Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects.

Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC.

Integrative analysis of multi-omics data reveals a pseudouridine-related lncRNA signature for prediction of glioma prognosis and chemoradiotherapy sensitivity.

Background: Glioblastoma is the most common type of glioma with a high incidence and poor prognosis, and effective medical treatment remains challenging. Pseudouridine (Ψ) is the first post-transcriptional modification discovered and one of the most abundant modifications to RNA. However, the prognostic value of Ψ-related lncRNAs (ΨrLs) for glioma patients has never been systematically evaluated.

Efficacy and safety of remote ischemic conditioning for acute ischemic stroke: A comprehensive meta-analysis from randomized controlled trials.

Background And Purpose: Remote ischemic conditioning (RIC) is a remote, transient, and noninvasive procedure providing temporary ischemia and reperfusion. However, there is no comprehensive literature investigating the efficacy and safety of RIC for the treatment of acute ischemic stroke. In the present study, we performed a comprehensive meta-analysis of the available studies.