Objective To investigate the effects of cucurbitacin B (CucB) on alleviating skin lesions and inflammation in psoriasis mice via the cGAS-STING signaling pathway. Methods The expression of genes associated with the cGAS-STING signaling pathway in psoriatic lesions and non-lesional skin was analyzed, and hallmark gene set enrichment analysis was performed. The cytotoxicity of CucB on BMDMs was evaluated using the CCK-8 assay. The expression levels of genes and proteins related to the cGAS-STING signaling pathway, along with the secretion of inflammatory cytokines, were measured at different concentrations of CucB using quantitative PCR, Western blotting, and ELISA. Imiquimod-induced psoriasis BALB/c mice were divided into four groups: normal group, model group, low-dose CucB group [0.1 mg/ (kg.d)], and high-dose CucB group [0.4 mg/ (kg.d)], with five mice per group. PASI scoring was performed to assess the severity of psoriasis after 6 days of treatment, and HE staining was conducted to observe pathological damage. Meanwhile, the mRNA levels of inflammatory cytokines and their secretion were detected by qPCR and ELISA. Results Most cGAS-STING signaling-related genes were upregulated in lesional skin of psoriasis patients, and the hallmark gene set enrichment analysis revealed that the most significantly upregulated genes were primarily associated with immune response signaling pathways. CucB inhibited dsDNA-induced phosphorylation of interferon regulatory factor 3 (IRF3) and STING proteins in both bone-marrow derived macrophages(BMDMs) and THP-1 cells. CucB also suppressed dsDNA-induced mRNA expression of IFNB1, TNF, IFIT1, CXCL10, ISG15, and reduced the secretion of cytokines such as IFN-β, IL-1β, and TNF-α in THP-1 cells. In the imiquimod-induced psoriasis mouse model, CucB treatment reduced psoriatic symptoms, alleviated skin lesions, and attenuated inflammation. ELISA and qPCR results showed that CucB significantly reduced serum secretion levels of IL-6, TNF-α, and IL-1β, as well as the mRNA levels of IL23A, IL1B, IL6, TNF, and IFNB1. Conclusion CucB inhibits cytoplasmic DNA-induced activationc of the GAS-STING pathway. CucB significantly attenuates skin lesions and inflammation in IMQ-induced psoriatic mice, and the potential molecular mechanism may be related to the down-regulation of the cGAS-STING pathway.
Animals ; Psoriasis/pathology* ; Signal Transduction/drug effects* ; Membrane Proteins/genetics* ; Mice ; Nucleotidyltransferases/genetics* ; Disease Models, Animal ; Mice, Inbred BALB C ; Skin/metabolism* ; Triterpenes/therapeutic use* ; Humans ; Cytokines/metabolism* ; Inflammation/drug therapy* ; Male

Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.

Obstructive sleep apnea(OSA)is a common sleep disorder characterized by repeated episodes of upper airway collapse and obstruction during sleep.Polysomnography is the gold standard for diagnosing OSA,but it is expensive,time-consuming,and can cause discomfort for patients.In recent years,acoustic-based approaches for detecting OSA have emerged as a research focus.This review summarizes recent advances in OSA automatic detection techniques based on snoring and speech signals,and systematically examines their applications in diagnosis,severity assessment,and localization of obstruction sites.Findings indicate that the acoustic features of snoring and speech signals hold significant value for OSA screening,and when combined with machine learning and deep learning models,it can achieve high diagnostic accuracy.Future research should focus on elucidating the relationship between acoustic features and the pathophysiological mechanisms of OSA,integrating multimodal information,and advancing the clinical application of wearable devices,with the aim of promoting intelligent,non-invasive,and cost-effective screening technologies for OSA.

Temporomandibular joint (TMJ) diseases are common clinical conditions. The number of patients with TMJ diseases is large, and the etiology, epidemiology, disease spectrum, and treatment of the disease remain controversial and unknown. To understand and master the current situation of the occurrence, development and prevention of TMJ diseases, as well as to identify the patterns in etiology, incidence, drug sensitivity, and prognosis is crucial for alleviating patients′suffering.This will facilitate in-depth medical research, effective disease prevention measures, and the formulation of corresponding health policies. Cohort construction and research has an irreplaceable role in precise disease prevention and significant improvement in diagnosis and treatment levels. Large-scale cohort studies are needed to explore the relationship between potential risk factors and outcomes of TMJ diseases, and to observe disease prognoses through long-term follw-ups. The consensus aims to establish a standard conceptual frame work for a cohort study on patients with TMJ disease while providing ideas for cohort data standards to this condition. TMJ disease cohort data consists of both common data standards applicable to all specific disease cohorts as well as disease-specific data standards. Common data were available for each specific disease cohort. By integrating different cohort research resources, standard problems or study variables can be unified. Long-term follow-up can be performed using consistent definitions and criteria across different projects for better core data collection. It is hoped that this consensus will be facilitate the development cohort studies of TMJ diseases.

To provide comprehensive, scientific, and precise big data supports for national pediatric cancer prevention and control, the National Center for Pediatric Cancer Surveillance constructed the Surveillance Platform in 2019. Based on stratified and service-oriented design concepts, and a microservices architecture, the platform constructed five layers: document storage, data storage, service, application, and visualization. The platform supported three data reporting methods: automatic collection, file import, and manual entry. It ensured data quality through both rule-based and process-based quality control measures. Additionally, strict data security measures had been established in areas such as security domains, permission management, and data de-identification to ensure the safety and reliability of the monitoring data. As to November 2024, the platform had covered 1 750 surveillance sites(hospitals) and collected information about 6 million pediatric cancer cases, achieving positive results. This practice had laid a solid foundation for the smooth implementation of national pediatric cancer surveillance work and provided scientific evidences for pediatric cancer prevention and control in China. In the future, the platform′s performance needs to be continuously optimized and upgraded. It should further integrate relevant datasets in this field and actively explore and expand new application scenarios with the help of cutting-edge technologies such as big language models.

