AIM To study the chemical constituents from Stelmatocrypton khasianum.(Benth.)Baill.and their in vitro anti-inflammatory activity and cytotoxic activity.METHODS Separation and purification were performed using thin layer chromatography,silica gel,semi-preparative HPLC and Sephadex LH-20,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The in vitro anti-inflammatory activity was evaluated by RAW264.7 model,and the cytotoxic activity was determined by CCK-8 method.RESULTS Fifteen compounds were isolated and identified as 2α,3β,19α,23-tetrahydroxy-urs-12-en-28-oic acid-3-O-β-D-glucopyranosyl-28-O-β-D-glucopyranosyl ester(1),dalzienoside(2),benzyl-(6-O-α-L-rhanmopyranosyl)O-β-D-glucopyranoside(3),corchorusoside C(4),cyclo(Ala-Tyr)(5),thymidine(6),(4S,5S)-5-hydroxy-4-hexanolide(7),2-methylpyridin-3-ol(8),butyl isobutyl phthalate(9),bis-(2-ethylhexyl)terephthalate(10),p-hydroxybenzaldehyde(11),vanilic acid(12),salicylic acid(13),isovanilic acid(14),3-hydroxy-p-anisaldehyde(15).The IC50 values of compounds 1,2 and 4 for NO were(27.69±5.51),(25.82±3.58)and(23.35±7.09)μmol/L,respectively.The IC50 value of compound 1 on MCF-7 cells was(18.15±6.45)μmol/L.The IC50 values of compound 4 on MCF-7 and HCCC-9810 cells were(19.43±2.66)and(21.76±5.81)μmol/L,respectively.CONCLUSION Compounds 2-11 are isolated from S.khasianum for the first time.Compounds 1,2 and 4 exhibit good in vitro anti-inflammatory activity,and 1,4 have cytotoxic activity.

To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

Objective:To investigate the feasibility of daily laparoscopic appendectomy for acute appendicitis in college students and establish a predictive model of the nomogram.Methods:A prospective analysis was performed on 82 college students with acute appendicitis who were admitted to hospital continuously from December 2023 to De-cember 2024.All patients received laparoscopic appendectomy and accelerated rehabilitation surgical measures combin-ing traditional Chinese and Western medicine,and were divided into two groups according to whether the daytime op-eration mode was achieved.Multivariate Logistic regression analysis was used to screen the independent influencing factors for achieving the day surgery model,and the prediction model was constructed,evaluated and verified.Results:Of the 82 patients,46(56%)achieved the daytime operation mode,the mean length of hospitalization was(41.37±6.33)h,and no serious complications were observed 30 days after surgery.The independent influencing factors for achieving this model were CRP≤13.9 mg/L,abdominal pain time≤22 h,appendix diameter≤10 mm,no drainage tube placed,and postoperative VAS score≤2.5(all P<0.05).The area under the curve of the prediction model was 0.905(95%CI:0.845～0.966),and the calibration chart indicated that the predicted probability of the model was in good agreement with the ac-tual probability,and the decision curve showed good clinical benefit.Conclusion:It is safe and feasible for college stu-dents to receive day surgery mode of laparoscopic appendectomy.The created nomogram model has good predictive ability,which can identify eligible patients as early as possible,and take timely intervention measures to improve the achievement rate.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

Objective:To investigate the feasibility of daily laparoscopic appendectomy for acute appendicitis in college students and establish a predictive model of the nomogram.Methods:A prospective analysis was performed on 82 college students with acute appendicitis who were admitted to hospital continuously from December 2023 to De-cember 2024.All patients received laparoscopic appendectomy and accelerated rehabilitation surgical measures combin-ing traditional Chinese and Western medicine,and were divided into two groups according to whether the daytime op-eration mode was achieved.Multivariate Logistic regression analysis was used to screen the independent influencing factors for achieving the day surgery model,and the prediction model was constructed,evaluated and verified.Results:Of the 82 patients,46(56%)achieved the daytime operation mode,the mean length of hospitalization was(41.37±6.33)h,and no serious complications were observed 30 days after surgery.The independent influencing factors for achieving this model were CRP≤13.9 mg/L,abdominal pain time≤22 h,appendix diameter≤10 mm,no drainage tube placed,and postoperative VAS score≤2.5(all P<0.05).The area under the curve of the prediction model was 0.905(95%CI:0.845～0.966),and the calibration chart indicated that the predicted probability of the model was in good agreement with the ac-tual probability,and the decision curve showed good clinical benefit.Conclusion:It is safe and feasible for college stu-dents to receive day surgery mode of laparoscopic appendectomy.The created nomogram model has good predictive ability,which can identify eligible patients as early as possible,and take timely intervention measures to improve the achievement rate.

