Results 121 to 130 of about 329,832 (294)

Integrated Metabolic and Inflammatory Clustering Reveals Distinct Risk Profiles for Digestive Diseases

open access: yesAdvanced Science, EarlyView.
This study analyzes 398 432 participants, identifying four distinct metabolic‐inflammatory subtypes. These subtypes show a significant association with digestive disease risk. Cluster‐associated metabolite signatures partially explain this link. Machine learning models using these metabolites accurately predict risk for ten digestive diseases. Key risk
Zhenhe Jin   +10 more
wiley   +1 more source

Guía para la consejería anti-tabáquica

open access: yesRevista Electrónica Dr. Zoilo E. Marinello Vidaurreta, 2019
El tabaquismo es una epidemia a nivel mundial. En ocasiones, los programas de control de enfermedades relacionadas con el tabaquismo no incluyen acciones de promoción y prevención contra esta adicción o las propuestas resultan muy generales e ...
Doris Cándida Fornaris-Marrero   +4 more
doaj  

Cultural adaptation of two school-based smoking prevention programs in Bogotá, Colombia. [PDF]

open access: yesTransl Behav Med, 2021
Sánchez-Franco S   +10 more
europepmc   +1 more source

Tailored Electro–Magnetic–Porous Multigradient Nanoarchitectonics for Absorption‐Dominated Electromagnetic Interference Shielding and Adaptive Multifunctionality

open access: yesAdvanced Science, EarlyView.
An electro–magnetic–porous multigradient nanofibrous electromagnetic interference (EMI) membrane is fabricated via shear‐induced in situ fibrillation and layer‐by‐layer assembly. Leveraging a multigradient‐induced “impedance matching–polarization–reabsorption” synergistic mechanism, the membrane achieves absorption‐dominated high‐efficiency EMI ...
Runze Shao   +5 more
wiley   +1 more source

Secondary school students' perceptions of a mobile application design for smoking prevention. [PDF]

open access: yesTob Prev Cessat, 2021
Uribe-Madrigal RD   +6 more
europepmc   +1 more source

Thermal Runaway Temperature Prediction of Lithium‐Ion Battery Under Extreme High‐Temperature Shock Using Experimental and Virtual Data

open access: yesAdvanced Science, EarlyView.
An integrated framework predicts lithium‐ion batteries (LIB) thermal runaway (TR) under extreme high‐temperature shock. By combining experimental data with the multiphysics‐generated virtual data, a hybrid deep learning approach is developed to accurately forecast temperature evolution for unseen scenarios.
Xiaoyu Li   +6 more
wiley   +1 more source

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