This study aims to explore the mechanism of Huanglian Jiedu Decoction(HJD) in treating acute liver injury(ALI) in the mouse model of sepsis induced by lipopolysaccharide(LPS). Fifty-four male C57BL/6 mice were randomized into six groups: blank group, model group, low-, medium-, and high-dose group HJD, and dexamethasone group. The mouse model of sepsis was established by intraperitoneal injection of LPS after 7 days of gavage with HJD, and dexamethasone(0.2 mL) was injected intraperitoneally 1.5 h after modeling. The murine sepsis score(MSS) was recorded 12 h after modeling. The levels of alanine aminotransferase(ALT) and aspartate aminotransferase(AST) in the liver tissue and tumor necrosis factor-α(TNF-α) and interleukin-6(IL-6) in the serum were measured by ELISA. Hematoxylin-eosin(HE) staining was used to observe the pathological changes of the mouse liver. The content of light chain 3 of microtubule-associated protein 1(LC3) was detected by immunofluorescence, and that of sirtuin 1(SIRT1) was detected by immunohistochemistry. The mRNA levels of adenosine 5'-monophosphate-activated protein kinase(AMPK), LC3, and P62 were detected by RT-PCR. Western blot was employed to determine the protein levels of AMPK, p-AMPK, and SIRT1 in the liver tissue. The results showed that compared with model group, drug interventions decreased the MSS and liver injury indicators, lowered the levels of inflammatory cytokines, improved the liver tissue structure, upregulated the protein levels of of p-AMPK/AMPK and SIRT1 and the mRNA levels of AMPK and LC3, and downregulated the mRNA level of P62. To sum up, HJD can regulate the autophagy level and reduce inflammation to ameliorate acute liver injury in septic mice by activating the AMPK/SIRT1 autophagy pathway.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Sirtuin 1/genetics* ; Male ; Mice ; Sepsis/metabolism* ; Mice, Inbred C57BL ; Autophagy/drug effects* ; AMP-Activated Protein Kinases/genetics* ; Liver/metabolism* ; Humans ; Signal Transduction/drug effects* ; Disease Models, Animal ; Tumor Necrosis Factor-alpha/genetics*

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Background: and Purpose This study aimed to investigate the association between residual inflammatory risk (RIR) and vulnerable plaques using high-resolution magnetic resonance imaging (HRMRI) in symptomatic intracranial atherosclerotic stenosis (ICAS). Methods: This retrospective study included 70%–99% symptomatic ICAS patients hospitalized from January 2016 to December 2022. Patients were classified into four groups based on high-sensitivity C-reactive protein (hs-CRP) and low-density lipoprotein cholesterol (LDL-C): residual cholesterol inflammatory risk (RCIR, hs-CRP ≥3 mg/L and LDL-C ≥2.6 mmol/L), RIR (hs-CRP ≥3 mg/L and LDL-C <2.6 mmol/L), residual cholesterol risk (RCR, hs-CRP <3 mg/L and LDL-C ≥2.6 mmol/L), and no residual risk (NRR, hs-CRP <3 mg/L and LDL-C <2.6 mmol/L). Vulnerable plaque features on HRMRI included positive remodeling, diffuse distribution, intraplaque hemorrhage, and strong enhancement. Results: Among 336 included patients, 21, 60, 58, and 197 were assigned to the RCIR, RIR, RCR, and NRR groups, respectively. Patients with RCIR (adjusted odds ratio [aOR], 3.606; 95% confidence interval [CI], 1.346–9.662; P=0.011) and RIR (aOR, 3.361; 95% CI, 1.774–6.368, P<0.001) had higher risks of strong enhancement than those with NRR. Additionally, patients with RCIR (aOR, 2.965; 95% CI, 1.060–8.297; P=0.038) were more likely to have intraplaque hemorrhage compared with those with NRR. In the sensitivity analysis, RCR (aOR, 2.595; 95% CI, 1.201–5.608; P=0.015) exhibited an additional correlation with an increased risk of intraplaque hemorrhage. Conclusion In patients with symptomatic ICAS, RIR is associated with a higher risk of intraplaque hemorrhage and strong enhancement, indicating an increased vulnerability to atherosclerotic plaques.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:The aim of this study is to evaluate the clinical efficacy of Oxalicao Formula combined with transacu-puncture in the treatment of chronic nonbacterial prostatitis(CNP)characterized by dampness-heat stasis.Methods:A total of 70 patients diagnosed with CNP and characterized by dampness-heat stasis were randomly divided into control group and treatment group,with 35 cases in each group.The patients in control group received Qianlie Beixi capsules.While the patients in treatment group were administered with oxalis decoction in conjunction with acupuncture therapy which lasted for 8 weeks.Pre-and post-treatment evalua-tions for NIH-Chronic Prostatitis Symptom Index(NIH-CPSI),Traditional Chinese Medicine(TCM)symptom scores,urodynamic pa-rameters,immune cell subsets and inflammatory factors were performed.Results:Ultimately,65 patients completed the study with 33 in the treatment group and 32 in the control group.After 8 weeks of intervention,the patients in both of groups demonstrated signifi-cant improvements(P＜0.05).Specifically,remarkable reductions in the NIH-CPSI total score including pain score,urination score,quality of life impact score,TCM symptom score and inflammatory cytokine levels were observed.Additionally,there were upward trends in maximum and average urinary flow rates as well as the CD4+/CD8+ratio of immune cells(P＜0.05).Compared to the con-trol group,the treatment group exhibited superior outcomes in reducing the NIH-CPSI total score,pain score,urination score,quality of life impact score,TCM symptom score,and inflammatory cytokine levels,and increasing in CD4+/CD8+ratios,maximum and av-erage urine flow rates(P＜0.05).Conclusion:The combination of Oxalicao Formula and transacupuncture for treating CNP charac-terized by dampness-heat stasis demonstrates significant therapeutic benefits,which has considerable clinical application value.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective This article aims to investigate the association between hypertension and the risk of GSD by conducting a national multicenter study,a systematic review,and a meta-analysis.Methods The study was conducted in three stages.In the first stage,subjects were recruited for health examination in four hospitals in Chengdu,Tianjin,Beijing,and Chongqing,China,from 2015 to 2020,and the multivariate logistic regression analysis was used to investigate the association between hypertension and the risk of GSD in each center.In the second stage,Embase,PubMed,Wanfang Data,VIP,and CNKI databases were searched for related studies published up to May 2021,and a meta-analysis was conducted to further verify such association.In the third stage,the random effects model was used for pooled analysis of the results of the multicenter cross-sectional study and the findings of previous literature.Results A total of 633 948 participants were enrolled in the cross-sectional study,and the prevalence rate of GSD was 7.844%.The multivariate logistic regression analysis showed that hypertension was positively associated with the risk of GSD(P＜0.05).Subgroup analysis showed that there was no significant difference in the association between hypertension and GSD between individuals with different sexes,ages,and subtypes of GSD.A total of 80 articles were included in the systematic review and the meta-analysis,and the results showed that the risk of GSD was increased by 1.022 times for every 10 mmHg increase in diastolic pressure and 1.014 times for every 10 mmHg increase in systolic pressure.Conclusion Hypertension significantly increases the risk of GSD,and the findings of this study will provide a basis for the etiology of GSD and the identification of high-risk groups.

