell maintenance, suppressing excessive inflammation, and enhancing anti-tumor immunity.
To extend healthy life expectancy in an aging society, it is crucial to prevent various diseases at pre-disease states. Although dynamical network biomarker theory has been developed for pre-disease detection, mathematical frameworks for pre-disease treatment have not been well established. Here I propose a control theory-based approach for pre-disease treatment, named Markov chain sparse control (MCSC), where time evolution of a probability distribution on a Markov chain is described as a discrete-time linear system. By designing a sparse controller, a few candidate states for intervention are identified. The validity of MCSC is demonstrated using numerical simulations and real-data analysis.
Pathogenic Rickettsia species target vascular endothelial cells and cause systemic vasculitis. As obligate intracellular bacterial pathogens, Rickettsia must secure nutritional resources within the cytoplasm of endothelial cells while simultaneously subverting the innate immune defense system. With advances in rickettsial and host genetics, recent studies have identified novel molecular mechanisms involved in the complex interactions between Rickettsia and endothelial cells. However, it remains unclear how Rickettsia shields pathogen-derived immune stimulants, such as lipopolysaccharides (LPS) and peptidoglycan fragments, from immune recognition during intracellular replication. Prior work described two Rickettsia conorii variants with kkaebi transposon insertions in the polysaccharide synthesis operon (pso). Biochemical and immunological analyses revealed that pso is responsible for the biosynthesis of O-antigen (O-Ag) and the proper assembly of surface proteins. In the present work, we document that pso variant HK2 exhibits reduced capacities to adhere to and invade microvascular endothelial cells. Despite the low intracellular abundance, HK2 induced significantly higher levels of proinflammatory cytokines and chemokines, leading to premature cell death. Notably, HK2 exhibited defective intracellular survival in bone marrow-derived macrophages. This inability to dampen endothelial cell-mediated immune stimulation and resist macrophage-induced bactericidal activities resulted in the rapid elimination of viable Rickettsia in the mouse model of spotted fever. Further, when tested as a live-attenuated vaccine, HK2 elicited robust protective immunity against lethal spotted fever pathogenesis. Our work highlights the crucial role of pso in enabling Rickettsia to evade immune surveillance during intracellular replication within endothelial cells, ultimately delaying pathogen-induced programmed cell death and escaping immune defense mechanisms.
BackgroundThe systemic immune-inflammation index (SII) and systemic inflammation response index (SIRI) are both novel biomarkers and predictors of inflammation. Psoriasis is a skin disease characterized by chronic inflammation. This study aimed to investigate the potential association between SII, SIRI, and adult psoriasis.MethodsData of adults aged 20 to 80 years from the National Health and Nutrition Examination Survey (NHANES) (2003–2006, 2009–2014) were utilized. The K-means method was used to group SII and SIRI into low, medium, and high-level clusters. Additionally, SII or SIRI levels were categorized into three groups: low (1st-3rd quintiles), medium (4th quintile), and high (5th quintile). The association between SII-SIRI pattern, SII or SIRI individually, and psoriasis was assessed using multivariate logistic regression models. The results were presented as odds ratios (ORs) and confidence intervals (CIs). Restricted cubic spline (RCS) regression, subgroup, and interaction analyses were also conducted to explore the potential non-linear and independent relationships between natural log-transformed SII (lnSII) levels or SIRI levels and psoriasis, respectively.ResultsOf the 18208 adults included in the study, 511 (2.81%) were diagnosed with psoriasis. Compared to the low-level group of the SII-SIRI pattern, participants in the medium-level group had a significantly higher risk for psoriasis (OR = 1.40, 95% CI: 1.09, 1.81, p-trend = 0.0031). In the analysis of SII or SIRI individually, both SII and SIRI were found to be positively associated with the risk of psoriasis (high vs. low group OR = 1.52, 95% CI: 1.18, 1.95, p-trend = 0.0014; OR = 1.48, 95% CI: 1.12, 1.95, p-trend = 0.007, respectively). Non-linear relationships were observed between lnSII/SIRI and psoriasis (both p-values for overall < 0.05, p-values for nonlinearity < 0.05). The association between SII levels and psoriasis was stronger in females, obese individuals, people with type 2 diabetes, and those without hypercholesterolemia.ConclusionWe observed positive associations between SII-SIRI pattern, SII, SIRI, and psoriasis among U.S. adults. Further well-designed studies are needed to gain a better understanding of these findings.
