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.

Eight compounds were isolated and purified from the ethyl acetate part of 70% acetone extract of Rehmannia glutinosa by various chromatographic techniques such as silica gel, MCI gel CHP-20, ODS, Toyopearl HW-40C, combined with TLC and semi-preparative HPLC. Their structures were elucidated by modern spectroscopy techniques (NMR, MS, UV, IR), and identified as neomartynoside A (1), osmanthuside B₆ (2), martynoside (3), isomartynoside (4), (E)-p-hydroxycinnamic acid (5), caffeic acid (6), ferulic acid (7), and methyl caffeate (8). Compound 1 is a new phenylethanol glycoside, which was identified as neomartynoside A. Compound 2 was isolated from Rehmannia glutinosa for the first time. In addition, compounds 2, 6 and 7 significantly increased relative glucose consumption, showing potential hypoglycemic activity.

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.

This study aims to explore the medication rules and mechanisms of treating chronic renal failure(CRF) by Jinling medical school based on data mining, network pharmacology, and experimental validation systematically and deeply. Firstly, the study selected the papers published by the inherited clinicians in Jinling medical school in Chinese journals using the subject headings named "traditional Chinese medicine(TCM) + chronic renal failure", "TCM + chronic renal inefficiency", or "TCM + consumptive disease" in China National Knowledge Infrastructure, Wanfang, and VIP Chinese Science and Technology Periodical Database and screened TCM formulas for treating CRF according to inclusion and exclusion criteria. The study analyzed the frequency of use of single TCM and the four properties, five tastes, channel tropism, and efficacy of TCM used with high frequency and performed association rule and clustering analysis, respectively. As a result, a total of 215 TCM formulas and 235 different single TCM were screened, respectively. The TCM used with high frequency included Astragali Radix, Rhei Radix et Rhizoma, Salviae Miltiorrhizae Radix et Rhizoma, Poria, and Atractylodis Macrocephalae Rhizoma(top 5). The single TCM characterized by "cold properties, sweet flavor, and restoring spleen channel" and the TCM with the efficacy of tonifying deficiency had the highest frequency of use, respectively. Then, the TCM with the rules of "blood-activating and stasis-removing" and "diuretic and dampness-penetrating" appeared. In addition, the core combination of TCM [(Hexin Formula, HXF)] included "Astragali Radix, Rhei Radix et Rhizoma, Poria, Salviae Miltiorrhizae Radix, and Angelicae Sinensis Radix". The network pharmacology analysis showed that HXF had 91 active compounds and 250 corresponding protein targets including prostaglandin-endoperoxide synthase 2(PTGS2), PTGS1, sodium voltage-gated channel alpha subunit 5(SCN5A), cholinergic receptor muscarinic 1(CHRM1), and heat shock protein 90 alpha family class A member 1(HSP90AA1)(top 5). Gene Ontology(GO) function analysis revealed that the core targets of HXF predominantly affected biological processes, cellular components, and molecular functions such as positive regulation of transcription by ribonucleic acid polymerase Ⅱ and DNA template transcription, formation of cytosol, nucleus, and plasma membrane, and identical protein binding and enzyme binding. Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis revealed that CRF-related genes were involved in a variety of signaling pathways and cellular metabolic pathways, primarily involving "phosphatidylinositol 3-kinase(PI3K)-protein kinase B(Akt) pathway" and "advanced glycation end products-receptor for advanced glycation end products". Molecular docking results showed that the active components in HXF such as isomucronulatol 7-O-glucoside, betulinic acid, sitosterol, and przewaquinone B might be crucial in the treatment of CRF. Finally, a modified rat model with renal failure induced by adenine was used, and the in vivo experimental confirmation was performed based on the above-mentioned predictions. The results verify that HXF can regulate mitochondrial autophagy in the kidneys and the PI3K-Akt-mammalian target of rapamycin(mTOR) signaling pathway activation at upstream, so as to alleviate renal tubulointerstitial fibrosis and then delay the progression of CRF.
Data Mining ; Drugs, Chinese Herbal/chemistry* ; Network Pharmacology ; Humans ; Kidney Failure, Chronic/metabolism* ; Medicine, Chinese Traditional ; China

Image 1.

