Hasil untuk "Nutritional diseases. Deficiency diseases"

Tien-Yu Chi

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.

Lixuan Tang, Xiao Yang, Liwen He et al.

Everlyn Matiri, Lacey Ramirez, Abdinasir Elmi et al.

ABSTRACT This program assessment explored the use of indigenous preserved animal‐source foods to improve complementary feeding practices, to identify the roles of mothers, fathers, and elder women in supporting infant and young child feeding (IYCF) practices and to develop recommendations for program implementation. The Trials of Improved Practices (TIPs) approach, food frequency, in‐depth interviews, and focus group discussions methodologies were used to collect information on complementary feeding and use of indigenous preserved animal‐source foods during a program assessment. Data was collected during Round 1‐dry season and Round 2‐prolonged drought. Sixty in‐depth interviews were carried out with mothers of children 6–23 months of age, 12 elder women, and six focus groups with 26 fathers for a total of 98 program participants in pastoral communities in Marsabit and Isiolo Counties, Kenya. Program sites were affected by limited access and availability of animal‐source foods and worsened household food insecurity. Nearly all mothers introduced camel milk, often fed raw, as a first food, before 1 year of age. Preserved meat and milk products were often prohibited or fed to older children due to cultural beliefs and norms. Most mothers experienced greater success in implementing TIPs recommendations during Round 1 versus Round 2 and stopped chewing food for the child, gave preserved meat, and fed eggs. Gendered divisions of labor and social norms around roles of fathers, elder women and mothers can hinder IYCF. Future programming should plan for climate‐induced shocks, including amplifying indigenous food preservation and addressing gender and social norms to improve IYCF.

Noushin Mohammadifard, Negar Ostadsharif, Ghazaleh Bahrami et al.

Abstract Background The association of sugar sweetened beverages (SSBs) and coronary artery disease (CAD) has not been well-established in Asians, where SSBs are the leading ultra-processed food product. Objective We aim to examine the association between SSBs and premature CAD (PCAD) in Iranian adults. Design Case-control. Participants A multi-centric study of Iranians including 2006 PCAD and 1131 healthy individuals as control group. Main outcome measures Dietary intakes were assessed using a validated food frequency questionnaire (FFQ). SSBs consist of artificial juice and sugar -sweetened drinks. The PCAD was determined based on the results of angiography and the occlusion percent of vessels. Statistical analysis The odds of PCAD across the quartiles of SSBs were assessed by binary logistic regression. Results The mean (SD) age of participants and SSB consumption was 51.5 years and 46.9 g/d, respectively. In the fully-adjusted model, compared with participants in the first quartile, those in the fourth quartile had higher risk of PCAD (OR = 1.50, 95% CI: 1.12, 2.00; P trend = 0.044). Consistently, SSB consumption was directly associated with the severity of PCAD. The higher SSB consumption, the greater risk for the severe PCAD (OR Q4 vs. Q1 = 1.34, 95% CI: 1.06, 1.68; P < 0.001). Conclusion This study demonstrated that higher consumption of SSB might be associated with higher risk of PCAD. However, more prospective cohort studies are necessary to confirm this association.

Bolin Liu, Alexander Fulton, Hector Zenil

Understanding disease relationships through blood biomarkers offers a pathway toward data driven taxonomy and precision medicine. We constructed a digital blood twin from 103 disease signatures comprising longitudinal hematological and biochemical analytes. Profiles were standardized into a unified disease analyte matrix, and pairwise Pearson correlations were computed to assess similarity. Hierarchical clustering revealed robust grouping of hematopoietic disorders, while metabolic, endocrine, and respiratory diseases were more heterogeneous, reflecting weaker cohesion. To evaluate structure, the tree was cut at a stringent threshold, yielding 16 groups. Enrichment of the largest heterogeneous cluster (Cluster 9) showed convergence on cytokine-signaling pathways, indicating shared immunological and inflammatory mechanisms across clinical boundaries. Dimensionality reduction with PCA and UMAP corroborated these results, consistently separating hematological diseases. Random Forest feature selection identified neutrophils, mean corpuscular volume, red blood cell count, and platelets as the most discriminative analytes, reinforcing hematopoietic markers as key drivers. Collectively, these findings show that blood-derived digital signatures can recover clinically meaningful clusters while revealing mechanistic overlaps across categories. The coherence of hematological diseases contrasts with the dispersion of systemic and metabolic disorders, underscoring both the promise and limits of blood-based classification. This framework highlights the potential of integrating routine laboratory data with computational methods to refine disease ontology, map comorbidities, and advance precision diagnostics.

