Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers.

The principal aim of this investigation is to identify pivotal biomarkers linked to the prognosis of osteosarcoma (OS) through the application of artificial intelligence (AI), with an ultimate goal to enhance prognostic prediction. Expression profiles from 88 OS cases and 396 normal samples were procured from accessible public databases. Prognostic models were established using univariate COX regression analysis and an array of AI methodologies including the XGB method, RF method, GLM method, SVM method, and LASSO regression analysis. Multivariate COX regression analysis was also employed. Immune cell variations in OS were examined using the CIBERSORT software, and a differential analysis was conducted. Routine blood data from 20,679 normal samples and 437 OS cases were analyzed to validate lymphocyte disparity. Histological assessments of the study's postulates were performed through immunohistochemistry and hematoxylin and eosin (HE) staining. AI facilitated the identification of differentially expressed genes, which were utilized to construct a prognostic model. This model discerned that the survival rate in the high-risk category was significantly inferior compared to the low-risk cohort (p < 0.05). SERPINE2 was found to be positively associated with memory B cells, while CPT1B correlated positively with CD8 T cells. Immunohistochemical assessments indicated that SERPINE2 was more prominently expressed in OS tissues relative to adjacent non-tumorous tissues. Conversely, CPT1B expression was elevated in the adjacent non-tumorous tissues compared to OS tissues. Lymphocyte counts from routine blood evaluations exhibited marked differences between normal and OS groups (p < 0.001). The study highlights SERPINE2 and CPT1B as crucial biomarkers for OS prognosis and suggests that dysregulation of lymphocytes plays a significant role in OS pathogenesis. Both SERPINE2 and CPT1B have potential utility as prognostic biomarkers for OS.

Chinese herbal compound prescription for Endometriosis: A protocol for systematic review and meta-analysis.

BACKGROUND: Endometriosis (EMT) is one of the common diseases of women of childbearing age. EMT destroys the anatomical structure of the pelvis, which leads to abnormal ovulation and endocrine abnormalities. It also affects embryo implantation and makes patients infertile. Recently, it is confirmed that Chinese medicine also have an excellent clinical efficacy on EMT. Compared with the conventional western medicine treatment, it effectively relieve pain and other concomitant symptoms. METHODS AND ANALYSIS: The following databases will be searched for relevant information before July 2020: PubMed, Embase, Cochrane Library, Web of Science, and CNKI. MAJOR RESULTS: the overall effective rate, VAS score. SECONDARY OUTCOMES: blood serum estradiol (E2), progesterone (P), Follicle-Stimulating Hormone (FSH), adverse events. Data will be collected independently by 2 researchers, and the risk of bias in meta-analysis will be evaluated according to "Cochrane Handbook for Systematic Reviews of Interventions". All data analysis will be conducted using Review Manager V.5.3. and Stata V.12.0. RESULTS: The curative effect and safety of Chinese herbal compound prescription treatment for EMT patients will be evaluated systematically. CONCLUSION: The systematic review of this study will summarize the currently published evidence of Chinese herbal compound prescription treatment for EMT to further guide its promotion and application. ETHICS AND DISSEMINATION: The private information from individuals will not be published. This systematic review also will not involve endangering participant rights. Ethical approval is not required. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. OPEN SCIENCE FRAMEWORK (OSF) REGISTRATION NUMBER:: https://osf.io/p5nrk.