Endodontic-related diseases constitute the fourth most expensive pathologies in industrialized countries. Specifically, endodontics is the part of dentistry focused on treating disorders of the dental pulp and its consequences. In order to treat these problems, especially endodontic infections, dental barriers and complex root canal anatomy should be overcome. This constitutes an unmet medical need since the rate of successful disinfection with the currently marketed drugs is around 85%. Therefore, nanoparticles constitute a suitable alternative in order to deliver active compounds effectively to the target site, increasing their therapeutic efficacy. Therefore, in the present review, an overview of dental anatomy and the barriers that should be overcome for effective disinfection will be summarized. In addition, the versatility of nanoparticles for drug delivery and their specific uses in dentistry are comprehensively discussed. Finally, the latest findings, potential applications and state of the art nanoparticles with special emphasis on biodegradable nanoparticles used for endodontic disinfection are also reviewed.
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http://dx.doi.org/10.3390/pharmaceutics14071519 | DOI Listing |
Comput Methods Programs Biomed
January 2025
Shanghai Maritime University, Shanghai 201306, China. Electronic address:
Background And Objective: Inferring large-scale brain networks from functional magnetic resonance imaging (fMRI) provides more detailed and richer connectivity information, which is critical for gaining insight into brain structure and function and for predicting clinical phenotypes. However, as the number of network nodes increases, most existing methods suffer from the following limitations: (1) Traditional shallow models often struggle to estimate large-scale brain networks. (2) Existing deep graph structure learning models rely on downstream tasks and labels.
View Article and Find Full Text PDFAdv Sci (Weinh)
January 2025
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
View Article and Find Full Text PDFSci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
View Article and Find Full Text PDFSci Rep
January 2025
Computer Vision Center, Universitat Autònoma de Barcelona, Barcelona, 08193, Spain.
In this study, we explore an enhancement to the U-Net architecture by integrating SK-ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral Imaging (MSI). SK-ResNeXt introduces cardinality and adaptive kernel sizes, allowing U-Net to better capture multi-scale features and adjust more effectively to variations in spatial resolution, thereby enhancing the model's ability to segment complex land cover types. We evaluate this approach using the Five-Billion-Pixels dataset, composed of 150 large-scale RGB-NIR images and over 5 billion labeled pixels across 24 categories.
View Article and Find Full Text PDFBMC Health Serv Res
January 2025
Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, USA.
Background: Despite their ubiquity across sub-Saharan Africa, private pharmacies are underutilized for HIV service delivery beyond the sale of HIV self-test kits. To understand what uptake of HIV prevention and treatment services might look like if private pharmacies offered clients free HIV self-testing and referral to clinic-based HIV services, we conducted a pilot study in Kenya.
Methods: At 20 private pharmacies in Kisumu County, Kenya, pharmacy clients (≥ 18 years) purchasing sexual health-related products (e.
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