MD Mehraz Hosen, Md. Hasibul Islam

Tomato crop health plays a critical role in ensuring agricultural productivity and food security. Timely and accurate detection of diseases affecting tomato plants is vital for effective disease management. In this study, we propose a deep learning-based approach for Tomato Leaf Disease Detection using two well-established convolutional neural networks (CNNs), namely VGG19 and Inception v3. The experiment is conducted on the Tomato Villages Dataset, encompassing images of both healthy tomato leaves and leaves afflicted by various diseases. The VGG19 model is augmented with fully connected layers, while the Inception v3 model is modified to incorporate a global average pooling layer and a dense classification layer. Both models are trained on the prepared dataset, and their performances are evaluated on a separate test set. This research employs VGG19 and Inception v3 models on the Tomato Villages dataset (4525 images) for tomato leaf disease detection. The models' accuracy of 93.93% with dropout layers demonstrates their usefulness for crop health monitoring. The paper suggests a deep learning-based strategy that includes normalization, resizing, dataset preparation, and unique model architectures. During training, VGG19 and Inception v3 serve as feature extractors, with possible data augmentation and fine-tuning. Metrics like accuracy, precision, recall, and F1 score are obtained through evaluation on a test set and offer important insights into the strengths and shortcomings of the model. The method has the potential for practical use in precision agriculture and could help tomato crops prevent illness early on.

Pascal Helson, Arvind Kumar

Despite the diversity and volume 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 (Brain Swap), 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.

Janet Wang, Xin Hu, Yunbei Zhang et al.

Skin Neglected Tropical Diseases (NTDs) impose severe health and socioeconomic burdens in impoverished tropical communities. Yet, advancements in AI-driven diagnostic support are hindered by data scarcity, particularly for underrepresented populations and rare manifestations of NTDs. Existing dermatological datasets often lack the demographic and disease spectrum crucial for developing reliable recognition models of NTDs. To address this, we introduce eSkinHealth, a novel dermatological dataset collected on-site in Côte d'Ivoire and Ghana. Specifically, eSkinHealth contains 5,623 images from 1,639 cases and encompasses 47 skin diseases, focusing uniquely on skin NTDs and rare conditions among West African populations. We further propose an AI-expert collaboration paradigm to implement foundation language and segmentation models for efficient generation of multimodal annotations, under dermatologists' guidance. In addition to patient metadata and diagnosis labels, eSkinHealth also includes semantic lesion masks, instance-specific visual captions, and clinical concepts. Overall, our work provides a valuable new resource and a scalable annotation framework, aiming to catalyze the development of more equitable, accurate, and interpretable AI tools for global dermatology.

Lisa Garnweidner-Holme, Yngvild Frivold, Gigja Max et al.

Abstract Objective: To assess the selection of foods and beverages in children’s sports arenas in Norway. Design: A cross-sectional study design with a digital questionnaire was used. Descriptive statistics were used to present the results. Moreover, Pearson’s χ 2 tests examined the factors that could aid in distinguishing clubs with healthy or unhealthy consumables. Setting: Children’s sports clubs in Norway. Participants: Representatives from 301 children’s sports clubs in Norway answered the questionnaire between September and November 2021. Results: In total, 89·4% of the participating sports clubs (n 301) offered soda drinks with sugar. Most of the sports clubs (88 %) reported to offer batter-based cakes such as pancakes and waffles and 63·8 % offered cakes. Furthermore, 47·5% sold hot dishes with processed meat, such as hamburgers and hot dogs. More than 80% of the sports clubs offered sweets and snacks, while 44·5% did not offer fruits, vegetables and/or berries. Notably, the important factors that distinguished sports clubs with healthier food selections from those with unhealthier selections were the presence of guidelines for the food offered and purchase agreements with food suppliers. Conclusions: Educational, governmental guidelines for the promotion of healthy eating and establishing agreements with suppliers of healthier foods could help to overcome barriers to unhealthy food selection.

Guangzan Yu, Lulu Liu, Qian Ma et al.

