In recent years, the application of artificial intelligence technology in psychiatry has gradually received attention, providing new methods and ideas for clinical diagnosis and treatment in psychiatry. Natural language processing (NLP) is an artificial intelligence technology with robust text data analysis and processing capacities. This paper reviews the literature on applying natural language processing in common psychiatric disorders, electronic medical records in psychiatry, and psychiatric biomarkers, to provide clinicians with a comprehensive understanding of NLP.

In recent years, the application of artificial intelligence technology in psychiatry has gradually received attention, providing new methods and ideas for clinical diagnosis and treatment in psychiatry. Natural language processing (NLP) is an artificial intelligence technology with robust text data analysis and processing capacities. This paper reviews the literature on applying natural language processing in common psychiatric disorders, electronic medical records in psychiatry, and psychiatric biomarkers, to provide clinicians with a comprehensive understanding of NLP.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.

Aim To investigate the protective effect of ginsenoside Rb1 against apoptosis induced by H_2O_2. Methods H_2O_2 was used to build an oxidative stress-induced injury model in neonatal rat cardiomyocytes. After treated with gensenoside Rb1(20, 40, 80 mg?L -1),the apoptosis rate, the content of malondialdehyde (MDA), and the activity of superoxide dimutase (SOD) of the cardiomyocytes were examined. The intracellular calcium indicated by the fluorescence in cells were measured by the laser confocal microscope. Results Compared with the model group, the apoptosis rate and the content of MDA of the cardiomyocytes decreased greatly (P