To introduce and analyze guideline terminology related to clinical question formulation, evidence retrieval and appraisal, and recommendation development.

To develop a guideline terminology system and promote its standardization, thereby enhancing medical staff's accurate understanding and correct application of guidelines.

In this study, araucarene diterpenes, characterized by a pimarene skeleton with a variably oxidized side chain at C-13, were investigated. A total of 16 araucarene diterpenoids and their derivatives were isolated from the woods of Agathis dammara, including 11 previously unreported compounds: dammaradione (1), dammarones D-G (2, 5, 14, 15), dammaric acids B-F (8-12), and dammarol (16). The structures of these new compounds were elucidated using high-resolution electrospray ionization mass spectroscopy (HR-ESI-MS) and one-dimensional/two-dimensional (1D/2D) nuclear magnetic resonance (NMR), while their absolute configurations were determined through the electronic circular dichroism (ECD) exciton chirality method and Snatzke's method. The hypoglycemic activity of all isolated compounds was evaluated using a transgenic zebrafish model, and a structure-activity relationship (SAR) analysis was conducted. Araucarone (3) and dammaric acid C (9), serving as representative compounds, demonstrated significant hypoglycemic effects on zebrafish. The primary mechanism involves the promotion of pancreatic β cell regeneration and glucose uptake. Specifically, these compounds enhance the differentiation of pancreatic endocrine precursor cells (PEP cells) into β cells in zebrafish.
Zebrafish ; Animals ; Diterpenes/isolation & purification* ; Insulin-Secreting Cells/cytology* ; Glucose/metabolism* ; Hypoglycemic Agents/isolation & purification* ; Molecular Structure ; Structure-Activity Relationship ; Plant Extracts/pharmacology* ; Regeneration/drug effects*

Objective To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer.Methods We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer,who were randomly divided into a training dataset(60%)and a testing dataset(40%).Using univariate analysis,multiple logistic regression analysis,and LASSO regression analysis,we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer.We employed an integrated learning approach that combined GBM,XGBOOST,and MLP algorithms for optimization of the model performance through parameter adjustments.Results The clinical pregnancy rate following fresh embryo transfer was significantly influenced by female age,Gn initiation dose,number of assisted reproduction cycles,and number of embryos transferred.The variables included in the LASSO model selection included female age,FSH levels,duration and initial dose of Gn usage,number of assisted reproduction cycles,retrieved oocytes,embryos transferred,endometrial thickness on HCG day,and progesterone level on HCG day.The nomogram demonstrated an accuracy of 0.642(95%CI:0.605-0.679)in the training dataset and 0.652(95%CI:0.600-0.704)in the validation dataset.The predictive ability of the model was further improved using ensemble learning methods and achieved predicative accuracies of 0.725(95%CI:0.680-0.770)in the training dataset and 0.718(95%CI:0.675-0.761)in the validation dataset.Conclusions The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.

Objective To establish a nomogram model for predicting clinical pregnancy rate in patients with endometriosis undergoing fresh embryo transfer.Methods We retrospectively collected the data of 464 endometriosis patients undergoing fresh embryo transfer,who were randomly divided into a training dataset(60%)and a testing dataset(40%).Using univariate analysis,multiple logistic regression analysis,and LASSO regression analysis,we identified the factors associated with the fresh transplantation pregnancy rate in these patients and developed a nomogram model for predicting the clinical pregnancy rate following fresh embryo transfer.We employed an integrated learning approach that combined GBM,XGBOOST,and MLP algorithms for optimization of the model performance through parameter adjustments.Results The clinical pregnancy rate following fresh embryo transfer was significantly influenced by female age,Gn initiation dose,number of assisted reproduction cycles,and number of embryos transferred.The variables included in the LASSO model selection included female age,FSH levels,duration and initial dose of Gn usage,number of assisted reproduction cycles,retrieved oocytes,embryos transferred,endometrial thickness on HCG day,and progesterone level on HCG day.The nomogram demonstrated an accuracy of 0.642(95%CI:0.605-0.679)in the training dataset and 0.652(95%CI:0.600-0.704)in the validation dataset.The predictive ability of the model was further improved using ensemble learning methods and achieved predicative accuracies of 0.725(95%CI:0.680-0.770)in the training dataset and 0.718(95%CI:0.675-0.761)in the validation dataset.Conclusions The established prediction model in this study can help in prediction of clinical pregnancy rates following fresh embryo transfer in patients with endometriosis.

