PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

The leaves of sea buckthorn(Hippophae rhamnoides), considered as common food raw materials, have records of medicinal use and diverse pharmacological activities, showing a potential medicinal value. However, the active substances in the sea buckthorn leaves and their mechanisms of action remain unclear. In addition, due to the extensive source and large variety variations, the quality evaluation criteria of sea buckthorn leaves remain to be developed. To solve the problems, this study predicted the main active components, core targets, key pathways, and potential pharmacological effects of sea buckthorn leaves by network pharmacology and molecular docking. Furthermore, ultra-performance liquid chromatography with diode-array detection(UPLC-DAD) was employed to determine the content of active components and establish the chemical fingerprint, on the basis of which the quality markers of sea buckthorn leaves were predicted and then verified by the enzyme activity inhibition method. The results indicated that sea buckthorn leaves had potential therapeutic effects on a variety of digestive tract diseases, metabolic diseases, tumors, and autoimmune diseases, which were consistent with the ancient records and the results of modern pharmacological studies. The core targets of sea buckthorn leaves included PTPN11, AKT1, PIK3R1, ESR1, and SRC, which were mainly involved in the PI3K-AKT, MAPK, and HIF-1 signaling pathways. In conclusion, the active components of sea buckthorn leaves are associated with the rich flavonoids and tannins, among which quercitrin, narcissoside, and ellagic acid can be used as the quality markers of sea buckthorn leaves. The findings provide a reference for the quality control and further development and utilization of sea buckthorn leaves as medicinal materials.
Hippophae/chemistry* ; Network Pharmacology ; Molecular Docking Simulation ; Phosphatidylinositol 3-Kinases/metabolism* ; Flavonoids/analysis* ; Fruit/chemistry*

Aim To explore the mechanism of process¬ing and increasing efficiency of Arisaematis rhizomz preparatum. Methods UPLC-Q-TOF-MS/MS tech¬nology was used to detect the chemical components be¬fore and after processing of Arisaematis rhizomz prepara¬tum, and its mechanism of action was analysed in the treatment of 44 asthma and phlegm " by using network pharmacology. A rat model of allergic asthma was es- tablished to compare the efficacy of Arisaematis rliizoma before and after processing. Results A total of 27 chemical components were identified, among which cur- cumin ,6-gingerol and other components increased after processing. Combined with the database prediction, the action mechanism of the 36 chemical components in the treatment of 44 asthma and phlegm" diseases was dis¬cussed and predicted through network pharmacology. The results of animal experiments showed that the effect of processed Arisaematis rhizoma on allergic asth¬ma was better than that of Arisaematis rhizoma, but there was no significant difference. Conclusions The addition of curcumin, 6-gingerol, camphor, demethyl- curcumin and other components after the processed Ari¬saematis rhizomz preparatum may be the reason for the synergistic effect of Arisaematis rhizomz preparatum in the treatment of allergic asthma.

Objective: To evaluate the feasibility and effectiveness of the application of internet-based HIV testing in men who have sex with men (MSM) in practical application and provide evidence for its application in the future. Methods: MSM who visited the internet-based intervention platform for at least one time from June to December 2020 were selected for the study. The information about platform visit, the number of self-test kits provided, the basic characteristics of the MSM and their satisfactory level were collected. And multivariable logistic regression analyses were conducted to identify the potential factors associated with the reporting of self-test results. Results: By the end of December 31^th, 2020, a total of 132 267 platform visits had been recorded, and 3 511 HIV self-test kits had been provided upon the MSM's requests, and 3 237 MSM (92.2%) reported self-test results. The HIV positive rate was 2.4% (69/2 855) and the confirmation rate of positive HIV test results was 86.7% (52/60). The MSM who asked for self-test kits online were mainly aged ≤30 years, had education level of college or above, and found their sexual partners through internet or dating software. Multivariable logistic regression analyses showed that repeat of online HIV self-test kits application (OR=3.50,95%CI:2.10-5.83), guarantee deposit of 50 yuan at application (OR=2.55,95%CI:1.33-4.89), monthly economic income 1-3 000 yuan (OR=1.54,95%CI:1.05-2.28) or no income (OR=1.71,95%CI:1.20-2.42) and online sexual partners finding (OR=1.49,95%CI:1.13-1.95) were associated with higher reporting rate of self-test results. The satisfactory rate the MSM to the service of platform was 99.5% (217/218). Conclusions: The study confirmed the feasibility and effectiveness of internet-based intervention for HIV tests in MSM, which could promote the self-test of HIV in MSM and facilitate the early detection of HIV infection through social media platforms and multi-channel promotion.
Male ; Humans ; Homosexuality, Male ; HIV Infections/prevention & control* ; Feasibility Studies ; Sexual and Gender Minorities ; HIV Testing ; Internet

