The gut microbiota plays a role in many human diseases, but high-throughput sequence analysis does not provide a straightforward path for defining healthy microbial communities. Therefore, understanding mechanisms that drive compositional changes during disease (gut dysbiosis) continues to be a central goal in microbiome research. Insights from the microbial pathogenesis field show that an ecological cause for gut dysbiosis is an increased availability of host-derived respiratory electron acceptors, which are dominant drivers of microbial community composition. Similar changes in the host environment also drive gut dysbiosis in several chronic human illnesses, and a better understanding of the underlying mechanisms informs approaches to causatively link compositional changes in the gut microbiota to an exacerbation of symptoms. The emerging picture suggests that homeostasis is maintained by host functions that control the availability of resources governing microbial growth. Defining dysbiosis as a weakening of these host functions directs attention to the underlying cause and identifies potential targets for therapeutic intervention.
Louise Belenger, Laure Kornreich, Céline Mignon
et al.
MyD88 deficiency is an innate immune deficiency that confers susceptibility to bacterial infections, particularly S. pneumoniae, S. aureus, and P. aeruginosa. This autosomal recessive deficiency was first described in 2008 by Van Bernuth et al. Most infections occur before the age of two and can be fatal in 35%. Fever and elevated inflammatory markers are not always present when infection occurs, making the diagnosis difficult. Here, we report two cases within the same family. The first patient presented with peritonitis and liver abscesses due to Pseudomonas aeruginosa infection at 1 month of age, and the second patient had endophthalmitis complicated by bacteremia and meningitis due to Pseudomonas aeruginosa at 20 days of age. These cases highlight the importance of considering MyD88 deficiency in neonates with severe bacterial infections, even without classic inflammatory signs.
Marília Bordignon Antonio, Thiago S. Torres, Paula M. Luz
et al.
Abstract Introduction HIV stigma negatively impacts multiple outcomes for persons living with HIV (PLHIV). Among older PLHIV, stigma may influence mental health and aging expectation. We aimed to evaluate factors related to HIV stigma and whether depression mediates its association with expectations regarding aging (ERA-12). Design Cross-sectional study of PLHIV ≥ 50 years, on antiretroviral therapy with suppressed viral load, enrolled in the Longitudinal Study of HIV & Aging in Brazil (ELEA-Brasil). Methods We assessed factors related to HIV stigma (12-item Berger, range:12–48, higher = higher stigma) and associated with ERA-12 (range: 0–100, lower = more negative aging expectation). Mediation analysis was performed to evaluate depression (Patient Health Questionnaire-9 [PHQ-9]; range:0–27, higher = higher symptoms) as a mediator of the association between stigma and ERA-12. Univariable and multivariable Poisson and linear regression models were applied, adjusted for demographics, time since HIV diagnosis, and substance use. Results We enrolled 702 PLHIV (median age 62 years; 64.9% men). Median and interquartile range (IQR) of HIV stigma, ERA-12 and PHQ-9 were 30 (25–35); 36 (22–47) and 3 (1–7) respectively. In adjusted analyses, older age (≥ 70 vs. 50–55 years) and shorter time since HIV diagnosis (10–19 years vs. ≥ 20 years) were significantly associated with lower HIV stigma. In adjusted models, higher HIV stigma (Coefficient = − 12.05 [95%CI − 17.84, − 6.26]) and lower education (Coefficient = − 7.60 [95%CI: − 13.40, − 1.81]) were associated with worse ERA-12. Depressive symptoms may reflect 25% of the overall effect of stigma on ERA-12. Conclusion We observed a high prevalence of HIV stigma, with depressive symptoms consistent with a potential mediating role in aging expectations.
Natalia Glazman, Jyoti Mangal, Pedro Borges
et al.
The application of causal discovery to diseases like Alzheimer's (AD) is limited by the static graph assumptions of most methods; such models cannot account for an evolving pathophysiology, modulated by a latent disease pseudotime. We propose to apply an existing latent variable model to real-world AD data, inferring a pseudotime that orders patients along a data-driven disease trajectory independent of chronological age, then learning how causal relationships evolve. Pseudotime outperformed age in predicting diagnosis (AUC 0.82 vs 0.59). Incorporating minimal, disease-agnostic background knowledge substantially improved graph accuracy and orientation. Our framework reveals dynamic interactions between novel (NfL, GFAP) and established AD markers, enabling practical causal discovery despite violated assumptions.
