Hasil untuk "Infectious and parasitic diseases"

Rowzatul Zannat, Abdullah Al Shafi, Abdul Muntakim

Increased access to reliable health information is essential for non-English-speaking populations, yet resources in Bangla for disease prediction remain limited. This study addresses this gap by developing a comprehensive Bangla symptoms-disease dataset containing 758 unique symptom-disease relationships spanning 85 diseases. To ensure transparency and reproducibility, we also make our dataset publicly available. The dataset enables the prediction of diseases based on Bangla symptom inputs, supporting healthcare accessibility for Bengali-speaking populations. Using this dataset, we evaluated multiple machine learning models to predict diseases based on symptoms provided in Bangla and analyzed their performance on our dataset. Both soft and hard voting ensemble approaches combining top-performing models achieved 98\% accuracy, demonstrating superior robustness and generalization. Our work establishes a foundational resource for disease prediction in Bangla, paving the way for future advancements in localized health informatics and diagnostic tools. This contribution aims to enhance equitable access to health information for Bangla-speaking communities, particularly for early disease detection and healthcare interventions.

Jaime A. Isern, Renzo Carlucci, Guillermo R. Labadie et al.

Parasitic diseases represent a severe global burden, with current treatments often limited by toxicity, drug resistance, and suboptimal efficacy in chronic infections. This review examines the emerging role of triazole-based compounds, originally developed as antifungals, in advanced antiparasitic therapy. Their unique structural properties, particularly those of 1,2,3- and 1,2,4-triazole isomers, facilitate diverse binding interactions and favorable pharmacokinetics. By leveraging innovative synthetic approaches, such as click chemistry (copper-catalyzed azide–alkyne cycloaddition) and structure-based design, researchers have repurposed and optimized triazole scaffolds to target essential parasite pathways, including sterol biosynthesis via CYP51 and other novel enzymatic routes. Preclinical studies in models of Chagas disease, leishmaniasis, malaria, and helminth infections demonstrate that derivatives like posaconazole, ravuconazole, and DSM265 exhibit potent in vitro and in vivo activity, although their primarily static effects have limited their success as monotherapies in chronic cases. Combination strategies and hybrid molecules have demonstrated the potential to enhance efficacy and mitigate drug resistance. Despite challenges in achieving complete parasite clearance and managing potential toxicity, interdisciplinary efforts across medicinal chemistry, parasitology, and clinical research highlight the significant potential of triazoles as components of next-generation, patient-friendly antiparasitic regimens. These findings support the further optimization and clinical evaluation of triazole-based agents to improve treatments for neglected parasitic diseases.

Sri Varsha Mulakala, G. Neeharika, P. Vinay Kumar et al.

The recent increase in morbidity is primarily due to chronic diseases including Diabetes, Heart disease, Lung cancer, and brain tumours. The results for patients can be improved, and the financial burden on the healthcare system can be lessened, through the early detection and prevention of certain disorders. In this study, we built a machine-learning model for predicting the existence of numerous diseases utilising datasets from various sources, including Kaggle, Dataworld, and the UCI repository, that are relevant to each of the diseases we intended to predict. Following the acquisition of the datasets, we used feature engineering to extract pertinent features from the information, after which the model was trained on a training set and improved using a validation set. A test set was then used to assess the correctness of the final model. We provide an easy-to-use interface where users may enter the parameters for the selected ailment. Once the right model has been run, it will indicate whether the user has a certain ailment and offer suggestions for how to treat or prevent it.

Alessandro Fontana

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.

Vivek Chetia, Abdul Taher Khan, Rahish Gogoi et al.