Abstract Background Cardiovascular diseases (CVD), chronic kidney disease (CKD), and lipids are positively correlated with the presence of depressive symptoms. However, investigation into the complex link that exists between cardiorenal syndrome (CRS) and lipid indices and depression remains scarce. Methods This study analyzed data from 11, 729 adults in the National Health and Nutritional Examination Surveys from 2005 to 2018. Weighted regression analysis was employed to examine the relationships between CRS and depression, CRS and the Patient Health Questionnaire-9 score, and lipid indices with depression. The restricted cubic spline analysis was used to determine whether there is a linear association between lipid indices and depression. Smooth curve fitting was employed to illustrate the relationship between lipids, depression, and cardiorenal diseases. Subgroup and sensitivity analyses were also conducted to enhance the stability of the results. Finally, we applied mediation analysis to explore whether the Atherogenic Index of Plasma (AIP), triglyceride glucose (TyG) index, and remnant cholesterol (RC) mediate the association between CRS and depression. Results After applying propensity score matching (PSM), 1,509 adults remained in the study. After PSM, more remarkable results were rendered that CRS was associated with depression compared with non-CRS (OR: 1. 240, 95% CI: 1. 237 ~ 1. 243), only-CVD (OR: 0. 646, 95% CI: 0. 644 ~ 0. 649), and only-CKD (OR: 1.432, 95% CI: 1.428 ~ 1.437) in a fully corrected model. Smooth curve fitting shows that the intersection point of the lines of CRS and non-CRS occurs at a higher value on the horizontal axis than the intersection point of the lines representing CVD and non-CVD. In the fully corrected model, AIP, TyG, and RC did not independently mediate the association between CRS and depression. Conclusion There was a significant association between CRS and depression and a linear relationship between AIP, TyG, and RC and depression. However, the above lipid indicators did not mediate the association between CRS and depression. Graphical Abstract

Chang Fu, Xiaocong Li, Yongxin Wang et al.

Abstract Background Triglyceride glucose (TyG) index combined with obesity-related indicators [triglyceride glucose-body mass index (TyG-BMI), triglyceride glucose-waist to height ratio (TyG-WHtR), triglyceride glucose-waist circumference (TyG-WC)], represents emerging methodologies for assessing insulin resistance. The objective of this investigation was to explore the correlation between TyG-related indices and gallstone disease. Methods The study included 3740 adults from the 2017–2020 period of the National Health and Nutrition Examination Survey. TyG-BMI, TyG-WC, and TyG-WHtR were integrated as both continuous and categorical variables within the multivariate logistic model, respectively to evaluate the connection between various TyG-related indices and gallstone disease. Additionally, restriction cubic splines and subgroup analysis were employed to deepen our understanding of this relationship. Results When analyzed as continuous variables, positive correlations were observed between TyG-BMI, TyG-WC, TyG-WHtR and gallstone disease. The OR(95%CI) were 1.063(1.045,1.082) for TyG-BMI (per 10-unit), 1.026(1.018,1.034) for TyG-WC (per 10-unit) and 1.483(1.314,1.676) for TyG-WHtR (per 1-unit), respectively. When categorized into quartiles, these three TyG-related indices still show statistically significant associations with gallstone disease. Descending in order, the diagnostic capability for gallstone disease is demonstrated as follows: TyG-WHtR (AUC = 0.667), TyG-BMI (AUC = 0.647), and TyG-WC (AUC = 0.640). Conclusion There were significantly positive associations between TyG-related indices, including TyG-BMI, TyG-WC, and TyG-WHtR, and gallstone disease. Of these indices, TyG-WHtR demonstrated the most favorable performance in identifying the risk of gallstone disease.

Yuchen Cao, Meichen Ai, Chunjun Liu

Abstract Objective This study aims to investigate the association between specific lipidomes and the risk of breast cancer (BC) using the Two-Sample Mendelian Randomization (TSMR) approach and Bayesian Model Averaging Mendelian Randomization (BMA-MR) method. Method The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran's Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study. Results By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916–0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894–0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER− BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034–1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091–1.119, P < 0.05) was an important causal effect in ER−BC. Conclusions The findings reveal that phosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC's intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.

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.

Guancheng Wan, Zewen Liu, Max S. Y. Lau et al.

Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nature of epidemics and fail to account for the specific mechanisms of disease transmission. In response to these challenges, we introduce an innovative end-to-end framework called Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (EARTH) in this paper. To learn continuous and regional disease transmission patterns, we first propose EANO, which seamlessly integrates the neural ODE approach with the epidemic mechanism, considering the complex spatial spread process during epidemic evolution. Additionally, we introduce GLTG to model global infection trends and leverage these signals to guide local transmission dynamically. To accommodate both the global coherence of epidemic trends and the local nuances of epidemic transmission patterns, we build a cross-attention approach to fuse the most meaningful information for forecasting. Through the smooth synergy of both components, EARTH offers a more robust and flexible approach to understanding and predicting the spread of infectious diseases. Extensive experiments show EARTH superior performance in forecasting real-world epidemics compared to state-of-the-art methods. The code will be available at https://github.com/Emory-Melody/EpiLearn.

