Publications by authors named "Y Gurt Ge"

The term "fluorescence" was first proposed nearly two centuries ago, yet its application in clinical medicine has a relatively brief history coming to the fore in the past decade. Nowadays, as fluorescence is gradually expanding into more medical applications, fluorescence image-guided surgery has become the new arena for this technology. It allows surgical teams to real-time visualize target tissues or anatomies intraoperatively to increase the precision of resection or preserve vital structures during open or laparoscopic surgeries.

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As a core genetic biomolecule in ecosystems, the metabolic processes of DNA, particularly DNA replication and damage repair, are regulated by Flap endonuclease 1 (FEN1). Abnormal expression and dysfunction of FEN1 may lead to genomic instability, which can induce a variety of chromosome-associated disorders, including tumours. FEN1 has emerged as a prominent tumour marker.

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Aim: To develop pH (pHe)-triggered membrane adhesive nanoliposome (pHTANL) of CD40a to enhance anti-tumor activity in pancreatic cancer while reducing systemic toxicity.

Materials And Methods: A small library of nanoliposomes (NL) with various lipid compositions were synthesized to prepare pH (pHe)-triggered membrane adhesive nanoliposome (pHTANL). Physical and functional characterization of pHTANL-CD40a was performed via dynamic light scattering (DLS), Transmission Electron Microscopy (TEM), confocal microscopy, and flow cytometry.

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Idesia polycarpa Maxim (IPM) cake meal, a major by-product of oil extraction, is often discarded in large quantities, resulting in considerable waste. This study explored the extraction of IPM polysaccharides (IPMPs) from cake meal using the innovative ultrasonic-assisted three-phase partitioning (UTPP) method, in comparison with conventional techniques, including acid, medium-temperature alkali, chelating agent, and enzyme extraction methods. The IPMP-UT prepared via UTPP method achieved superior extraction efficiency (10.

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Background: The increasing use of social media to share lived and living experiences of substance use presents a unique opportunity to obtain information on side effects, use patterns, and opinions on novel psychoactive substances. However, due to the large volume of data, obtaining useful insights through natural language processing technologies such as large language models is challenging.

Objective: This paper aims to develop a retrieval-augmented generation (RAG) architecture for medical question answering pertaining to clinicians' queries on emerging issues associated with health-related topics, using user-generated medical information on social media.

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