Publications by authors named "H Q Fu"

PGLa, an antimicrobial peptide (AMP), primarily exerts its antibacterial effects by disrupting bacterial cell membrane integrity. Previous theoretical studies mainly focused on the binding mechanism of PGLa with membranes, while the mechanism of water pore formation induced by PGLa peptides, especially the role of structural flexibility in the process, remains unclear. In this study, using all-atom simulations, we investigated the entire process of membrane deformation caused by the interaction of PGLa with an anionic cell membrane composed of dimyristoylphosphatidylcholine (DMPC) and dimyristoylphosphatidylglycerol (DMPG).

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The electroconversion of CO into ethylene (CH) offers a promising solution to environmental and energy challenges. Crown ether (CE) modification significantly enhances the CH selectivity of copper-based MOFs, improving CH faradaic efficiency (FE) in CuBTC, CuBDC, and CuBDC-NH by 3.1, 1.

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Background: Huanglian-ejiao decoction (HED) is a Chinese traditional medicinal formula evolved from the Shanghan Lun (Treatise on Febrile Diseases). However, HED ultimate mechanism of action remained indistinct. Therefore, this study aimed to investigate whether HED could exert anti-inflammatory effects on 2,4,6-Trinitrobenzenesulfonic acid (TNBS)-induced colitis (UC) model through the regulation of CD4T subsets and gut microbiota.

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The aim of this study was to develop and validate a nomogram predicting progression-free survival (PFS) for adult patients with positive acute lymphoblastic leukemia(Ph + ALL) who have undergone allogeneic hematopoietic stem cell transplantation(allo-HSCT) and tyrosine kinase inhibitor(TKI) treatment. Data were retrospectively collected from 176 adult patients diagnosed with Ph + ALL and treated with allo-HSCT and TKIs at The First Affiliated Hospital, Zhejiang University School of Medicine, between January 2015 and May 2023. 70% of the patients were randomly assigned to the training group(n = 124) and 30% of the patients were assigned to the validation group(n = 52).

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Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.

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