The medical metaverse is a combination of medicine and other cutting-edge technologies such as computer and information ones. In the medical metaverse, medical knowledge in the real world will be transformed into a digital form, so that activities concerning diagnosis, treatment, education and clinical practice can be carried out in a virtual environment. Based on the latest research advances at home and abroad, this review expounds on the medical metaverse from the aspects of supporting technologies, applications in clinic and medical education, current deficiencies and future development.

OBJECTIVE To compare the contents of schizandrin A, schizandrin B, schizandrin C, schisandrol A, schisandrol B, schisantherin A, anwuligan, and schisanhenol in Schisandrae Sphenantherae Fructus on market from 12 habitats. METHODS The samples were pre-treated by 96-well fitration plates. The assay was performed on ACE EXCEL 1.7 C18-AMIDE(100 mm×2.1 mm, 1.7 μm) column with 0.1% formic acid-water(A) and methanol(B), gradient elution, flow speed was 0.4 mL·min–1. Ion source was electric spray ion source, positive ion monitoring mode, multireaction monitoring mode for quantitative analysis. Linear, instrument precision, stability, repeatability, average recovery were investigated. RESULTS The content of schisantherin A in 10 of 12 producing areas reached the standard of ≥0.2% of Schisandrae Sphenantherae Fructus in 2020 Edition of Chinese Pharmacopoeia. CONCLUSION UHPLC-MS/MS is suitable for simultaneous determination of multiple components in Schisandrae Sphenantherae Fructus. The Schisandrae Sphenantherae Fructus in the market basically meet the national legal standards.

Objective To investigate the key genes associated with ferroptosis in peri-implantitis and explore the potential mecha-nisms regulating peri-implantitis.Methods Several datasets were obtained from the GEO database.Differential expressed genes were screened,and GO and KEGG analyses were performed.A PPI network was constructed using the STRING website.Key genes were val-idated using a test set,and the diagnostic value of key genes was determined.The content and proportion of 22 immune cells in peri-im-plantitis tissues were obtained through immune infiltration analysis.Key genes were validated by qRT-PCR and Western Blot(WB).Results There were 1 138 differential genes between peri-implantitis tissues and normal gingival tissues,of which 29 were related to ferroptosis.The gene expression in peri-implantitis tissues mainly involved processes such as immune response activation.Five key genes in the ferroptosis-related differential genes,namely SOX2,GJA1,IL1B,GPX2 and CHAC1,were differentially expressed in peri-implantitis tissues and had high diagnostic value.Immune infiltration analysis showed significant changes in immune cells such as memory B cells and plasma cells in peri-implantitis tissues.qRT-PCR and WB confirmed significant differential expression of mRNA and the protein transcribed by key genes.Conclusion Differential genes between peri-implantitis and ferroptosis are screened using bioinformatics analysis and biological validation,providing new insights into the study on peri-implantitis.

Platelet is the key factor in hemostasis and coagulation,its potential in inflammatory response and immune response has been widely concerned in recent years.A growing number of studies indicate that tissue damage in inflammatory bowel disease is not only the result of dysregulated immune response,but also the active participation of various other cellular systems,and platelet might play an important role in intestinal mucosal inflammation and immunity.This article reviewed the abnormal changes and possible mechanisms of platelet in inflammatory bowel disease,and the prospects of platelet as a specific treatment target for inflammatory bowel disease.