The proposed solution is Deep Learning Technique that will be able classify three types of tea leaves diseases from which two diseases are caused by the pests and one due to pathogens (infectious organisms) and environmental conditions and also show the area damaged by a disease in leaves. Namely Red Rust, Helopeltis and Red spider mite respectively. In this paper we have evaluated two models namely SSD MobileNet V2 and Faster R-CNN ResNet50 V1 for the object detection. The SSD MobileNet V2 gave precision of 0.209 for IOU range of 0.50:0.95 with recall of 0.02 on IOU 0.50:0.95 and final mAP of 20.9%. While Faster R-CNN ResNet50 V1 has precision of 0.252 on IOU range of 0.50:0.95 and recall of 0.044 on IOU of 0.50:0.95 with a mAP of 25%, which is better than SSD. Also used Mask R-CNN for Object Instance Segmentation where we have implemented our custom method to calculate the damaged diseased portion of leaves. Keywords: Tea Leaf Disease, Deep Learning, Red Rust, Helopeltis and Red Spider Mite, SSD MobileNet V2, Faster R-CNN ResNet50 V1 and Mask RCNN.

Maryem Arraji, Nadia Al Wachami, Younes Iderdar et al.

Objectives This study was performed to evaluate the prevalence of medication adherence and its determinants among Moroccan patients with type 2 diabetes mellitus. Methods A multicenter cross-sectional survey was conducted from February to June 2024, involving 584 patients from the Casablanca-Settat and Rabat-Sale-Kenitra regions of Morocco. Medication adherence was assessed using the general medication adherence scale. Associations between independent variables and adherence were analyzed using chi-square tests and multivariate logistic regression. Results Medication adherence was observed in 96.2% of participants. Multivariate analysis revealed significantly lower medication adherence among patients using 2 or more oral antidiabetic drugs (adjusted odds ratio [aOR], 0.026; 95% confidence interval [CI], 0.001–0.642; p=0.026) and those with a diabetes duration of 11 to 15 years (aOR, 0.037; 95% CI, 0.001–0.956; p=0.047). Conclusion Despite a high overall adherence rate, patients on dual or polytherapy and those with longer disease duration exhibited lower adherence in multivariate analysis. Targeted interventions are needed to improve adherence in these high-risk groups.

Tianqi Wei, Zhi Chen, Zi Huang et al.

Existing plant disease classification models have achieved remarkable performance in recognizing in-laboratory diseased images. However, their performance often significantly degrades in classifying in-the-wild images. Furthermore, we observed that in-the-wild plant images may exhibit similar appearances across various diseases (i.e., small inter-class discrepancy) while the same diseases may look quite different (i.e., large intra-class variance). Motivated by this observation, we propose an in-the-wild multimodal plant disease recognition dataset that contains the largest number of disease classes but also text-based descriptions for each disease. Particularly, the newly provided text descriptions are introduced to provide rich information in textual modality and facilitate in-the-wild disease classification with small inter-class discrepancy and large intra-class variance issues. Therefore, our proposed dataset can be regarded as an ideal testbed for evaluating disease recognition methods in the real world. In addition, we further present a strong yet versatile baseline that models text descriptions and visual data through multiple prototypes for a given class. By fusing the contributions of multimodal prototypes in classification, our baseline can effectively address the small inter-class discrepancy and large intra-class variance issues. Remarkably, our baseline model can not only classify diseases but also recognize diseases in few-shot or training-free scenarios. Extensive benchmarking results demonstrate that our proposed in-the-wild multimodal dataset sets many new challenges to the plant disease recognition task and there is a large space to improve for future works.

Anirudh Mazumder, Jianguo Liu

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.

Akua Sekyiwaa Osei-Nkwantabisa, Redeemer Ntumy

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.

Tahmina Akter, Rob Deardon

Here, we introduce a novel framework for modelling the spatiotemporal dynamics of disease spread known as conditional logistic individual-level models (CL-ILM's). This framework alleviates much of the computational burden associated with traditional spatiotemporal individual-level models for epidemics, and facilitates the use of standard software for fitting logistic models when analysing spatiotemporal disease patterns. The models can be fitted in either a frequentist or Bayesian framework. Here, we apply the new spatial CL-ILM to both simulated and semi-real data from the UK 2001 foot-and-mouth disease epidemic.

Salman Faroz

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.

Yannis Papanikolaou, Francesco Tuveri, Misa Ogura et al.

