A Recognition System for Diagnosing Salivary Gland Neoplasms Based on Vision Transformer.

Salivary gland neoplasms (SGNs) represent a group of human neoplasms characterized by a remarkable cytomorphologic diversity, which frequently poses diagnostic challenges. Accurate histologic categorization of salivary tumors is crucial to make precise diagnoses and guide decisions regarding patient management. Within the scope of this study, a computer-aided diagnosis model using Vision Transformer (ViT), a cutting-edge deep learning model in computer vision, has been developed to accurately classify the most prevalent subtypes of SGNs.

Identification of ACAA1 and HADHB as potential prognostic biomarkers based on a novel fatty acid oxidation-related gene model in head and neck squamous cell carcinoma: A retrospective study.

Objectives: To investigate the importance of fatty acid oxidation (FAO)-related genes in predicting the progression and prognosis of head and neck squamous cell carcinoma (HNSCC).

Methods: The FAO-related gene prognostic model was established employing Cox regression analyses, during which accuracy and sensitivity of the gene model were evaluated in The Cancer Genome Atlas (TCGA) internal testing and Gene Expression Omnibus (GEO) external validation cohorts. Ultimately, hub genes were identified among 13 model genes using STRING and Cytoscape, with preliminary validation carried out through immunohistochemistry.

GAD1-mediated GABA elicits aggressive characteristics of human oral cancer cells.

Studies suggest that the expression of glutamate decarboxylase 1 (GAD1), γ-aminobutyric acid (GABA), and GABA receptors are involved in tumor progression. However, the underlying mechanisms of high expression and potential functions of GAD1 and GABA in oral squamous cell carcinoma (OSCC) are not known. In this study, we found that the expressions of GAD1 and GABA were considerably increased in OSCC samples, which were closely associated with clinical stage and lymph node metastasis.

An Improved Partial Discharge Detection System Based on UV Pulses Detection.

Partial discharge (PD) usually reflects failures and potential hazards of equipment, so PD detection is important to protect the power system. The most reliable method now is the pulse current method (PCM), but the device of PCM is large and hard to carry. Ultraviolet (UV) pulse detection is another method to detect PD, which has a high precision, strong anti-interference ability, and a long effective distance.