To investigate the in vitro anti-skin-aging properties of extract and encapsulate this plant extract in nanostructured lipid carriers (CFE-NLCs) for dermal application. The biological properties of the plant extract, including enhanced procollagen type I synthesis and anti-matrix metalloproteinase activity, were evaluated to assess its cosmetic benefits. An artificial neural network (ANN) coupled with K-fold cross-validation was applied to investigate the effects of the formulants and optimize the CFE-NLCs. The physicochemical properties, percutaneous absorption, and irritation potential of the CFE-NLCs were analyzed. Liquid chromatography-mass spectrometry analysis revealed that CFE contained 5-O-caffeoylquinic acid as the vital constituent. Appropriate skin-care properties were also demonstrated with respect to enhanced type I procollagen synthesis and the inhibition of MMP-1, MMP-3, and MMP-9 in primary human dermal fibroblasts. The optimal CFE-NLCs exhibited better skin absorption and biocompatibility and lower irritation potential than the free botanical extract solution. The findings obtained highlight CFE-NLCs as promising skin-care ingredients.
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http://dx.doi.org/10.34172/apb.2022.082 | DOI Listing |
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
View Article and Find Full Text PDFFront Cell Dev Biol
January 2025
Department of Medical Informatics, Nantong University, Nantong, Jiangsu, China.
Introduction: Diabetic retinopathy (DR) has long been recognized as a common complication of diabetes, making accurate automated grading of its severity essential. Color fundus photographs play a crucial role in the grading of DR. With the advancement of artificial intelligence technologies, numerous researchers have conducted studies on DR grading based on deep features and radiomic features extracted from color fundus photographs.
View Article and Find Full Text PDFInt J Cardiol Heart Vasc
February 2025
Department of Radiology, Frimley Park Hospital NHS Foundation Trust, Camberley, Surrey, UK.
Background: The National Lung Screening Trial (NLST) has shown that screening with low dose CT in high-risk population was associated with reduction in lung cancer mortality. These patients are also at high risk of coronary artery disease, and we used deep learning model to automatically detect, quantify and perform risk categorisation of coronary artery calcification score (CACS) from non-ECG gated Chest CT scans.
Materials And Methods: Automated calcium quantification was performed using a neural network based on Mask regions with convolutional neural networks (R-CNN) for multiorgan segmentation.
Indian Dermatol Online J
December 2024
Financial Research and Executive Insights, Everest Group, Gurugram, Haryana, India.
Background: Artificial intelligence (AI) is revolutionizing healthcare by enabling systems to perform tasks traditionally requiring human intelligence. In healthcare, AI encompasses various subfields, including machine learning, deep learning, natural language processing, and expert systems. In the specific domain of onychology, AI presents a promising avenue for diagnosing nail disorders, analyzing intricate patterns, and improving diagnostic accuracy.
View Article and Find Full Text PDFImaging Neurosci (Camb)
November 2024
Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Synthetic data have emerged as an attractive option for developing machine-learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)-a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties.
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