Vision-language large learning model, GPT4V, accurately classifies the Boston Bowel Preparation Scale score.

Introduction: Large learning models (LLMs) such as GPT are advanced artificial intelligence (AI) models. Originally developed for natural language processing, they have been adapted for multi-modal tasks with vision-language input. One clinically relevant task is scoring the Boston Bowel Preparation Scale (BBPS).

Botulinum toxin type A in treating early-stage patients with small-angle acute acquired comitant esotropia.

Aim: To investigate botulinum toxin A (BTXA) efficacy on small-angle (≤25) acute acquired concomitant esotropia (AACE) in early-stage patients.

Methods: The electronic medical record data of AACE patients during March 2019 and June 2023 were collected in this retrospective and hospital-based cohort study. A total of 72 small-angle AACE patients received BTXA extraocular muscle injection.

Effects of slanted bilateral lateral recession conventional bilateral lateral recession on convergence insufficiency intermittent exotropia: a prospective study.

Aim: To observe the surgical effects of slanted bilateral lateral recession (S-BLR) versus conventional bilateral lateral recession (C-BLR) in convergence insufficiency intermittent exotropia (CI-IXT).

Methods: Using a randomized, double-blind, prospective design, 22 patients with CI-IXT who were admitted to Renmin Hospital of Wuhan University from July 2019 to December 2020 were included. Patients were randomly divided into either S-BLR or C-BLR group for their subsequent strabismus surgery.

Spatio-temporal model of Meox1 expression control involvement of Sca-1-positive stem cells in neointima formation through the synergistic effect of Rho/CDC42 and SDF-1α/CXCR4.

Aims: Neointimal hyperplasia remains a major obstacle in vascular regeneration. Sca-1-positive progenitor cells residing within the vascular adventitia play a crucial role in the assemblage of vascular smooth muscle cell (VSMC) and the formation of the intimal lesion. However, the underlying mechanisms during vascular injury are still unknown.

Fast CT metal artefacts correction based on derivative and region-based filling.

Introduction: Metal artefacts seriously degrade the quality of the CT images. Blurring around the junctions between metal and non-metal regions in CT images, metal artefacts often prevent right diagnoses, and even lead to misdiagnoses of patients. The aim of the study was to devise a fast and robust method to improve the quality of the artefact-contaminated CT images.