How does the threat of infectious disease influence sociality among generative agents? We used generative agent-based modeling (GABM), powered by large language models, to experimentally test hypotheses about the behavioral immune system. Across three simulation runs, generative agents who read news about an infectious disease outbreak showed significantly reduced social engagement compared to agents who received no such news, including lower attendance at a social gathering, fewer visits to third places (e.g., cafe, store, park), and fewer conversations throughout the town. In interview responses, agents explicitly attributed their behavioral changes to disease-avoidance motivations. A validity check further indicated that they could distinguish between infectious and noninfectious diseases, selectively reducing social engagement only when there was a risk of infection. Our findings highlight the potential of GABM as an experimental tool for exploring complex human social dynamics at scale.
Compartmental models like the Susceptible-Infected-Recovered (SIR)\cite{Kermack1927} and its extensions such as the Susceptible-Exposed-Infected-Recovered (SEIRS)\cite{Ottar2020,Ignazio2021,Grimm2021,Paoluzzi2021} are commonly used to model the spread of infectious diseases. We propose here, a modified SEIRS, namely, an SEIRSD model which comprises of (i) a reverse transmission from exposed to susceptible compartment to account for the probabilistic character of disease transmission seen in nature, and (ii) inclusion of mortality caused by infection in addition to death by other causes. We observed that, a reverse flow from exposed to susceptible class, has a significant impact on the height of infection peaks and their time of occurrence. In view of the recent surges of Covid-19 variants, this study is most relevant.
The clinical application of immune checkpoint inhibitor (ICI) has profoundly reshaped the therapeutic landscape of non-small cell lung cancer (NSCLC), heralding a new era of immunotherapy in oncology. However, despite the durable and remarkable clinical benefits observed in a subset of patients, a considerable proportion exhibit primary or acquired resistance, substantially limiting overall therapeutic efficacy. Immune resistance has emerged as one of the central challenges in ICI-based NSCLC treatment, stemming from an incomplete understanding of ICI mechanisms of action and the highly heterogeneous and dynamically complex nature of the NSCLC tumor microenvironment (TME). This review provides a comprehensive overview of the diverse molecular and cellular mechanisms underlying ICI resistance in NSCLC, highlights recent advances in combination therapeutic strategies aimed at overcoming resistance, and discusses the opportunities and challenges associated with their clinical translation and application.
In the infectious disease literature, significant effort has been devoted to studying dynamics at a single scale. For example, compartmental models describing population-level dynamics are often formulated using differential equations. In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically. Classic stochastic simulation algorithms, such as the next reaction method, can be employed to solve these Markovian models exactly. The intricate coupling between models at different scales underscores the importance of multiscale modelling in infectious diseases. However, several computational challenges arise when the multiscale model becomes non-Markovian. In this paper, we address these challenges by developing a novel exact stochastic simulation algorithm. We apply it to a showcase multiscale system where all individuals share the same deterministic within-host model while the population-level dynamics are governed by a stochastic formulation. We demonstrate that as long as the within-host information is harvested at a reasonable resolution, the novel algorithm will always be accurate. Furthermore, our implementation is still efficient even at finer resolutions. Beyond infectious disease modelling, the algorithm is widely applicable to other multiscale systems, providing a versatile, accurate, and computationally efficient framework.
An understanding of the disease spreading phenomenon based on a mathematical model is extremely needed for the implication of the correct policy measures to contain the disease propagation. Here, we report a new model namely the Ising-SIR model describing contagious disease spreading phenomena including both airborne and direct contact disease transformations. In the airborne case, a susceptible agent can catch the disease either from the environment or its infected neighbors whereas in the second case, the agent can be infected only through close contact with its infected neighbors. We have performed Monte Carlo simulations on a square lattice using periodic boundary conditions to investigate the dynamics of disease spread. The simulations demonstrate that the mechanism of disease spreading plays a significant role in the growth dynamics and leads to different growth exponent. In the direct contact disease spreading mechanism, the growth exponent is nearly equal to two for some model parameters which agrees with earlier empirical observations. In addition, the model predicts various types of spatiotemporal patterns that can be observed in nature.
Mahendra Kumar Gohil, Anirudha Bhattacharjee, Rwik Rana
et al.
