Results 51 to 60 of about 2,747,980 (293)

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

Frontiers in Bioscience-Landmark, 2021
Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those.
Smiksha Munjral, Puneet Ahluwalia, Ankush D. Jamthikar, Anudeep Puvvula, Luca Saba, Gavino Faa, Inder M Singh, Paramjit S. Chadha, Monika Turk, Amer M. Johri, Narendra N Khanna, Klaudija Viskovic, Sophie Mavrogeni, John R Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P Sfikakis, George Tsoulfas, Athanasios Protogerou, Prasanna Misra, Vikas Agarwal, George D. Kitas, Raghu Kolluri, Jagjit Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Meyypan Sockalingam, Ajit Saxena, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Vijay Viswanathan, P K Krishnan, Tomaz Omerzu, Subbaram Naidu, Andrew Nicolaides, Jasjit S. Suri   +39 more
doaj   +1 more source

Applying an Ethical Lens to the Treatment of People With Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT The practice of neurology requires an understanding of clinical ethics for decision‐making. In multiple sclerosis (MS) care, there are a wide range of ethical considerations that may arise. These involve shared decision‐making around selection of a disease‐modifying therapy (DMT), risks and benefits of well‐studied medications in comparison to
Methma Udawatta, Farrah J. Mateen
wiley   +1 more source

A Scoping Review on Artificial Intelligence–Supported Interventions for Nonpharmacologic Management of Chronic Rheumatic Diseases

Arthritis Care &Research, EarlyView.
This review summarizes artificial intelligence (AI)‐supported nonpharmacological interventions for adults with chronic rheumatic diseases, detailing their components, purpose, and current evidence base. We searched Embase, PubMed, Cochrane, and Scopus databases for studies describing AI‐supported interventions for adults with chronic rheumatic diseases.
Nirali Shah, Alexis Castellanos, Yen Chen, John Piette, Amy Bucher, Susan Murphy   +5 more
wiley   +1 more source

Prediction of depressive disorder using machine learning approaches: findings from the NHANES

BMC Medical Informatics and Decision Making
Background Depressive disorder, particularly major depressive disorder (MDD), significantly impact individuals and society. Traditional analysis methods often suffer from subjectivity and may not capture complex, non-linear relationships between risk ...
Thien Vu, Research Dawadi, Masaki Yamamoto, Jie Ting Tay, Naoki Watanabe, Yuki Kuriya, Ai Oya, Phap Ngoc Hoang Tran, Michihiro Araki   +8 more
doaj   +1 more source

Development of home artificial nutrition in Italy over a seven year period: 2005–2012

BMC Nutrition, 2017
BackgroundIn 2005, the Italian Society for Artificial Nutrition and Metabolism carried out a national survey on the prevalence of home artificial nutrition (HAN), enteral (HEN) and parenteral (HPN).
L. Pironi
semanticscholar   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Smarter Sensors Through Machine Learning: Historical Insights and Emerging Trends across Sensor Technologies

Advanced Functional Materials, EarlyView.
This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee, Osman Gul, Yeongjae Kwon, Jaeseok Jeong, Seokjoo Cho, Jihyeon Ahn, Jehee Yu, Cheolmin Kim, Donho Lee, Hyeonseok Han, Byeongju Lee, Jungrak Choi, Ji‐Hwan Ha, Yongrok Jeong, Kyungnam Kang, Ali Javey, Junseong Ahn, Inkyu Park   +17 more
wiley   +1 more source

Large language models and questions from older adults: a human and machine-based evaluation study

Discover Artificial Intelligence
Large language models (LLMs) hold the potential to offer substantial advantages in information generation and comprehension. This study seeks to evaluate the extent to which these models can effectively meet the needs of older adults by examining ...
Research Dawadi, Thien Vu, Jie Ting Tay, Phap Tran Ngoc Hoang, Ai Oya, Masaki Yamamoto, Naoki Watanabe, Yuki Kuriya, Michihiro Araki   +8 more
doaj   +1 more source

Controlled Movement of Soft Actuators using Multi‐Responsive Microgel Arrays and Microcirculatory Systems

Advanced Functional Materials, EarlyView.
Microscale hydrogels (microgels) feature improved mass transport characteristics supportive of fast actuation and chemical tunability amenable to programmed stimuli response. A unique soft actuator architecture is realized by encapsulating microgels in soft microcirculatory systems which enable the convenient delivery of liquid stimuli for powering and
Nengjian Huang, Brennan P. Watts, Stephen A. Morin   +2 more
wiley   +1 more source

Bio‐Orthogonally Crosslinked Supramolecular Polymer Bottlebrush Hydrogels for Long‐Term 3D Cell Culture

Advanced Functional Materials, EarlyView.
Fibrous benzenetrispeptide (BTP) hydrogels, fabricated via strain‐promoted azide‐alkyne cycloaddition (SPAAC) crosslinking, form robust, bioinert networks. These hydrogels can support 3D cell culture, where cell viability and colony growth depend on the fiber content.
Ceren C. Pihlamagi, David Pretzel, Peer Nölte, Kourosh Rezaei, Ulrich S. Schubert, Johannes C. Brendel   +5 more
wiley   +1 more source