Ghazaleh Babanejaddehaki, Aijun An, Manos Papagelis

Infectious diseases occur when pathogens from other individuals or animals infect a person, resulting in harm to both individuals and society as a whole. The outbreak of such diseases can pose a significant threat to human health. However, early detection and tracking of these outbreaks have the potential to reduce the mortality impact. To address these threats, public health authorities have endeavored to establish comprehensive mechanisms for collecting disease data. Many countries have implemented infectious disease surveillance systems, with the detection of epidemics being a primary objective. The clinical healthcare system, local/state health agencies, federal agencies, academic/professional groups, and collaborating governmental entities all play pivotal roles within this system. Moreover, nowadays, search engines and social media platforms can serve as valuable tools for monitoring disease trends. The Internet and social media have become significant platforms where users share information about their preferences and relationships. This real-time information can be harnessed to gauge the influence of ideas and societal opinions, making it highly useful across various domains and research areas, such as marketing campaigns, financial predictions, and public health, among others. This article provides a review of the existing standard methods developed by researchers for detecting outbreaks using time series data. These methods leverage various data sources, including conventional data sources and social media data or Internet data sources. The review particularly concentrates on works published within the timeframe of 2015 to 2022.

Zihao Zhao, Yi Jing, Fuli Feng et al.

Medication recommendation systems have gained significant attention in healthcare as a means of providing tailored and effective drug combinations based on patients' clinical information. However, existing approaches often suffer from fairness issues, as recommendations tend to be more accurate for patients with common diseases compared to those with rare conditions. In this paper, we propose a novel model called Robust and Accurate REcommendations for Medication (RAREMed), which leverages the pretrain-finetune learning paradigm to enhance accuracy for rare diseases. RAREMed employs a transformer encoder with a unified input sequence approach to capture complex relationships among disease and procedure codes. Additionally, it introduces two self-supervised pre-training tasks, namely Sequence Matching Prediction (SMP) and Self Reconstruction (SR), to learn specialized medication needs and interrelations among clinical codes. Experimental results on two real-world datasets demonstrate that RAREMed provides accurate drug sets for both rare and common disease patients, thereby mitigating unfairness in medication recommendation systems.

Thien-Minh Le, Jukka-Pekka Onnela

Infectious disease modeling is used to forecast epidemics and assess the effectiveness of intervention strategies. Although the core assumption of mass-action models of homogeneously mixed population is often implausible, they are nevertheless routinely used in studying epidemics and provide useful insights. Network models can account for the heterogeneous mixing of populations, which is especially important for studying sexually transmitted diseases. Despite the abundance of research on mass-action and network models, the relationship between them is not well understood. Here, we attempt to bridge the gap by first identifying a spreading rule that results in an exact match between disease spreading on a fully connected network and the classic mass-action models. We then propose a method for mapping epidemic spread on arbitrary networks to a form similar to that of mass-action models. We also provide a theoretical justification for the procedure. Finally, we show the advantages of the proposed methods using synthetic data that is based on an empirical network. These findings help us understand when mass-action models and network models are expected to provide similar results and identify reasons when they do not.

Chunyan Ao, Zhichao Xiao, Lixin Guan et al.

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.

Luca Aiello, Sudipto Banerjee

Epidemiologists commonly use regional aggregates of health outcomes to map mortality or incidence rates and identify geographic disparities. However, to detect health disparities across regions, it is necessary to identify "difference boundaries" that separate neighboring regions with significantly different spatial effects. This can be particularly challenging when dealing with multiple outcomes for each unit and accounting for dependence among diseases and across areal units. In this study, we address the issue of multivariate difference boundary detection for correlated diseases by formulating the problem in terms of Bayesian pairwise multiple comparisons by extending it through the introduction of adjacency modeling and disease graph dependencies. Specifically, we seek the posterior probabilities of neighboring spatial effects being different. To accomplish this, we adopt a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence by endowing the spatial random effects with a discrete probability law. Our method is evaluated through simulation studies and applied to detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.