Medicinal plants have provided numerous medicinal active ingredients for thousands of years and these ingredients have been used in Chinese medicine (CM) and traditional pharmacologies worldwide. Recently, the exploitation and utilisation of medicinal plant resources has increased significantly. The results of the studies have led to the identification of many active components, such as steroidal alkaloids, saponins, terpenoids, and glycosides, in various medicinal plants with different evolutionary levels. Moreover, research on the chemical classification, molecular phylogeny, and pharmacological activity of medicinal plants is increasing in popularity. Pharmacophylogeny is an interdisciplinary topic that studies the correlation between plant phylogeny, chemical composition, and curative effects (pharmacological activity and the traditional curative effect) of medicinal plants. In addition, it provides the basic tools to enable research and development of CM resources. This literature review, based on the genetic relationship between phytogroup and species, highlights the formation process, research content, applications, and future directions of pharmacophylogeny.
Alkaloids ; Glycosides ; Plant Extracts/chemistry* ; Plants, Medicinal/chemistry* ; Saponins ; Terpenes

Although there is guidance from different regulatory agencies, there are opportunities to bring greater consistency and stronger applicability to address the practical issues of establishing and operating a data monitoring committee (DMC) for clinical studies of Chinese medicine. We names it as a Chinese Medicine Data Monitoring Committee (CMDMC). A panel composed of clinical and statistical experts shared their experience and thoughts on the important aspects of CMDMCs. Subsequently, a community standard on CMDMCs (T/CACM 1323-2019) was issued by the China Association of Chinese Medicine on September 12, 2019. This paper summarizes the key content of this standard to help the sponsors of clinical studies establish and operate CMDMCs, which will further develop the scientific integrity and quality of clinical studies.

Objective To evaluate the efficiency of a recombinase-aided amplification (RAA) assay for the detection of Schistosoma japonicum infections in Oncomelania hupensis snails. Methods A group test was employed. Fifty Oncomelania snails were collected as a detection sample. The detection samples without infected snails were designated as negative specimens, while the detection samples that contained different numbers of infected snails were designated as positive specimens. A total of 10 negative specimens, 10 positive specimens containing 1 infected snail, 20 positive specimens containing 2 infected snails and 10 positive specimens containing 3 infected snails were assigned. Following random grouping, 40 specimens were subject to the florescent RAA assay using a blind method. The miradium shedding method served as a gold standard, and the sensitivity, specificity, Youden’s index and coincidence rate of the florescent RAA assay were estimated. In addition, 20 samples consisted of 5 negative specimens and 15 positive specimens with 1, 2 and 3 infected snails respectively were grouped randomly. The same specimens were detected using the crushing method and fluorescent RAA assay with the blind method in a paired-design manner. Then, the test results were compared and analyzed. Results Florescent RAA assay detected 29 positives in the 30 specimens containing different numbers of infected snails, with a sensitivity of 96.67%, and 8 negatives in the 10 detection specimens without infected snails, with a specificity of 80.00%, showing a Youden’s index of 0.77. The coincidence rate was 100% among 10 repeated assays for a detection specimen. In addition, there was no significant difference in the detection of infected snails between the florescent RAA assay and the crushing method (χ² = 0, P > 0.05), and the actual coincidence rates of the florescent RAA assay and crushing method were 95.00% (19/20) and 90.00% (18/20) with the real results, respectively. Conclusion Fluorescent RAA assay has a favorable efficiency for the detection of S. japonicum infections in Oncomelania snails, which shows a potential in screening of S. japonicum-infected Oncomelania snails.

Objective:To investigate the relationship of triglyceride (TG), fasting blood glucose (FPG) and triglyceride glucose product index (TyG) with the incidence of hypertension, and provide basic data for the prevention and treatment of hypertension in the population.Methods:A total of 23 581 individuals who met the research criteria in Jinchang cohort were selected as the research subjects, the Cox proportional hazard model was used to analyze the relationship of TG, FPG, and TyG with the risk of hypertension. A stratified analysis was conducted by sex.Results:After adjusting for confounding factors, compared with the normal TG group, the HR(95% CI) of the elevated TG margin group and the elevated group were 1.16 (1.01-1.34) and 1.49 (1.30-1.70), respectively in the total population. Among men, they were 1.13 (1.01-1.27) and 1.17 (1.06-1.30), and among women, they were 1.05 (0.88-1.26) and 1.06 (0.88-1.28). Compared with the normal FPG group, the HR (95% CI) of the FPG-impaired group were 1.29 (1.13-1.48) in the total population, 1.26 (1.08-1.48) in men and 1.59 (1.14-2.21) in women. Taking the lowest quartile array as a reference, the HR (95% CI) of the highest quartile array of TyG was 1.73 (1.45-2.07) in the total population, 1.32 (1.14-1.53) in men and 1.87 (1.37-2.54) in women. TG, FPG had a nonlinear dose-response relationship with the risk of hypertension, while TyG had a linear correlation with the risk of hypertension. Conclusions:Higher TG, FPG, and TyG levels are independent risk factors for the incidence of hypertension. People with higher TG, FPG and TyG are at high risk for hypertension, to which close attention should be paid in the prevention and treatment of hypertension.

