AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

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.

The therapeutic effects of Yueju Pills on depression and cardiovascular diseases have been widely recognized. Previous studies have shown that the drug can significantly improve depressive-like behaviors induced by chronic unpredictable mild stress(CUMS) combined with atherosclerosis(AS). Given the complex pathogenesis of psychocardiac diseases, this study integrated metabolomics and network pharmacology to systematically elucidate the mechanism of Yueju Pills in alleviating depressive symptoms in psychocardiac diseases. The results demonstrate that, after Yueju Pill intervention, the levels of 9 abnormal metabolites in the hippocampus restore to normal ranges, primarily involving key pathways or signaling pathways, including the cyclic adenosine monophosphate(cAMP), mammalian target of rapamycin(mTOR), glycine/serine/threonine metabolism, and aminoacyl-tRNA biosynthesis. In a high-fat diet-induced CUMS ApoE~(-/-) mouse model, Yueju Pills significantly increases adenosine monophosphate(AMP) levels and decreases L-alanine and D-glyceric acid levels in the hippocampus. In conclusion, Yueju Pills exert antidepressant effects by regulating multiple metabolic axes, including glycine/serine/threonine metabolism and the cAMP, mTOR signaling pathways. Network pharmacology predictions reveal that the treatment of CUMS combined with AS by its core active components may be realized through modulating pathways concerning neuroinflammation and synaptic plasticity, including serine/threonine-protein kinase 1(AKT1), mitogen-activated protein kinase 1(MAPK1), and prostaglandin-endoperoxide synthase 2(PTGS2). This study provides a theoretical reference for the clinical application of Yueju Pills in alleviating the depressive symptoms of psychocardiac diseases.
Animals ; Network Pharmacology ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Metabolomics ; Male ; Depression/genetics* ; Humans ; Hippocampus/drug effects* ; Mice, Inbred C57BL ; Signal Transduction/drug effects*

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the correlation between the hemodynamic pattern of non-culprit vessel stenosis and long-term vessel-oriented composite outcome(VOCO) in patients with acute ST-segment elevation myocardial infarction (STEMI).Methods:From January 2019 to December 2021, 233 consecutive patients with STEMI and non-culprit vessel stenosis were prospectively enrolled at Shanghai East Hospital. The median follow-up duration was 3.9 years. The 367 non-culprit vessels of the 233 patients were divided into the VOCO group (33 vessels, 9.0%) and the non-VOCO group (334 vessels, 91.0%). Parameters pertaining to the hemodynamic pattern of non-culprit vessel stenosis between the two groups were compared. Receiver operating characteristic (ROC) curves were used to assess the correlation between hemodynamic pattern and VOCO, and Cox multivariate regression and logistic multivariate regression analyses were applied to identify independent predictors of VOCO.Results:The 233 enrolled patients were aged (62.5±12.9) years, with 193 males (82.8%). In the VOCO group, the maximum quantitative flow ratio (QFR) decreased within 20 mm of the QFR-assessed segment, the difference in QFR across the entire vessel, the length of functionally significant vessel, and the maximum gradient of QFR decrease (dQFR/dsmax) were significantly greater than those in the non-VOCO group. ROC curve analysis showed that the optimal threshold for predicting VOCO using dQFR/dsmax was 0.009 6 (area under the curve: 0.691, 95% CI: 0.606-0.775, P<0.001). Multivariable Cox regression analysis revealed that dQFR/dsmax was an independent predictor of VOCO ( HR=1.199, 95% CI: 1.070-1.343, P=0.002). When anatomical and functional stenosis severities were included in the model, a high pullback pressure gradient (PPG) index ( HR=1.572, 95% CI: 1.052-2.351, P=0.027) emerged as an independent predictor of VOCO. Multivariable logistic regression analysis revealed that a low PPG index( OR=2.851, 95% CI: 1.945-4.178, P<0.001) was an independent predictor of QFR≤0.80 without long-term VOCO. Conclusion:In patients with STEMI, localized hemodynamic patterns of coronary artery stenosis, characterized by high dQFR/dsmax and high PPG index, are associated with long-term VOCO.