This article systematically summarizes the clinical experience of Professor Yang Jun,a nationally renowned traditional Chinese medicine(TCM)physician,in applying refined direct moxibustion(applying a moxibustion pen made by Chinese medical extract)to treat functional dyspepsia(FD)of the stuffiness-fullness type.Based on decades of clinical practice,Professor Yang innovatively established a moxibustion therapy system for FD,which centers on TCM syndrome differentiation and treatment.The system emphasizes the refined identification of epigastric stuffiness and fullness syndrome,particularly focusing on the relative significance of abdominal distension and poor appetite.Its therapeutic features lie in establishing the principle of"prioritizing mind regulation while holistically harmonizing body and spirit",combined with personalized moxibustion dosage control and a unique refined direct moxibustion technique.By optimizing the configuration of each step in moxibustion therapy,the system maximizes therapeutic efficacy,providing novel theoretical foundations and clinical strategies for moxibustion treatment of stuffiness-fullness type of FD.

This article introduces Professor Yang Jun's clinical experience in treating bronchial asthma using warming needle moxibustion via the governor vessel-unblocking and conception vessel-regulating method.Professor Yang posits that asthma pathogenesis-whether triggered by internal imbalances or external pathogens-ultimately stems from yin-yang disharmony leading to rebellious lung qi and impaired diffusion/descending functions.Thus,restoring dynamic yin-yang balance constitutes the core therapeutic principle.As the governor and conception vessels govern the body's yin-yang regulation,Professor Yang's decades of clinical practice substantiate that"harmonizing these vessels determines life's vitality".His protocol combines warming needle moxibustion with press needles to activate governor-conception vessel functions,achieving five therapeutic effects:(1)yin-yang harmonization,(2)qi movement regulation,(3)meridian unblocking,(4)visceral stabilization,and(5)pathogen elimination,demonstrating remarkable efficacy.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.

Objective To develop an intelligent model for differential diagnosis of atrioventricular nodal re-entrant tachycardia(AVNRT)and atrioventricular re-entrant tachycardia(AVRT)using 12-lead wearable electrocardiogram devices.Methods A total of 356 samples of 12-lead supraventricular tachycardia(SVT)electrocardiograms recorded by wearable devices were randomly divided into training and validation sets using 5-fold cross validation to establish the intelligent classification model,and 101 patients with the diagnosis of SVT undergoing electrophysiological studies and radiofrequency ablation from October,2021 to March,2023 were selected as the testing set.The changes in electrocardiogram parameters before and during induced tachycardia were compared.Based on multiscale deep neural network,an intelligent diagnosis model for classifying SVT mechanisms was constructed and validated.The 3-lead electrocardiogram signals from Ⅱ,Ⅲ,and V1 were extracted to build new classification models,whose diagnostic efficacy was compared with that of the 12-lead model.Results Of the 101 patients with SVT in the testing set,68 were diagnosed with AVNRT and 33 were diagnosed with AVRT by electrophysiological study.The pre-trained model achieved a high area under the precision-recall curve(0.9492)and F1 score(0.8195)for identifying AVNRT in the validation set.The total F1 scores of the lead Ⅱ,Ⅲ,V1,3-lead and 12-lead intelligent diagnostic models in the testing set were 0.5597,0.6061,0.3419,0.6003 and 0.6136,respectively.Compared with the 12-lead classification model,the lead-Ⅲ model had a net reclassification index improvement of-0.029(P=0.878)and an integrated discrimination index improvement of-0.005(P=0.965).Conclusion The intelligent diagnostic model based on multiscale deep neural network using wearable electrocardiogram devices has an acceptable accuracy for classifying SVT mechanisms.