Reconstructing transmission networks is essential for identifying key factors like superspreaders and high-risk locations, which are critical for developing effective pandemic prevention strategies. In this study, we developed a Bayesian framework that integrates genomic and temporal data to reconstruct transmission networks for infectious diseases. The Bayesian transmission model accounts for the latent period and differentiates between symptom onset and actual infection time, enhancing the accuracy of transmission dynamics and epidemiological models. Additionally, the model allows for the transmission of multiple pathogen lineages, reflecting the complexity of real-world transmission events more accurately than models that assume a single lineage transmission. Simulation results show that the Bayesian model reliably estimates both the model parameters and the transmission network. Moreover, hypothesis testing effectively identifies direct transmission events. This approach highlights the crucial role of genetic data in reconstructing transmission networks and understanding the origins and transmission dynamics of infectious diseases.
The extraction of biomedical data has significant academic and practical value in contemporary biomedical sciences. In recent years, drug repositioning, a cost-effective strategy for drug development by discovering new indications for approved drugs, has gained increasing attention. However, many existing drug repositioning methods focus on mining information from adjacent nodes in biomedical networks without considering the potential inter-relationships between the feature spaces of drugs and diseases. This can lead to inaccurate encoding, resulting in biased mined drug-disease association information. To address this limitation, we propose a new model called Dual-Feature Drug Repurposing Neural Network (DFDRNN). DFDRNN allows the mining of two features (similarity and association) from the drug-disease biomedical networks to encode drugs and diseases. A self-attention mechanism is utilized to extract neighbor feature information. It incorporates two dual-feature extraction modules: the single-domain dual-feature extraction (SDDFE) module for extracting features within a single domain (drugs or diseases) and the cross-domain dual-feature extraction (CDDFE) module for extracting features across domains. By utilizing these modules, we ensure more appropriate encoding of drugs and diseases. A cross-dual-domain decoder is also designed to predict drug-disease associations in both domains. Our proposed DFDRNN model outperforms six state-of-the-art methods on four benchmark datasets, achieving an average AUROC of 0.946 and an average AUPR of 0.597. Case studies on two diseases show that the proposed DFDRNN model can be applied in real-world scenarios, demonstrating its significant potential in drug repositioning.
Alessandro Viani, Boris A Gutman, Emile d'Angremont
et al.
Modelling the progression of Degenerative Diseases (DD) is essential for detection, prevention, and treatment, yet it remains challenging due to the heterogeneity in disease trajectories among individuals. Factors such as demographics, genetic conditions, and lifestyle contribute to diverse phenotypical manifestations, necessitating patient stratification based on these variations. Recent methods like Subtype and Stage Inference (SuStaIn) have advanced unsupervised stratification of disease trajectories, but they face potential limitations in robustness, interpretability, and temporal granularity. To address these challenges, we introduce Disease Progression Modelling and Stratification (DP-MoSt), a novel probabilistic method that optimises clusters of continuous trajectories over a long-term disease time-axis while estimating the confidence of trajectory sub-types for each biomarker. We validate DP-MoSt using both synthetic and real-world data from the Parkinson's Progression Markers Initiative (PPMI). Our results demonstrate that DP-MoSt effectively identifies both sub-trajectories and subpopulations, and is a promising alternative to current state-of-the-art models.
Yumeng Yang, Ashley Gilliam, Ethan B Ludmir
et al.
Clinical trials are pivotal in medical research, and NLP can enhance their success, with application in recruitment. This study aims to evaluate the generalizability of eligibility classification across a broad spectrum of clinical trials. Starting with phase 3 cancer trials, annotated with seven eligibility exclusions, then to determine how well models can generalize to non-cancer and non-phase 3 trials. To assess this, we have compiled eligibility criteria data for five types of trials: (1) additional phase 3 cancer trials, (2) phase 1 and 2 cancer trials, (3) heart disease trials, (4) type 2 diabetes trials, and (5) observational trials for any disease, comprising 2,490 annotated eligibility criteria across seven exclusion types. Our results show that models trained on the extensive cancer dataset can effectively handle criteria commonly found in non-cancer trials, such as autoimmune diseases. However, they struggle with criteria disproportionately prevalent in cancer trials, like prior malignancy. We also experiment with few-shot learning, demonstrating that a limited number of disease-specific examples can partially overcome this performance gap. We are releasing this new dataset of annotated eligibility statements to promote the development of cross-disease generalization in clinical trial classification.
