A deep learning-based technique for the diagnosis of epidural spinal cord compression on thoracolumbar CT.

Purpose: To develop a deep learning (DL) model for epidural spinal cord compression (ESCC) on CT, which will aid earlier ESCC diagnosis for less experienced clinicians.

Methods: We retrospectively collected CT and MRI data from adult patients with suspected ESCC at a tertiary referral institute from 2007 till 2020. A total of 183 patients were used for training/validation of the DL model.

A prospective comparative study on the effectiveness of two different non-adherent polyurethane dressings on split-thickness skin graft donor sites.

Aim: To analyze differences in re-epithelization, exudate absorbency, ease and pain on dressing removal between ALLEVYN™ Non-Adhesive and Betaplast™ N.

Methodology: Patients admitted to the general ward undergoing split skin grafting were recruited. Allevyn and Betaplast were applied on the donor site.

Quantitative investigation on the critical thickness of the dielectric shell for metallic nanoparticles determined by the plasmon decay length.

Inert dielectric shells coating the surface of metallic nanoparticles (NPs) are important for enhancing the NPs' stability, biocompatibility, and realizing targeting detection, but they impair NPs' sensing ability due to the electric fields damping. The dielectric shell not only determines the distance of the analyte from the NP surface, but also affects the field decay. From a practical point of view, it is extremely important to investigate the critical thickness of the shell, beyond which the NPs are no longer able to effectively detect the analytes.