The enterprise master patient index is an important tool for identifying the diagnosis and treatment records of the same patient in heterogeneous medical data from multiple sources. From June to December 2021, the National Children′s Cancer Monitoring Center screened and determined the enterprise master patient index index system and its recognition logic by literature analysis and expert consultation. Based on the National Children′s Cancer surveillance Platform (hereinafter referred to as the surveillance platform), a corresponding intelligent recognition algorithm system was developed. After multiple rounds of real data verification and adjustment, a enterprise master patient index suitable for multi-source heterogeneous medical data was constructed. From January 2022 to March 2024, the intelligent recognition algorithm system had completed the recognition of 2.46 million pediatric tumor case report cards, established 0.33 million primary indexes and their unique identification codes for malignant tumor patients, and improved the data management and application efficiency of the surveillance platform. The enterprise master patient index based on surveillance platforms had played an important role in the registration and follow-up of pediatric cancer cases and related medical research, which could provide references for the construction of master indexes on other medical big data platforms in China.

The national pediatric cancer surveillance data known as the pediatric cancer case report card(report card), had the characteristics of wide sources, diverse collection methods and a large amount of information. Based on the characteristics of the surveillance data, the National Center for Pediatric Cancer Surveillance (surveillance center) established quality control program for surveillance data according to the relevant norms and standards from China and other countries. The program defined the variables, requirements and rules for the quality control of surveillance data. The surveillance center designed different quality control processes according to the way of data reporting including manual filling/file import and port docking, and formulated a series of supporting measures to achieve the completeness, accuracy and standardization of surveillance data. By analyzing the report cards of patients discharged from hospital from 2021 to 2023, the surveillance center found that the number of problem report cards decreased from 40.6% (202 185 cards / 497 538 cards) before feedback to 31.1% (157 725 cards / 506 817 cards) after feedback. The data quality control program not only improved the quality of surveillance data, but also provided references for the establishment of the data quality control program of other registration systems of medical field.

Building a nationwide representative sibling information database of pediatric cancer is of great significance for the research of pediatric cancer. In October 2022, based on the national pediatric cancer surveillance platform, the National Center for Pediatric Cancer Surveillance(NCPCS) identified and integrated the information of pediatric cancer cases using the patient master index, and then determined and retrieved the diagnosis and treatment information of pediatric cancer siblings through the sibling pair matching algorithm system, to establish the sibling information database. The information database was stored in the sibling database module of the surveillance platform, which realized the dynamic update, retrieval, download, and analysis of sibling information. The database provided data and technical support for the further childhood cancer research among siblings, as well as provided a reference for the construction of research-oriented databases for other disease surveillance systems. As of March 2024, this database had included 2 980 childhood cancer patients, collecting nearly 30 000 related medical records. In the future, NCPCS should further improve the sensitivity of sibling decision logic and expand the functionality of the sibling information database, so as to better meet the diverse needs of clinical and scientific research.

In-depth understanding of the clinical diagnosis and treatment practices of various health workers is of great significance for optimizing the allocation of health workforce. In 2023, based on the surveillance platform of National Center for Pediatric Cancer Surveillance(NCPCS), the NCPCS effectively integrated human resources data with clinical diagnosis and treatment data. By clarifying the conceptual and logical structures of the database, a clinical health workforce database was constructed using a distributed relational database. This database adhered to the data quality control principles of uniqueness, integrity, logic, and validity, and implemented scientific and effective data security protection strategies throughout the entire data life cycle. In practical applications, statistical analyses could be conducted on this database from two dimensions: health workforce and diagnosis-treatment processes, assisting relevant departments and hospitals in the refined management of health workforce allocation and promoting discipline construction. As of May 2024, the database had incorporated 931 hospitals, with the number of various health workers exceeding 640 000. The clinical health workforce database provided references for health administrative departments and hospitals at all levels to grasp the clinical practices of various health workers, and to achieve a clinical-demand-oriented allocation of health workforce.

To provide comprehensive, scientific, and precise big data supports for national pediatric cancer prevention and control, the National Center for Pediatric Cancer Surveillance constructed the Surveillance Platform in 2019. Based on stratified and service-oriented design concepts, and a microservices architecture, the platform constructed five layers: document storage, data storage, service, application, and visualization. The platform supported three data reporting methods: automatic collection, file import, and manual entry. It ensured data quality through both rule-based and process-based quality control measures. Additionally, strict data security measures had been established in areas such as security domains, permission management, and data de-identification to ensure the safety and reliability of the monitoring data. As to November 2024, the platform had covered 1 750 surveillance sites(hospitals) and collected information about 6 million pediatric cancer cases, achieving positive results. This practice had laid a solid foundation for the smooth implementation of national pediatric cancer surveillance work and provided scientific evidences for pediatric cancer prevention and control in China. In the future, the platform′s performance needs to be continuously optimized and upgraded. It should further integrate relevant datasets in this field and actively explore and expand new application scenarios with the help of cutting-edge technologies such as big language models.