The evolutionary origins of ageing and age-associated diseases continue to pose a fundamental question in biology. This study is concerned with a recently proposed framework, which conceptualises development and ageing as a continuous process, driven by genetically encoded epigenetic changes in target sets of cells. According to the Evolvable Soma Theory of Ageing (ESTA), ageing reflects the cumulative manifestation of epigenetic changes that are predominantly expressed during the post-reproductive phase. These late-acting modifications are not yet evolutionarily optimised but are instead subject to ongoing selection, functioning as somatic "experiments" through which evolution explores novel phenotypic variation. These experiments are often detrimental, leading to progressive physical decline and eventual death, while a small subset may produce beneficial adaptations, that evolution can exploit to shape future developmental trajectories. According to ESTA, ageing can be understood as evolution in action, yet old age is also the strongest risk factor for major diseases such as cardiovascular diseases, cancer, neurodegenerative disorders, and metabolic syndrome. We argue that this association is not merely correlational but causal: the same epigenetic process that drive development and ageing also underlie age-associated diseases. Growing evidence points to epigenetic regulation as a central factor in these pathologies, since no consistent patterns of genetic mutations have been identified, whereas widespread regulatory and epigenetic disruptions are observed. From this perspective, evolution is not only the driver of ageing but also the ultimate source of the diseases that accompany it, making it the root cause of most age-related pathologies.
Carson Dudley, Reiden Magdaleno, Christopher Harding
et al.
Infectious disease forecasting in novel outbreaks or low-resource settings is hampered by the need for large disease and covariate data sets, bespoke training, and expert tuning, all of which can hinder rapid generation of forecasts for new settings. To help address these challenges, we developed Mantis, a foundation model trained entirely on mechanistic simulations, which enables out-of-the-box forecasting across diseases, regions, and outcomes, even in settings with limited historical data. We evaluated Mantis against 48 forecasting models across six diseases with diverse modes of transmission, assessing both point forecast accuracy (mean absolute error) and probabilistic performance (weighted interval score and coverage). Despite using no real-world data during training, Mantis achieved lower mean absolute error than all models in the CDC's COVID-19 Forecast Hub when backtested on early pandemic forecasts which it had not previously seen. Across all other diseases tested, Mantis consistently ranked in the top two models across evaluation metrics. Mantis further generalized to diseases with transmission mechanisms not represented in its training data, demonstrating that it can capture fundamental contagion dynamics rather than memorizing disease-specific patterns. These capabilities illustrate that purely simulation-based foundation models such as Mantis can provide a practical foundation for disease forecasting: general-purpose, accurate, and deployable where traditional models struggle.
Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multiple progression patterns depending on a patient's individual genetics and environmental effects due to lifestyles. We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles. In particular, an end-to-end pipeline is designed to simultaneously infer the characteristics of patients from genetic markers using a variational autoencoder and how it drives the disease progressions using an RNN-based state-space model based on clinical observations. Our proposed model shows improvement on real-world and synthetic clinical data.
Despite the diversity of brain data acquired and advanced AI-based algorithms to analyze them, brain features are rarely used in clinics for diagnosis and prognosis. Here we argue that the field continues to rely on cohort comparisons to seek biomarkers, despite the well-established degeneracy of brain features. Using a thought experiment, we show that more data and more powerful algorithms will not be sufficient to identify biomarkers of brain diseases. We argue that instead of comparing patient versus healthy controls using single data type, we should use multimodal (e.g. brain activity, neurotransmitters, neuromodulators, brain imaging) and longitudinal brain data to guide the grouping before defining multidimensional biomarkers for brain diseases.
Muhammad Ahmad, Sardar Usman, Ildar Batyrshin
et al.
Background: Lung disease is a significant health issue, particularly in children and elderly individuals. It often results from lung infections and is one of the leading causes of mortality in children. Globally, lung-related diseases claim many lives each year, making early and accurate diagnoses crucial. Radiographs are valuable tools for the diagnosis of such conditions. The most prevalent lung diseases, including pneumonia, asthma, allergies, chronic obstructive pulmonary disease (COPD), bronchitis, emphysema, and lung cancer, represent significant public health challenges. Early prediction of these conditions is critical, as it allows for the identification of risk factors and implementation of preventive measures to reduce the likelihood of disease onset Methods: In this study, we utilized a dataset comprising 3,475 chest X-ray images sourced from from Mendeley Data provided by Talukder, M. A. (2023) [14], categorized into three classes: normal, lung opacity, and pneumonia. We applied five pre-trained deep learning models, including CNN, ResNet50, DenseNet, CheXNet, and U-Net, as well as two transfer learning algorithms such as Vision Transformer (ViT) and Shifted Window (Swin) to classify these images. This approach aims to address diagnostic issues in lung abnormalities by reducing reliance on human intervention through automated classification systems. Our analysis was conducted in both binary and multiclass settings. Results: In the binary classification, we focused on distinguishing between normal and viral pneumonia cases, whereas in the multi-class classification, all three classes (normal, lung opacity, and viral pneumonia) were included. Our proposed methodology (ViT) achieved remarkable performance, with accuracy rates of 99% for binary classification and 95.25% for multiclass classification.
Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06% on a label-level when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.
Heart disease is a serious worldwide health issue because it claims the lives of many people who might have been treated if the disease had been identified earlier. The leading cause of death in the world is cardiovascular disease, usually referred to as heart disease. Creating reliable, effective, and precise predictions for these diseases is one of the biggest issues facing the medical world today. Although there are tools for predicting heart diseases, they are either expensive or challenging to apply for determining a patient's risk. The best classifier for foretelling and spotting heart disease was the aim of this research. This experiment examined a range of machine learning approaches, including Logistic Regression, K-Nearest Neighbor, Support Vector Machine, and Artificial Neural Networks, to determine which machine learning algorithm was most effective at predicting heart diseases. One of the most often utilized data sets for this purpose, the UCI heart disease repository provided the data set for this study. The K-Nearest Neighbor technique was shown to be the most effective machine learning algorithm for determining whether a patient has heart disease. It will be beneficial to conduct further studies on the application of additional machine learning algorithms for heart disease prediction.
The medical domain is vast and diverse, with many existing embedding models focused on general healthcare applications. However, these models often struggle to capture a deep understanding of diseases due to their broad generalization across the entire medical field. To address this gap, I present DisEmbed, a disease-focused embedding model. DisEmbed is trained on a synthetic dataset specifically curated to include disease descriptions, symptoms, and disease-related Q\&A pairs, making it uniquely suited for disease-related tasks. For evaluation, I benchmarked DisEmbed against existing medical models using disease-specific datasets and the triplet evaluation method. My results demonstrate that DisEmbed outperforms other models, particularly in identifying disease-related contexts and distinguishing between similar diseases. This makes DisEmbed highly valuable for disease-specific use cases, including retrieval-augmented generation (RAG) tasks, where its performance is particularly robust.
Plant disease recognition is a critical task that ensures crop health and mitigates the damage caused by diseases. A handy tool that enables farmers to receive a diagnosis based on query pictures or the text description of suspicious plants is in high demand for initiating treatment before potential diseases spread further. In this paper, we develop a multimodal plant disease image retrieval system to support disease search based on either image or text prompts. Specifically, we utilize the largest in-the-wild plant disease dataset PlantWild, which includes over 18,000 images across 89 categories, to provide a comprehensive view of potential diseases relating to the query. Furthermore, cross-modal retrieval is achieved in the developed system, facilitated by a novel CLIP-based vision-language model that encodes both disease descriptions and disease images into the same latent space. Built on top of the retriever, our retrieval system allows users to upload either plant disease images or disease descriptions to retrieve the corresponding images with similar characteristics from the disease dataset to suggest candidate diseases for end users' consideration.
Catarina Botelho, Alberto Abad, Tanja Schultz
et al.
Speech is a rich biomarker that encodes substantial information about the health of a speaker, and thus it has been proposed for the detection of numerous diseases, achieving promising results. However, questions remain about what the models trained for the automatic detection of these diseases are actually learning and the basis for their predictions, which can significantly impact patients' lives. This work advocates for an interpretable health model, suitable for detecting several diseases, motivated by the observation that speech-affecting disorders often have overlapping effects on speech signals. A framework is presented that first defines "reference speech" and then leverages this definition for disease detection. Reference speech is characterized through reference intervals, i.e., the typical values of clinically meaningful acoustic and linguistic features derived from a reference population. This novel approach in the field of speech as a biomarker is inspired by the use of reference intervals in clinical laboratory science. Deviations of new speakers from this reference model are quantified and used as input to detect Alzheimer's and Parkinson's disease. The classification strategy explored is based on Neural Additive Models, a type of glass-box neural network, which enables interpretability. The proposed framework for reference speech characterization and disease detection is designed to support the medical community by providing clinically meaningful explanations that can serve as a valuable second opinion.
