Ocular inoculation of toad venom: toxic cataract and proteomic profiling.

Purpose: To report a singular case of cataract caused by toad venom inoculation and to scrutinize the pathological mechanisms through proteomic sequencing of the lens specimen.

Methods: A young Chinese male presented with progressively deteriorating vision in his right eye subsequent to a history of toad venom inoculation. He was diagnosed with a toxic cataract, and underwent phacoemulsification cataract surgery.

Multifrequency spherical cloak in microwave frequencies enabled by deep learning.

Designing invisibility devices for required frequency bands is important in anti-detection methods in various fields such as communications, construction, and others. However, traditional design methods are time-consuming, with manual adjustment of parameters and continuous trial and error. Fortunately, the data-driven approach based on deep learning has revolutionized the field.

Knowledge translation initiatives at the Transitional Pain Service: insights from healthcare provider outreach and patient education.

Evidence-based treatment of chronic pain requires a multidisciplinary approach grounded in the biopsychosocial model. Implementing this approach within health systems relies on its acceptance by both healthcare providers and patients. While pioneering multidisciplinary pain clinics can serve as a model for implementation, a systematic effort is needed to share knowledge effectively and broadly.

Oestrogen suppresses the adipogenesis of fibro/adipogenic progenitors through reactivating the METTL3-ESR1-mediated loop in post-menopausal females.

Background: Post-menopausal women experience more severe muscular fatty infiltration, though the mechanisms remain unclear. The decline in estrogen levels is considered as a critical physiological alteration during post-menopause. Fibro/adipogenic progenitors (FAPs) are identified as major contributors to muscular fatty infiltration.

A structural health monitoring data reconstruction method based on VMD and SSA-optimized GRU model.

In the field of Structural Health Monitoring (SHM), complete datasets are fundamental for modal identification analysis and risk prediction. However, data loss due to sensor failures, transmission interruptions, or hardware issues is a common problem. To address this challenge, this study develops a method combining Variational Mode Decomposition (VMD) and Sparrow Search Algorithm (SSA)-optimized Gate Recurrent Unit (GRU) for recovering structural response data.