Following the approval of Cervarix for the immunization of girls and women in China against high-risk human papillomavirus types 16 and 18, a non-interventional post-authorization safety study was performed. A multi-center prospective cohort study assessed safety following Cervarix vaccination of Chinese girls and women aged 9–45 years between 31 May 2018 and 3 December 2020. Adverse events following immunization (AEFIs), potential immune-mediated diseases (pIMDs), and pregnancy-related outcomes were collected up to 12 months from the third immunization or 24 months from the first immunization, whichever came first. Among 3,013 women who received 8,839 Cervarix doses, 167 (5.5%) reported ≥ 1 any AEFI, and 22 (0.7%) reported 40 serious AEFIs. During the 30 days after each dose, 147 women (4.9%) reported 211 medically attended AEFIs, including 3 serious AEFIs reported by 1 woman (0.03%). One woman reported a pIMD. Cervarix was inadvertently administered to 65 women (2.2%) within 60 days before conception or during pregnancy. Of these women, 34 (52.3%) gave birth to live infant(s) with no apparent congenital anomalies, and 1 (1.5%) woman gave birth to a live infant with a congenital anomaly. No serious AEFIs or pIMDs were considered to be related to the vaccination. In Chinese women aged 9–45 years, immunization with the Cervarix three-dose schedule was well tolerated. Overall, no safety concerns were identified, although rare adverse events may have been missed due to the study sample size.Clinical trial registration: NCT03438006.
Mirsaeed Abdollahi, Ali Jafarizadeh, Amirhosein Ghafouri Asbagh
et al.
Background: Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods - in particular, deep learning (DL) - has been on the rise lately for the analysis of different CVD-related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand heart function and anticipate changes based on microvascular characteristics and function, researchers are currently exploring the integration of AI with non-invasive retinal scanning. There is great potential to reduce the number of cardiovascular events and the financial strain on healthcare systems by utilizing AI-assisted early detection and prediction of cardiovascular diseases on a large scale. Method: A comprehensive search was conducted across various databases, including PubMed, Medline, Google Scholar, Scopus, Web of Sciences, IEEE Xplore, and ACM Digital Library, using specific keywords related to cardiovascular diseases and artificial intelligence. Results: The study included 87 English-language publications selected for relevance, and additional references were considered. This paper provides an overview of the recent developments and difficulties in using artificial intelligence and retinal imaging to diagnose cardiovascular diseases. It provides insights for further exploration in this field. Conclusion: Researchers are trying to develop precise disease prognosis patterns in response to the aging population and the growing global burden of CVD. AI and deep learning are revolutionizing healthcare by potentially diagnosing multiple CVDs from a single retinal image. However, swifter adoption of these technologies in healthcare systems is required.
In recent decades, traditional drug research and development have been facing challenges such as high cost, long timelines, and high risks. To address these issues, many computational approaches have been suggested for predicting the relationship between drugs and diseases through drug repositioning, aiming to reduce the cost, development cycle, and risks associated with developing new drugs. Researchers have explored different computational methods to predict drug-disease associations, including drug side effects-disease associations, drug-target associations, and miRNAdisease associations. In this comprehensive review, we focus on recent advances in predicting drug-disease association methods for drug repositioning. We first categorize these methods into several groups, including neural network-based algorithms, matrixbased algorithms, recommendation algorithms, link-based reasoning algorithms, and text mining and semantic reasoning. Then, we compare the prediction performance of existing drug-disease association prediction algorithms. Lastly, we delve into the present challenges and future prospects concerning drug-disease associations.
The use of unmanned aerial vehicles (UAV) is revolutionizing the agricultural industry. Cashews are grown by approximately 70% of small and marginal farmers, and the cashew industry plays a critical role in their economic development. To take timely counter measures against plant diseases and infections, it is imperative to monitor and detect diseases as early as possible and take suitable measures. Using UAVs, such as those that are equipped with artificial intelligence, can assist farmers by providing early detection of crop diseases and precision pesticide application. An edge computing paradigm of Artificial Intelligence is employed to process this image in order to make decisions with the least amount of latency possible. As a result of these decisions, the stage of infestation, the crops affected, the method of prevention of spreading the disease, and what type and amount of pesticides need to be applied can be determined. UAVs equipped with sensors detect disease patterns quickly and accurately over large areas. Combined with AI algorithms, these machines can analyse data from a variety of sources such as temperature, humidity, CO2 levels and soil composition. This allows them to recognize disease symptoms before they become visible. Early detection allows for more effective control strategies that can reduce costs caused by lost production due to infestations or crop failure. Using an end-to-end training architecture, mobileNetV2 determines how to classify anthracnose disease in cashew leaves. A standard PlantVillage dataset is used for performance evaluation and for standardization. Additionally, samples captured with a drone present a variety of image samples captured in a variety of conditions, which complicates the analysis. According to our analysis, we were able to identify the anthracnose with 95% accuracy and the healthy leaves with 99% accuracy.