J Microbiol Immunol Infect
January 2025
Purpose: This retrospective study aimed to investigate demographic characteristics, predisposing factors, and clinical outcomes in patients with parasitic keratitis.
Methods: Medical records of patients with molecularly confirmed Acanthamoeba or microsporidia, identified through corneal scraping specimens (collected between September 21, 2017, and June 27, 2023), were reviewed. Demographic data, clinical profiles, such as symptom duration before confirmed diagnosis, antiviral treatment pre-diagnosis, contact lens use, tap water and soil contamination, ocular trauma, and treatment regimens, were analyzed.
Diabetic wounds are characterized by chronic inflammation, reduced angiogenesis, and insufficient collagen deposition, leading to impaired healing. Extracellular vesicles (EVs) derived from adipose-derived mesenchymal stem cells (ADSC) offer a promising cell-free therapeutic strategy, yet their efficacy and immunomodulation can be enhanced through bioactivation. In this study, we developed calcium silicate (CS)-stimulated ADSC-derived EVs (CSEV) incorporated into collagen hydrogels to create a sustained-release system for promoting diabetic wound healing.
View Article and Find Full Text PDFBackground: The Patient Infotainment Terminal (PIT) plays a pivotal role in Smart Health, enabling hospitals to actively pursue the objective of fostering Shared Decision-Making. By providing General information, Medical information, and Entertainment options, the system fosters effective patient-clinician communication and significantly elevates the standard of care.
Objective: This study aimed to investigate how registered nurses utilized the PIT and prioritized functions based on their perception of importance and satisfaction to find out high-importance but low-satisfaction PIT functions.
Akuammicine (AKC), an indole alkaloid, is a kappa opioid receptor (KOR) full agonist with a moderate affinity. 10-Iodo-akuammicine (I-AKC) and 10-Bromo-akuammicine (Br-AKC) showed higher affinities for the KOR with K values of 2.4 and 5.
View Article and Find Full Text PDFObjective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer (LCa) survival outcome predictions, analyzing handcrafted and deep radiomic features (HRF/DRF) from PET/CT scans with hybrid machine learning systems (HMLSs).
Methods: We collected 199 LCa patients with both PET and CT images, obtained from TCIA and our local database, alongside 408 HNCa PET/CT images from TCIA. We extracted 215 HRFs and 1024 DRFs by PySERA and a 3D autoencoder, respectively, within the ViSERA 1.