In this work we present a deep learning approach to conduct hypothesis-free, transcriptomics-based matching of drugs for diseases. Our proposed neural network architecture is trained on approved drug-disease indications, taking as input the relevant disease and drug differential gene expression profiles, and learns to identify novel indications. We assemble an evaluation dataset of disease-drug indications spanning 68 diseases and evaluate in silico our approach against the most widely used transcriptomics-based matching baselines, CMap and the Characteristic Direction. Our results show a more than 200% improvement over both baselines in terms of standard retrieval metrics. We further showcase our model's ability to capture different genes' expressions interactions among drugs and diseases. We provide our trained models, data and code to predict with them at https://github.com/healx/dgem-nn-public.

T. V. Afanasieva, A. P. Kuzlyakin, A. V. Komolov

Cardiovascular diseases are widespread among patients with chronic noncommunicable diseases and are one of the leading causes of death, including in the working age. The article presents the relevance of the development and application of patient-oriented systems, in which machine learning (ML) is a promising technology that allows predicting cardiovascular diseases. Automated machine learning (AutoML) makes it possible to simplify and speed up the process of developing AI/ML applications, which is key in the development of patient-oriented systems by application users, in particular medical specialists. The authors propose a framework for the application of automatic machine learning and three scenarios that allowed for data combining five data sets of cardiovascular disease indicators from the UCI Machine Learning Repository to investigate the effectiveness in detecting this class of diseases. The study investigated one AutoML model that used and optimized the hyperparameters of thirteen basic ML models (KNeighborsUnif, KNeighborsDist, LightGBMXT, LightGBM, RandomForestGini, RandomForestEntr, CatBoost, ExtraTreesGini, ExtraTreesEntr, NeuralNetFastA, XGBoost, NeuralNetTorch, LightGBMLarge) and included the most accurate models in the weighted ensemble. The results of the study showed that the structure of the AutoML model for detecting cardiovascular diseases depends not only on the efficiency and accuracy of the basic models used, but also on the scenarios for preprocessing the initial data, in particular, on the technique of data normalization. The comparative analysis showed that the accuracy of the AutoML model in detecting cardiovascular disease varied in the range from 87.41% to 92.3%, and the maximum accuracy was obtained when normalizing the source data into binary values, and the minimum was obtained when using the built-in AutoML technique.

Thomas Bellotti

Numerical analysis for linear constant-coefficients Finite Difference schemes was developed approximately fifty years ago. It relies on the assumption of scheme stability and in particular -- for the $L^2$ setting -- on the absence of multiple roots of the amplification polynomial on the unit circle. This allows to decouple, while discussing the convergence of the method, the study of the consistency of the scheme from the precise knowledge of its parasitic/spurious modes, so that multi-step methods can be studied essentially as they were one-step schemes. In other words, the global truncation error can be inferred from the local truncation error. Furthermore, stability alleviates the need to delve into the complexities of floating-point arithmetic on computers, which can be challenging topics to address. In this paper, we show that in the case of ``weakly'' unstable schemes with multiple roots on the unit circle, although the schemes may remain stable, the consideration of parasitic modes is essential in studying their consistency and, consequently, their convergence. Otherwise said, the lack of genuine stability prevents bounding the global truncation error using the local truncation error, and one is thus compelled to study the former on its own. This research was prompted by unexpected numerical results on lattice Boltzmann schemes, which can be rewritten in terms of multi-step Finite Difference schemes. Initial expectations suggested that third-order initialization schemes would suffice to maintain the accuracy of a fourth-order multi-step scheme. However, this assumption proved incorrect for ``weakly'' unstable schemes. This borderline scenario underscores the significance of genuine stability in facilitating the construction of Lax-Richtmyer-like theorems and in mastering the impact of round-off errors. Despite the simplicity and apparent lack of practical usage of the linear transport equation at constant velocity considered throughout the paper, we demonstrate that high-order lattice Boltzmann schemes for this equation can be used to tackle non-linear systems of conservation laws relying on a Jin-Xin approximation and high-order splitting formulae.

Lamprini Veneti, Jacob Dag Berild, Sara Viksmoen Watle et al.