To explore the application of sequential analysis in post-market safety dynamic surveillance of vaccines. Under the dynamic monitoring data of vaccines post-market approval, this research introduces the fundamental principles of maximizing sequential probability ratio test (MaxSPRT) and Bayesian sequential analysis, employing R software. Through an example of dynamic safety monitoring data of vaccines post-market approval, we analyze using the MaxSPRT and Bayesian sequential analysis. The MaxSPRT identified a safety signal in week 4 ( P<0.05), while Bayesian sequential analysis indicated that the 95% highest density interval for the RR value at week 4 is 1.13-3.27, suggesting the first appearance of a safety signal at week 4. The MaxSPRT and Bayesian sequential analysis effectively leverage continuously accumulating dynamic monitoring data, thereby serving as a valuable method for post-market safety surveillance of vaccines.

AIM:To evaluate the application effec-tiveness of a Bayesian mixture model based on the principal stratum strategy for estimating the com-plier average causal effect(CACE)in clinical trials with non-compliance.METHODS:Using a non-infe-riority randomized controlled trial investigating a novel drug for primary type 2 diabetes mellitus(non-inferiority margin:-0.4)as a case study,the primary analysis applied a Bayesian mixture model under the monotonicity assumption to estimate CACE of between-group differences in glycated he-moglobin(HbA1c)changes within the compliant stratum,followed by non-inferiority testing.Sensi-tivity analyses included a Bayesian mixture model relaxing the monotonicity assumption and compar-ing results with per-protocol set(PPS)analysis.RE-SULTS:In the primary analysis,the posterior mean of CACE for HbA1c change in the compliant stratum was 0.081％,with a one-sided 97.5％credible inter-val lower bound of-0.124,exceeding the non-infe-riority margin(-0.4％),supporting the non-inferiori-ty efficacy of the novel drug in the compliant stra-tum(P(H1|Data)=1).Consistent findings were ob-served in PPS analyses(estimated effect:0.136％;one-sided 97.5％credible interval lower bound:-0.069％),further validating methodological robust-ness.CONCLUSION:In clinical trials with noncom-pliance as an intercurrent event,the Bayesian mix-ture model under the principal stratum strategy ef-fectively adjusts for compliance-related bias and yields conservative,robust estimates of causal ef-fects,supporting its value in efficacy evaluation un-der complex compliance scenarios.

AIM To study the chemical constituents from Stelmatocrypton khasianum.(Benth.)Baill.and their in vitro anti-inflammatory activity and cytotoxic activity.METHODS Separation and purification were performed using thin layer chromatography,silica gel,semi-preparative HPLC and Sephadex LH-20,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The in vitro anti-inflammatory activity was evaluated by RAW264.7 model,and the cytotoxic activity was determined by CCK-8 method.RESULTS Fifteen compounds were isolated and identified as 2α,3β,19α,23-tetrahydroxy-urs-12-en-28-oic acid-3-O-β-D-glucopyranosyl-28-O-β-D-glucopyranosyl ester(1),dalzienoside(2),benzyl-(6-O-α-L-rhanmopyranosyl)O-β-D-glucopyranoside(3),corchorusoside C(4),cyclo(Ala-Tyr)(5),thymidine(6),(4S,5S)-5-hydroxy-4-hexanolide(7),2-methylpyridin-3-ol(8),butyl isobutyl phthalate(9),bis-(2-ethylhexyl)terephthalate(10),p-hydroxybenzaldehyde(11),vanilic acid(12),salicylic acid(13),isovanilic acid(14),3-hydroxy-p-anisaldehyde(15).The IC50 values of compounds 1,2 and 4 for NO were(27.69±5.51),(25.82±3.58)and(23.35±7.09)μmol/L,respectively.The IC50 value of compound 1 on MCF-7 cells was(18.15±6.45)μmol/L.The IC50 values of compound 4 on MCF-7 and HCCC-9810 cells were(19.43±2.66)and(21.76±5.81)μmol/L,respectively.CONCLUSION Compounds 2-11 are isolated from S.khasianum for the first time.Compounds 1,2 and 4 exhibit good in vitro anti-inflammatory activity,and 1,4 have cytotoxic activity.

