Publications by authors named "A Oshima"

Objective: The impact of suture materials on surgical site infections (SSIs) has been well documented in various surgical fields; however, it has not been thoroughly examined in oral oncological surgery with free-flap reconstruction. This study aimed to evaluate the incidence of oral SSIs associated with the use of monofilament and braided sutures for flap fixation.

Methods: A retrospective chart review of patients who underwent oral oncological resection with free-flap reconstruction was conducted between May 2020 and April 2024.

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Lysophosphatidic acid (LPA) and sphingosine-1-phosphate (S1P) are bioactive lysophospholipids derived from cell membranes that activate the endothelial differentiation gene family of G protein-coupled receptors. Activation of these receptors triggers multiple downstream signaling cascades through G proteins such as Gi/o, Gq/11, and G12/13. Therefore, LPA and S1P mediate several physiological processes, including cytoskeletal dynamics, neurite retraction, cell migration, cell proliferation, and intracellular ion fluxes.

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Background: Previous computed tomography studies have reported that the superior mesenteric artery is often located ventrally to the superior mesenteric vein; however, the precise location of the peripheral jejunal arteriovenous system is unknown. This study investigated the arteriovenous positioning of the free jejunal flaps during reconstructive surgery.

Methods: This retrospective cohort study included 78 patients who underwent free jejunal flap reconstruction between June 2021 and May 2023.

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At present, mid-sized binding peptides have emerged as a new class of drug modalities. We have de novo designed a helix-loop-helix (HLH) peptide (MW: ∼4,500), constructed phage-displayed libraries, and screened the libraries against a variety of disease-related proteins to successfully obtain molecular-targeting HLH peptides. The next essential step in developing HLH peptides into therapeutics involves affinity engineering to optimize binding affinity and specificity.

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Traditionally, coronary angiography was restricted to visual estimation of contrast-filled lumen in coronary obstructive diseases. Over the previous decades, considerable development has been made in quantitatively analyzing coronary angiography, significantly improving its accuracy and reproducibility.  Notably, the integration of artificial intelligence (AI) and machine learning into quantitative coronary angiography (QCA) holds promise for further enhancing diagnostic accuracy and predictive capabilities.

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