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A nationwide cross-sectional study in Saudi Arabia for the assessment of understanding and practices of clinicians towards personalized genetic testing.
Sci Rep
December 2024
Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia.
In order to plan and facilitate the culture of personalized / precision medicine in medical practices within any healthcare institution, it is requisite for healthcare professionals like clinicians to have a clear understanding and approach towards the practices of personalized genetic testing. This nationwide cross-sectional study aimed to measure the perceptions and knowledge of clinicians towards personalized genetic testing and assess their current practices of personalized genetic testing in clinical settings through an online self-administered questionnaire in Saudi Arabia. The results of the study revealed that almost two-fifths of participants were responsible for ordering genetic tests directly (39.
View Article and Find Full Text PDFAttention-guided convolutional network for bias-mitigated and interpretable oral lesion classification.
Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
View Article and Find Full Text PDFUse of large language models as artificial intelligence tools in academic research and publishing among global clinical researchers.
Sci Rep
December 2024
Department of Dermatology, Niazi Hospital, Lahore, Pakistan.
With breakthroughs in Natural Language Processing and Artificial Intelligence (AI), the usage of Large Language Models (LLMs) in academic research has increased tremendously. Models such as Generative Pre-trained Transformer (GPT) are used by researchers in literature review, abstract screening, and manuscript drafting. However, these models also present the attendant challenge of providing ethically questionable scientific information.
View Article and Find Full Text PDFSurface enzyme-polymerization endows Janus hydrogel tough adhesion and regenerative repair in penetrating orocutaneous fistulas.
Nat Commun
December 2024
Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Penetrating orocutaneous or oropharyngeal fistulas (POFs), severe complications following unsuccessful oral or oropharyngeal reconstruction, remain complex clinical challenges due to lack of supportive tissue, contamination with saliva and chewed food, and dynamic oral environment. Here, we present a Janus hydrogel adhesive (JHA) with asymmetric functions on opposite sides fabricated via a facile surface enzyme-initiated polymerization (SEIP) approach, which self-entraps surface water and blood within an in-situ formed hydrogel layer (RL) to effectively bridge biological tissues with a supporting hydrogel (SL), achieving superior wet-adhesion and seamless wound plugging. The tough SL hydrogel interlocked with RL dissipates energy to withstand external mechanical stimuli from continuous oral motions like chewing and swallowing, thus reducing stress-induced damage.
View Article and Find Full Text PDFFast and sensitive multivalent spatial pattern-recognition for circular RNA detection.
Nat Commun
December 2024
Department of Pharmacology, Jiangsu Provincial Key Laboratory of Critical Care Medicine, School of Medicine, Southeast University, Nanjing, China.
While circular RNAs (circRNAs) exhibit lower abundance compared to corresponding linear RNAs, they demonstrate potent biological functions. Nevertheless, challenges arise from the low concentration and distinctive structural features of circRNAs, rendering existing methods operationally intricate and less sensitive. Here, we engineer an intelligent tetrahedral DNA framework (TDF) possessing precise spatial pattern-recognition properties with exceptional sensing speed and sensitivity for circRNAs.
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