In clinical practice, the administration of adjuvant chemotherapy (ACT) following tumor surgical resection raises a critical dilemma for stage II colon cancer (CC) patients. The prognostic features used to identify high-risk CC patients rely on the pathological assessment of tumor cells. Currently, these factors are considered for stratifying patients who may benefit from ACT at early CC stages. However, the extent to which these factors predict clinical outcomes (i.e. recurrence, survival) remains highly controversial, also uncertainty persists regarding patients’ response to treatment, necessitating further investigation. Therefore, an imperious need is to explore novel biomarkers that can reliably stratify patients at risk, to optimize adjuvant treatment decisions. Recently, we evaluated the prognostic and predictive value of Immunoscore (IS), an immune digital-pathology assay, in stage II CC patients. IS emerged as the sole significant parameter for predicting disease-free survival (DFS) in high-risk patients. Moreover, IS effectively stratified patients who would benefit most from ACT based on their risk of recurrence, thus predicting their outcomes. Notably, our findings revealed that digital IS outperformed the visual quantitative assessment of the immune response conducted by expert pathologists. The latest edition of the WHO classification for digestive tumor has introduced the evaluation of the immune response, as assessed by IS, as desirable and essential diagnostic criterion. This supports the revision of current cancer guidelines and strongly recommends the implementation of IS into clinical practice as a patient stratification tool, to guide CC treatment decisions. This approach may provide appropriate personalized therapeutic decisions that could critically impact early-stage CC patient care.
Immunologic diseases. Allergy, Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Vadym Sulimenko, Vladimíra Sládková, Tetyana Sulimenko
et al.
Aggregation of high-affinity IgE receptors (FcϵRIs) on granulated mast cells triggers signaling pathways leading to a calcium response and release of inflammatory mediators from secretory granules. While microtubules play a role in the degranulation process, the complex molecular mechanisms regulating microtubule remodeling in activated mast cells are only partially understood. Here, we demonstrate that the activation of bone marrow mast cells induced by FcϵRI aggregation increases centrosomal microtubule nucleation, with G protein-coupled receptor kinase-interacting protein 2 (GIT2) playing a vital role in this process. Both endogenous and exogenous GIT2 were associated with centrosomes and γ-tubulin complex proteins. Depletion of GIT2 enhanced centrosomal microtubule nucleation, and phenotypic rescue experiments revealed that GIT2, unlike GIT1, acts as a negative regulator of microtubule nucleation in mast cells. GIT2 also participated in the regulation of antigen-induced degranulation and chemotaxis. Further experiments showed that phosphorylation affected the centrosomal localization of GIT2 and that during antigen-induced activation, GIT2 was phosphorylated by conventional protein kinase C, which promoted microtubule nucleation. We propose that GIT2 is a novel regulator of microtubule organization in activated mast cells by modulating centrosomal microtubule nucleation.
The agricultural sector plays an essential role in the economic growth of a country. Specifically, in an Indian context, it is the critical source of livelihood for millions of people living in rural areas. Plant Disease is one of the significant factors affecting the agricultural sector. Plants get infected with diseases for various reasons, including synthetic fertilizers, archaic practices, environmental conditions, etc., which impact the farm yield and subsequently hinder the economy. To address this issue, researchers have explored many applications based on AI and Machine Learning techniques to detect plant diseases. This research survey provides a comprehensive understanding of common plant leaf diseases, evaluates traditional and deep learning techniques for disease detection, and summarizes available datasets. It also explores Explainable AI (XAI) to enhance the interpretability of deep learning models' decisions for end-users. By consolidating this knowledge, the survey offers valuable insights to researchers, practitioners, and stakeholders in the agricultural sector, fostering the development of efficient and transparent solutions for combating plant diseases and promoting sustainable agricultural practices.
Currently intelligent diagnosis systems lack the ability of continually learning to diagnose new diseases once deployed, under the condition of preserving old disease knowledge. In particular, updating an intelligent diagnosis system with training data of new diseases would cause catastrophic forgetting of old disease knowledge. To address the catastrophic forgetting issue, an Adapter-based Continual Learning framework called ACL is proposed to help effectively learn a set of new diseases at each round (or task) of continual learning, without changing the shared feature extractor. The learnable lightweight task-specific adapter(s) can be flexibly designed (e.g., two convolutional layers) and then added to the pretrained and fixed feature extractor. Together with a specially designed task-specific head which absorbs all previously learned old diseases as a single "out-of-distribution" category, task-specific adapter(s) can help the pretrained feature extractor more effectively extract discriminative features between diseases. In addition, a simple yet effective fine-tuning is applied to collaboratively fine-tune multiple task-specific heads such that outputs from different heads are comparable and consequently the appropriate classifier head can be more accurately selected during model inference. Extensive empirical evaluations on three image datasets demonstrate the superior performance of ACL in continual learning of new diseases. The source code is available at https://github.com/GiantJun/CL_Pytorch.
