Obesity

Predicting candidate biomarkers for COVID-19 associated with leukemia in children.

Since the COVID-19 pandemic, a significant number of pediatric leukemia patients have shown to have also contracted COVID-19 several weeks or months prior to the development of their cancer. Current research indicates the expression of MDA5, encoded by , is associated with increased immunity to COVID-19 in children. Children are also known to have a much lower risk of developing leukemia. Our hypo...

A comprehensive and bias-free machine learning approach for risk prediction of preeclampsia with severe features in a nulliparous study cohort.

Preeclampsia is one of the leading causes of maternal morbidity, with consequences during and after pregnancy. Because of its diverse clinical presentation, preeclampsia is an adverse pregnancy outcome that is uniquely challenging to predict and manage. In this paper, we developed racial bias-free machine learning models that predict the onset of preeclampsia with severe features or eclampsia at d...

Adipsin and Leptin Levels in Type 2 Diabetic Patients on Sitagliptin and Metformin Versus Metformin Therapy.

OBJECTIVES: Adipsin and leptin are adipokines that link adipose tissue dysfunction and increased fat accumulation to obesity-related metabolic disorde...

Breath Analyzer for Real-Time Exercise Fat Burning Prediction: Oral and Alveolar Breath Insights with CNN.

The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fa...

Optimizing hypertension prediction using ensemble learning approaches.

Hypertension (HTN) prediction is critical for effective preventive healthcare strategies. This study investigates how well ensemble learning technique...

Comparison of the Accuracy, Completeness, Reproducibility, and Consistency of Different AI Chatbots in Providing Nutritional Advice: An Exploratory Study.

: The use of artificial intelligence (AI) chatbots for obtaining healthcare advice is greatly increased in the general population. This study assessed...

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference.

The human microbiome, the community of microorganisms that reside on and inside the human body, is critically important for health and disease. Howeve...

Biofilm architecture and dynamics of the oral ecosystem.

The oral cavity, being a nutritionally enriched environment, has been proven to be an ideal habitat for biofilm development. Various microenvironments...

Profiling the AI speaker user: Machine learning insights into consumer adoption patterns.

The objective of this study is to identify the characteristics of users of AI speakers and predict potential consumers, with the aim of supporting eff...

Investigating the anti-obesity potential of leaf bioactive compounds through machine learning and computational biology methods.

Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseases, type 2 diabetes, cancer, and neuropsychiatric ...

Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications...

Cytotoxicity, Antiadipogenic, Low-Density Lipoprotein Oxidation Inhibitory Activities, and Acute Toxicity Study of Hydroethanolic Leaf and Bark Extracts.

Obesity is increasingly taking an important stage as a cause of death worldwide, and interventions with a good cost-effectiveness ratio are needed. ...

Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants.

OBJECTIVE: To develop and validate a machine learning model incorporating dietary antioxidants to predict cardiovascular disease (CVD)-cancer comorbid...

XGBoost-based nomogram for predicting lymph node metastasis in endometrial carcinoma.

This study aims to construct and optimize risk prediction models for lymph node metastasis (LNM) in endometrial carcinoma (EC) patients, thus improvin...

Lifestyle factors and other predictors of common mental disorders in diagnostic machine learning studies: A systematic review.

BACKGROUND: Machine Learning (ML) models have been used to predict common mental disorders (CMDs) and may provide insights into the key modifiable fac...

An explainable predictive model for anxiety symptoms risk among Chinese older adults with abdominal obesity using a machine learning and SHapley Additive exPlanations approach.

BACKGROUND: Early detection of anxiety symptoms can support early intervention and may help reduce the burden of disease in later life in the elderly ...

Predicting 30-day reoperation following primary total knee arthroplasty: machine learning model outperforms the ACS risk calculator.

The ACS risk calculator (ARC) has proven less effective in predicting patient-specific risk of early reoperation after primary total knee arthroplasty...

AcidAGE: a biological age determination neural network based on urine organic acids.

Organic acids reflect the course of all important metabolic processes and the effects of diet, nutrient deficiency, lifestyle, and microbiota composit...

Machine learning classification meets migraine: recommendations for study evaluation.

The integration of machine learning (ML) classification techniques into migraine research has offered new insights into the pathophysiology and classi...

Prediction of prolonged mechanical ventilation in the intensive care unit via machine learning: a COVID-19 perspective.

Early recognition of risk factors for prolonged mechanical ventilation (PMV) could allow for early clinical interventions, prevention of secondary com...