Objectives: We estimated the BNT162b2 vaccine effectiveness (VE) against any (symptomatic or not) SARS-CoV-2 Delta and Omicron infection among adolescents (aged 12-17 years) in Norway from August 2021 to January 2022. Methods: We used Cox proportional hazard models, where vaccine status was included as a time-varying covariate and models were adjusted for age, sex, comorbidities, residence county, birth country, and living conditions. Results: The VE against Delta infection peaked at 68% (95% confidence interval [CI]: 64-71%) and 62% (95% CI: 57-66%) in days 21-48 after the first dose among those aged 12-15 years and 16-17 years, respectively. Among those aged 16-17 years who received two doses, the VE against Delta infection peaked at 93% (95% CI: 90-95%) in days 35-62 and decreased to 84% (95% CI: 76-89%) in ≥63 days after vaccination. We did not observe a protective effect against Omicron infection after receiving one dose. Among those aged 16-17 years, the VE against Omicron infection peaked at 53% (95% CI: 43-62%) in 7-34 days after the second dose and decreased to 23% (95% CI: 3-40%) in ≥63 days after vaccination. Conclusion: We found a reduced protection after two BNT162b2 vaccine doses against any Omicron infection compared to Delta. Effectiveness decreased with time from vaccination for both variants. The impact of vaccination among adolescents on reducing infection and thus transmission is limited during the Omicron dominance.

Xiao-Lan Gu, Rui Wang, Chuan-Min Zhou et al.

We sequenced DNA from spleens of rodents captured in rural areas of Qingdao, East China, during 2013–2015. We found 1 Apodemus agrarius mouse infected with Rickettsia conorii, indicating a natural Mediterranean spotted fever foci exists in East China and that the range of R. conorii could be expanding.

Meital Elbaz, Avi Gadoth, Daniel Shepshelovich et al.

Tick-borne encephalitis (TBE) is a viral infection of the central nervous system that occurs in many parts of Europe and Asia. Humans mainly acquire TBE through tick bites, but TBE occasionally is contracted through consuming unpasteurized milk products from viremic livestock. We describe cases of TBE acquired through alimentary transmission in Europe during the past 4 decades. We conducted a systematic review and meta-analysis of 410 foodborne TBE cases, mostly from a region in central and eastern Europe. Most cases were reported during the warmer months (April–August) and were associated with ingesting unpasteurized dairy products from goats. The median incubation period was short, 3.5 days, and neuroinvasive disease was common (38.9%). The clinical attack rate was 14% (95% CI 12%–16%), and we noted major heterogeneity. Vaccination programs and public awareness campaigns could reduce the number of persons affected by this potentially severe disease.

Aulia Rahmi Pawestri, Kanthinich Thima, Somphob Leetachewa et al.

Jose L. Herrera-Diestra, Michael Tildesley, Katriona Shea et al.

The structure of contact networks affects the likelihood of disease spread at the population scale and the risk of infection at any given node. Though this has been well characterized for both theoretical and empirical networks for the spread of epidemics on completely susceptible networks, the long-term impact of network structure on risk of infection with an endemic pathogen has been less well characterized. Here, we analyze detailed records of the transportation of cattle between farms in Turkey to characterize the global and local attributes of the shipments network between 2007-2012, building an aggregated static directed - weighted network. We study the correlation between network properties and the likelihood of infection with, or exposure to, foot-and-mouth (FMD) disease over the same time period using recorded outbreaks. The shipments network shows properties of small-worldness and scale-freeness (intermediate degrees), with an exponential cut-off for high degrees. The shipments network illustrates strong modularity and lack of assortativity. The shipments network shows signs of spatial constraints, with few long-distance connections, and a strong similarity to other spatially constrained networks. We find that farms that were either infected or at high risk of infection with FMD (within one link from an infected farm) had higher values of centrality; farms that were never less than 2 links from an infected farm had disproportionately low centrality. However, the correlation of the rankings of farms shows that central farms (high eigenvector centrality) are not necessarily those with more connections to/from it (in/out degree). Several central farms serve as bridges of densely connected farms (high betweenness centrality). These results suggest that to detect FMD spread, surveillance efforts could be focused preferentially on farms with centralities greater than the mean.