Objective:To explore the relationship between lipid indicators and the incidence of diabetes, and to compare the diabetes prediction and identification power of traditional lipid combined lipid indicators, in order to explore the best alternative indicators for identifying and predicting diabetes.Methods:Based on the Jinchang cohort, a nested case-control study was conducted in 1 025 new cases of diabetes after excluding patients with malignant tumor and related endocrine, circulatory system disease, then an age (±2 years), gender matched 1∶1 control group of 1 025 cases was set to analyze the relationship between the incidence of diabetes and lipid parameters.Results:Among the traditional lipid parameters, the fourth quartile of TG, TC, and LDL-C indicated higher risks of developing diabetes, which was 14.00 times (95% CI: 9.73-20.15), 2.15 times (95% CI: 1.65-2.79) and 1.66 times (95% CI: 1.29-2.14) than that of the first quartile, respectively. The risk of developing diabetes indicated by the fourth quartile of HDL-C was 0.21 times than that indicated by the first quartile (95% CI: 0.15-0.28). In the combined lipid parameters, the fourth quartile of TG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C indicated higher risks of developing diabetes, which was 14.86 times (95% CI: 10.35-21.34), 8.12 times (95% CI: 5.94-11.01), 5.85 times (95% CI:4.34-7.88) and 5.20 times (95% CI: 3.85-7.03) than that indicated by the first quartile, respectively. The areas under the ROC curve of TG, TC, HDL-C, LDL-C, TG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C were 0.76 (95% CI: 0.74-0.78), 0.59 (95% CI: 0.57-0.61), 0.67 (95% CI: 0.65-0.69), 0.57 (95% CI: 0.55-0.59), 0.77 (95% CI: 0.75-0.78), 0.73 (95% CI: 0.71-0.75), 0.69 (95% CI: 0.67-0.71) and 0.66 (95% CI: 0.64-0.68), respectively. The optimal diabetes predicting point cuts of TG, TC, HDL-C, LDL-C, TG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C were 1.40, 4.70, 1.28, 3.25, 1.17, 3.43, 2.46, and 3.58 mmol/L, respectively. Conclusions:Lipid metabolic disorder is a risk factor for diabetes. TG and TG/HDL-C are the good lipid metabolism indicators for the prediction of diabetic.

Objective:To explore the relationship of body mass index and blood pressure with the incidence of diabetes in Jinchang cohort.Methods:We designed a nested case-control study, a total of 29 572 workers who had no history of diabetes in baseline survey in Jinchang cohort were selected as the study cohort from June 2011 to December 2013. After 2 year follow-up, 1 021 workers with first diagnosed diabetes were selected as the case group, after 1∶1 matching according to the same gender and age ±2 years among those without diabetes, circulatory system, or endocrine system diseases during the same follow-up period, 1 021 controls was selected and 2 042 subjects were finally included. We used multivariate conditional logistic regression model, additive interaction model and multiplicative interaction model to explore the relationship of body mass index and blood pressure with the incidence of diabetes.Results:After adjusting for factors such as occupation, alcohol use, family history of diabetes, hyperuricemia, hypercholesterolemia, hypertriglyceridemia, low-HDL cholesterolemia and high-LDL cholesterolemia, multivariate conditional logistic regression analysis showed that the risk of diabetes increased with body mass index and blood pressure. Hypertension and overweight/obesity had a multiplicative interaction on the incidence of diabetes. The risks of diabetes in men and women with hypertension and overweight/obese were 2.04 times (95% CI: 1.54-2.69) and 3.88 times (95% CI: 2.55-5.91) higher than those in men and women with normal body weight and blood pressure, respectively. In the combination of BMI and blood pressure, obese individuals with SBP≥160 mmHg were 4.57 times (95% CI: 2.50-8.34) more likely to have diabetes than those with normal BMI and SBP, obese individuals with DBP≥90 mmHg were 3.40 times (95% CI: 2.19-5.28) more likely to have diabetes than those with normal BMI and DBP. Conclusions:Overweight/obesity and hypertension can increase the risk of diabetes. Health education about body weight and blood pressure controls should be strengthened to reduce the risk of diabetes.