Marlen Vitales-Noyola, Berenice Hernández-Castro, Diana Alvarado-Hernández
et al.
Rheumatoid arthritis (RA) is a chronic autoimmune condition characterized, among others, by tissue damage and activation/differentiation of proinflammatory lymphocytes. Accordingly, several studies have concluded that type 17 T helper (Th17) cells seem to have an important role in the pathogenesis of this condition. However, the strategy for the identification and analysis of proinflammatory Th17 cells in those studies has not been consistent and has usually been different from what was originally described. Therefore, we decided to evaluate the levels of Th17 cells in patients with RA employing an extended immune phenotype by using an eight-color multiparametric flow cytometry analysis. For this purpose, blood samples were obtained from 30 patients with RA and 16 healthy subjects, and the levels of Th17 and type 22 helper (Th22) lymphocytes were analyzed as well as the in vitro differentiation of peripheral blood mononuclear cells into Th17 lymphocytes induced by interleukin-23 (IL-23) and IL-1β. We found significant enhanced levels of total Th17 lymphocytes (defined as CD4+IL-17+) as well as enhanced numbers of their pathogenic (defined as CD4+CXCR3+IL-17+IL-22+CD243+CD161+IFN-γ+IL-10-) and nonpathogenic (CD4+CXCR3+IL-17+IL-22-CD243-CD161-IFN-γ-IL-10+) cell subsets in patients with RA. Likewise, the number of Th22 (CD4+CXCR3+/-IL-17-IL-22+) was also increased in these patients compared to healthy controls. However, the in vitro induction/differentiation of pathogenic Th17 cells showed similar results in controls and patients with RA. Likewise, no significant associations were detected in patients with RA between the levels of Th17 or Th22 cells and clinical or laboratory parameters. Our data indicate that patients with RA show enhanced blood levels of the different subsets of Th17 cells and Th22 lymphocytes tested in this study and suggest that these levels are not apparently associated with clinical or laboratory parameters.
Bryan A Johnson, Yiyang Zhou, Kumari G Lokugamage
et al.
While SARS-CoV-2 continues to adapt for human infection and transmission, genetic variation outside of the spike gene remains largely unexplored. This study investigates a highly variable region at residues 203-205 in the SARS-CoV-2 nucleocapsid protein. Recreating a mutation found in the alpha and omicron variants in an early pandemic (WA-1) background, we find that the R203K+G204R mutation is sufficient to enhance replication, fitness, and pathogenesis of SARS-CoV-2. The R203K+G204R mutant corresponds with increased viral RNA and protein both in vitro and in vivo. Importantly, the R203K+G204R mutation increases nucleocapsid phosphorylation and confers resistance to inhibition of the GSK-3 kinase, providing a molecular basis for increased virus replication. Notably, analogous alanine substitutions at positions 203+204 also increase SARS-CoV-2 replication and augment phosphorylation, suggesting that infection is enhanced through ablation of the ancestral 'RG' motif. Overall, these results demonstrate that variant mutations outside spike are key components in SARS-CoV-2's continued adaptation to human infection.
Alopecia universalis (AU) is an autoimmune disorder characterized by non-scarring hair loss in the scalp, eyebrows, beard, and nearly the entire body, negatively affecting patient prognosis. Available treatments are usually unsatisfactory. The autoimmune attacks of hair follicles induced by CD8+ T cells and the collapse of hair follicle immune privilege are believed to be the leading causes of AU. Additionally, interferon (IFN)-γ plays an important role in triggering the collapse of hair follicle immune privilege and impairing hair follicle stem cells. Furthermore, the upregulation of Janus kinase (JAK)3 and phospho-signal transducer and activator of transcription (pSTAT)3/STAT1 in alopecia areata patients suggest that JAK inhibitors can be a potentially promising choice for AU patients for the reason that JAK inhibitors can interfere with JAK-STAT signaling pathways and inhibit IFN-γ. Herein, we report a case of AU successfully treated with tofacitinib. However, this beneficial response in the patient was accompanied by a remarkable increase in peripheral blood cytokine levels during tofacitinib treatment.