Alterations in resting-state EEG functional connectivity in patients with major depressive disorder receiving electroconvulsive therapy: A systematic review.

Electroconvulsive therapy (ECT) is highly efficacious for the treatment of major depressive disorder (MDD), but its mechanisms still require clarification. Even though depression is associated with alterations in functional connectivity (FC), EEG studies investigating effects of ECT on FC have not been systematically reviewed. Understanding these effects may help to identify the role of functional brain circuits in depression and its remission.

Exploring the Metabolic Impact of FLASH Radiotherapy.

FLASH radiotherapy (FLASH RT) is an innovative modality in cancer treatment that delivers ultrahigh dose rates (UHDRs), distinguishing it from conventional radiotherapy (CRT). FLASH RT has demonstrated the potential to enhance the therapeutic window by reducing radiation-induced damage to normal tissues while maintaining tumor control, a phenomenon termed the FLASH effect. Despite promising outcomes, the precise mechanisms underlying the FLASH effect remain elusive and are a focal point of current research.

Computer-aided design and fabrication of statistically shaped nasal prostheses: A feasibility study.

Statement Of Problem: A nasal prosthesis may compensate for a partial or complete defect of the nose associated with trauma or amputation. However, the design and production is time-consuming, expensive, and expertize-dependent. Computer-generated prosthesis models and 3D printing can optimize the process.

Comparable performance between automatic and manual laryngeal and hypopharyngeal GTV delineations validated with pathology.

Purpose: Deep learning is a promising approach to increase reproducibility and time-efficiency of GTV delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, which are subjective and tend to overestimate tumor volume. This study aimed to validate a deep learning model for laryngeal and hypopharyngeal GTV segmentation with pathology and to compare its performance with clinicians' manual delineations.

Materials And Methods: A retrospective dataset of 193 laryngeal and hypopharyngeal cancer patients was used to train a deep learning model with clinical GTV delineations as reference.