Probing the Conductive and Tribological Behaviors of Solid Additives in Multiply Alkylated Cyclopentanes for Sliding Electrical Contact

Sliding electrical contacts need to be lubricated by conductive lubricants to perform low energy dissipation, high reliability, and long service life. This work studied the thermal stability, anti-corrosion capacity, and conductive, and tribological behaviors of several solid additives in multiply alkylated cyclopentanes (MACs), including carbon nanotubes (CNTs), multilayer graphene (MG), and silver microparticles. The results showed that all the additives possessed favorable thermal stability and corrosion resistance; in particular, CNTs and MG exhibited lower and more stable electrical contact resistance (ECR) and better lubricity abilities than Ag microparticles. Moreover, based on the characterization of the worn surfaces and the film thickness calculation, the favorable conductive and tribological properties of CNTs and MG were related to the high conductivity and specific structure of the additives and the good chemical inertness of MACs

Application of Artificial Intelligence Methods on Osteoporosis Classification with Radiographs—A Systematic Review

Osteoporosis is a complex endocrine disease characterized by a decline in bone mass and microstructural integrity. It constitutes a major global health problem. Recent progress in the field of artificial intelligence (AI) has opened new avenues for the effective diagnosis of osteoporosis via radiographs. This review investigates the application of AI classification of osteoporosis in radiographs. A comprehensive exploration of electronic repositories (ClinicalTrials.gov, Web of Science, PubMed, MEDLINE) was carried out in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 statement (PRISMA). A collection of 31 articles was extracted from these repositories and their significant outcomes were consolidated and outlined. This encompassed insights into anatomical regions, the specific machine learning methods employed, the effectiveness in predicting BMD, and categorizing osteoporosis. Through analyzing the respective studies, we evaluated the effectiveness and limitations of AI osteoporosis classification in radiographs. The pooled reported accuracy, sensitivity, and specificity of osteoporosis classification ranges from 66.1% to 97.9%, 67.4% to 100.0%, and 60.0% to 97.5% respectively. This review underscores the potential of AI osteoporosis classification and offers valuable insights for future research endeavors, which should focus on addressing the challenges in technical and clinical integration to facilitate practical implementation of this technology

Additional file 1: of MicroRNA-101 is a potential prognostic indicator of laryngeal squamous cell carcinoma and modulates CDK8

Figure S1. The simple figure describing the construction of lentivirus

Additional file 2: of MicroRNA-101 is a potential prognostic indicator of laryngeal squamous cell carcinoma and modulates CDK8

Figure S2. Hep-2 cells 72 h after transduction. (A) Fluorescence microscopic images of cells in the miR-101-treated group. (B) Light microscopic images of cells in the miR-101-treated group. (C) Fluorescence microscopic images of cells in the negative control group. (D) Light microscopic images of cells in the negative control group

Diagnostic Accuracy of CT for Metastatic Epidural Spinal Cord Compression

10.3390/cancers14174231CANCERS141