Results 121 to 130 of about 235,297 (357)

Aptamer Engineering: Strategies for Discovering Functional Nucleic Acids for Next‐Generation Diagnostics and Biosensing

open access: yesAdvanced Science, EarlyView.
The advent of aptamers has highlighted their potential as alternatives to antibodies, overcoming limitations of structural instability and production cost. However, conventional approaches such as SELEX remain slow and labor‐intensive. This review examines recent advances in aptamer engineering, emphasizing in vitro and AI‐driven in silico strategies ...
John V. L. Nguyen   +5 more
wiley   +1 more source

SERS‐AI‐LUA‐Driven Salivary Diagnosis of Head and Neck Cancer Using Graphene‐Assisted Plasmonic Nanocorals

open access: yesAdvanced Science, EarlyView.
The developed graphene‐assisted plasmonic nanocoral platform enables sensitive, label‐free surface‐enhanced Raman scattering analysis of salivary metabolites for head and neck cancer (HNC) detection. When combined with machine learning classification and nonnegative least squares‐based spectral deconvolution, this approach achieves 98% diagnostic ...
Hyo Jeong Seo   +11 more
wiley   +1 more source

Patience Pays Off: A Case of Self Exfoliation of Large Parotid Sialolith [PDF]

open access: yesJournal of Krishna Institute of Medical Sciences University, 2019
Sialolithiasis is one of the common diseases affecting the salivary glands. The submandibular gland is the most commonly affected gland among the three major salivary glands.
Roopashri Rajesh Kashyap   +1 more
doaj  

Self-exfoliation of large submandibular stone-report of two cases

open access: yesContemporary Clinical Dentistry, 2012
Sialoliths are the most common diseases of the salivary glands. They may occur in any of the salivary gland ducts but are most common in Wharton′s duct and the submandibular gland.
Anita Singhal   +3 more
doaj   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

open access: yesAdvanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

Detection of immunogenic proteins from Anopheles sundaicussalivary glands in the human serum

open access: yesRevista da Sociedade Brasileira de Medicina Tropical, 2015
INTRODUCTION:The saliva of mosquitoes has an important role in the transmission of several diseases, including malaria, and contains substances with vasomodulating and immunomodulating effects to counteract the host physiological mechanisms and enhance ...
Yunita Armiyanti   +6 more
doaj   +1 more source

Spectrum of salivary gland diseases: A 24-year single-institution retrospective study. [PDF]

open access: yesJ Oral Maxillofac Pathol, 2023
Sabarinath B   +4 more
europepmc   +1 more source

A Solution for Exosome‐Based Analysis: Surface‐Enhanced Raman Spectroscopy and Artificial Intelligence

open access: yesAdvanced Intelligent Discovery, EarlyView.
Exosomes are emerging as powerful biomarkers for disease diagnosis and monitoring. This review highlights the integration of surface‐enhanced Raman spectroscopy with artificial intelligence to enhance molecular fingerprinting of exosomes. Machine learning and deep learning techniques improve spectral interpretation, enabling accurate classification of ...
Munevver Akdeniz   +2 more
wiley   +1 more source

Machine Learning‐Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy

open access: yesInternational Forum of Allergy &Rhinology, EarlyView.
ABSTRACT Background Sinusitis is a prevalent disease for which nasal endoscopy (NE) is an optimal diagnostic modality. However, NE accuracy is limited by inter‐operator variability in landmark identification and localization of mucus that is necessary for sinusitis diagnosis. We sought to develop a novel multi‐class machine learning (ML) framework that
Dipesh Gyawali   +12 more
wiley   +1 more source

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