The main sources of natural drugs include various biological species such as plants, animals, and microorganisms. The accurate identification of these species is the bedrock of natural drug development. We propose a novel method of species identification in this paper: analysis of whole-genome (AGE), a molecular diagnostic method used to identify species by finding species-specific sequences from the whole genome and precisely recognizing the specific target sequences. We elaborate that the principle for species identification based on AGE is that the genome sequences of diverse species must differ and divide the implementation strategy of the method into two levels of research and application. Based on our analysis of its characteristics, the method would have the potential advantages of reliable principle, high specificity, and wide applicability. Moreover, three crucial concerns related to building method systems including genome acquisition, bioinformatics analysis, and database construction, are further discussed. In summary, we offer theoretical underpinnings and methodological guidance for the development of bioinformatics software and commercial kits, indicating AGE has great application potential in objects, subjects, and industries.

Objectives: To examine the influence of adjuvant chemotherapy after radical resection on the survival of patients with intrahepatic cholangiocarcinoma(ICC) and to identify patients who may benefit from it. Methods: The clinical and pathological data of 654 patients with ICC diagnosed by postoperative pathology from December 2011 to December 2017 at 13 hospitals in China were collected retrospectively. According to the inclusion and exclusion criteria,455 patients were included in this study,including 69 patients (15.2%) who received adjuvant chemotherapy and 386 patients (84.8%) who did not receive adjuvant chemotherapy. There were 278 males and 177 females,with age of 59 (16) years (M(IQR))(range:23 to 88 years). Propensity score matching (PSM) method was used to balance the difference between adjuvant chemotherapy group and non-adjuvant chemotherapy group. Kaplan-Meier method was used to plot the survival curve,the Log-rank test was used to compare the difference of overall survival(OS) and recurrence free survival(RFS)between the two groups. Univariate analysis was used to determine prognostic factors for OS. Multivariate Cox proportional hazards models were then performed for prognostic factors with P<0.10 to identify potential independent risk factors. The study population were stratified by included study variables and the AJCC staging system,and a subgroup analysis was performed using the Kaplan-Meier method to explore the potential benefit subgroup population of adjuvant chemotherapy. Results: After 1∶1 PSM matching,69 patients were obtained in each group. There was no significant difference in baseline data between the two groups (all P>0.05). After PSM,Cox multivariate analysis showed that lymph node metastasis (HR=3.06,95%CI:1.52 to 6.16,P=0.039),width of resection margin (HR=0.56,95%CI:0.32 to 0.99,P=0.044) and adjuvant chemotherapy (HR=0.51,95%CI:0.29 to 0.91,P=0.022) were independent prognostic factors for OS. Kaplan-Meier analysis showed that the median OS time of adjuvant chemotherapy group was significantly longer than that of non-adjuvant chemotherapy group (P<0.05). There was no significant difference in RFS time between the adjuvant chemotherapy group and the non-adjuvant chemotherapy group (P>0.05). Subgroup analysis showed that,the OS of female patients,without HBV infection,carcinoembryonic antigen<9.6 μg/L,CA19-9≥200 U/ml,intraoperative bleeding<400 ml,tumor diameter>5 cm,microvascular invasion negative,without lymph node metastasis,and AJCC stage Ⅲ patients could benefit from adjuvant chemotherapy (all P<0.05). Conclusion: Adjuvant chemotherapy can prolong the OS of patients with ICC after radical resection,and patients with tumor diameter>5 cm,without lymph node metastasis,AJCC stage Ⅲ,and microvascular invasion negative are more likely to benefit from adjuvant chemotherapy.