Over the past decade, several image-processing methods and algorithms have been proposed for identifying plant diseases based on visual data. DNN (Deep Neural Networks) have recently become popular for this task. Both traditional image processing and DNN-based methods encounter significant performance issues in real-time detection owing to computational limitations and a broad spectrum of plant disease features. This article proposes a novel technique for identifying and localising plant disease based on the Quad-Tree decomposition of an image and feature learning simultaneously. The proposed algorithm significantly improves accuracy and faster convergence in high-resolution images with relatively low computational load. Hence it is ideal for deploying the algorithm in a standalone processor in a remotely operated image acquisition and disease detection system, ideally mounted on drones and robots working on large agricultural fields. The technique proposed in this article is hybrid as it exploits the advantages of traditional image processing methods and DNN-based models at different scales, resulting in faster inference. The F1 score is approximately 0.80 for four disease classes corresponding to potato and tomato crops.
Md. Simul Hasan Talukder, Sharmin Akter, Abdullah Hafez Nur
et al.
Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called sugarcaneNet2024 that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models, particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169, Xception, and ResNet152V2. Initially, we added three more dense layers with 0.0001 LASSO regularization, three 30% dropout layers, and three batch normalizations with renorm enabled at the bottom of these pre-trained models to improve the performance. The accuracy of sugarcane leaf disease classification was greatly increased by this addition. Following this, several comparative studies between the average ensemble and individual models were carried out, indicating that the ensemble technique performed better. The average ensemble of all modified pre-trained models produced outstanding outcomes: 100%, 99%, 99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively. Performance was further enhanced by the implementation of an optimized weighted average ensemble technique incorporated with grid search. This optimized sugarcaneNet2024 model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100% , respectively.
Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGBoost, CatBoost, and LightGBM. The model's capability to process complex data and provide per-pixel disease-affecting probabilities enables precise, targeted interventions, contributing to more sustainable disease management practices. Our findings underscore the transformative potential of combining Transformer models with remote sensing data in precision agriculture, offering a scalable solution for improving crop health and productivity while reducing environmental impact.
AbstractHuman noroviruses (HuNoV) are the leading cause of acute gastroenteritis worldwide. The humoral immune response plays an important role in clearing HuNoV infections and elucidating the antigenic landscape of HuNoV during an infection can shed light on antibody targets to inform vaccine design. Here, we utilized Jun-Fos-assisted phage display of a HuNoV genogroup GI.1 genomic library and deep sequencing to simultaneously map the epitopes of serum antibodies of six individuals infected with GI.1 HuNoV. We found both unique and common epitopes that were widely distributed among both nonstructural proteins and the major capsid protein. Recurring epitope profiles suggest immunodominant antibody footprints among these individuals. Analysis of sera collected longitudinally from three individuals showed the presence of existing epitopes in the pre-infection sera, suggesting these individuals had prior HuNoV infections. Nevertheless, newly recognized epitopes surfaced seven days post-infection. These new epitope signals persisted by 180 days post-infection along with the pre-infection epitopes, suggesting a persistent production of antibodies recognizing epitopes from previous and new infections. Lastly, analysis of a GII.4 genotype genomic phage display library with sera of three persons infected with GII.4 virus revealed epitopes that overlapped with those identified in GI.1 affinity selections, suggesting the presence of GI.1/GII.4 cross-reactive antibodies. The results demonstrate that genomic phage display coupled with deep sequencing can characterize HuNoV antigenic landscapes from complex polyclonal human sera to reveal the timing and breadth of the human humoral immune response to infection.
In the past decade, there has been a surge in research examining the use of voice and speech analysis as a means of detecting neurodegenerative diseases such as Alzheimer's. Many studies have shown that certain acoustic features can be used to differentiate between normal aging and Alzheimer's disease, and speech analysis has been found to be a cost-effective method of detecting Alzheimer's dementia. The aim of this review is to analyze the various algorithms used in speech-based detection and classification of Alzheimer's disease. A literature survey was conducted using databases such as Web of Science, Google Scholar, and Science Direct, and articles published from January 2020 to the present were included based on keywords such as ``Alzheimer's detection'', "speech," and "natural language processing." The ADReSS, Pitt corpus, and CCC datasets are commonly used for the analysis of dementia from speech, and this review focuses on the various acoustic and linguistic feature engineering-based classification models drawn from 15 studies. Based on the findings of this study, it appears that a more accurate model for classifying Alzheimer's disease can be developed by considering both linguistic and acoustic data. The review suggests that speech signals can be a useful tool for detecting dementia and may serve as a reliable biomarker for efficiently identifying Alzheimer's disease.