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AI-Driven Precision Nutrition for Predicting Iron Deficiency Using Genomics, Polygenic Risk Scores, Dietary Patterns, and Personalized Dietary Recommendations

Background: Iron Deficiency Anemia (IDA) is one of the most prevalent nutritional disorders globally and a leading cause of Disability Adjusted Life Years (DALYs). Conventional diagnostic methods fail to detect deficiencies at an early stage and rarely account for individual genetic5 predisposition. Methods: This study proposes an end-to-end AI-driven precision nutrition pipeline integrating publi...

Combined triglyceride-glucose and frailty index (TyGFI) and risk of endometrial cancer in U.S. women aged >=45: NHANES 2011-2018 analysis integrating data engineering and machine learning with logistic modeling

Endometrial cancer (EC) incidence is closely linked to metabolic and hormonal factors. The TyGFI, a composite indicator integrating the triglyceride-glucose index and frailty index, may capture combined risk dimensions relevant to EC etiology and prediction. This study aimed to evaluate the association between TyGFI and EC prevalence among U.S. women aged 45 years and older, and to explore its pre...

DisciplineGen-1M: A Large-Scale Dataset for Multidisciplinary Visual Generation and Editing

Recent image generation and editing models can produce visually appealing natural images, yet they remain unreliable when the target image is a knowle...

CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents

LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading. ...

Response consistency of ChatGPT-4o for Type 2 Diabetes Nutrition and Physical-activity Recommendations: A Pilot NLP-based Assessment of GPT outputs

Generative AI tools such as ChatGPT are increasingly used by the public to seek guidance on diet and physical activity for type 2 diabetes (T2D) preve...

Predicting county-level diagnosed diabetes prevalence in the United States using explainable gradient boosting and geographic interpretation

Diagnosed diabetes affects approximately 38.4 million Americans, but its burden is not evenly distributed across U.S. counties. Existing machine-learn...

Screening for Probable Undiagnosed Hypertension in US Adults Using Interpretable Machine Learning: An NHANES 2017-2018 Study

Background Hypertension remains one of the most challenging healthcare problems in the community. It is a common, measurable, and treatable condition ...

Ingredient-Level Food Image Segmentation for Nutrition Awareness

Food images often contain several visible ingredients, so assigning one dish label to an entire image hides important visual structure. This work stud...

Ingredient-Level Food Image Segmentation for Nutrition Awareness

Food images often contain several visible ingredients, so assigning one dish label to an entire image hides important visual structure. This work stud...

Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation

Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy...

Comparative Analysis of Machine Learning Models vs. Traditional Clinical Calculators for Cardiovascular Risk Prediction

Background: Cardiovascular diseases (CVD) remain the leading global cause of mortality, responsible for approximately 31% of all deaths worldwide in 2...

Beyond the Blood Draw: Explainable Machine Learning for Non-Invasive Dysglycemia Risk Screening

Dysglycemia, encompassing both prediabetes and diabetes, affects huge numbers of adults worldwide, yet many of them remain undiagnosed. We developed a...

Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response

Autonomous network-security response systems promise to reduce Security Operations Centre (SOC) reaction latency, but reward-only multi-agent reinforc...

NutriMLLM: Multimodal Large Language Models for Dietary Micronutrient Analysis

Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large mul...

Beyond the annotated: protein foundation models enable robust prediction of microbial root competence

Background Root competence, the ability of soil bacteria to establish and grow on plant roots, is a key ecological trait influencing plant nutrition, ...

Progeny differentiation in faba bean using hyperspectral images and machine learning

Though currently a minor crop, faba bean is a promising source of plant-based protein as global diets shift towards more plant-based nutrition. To rea...

LOCOPOTS: a low-cost high-throughput screening platform for in vitro potato phenotyping under abiotic stress

Potato crop is highly vulnerable to abiotic stresses like salinity and low nutrient availability. Rapid identification of stress-resilient genotypes i...

Use of Large Language Models by U.S. Adults to Support Exercise: A Survey Study

Background: Large Language Model (LLM) chatbots are increasingly used for exercise and fitness topics, yet users' experience with these tools remains ...

Whom Does Algorithmic Risk Stratification Miss? A Fairness Audit of Machine Learning Targeting for Concurrent Maternal-Child Double Burden of Malnutrition Across 30 Low- and Middle-Income Countries

Author Summary In many low- and middle-income countries, mothers who are overweight often live in the same household as children who are too short or ...

The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches

The vulnerabilities of deep neural networks against singularities have raised serious concerns regarding their deployment in the physical world. One o...