Dual-mode-driven nanomotors targeting inflammatory macrophages for the MRI and synergistic treatment of atherosclerosis.

With the progress of atherosclerosis (AS), the arterial lumen stenosis and compact plaque structure, the thickening intima and the narrow gaps between endothelial cells significantly limit the penetration efficiency of nanoprobe to plaque, weakening the imaging sensitivity and therapy efficiency. Thus, in this study, a HO-NIR dual-mode nanomotor, Gd-doped mesoporous carbon nanoparticles/Pt with rapamycin (RAPA) loading and AntiCD36 modification (Gd-MCNs/Pt-RAPA-AC) was constructed. The asymmetric deposition of Pt on Gd-MCNs catalyzed HO at the inflammatory site to produce O, which could promote the self-motion of the nanomotor and ease inflammation microenvironment of AS plaque.

Relationship between ferroptosis and healing of diabetic foot ulcer: a prospective clinical study.

Objective: Diabetic foot ulcer (DFU) is one of the common complications in patients with diabetes mellitus (DM). In order to find a method to monitor and treat the refractory DFU, the ferroptosis level in DFU and traumatic wounds (TW) was monitored and the difference between them was analyzed. At the same time, this study further analyzed the correlation of ferroptosis levels with DM severity and DFU's healing.

Virtual biopsy for non-invasive identification of follicular lymphoma histologic transformation using radiomics-based imaging biomarker from PET/CT.

Background: This study aimed to construct a radiomics-based imaging biomarker for the non-invasive identification of transformed follicular lymphoma (t-FL) using PET/CT images.

Methods: A total of 784 follicular lymphoma (FL), diffuse large B-cell lymphoma, and t-FL patients from 5 independent medical centers were included. The unsupervised EMFusion method was applied to fuse PET and CT images.

Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses.

Chatbot-based multimodal AI holds promise for collecting medical histories and diagnosing ophthalmic diseases using textual and imaging data. This study developed and evaluated the ChatGPT-powered Intelligent Ophthalmic Multimodal Interactive Diagnostic System (IOMIDS) to enable patient self-diagnosis and self-triage. IOMIDS included a text model and three multimodal models (text + slit-lamp, text + smartphone, text + slit-lamp + smartphone).

An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer.

To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.