Comment détecte-t-on les polyphénols dans les aliments ?

Les polyphénols peuvent être détectés par des méthodes chromatographiques comme HPLC.

Quels tests mesurent les niveaux de polyphénols ?

Des tests de spectrophotométrie peuvent quantifier les polyphénols dans les extraits.

Quels effets les polyphénols ont-ils sur la santé ?

Ils peuvent réduire l'inflammation et le stress oxydatif, améliorant ainsi la santé globale.

Les polyphénols causent-ils des effets secondaires ?

En général, ils sont sûrs, mais des doses élevées peuvent causer des troubles digestifs.

Les polyphénols préviennent-ils des maladies ?

Oui, leur consommation est associée à une réduction du risque de maladies cardiovasculaires.

Quels aliments sont riches en polyphénols ?

Les fruits, légumes, thé, chocolat noir et vin rouge sont de bonnes sources.

Les polyphénols sont-ils utilisés en médecine ?

Oui, ils sont étudiés pour leur potentiel dans le traitement de maladies chroniques.

Comment les polyphénols aident-ils à traiter l'obésité ?

Ils peuvent moduler le métabolisme lipidique et réduire l'inflammation associée à l'obésité.

Les polyphénols peuvent-ils interagir avec des médicaments ?

Oui, certains polyphénols peuvent affecter l'absorption de médicaments anticoagulants.

Y a-t-il des risques liés à une surconsommation de polyphénols ?

Une surconsommation peut entraîner des troubles gastro-intestinaux et des déséquilibres nutritionnels.

Qui devrait limiter les polyphénols ?

Les personnes sous anticoagulants doivent consulter un médecin avant d'augmenter leur consommation.

Les allergies alimentaires affectent-elles la consommation de polyphénols ?

Oui, certaines personnes allergiques à des fruits ou légumes doivent éviter les polyphénols de ces sources.

Polyphénols - Informations médicales vérifiées

Survey and Evaluation of Hypertension Machine Learning Research.

02 05 2023

DOI: 10.1161/JAHA.122.027896 PMID: 37119074

Background Machine learning (ML) is pervasive in all fields of research, from automating tasks to complex decision-making. However, applications in different specialities are variable and generally li...

Humans Machine Learning Hypertension Blood Pressure Surveys and Questionnaires

Machine Learning Techniques for Antimicrobial Resistance Prediction of

2023

DOI: 10.1155/2023/5236168 PMID: 36909968

Due to the growing availability of genomic datasets, machine learning models have shown impressive diagnostic potential in identifying emerging and reemerging pathogens. This study aims to use machine... A machine learning technique called Random Forest (RF) and BioWeka was used for classification accuracy assessment and logistic regression (LR) for statistical analysis.... Our results show 44.66% of isolates were resistant to twelve antimicrobial agents and 55.33% were sensitive. The mean classification accuracy was obtained ≥98% for BioWeka and ≥96 for RF on these fami... The ability to accurately detect antibiotic resistance could help clinicians make educated decisions about empiric therapy based on the local antibiotic resistance pattern. Moreover, infection prevent...

Humans Anti-Bacterial Agents Pseudomonas aeruginosa Drug Resistance, Bacterial Anti-Infective Agents +1 autres

Inferred networks, machine learning, and health data.

2023

DOI: 10.1371/journal.pone.0280910 PMID: 36689443

This paper presents a network science approach to investigate a health information dataset, the Sexual Acquisition and Transmission of HIV Cooperative Agreement Program (SATHCAP), to uncover hidden re...

Humans Algorithms Cluster Analysis Machine Learning HIV Infections

Inferring turbulent environments via machine learning.

31 Dec 2022

DOI: 10.1140/epje/s10189-022-00258-3 PMID: 36586035

The problem of classifying turbulent environments from partial observation is key for some theoretical and applied fields, from engineering to earth observation and astrophysics, e.g., to precondition...

Bayes Theorem Machine Learning Neural Networks, Computer

Protein Function Analysis through Machine Learning.

06 09 2022

DOI: 10.3390/biom12091246 PMID: 36139085

Machine learning (ML) has been an important arsenal in computational biology used to elucidate protein function for decades. With the recent burgeoning of novel ML methods and applications, new ML app...

Computational Biology Ligands Machine Learning Molecular Docking Simulation Molecular Dynamics Simulation +2 autres

Machine learning for risk stratification in kidney disease.

01 11 2022

DOI: 10.1097/MNH.0000000000000832 PMID: 36004937

Risk stratification for chronic kidney is becoming increasingly important as a clinical tool for both treatment and prevention measures. The goal of this review is to identify how machine learning too... The two key machine learning paradigms to predictively stratify kidney disease risk are genomics-based and electronic health record based approaches. These methods can provide both quantitative inform... The four key methods to stratify chronic kidney disease risk are genomics, multiomics, supervised and unsupervised machine learning methods. Polygenic risk scores utilize whole genome sequencing data ...

Biomarkers Electronic Health Records Humans Machine Learning Renal Insufficiency, Chronic +1 autres

Are smartphones and machine learning enough to diagnose tremor?

Nov 2022

DOI: 10.1007/s00415-022-11293-7 PMID: 35861853

Patients with essential tremor (ET), Parkinson's disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics.... To use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natu... A supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of ... The classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity o... Machine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremo...

Essential Tremor Humans Machine Learning Parkinson Disease Smartphone +1 autres

Machine Learning for Renal Pathologies: An Updated Survey.

01 Jul 2022

DOI: 10.3390/s22134989 PMID: 35808481

Within the literature concerning modern machine learning techniques applied to the medical field, there is a growing interest in the application of these technologies to the nephrological area, especi...

Databases, Factual Kidney Machine Learning

Machine Learning Models for Inpatient Glucose Prediction.

08 2022

DOI: 10.1007/s11892-022-01477-w PMID: 35759171

Glucose management in the hospital is difficult due to non-static factors such as antihyperglycemic and steroid doses, renal function, infection, surgical status, and diet. Given these complex and dyn... The published literature on machine learning algorithms has varied in terms of population studied, outcomes of interest, and validation methods. There have been tools developed that utilize data from ...

Blood Glucose Glucose Humans Hypoglycemic Agents Inpatients +2 autres

Automation of Cephalometrics Using Machine Learning Methods.

2022

DOI: 10.1155/2022/3061154 PMID: 35774443

Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient's lateral radiograph of the face was utilised to construct a tracing from the trac...

Automation Cephalometry Humans Machine Learning Neural Networks, Computer

Polyphénols : Questions médicales fréquentes

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