Objective:To analyze the genes related to platelet activation in essential thrombocythemia (ET)based on transcriptome sequencing technology (RNA-seq ),and to explore the potential targets related to ET thrombosis. Methods:Blood samples from ET patients and healthy individuals were collected for RNA-seq,and differentially expressed lncRNAs,miRNAs,and mRNAs were selected to construct a lncRNA-miRNA-mRNA regulatory network. Differential mRNAs in the regulatory network were enriched and analyzed using Gene Ontology (GO ) and Kyoto Encyclopedia of Genes and Genomes (KEGG).The real-time PCR method was applied to validate differential mRNAs on crucial signaling pathways.Results:A total of 32 lncRNAs (3 up-regulated,29 down-regulated),16 miRNAs (8 up-regulated,8 down-regulated),and 35 mRNAs (27 up-regulated,8 down-regulated)were identified as differentially expressed.Among them,5 lncRNAs,12 miRNAs,and 19 mRNAs constituted the regulatory network.KEGG enrichment analysis showed that the differential mRNAs were related to the platelet activation signaling pathway,and there were 6 differential mRNAs related to platelet activation,namely F2R,ITGA2B,ITGB1,ITGB3,PTGS1,and GP1 BB,which were all up-regulated in their expression.RT-PCR results showed that the expression of five mRNAs including F2R,ITGA2B,ITGB1,ITGB3,and GP1BB were upregulated in ET patients compared with healthy subjects,and consistent with RNA-seq results,while PTGS1 expression was not significantly different.Conclusion:Differential mRNAs in ET patients are related to the platelet activation pathway,and F2R,ITGA2B,ITGB1,ITGB3,and GP1BB mRNAs may serve as novel targets associated with platelet activation in ET.

Objective:To assess the predictive accuracy and practical utility of established risk prediction models for enteral nutrition intolerance in critically ill patients. Methods:A meta-analysis was conducted to identify existing risk prediction models for enteral nutrition intolerance in critically ill patients. Eligible patients admitted to the Department of Critical Care Medicine and various ICUs of General Hospital of Eastern Theater Command from March 2023 to August 2023, meeting natriuresis criteria, were included in the study. The discrimination and calibration of the two models were assessed using the area under the receiver operating characteristic curve (AUROC) and the Hosmer-Lemeshow goodness-of-fit test (H-L test). Results:Two models were analyzed, encompassing a total of 395 patients, among whom 161 experienced intolerances, resulting in an incidence rate of 40.8％. Model 1 demonstrated an AUROC of 0.838 (95％CI:0.798 ～ 0.873), while model 2 yielded an AUROC of 0.744 (95％CI:0.698 ～ 0.786). The Delong method was utilized to compare the AUROC values of the two models, revealing a statistically significant difference (P=0.0043). Notably, the model 1 exhibited superior performance compered to model 2. The H-L test for model 1 indicated fair calibration (X2=61.116, P<0.001), whereas model 2 demonstrated better calibration (X2=3.659, P=0.887). Conclusion:Model 1 exhibits superior discriminatory ability compared tomodel 2, while the calibration of model 2 surpasses that of model 1. Model 1 is well-suited for dynamic prediction, accommodating changes in patient condition over time. Conversely, Model 2 is appropriated for initial prediction following enteral nutrition initiation. Healthcare professionals can integrate bothmodels based on the specific clinical conditions to enhance predictive accutacy. Additionally, they can undertake high-quality research to develop a novel risk prediction model.