Objective: To characterize perioperative dynamic changes in immune-cell phenotypes and inflammatory cytokines in patients undergoing CPB (cardiopulmonary bypass) cardiac surgery, and to explore their associations with postoperative outcomes. Methods: In this prospective cohort study, 120 adult patients who underwent elective cardiac surgery under CPB at West China Hospital from May 2022 to March 2023 were enrolled. Perioperative immune-cell phenotypes and concentrations of 40 inflammation-related cytokines were measured. The primary outcomes were the sequential organ failure assessment (SOFA) score at 24 h after surgery and ΔSOFA (the peak SOFA score within 48 h after surgery minus the preoperative SOFA score). Secondary outcomes included major adverse cardiovascular events (MACE), acute kidney injury (AKI), respiratory failure, severe liver injury, and infection. Results: The mean age of enrolled patients was 57±10 years. Of these, 52% (62/120) were male and 90% (108/120) underwent valve surgery. During the rewarming to the end of CPB, neutrophil counts rapidly increased (7.39×10 /L vs preoperative 3.07×10 /L, P<0.001), with significant upregulation of CD11b (7.30×10 /L vs preoperative 3.05×10 /L, P<0.001) and CD54 (7.15×10 /L vs preoperative 2.99×10 /L, P<0.001). Lymphocyte counts increased at the end of CPB (1.75×10 /L vs preoperative 1.12×10 /L, P<0.001) but decreased significantly at 24 h after surgery (0.59×10 /L vs preoperative 1.12×10 /L, P<0.001). Plasma analysis showed that multiple pro-inflammatory cytokines increased during CPB and remained elevated up to 24 h after surgery; five chemokines and the anti-inflammatory cytokine IL-10 peaked at the end of CPB. The SOFA score increased from 1 (1, 2) preoperatively to 7 (5, 10) at 24 h after surgery, with a ΔSOFA of 6 (4, 8). Within 30 days after surgery, 48 patients (40.0%) developed AKI, 17 (14.2%) developed infection, 4 (3.3%) developed severe liver injury, 3 (2.5%) developed respiratory failure, and 3 (2.5%) experienced MACE. During the 2-year follow-up, 8 patients (6.7%) experienced MACE and 5 (4.2%) died. Conclusion: Multi-organ dysfunction is common after cardiac surgery under CPB (median ΔSOFA, 6), accompanied by perioperative activation of multiple immune-cell subsets and upregulation of pro-inflammatory, anti-inflammatory, and chemotactic mediators. This study provides data-driven evidence and research clues for further investigation of the associations between CPB-related immune perturbations and postoperative organ dysfunction and clinical outcomes.

This article systematically analyzes the historical evolution of the name， medicinal parts， origin， harvesting， processing and other aspects of Houttuyniae Herba（HH） by referring to the medical books， prescription books and other documents of the past dynasties， combined with the research materials related to modern and contemporary times， in order to provide a basis for the development of famous classical formulas containing this herb. In ancient literature， HH was often referred to as "Ji" and "Jicai"， the name of "Ji" was first recorded in Mingyi Bielu during the Han and Wei dynasties， and the name of Yuxingcao was first seen in Lyuchanyan Bencao during the southern Song dynasty and has continued to this day. The origin of HH used throughout history is consistent， all of which are the whole herb or aboveground parts of Houttuynia cordata in Saururaceae family. HH recorded throughout history has a wide range of production areas， mostly self-produced self-marketing. In ancient times， fresh HH was often used as medicine by pounding its juice without involving any processing steps. Both fresh and dried products can be used as medicine， the fresh products uses the whole plant， while the dried products uses the aboveground parts， which are cleaned， selected and processed before use. Fresh products are harvested regardless of season， while dried products are harvested in both summer and autumn， with summer as the best. In ancient times， there were no specific requirements for the quality of HH， while in modern times， "intact stems and leaves with a strong fishy smell" are preferred. In addition， the medicinal properties of HH have undergone significant changes from ancient to modern times. In the early period， it was believed that its medicinal property was slightly warm， until the 1977 edition of Chinese Pharmacopoeia officially changed it to slightly cold. Both ancient and modern literature states that HH can be used for the treatment of carbuncle and malignant sores， Lyuchanyan Bencao for the first time introduced HH fresh juice can relieve summer heat， since Diannan Bencao recorded that it can be used for lung carbuncle， and gradually developed into the first choice for the treatment of lung carbuncle. Based on the research results， it is suggested that fresh herb or dried aboveground parts of H. cordata are used as medicine when developing famous classical formulas.

OBJECTIVE To investigate the potential mechanism of Sanqi danshen tablets in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Core targets of Sanqi danshen tablets in the treatment of NAFLD were explored by network pharmacological methods. Gene ontology （GO） functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis were also performed. Based on the results obtained from network pharmacological studies， using SD rats as subjects， the NAFLD model was induced by feeding them high-fat diet. The effects of Sanqi danshen tablets on pathological changes such as lipid droplet vacuoles and lipid accumulation in the liver tissue of NAFLD rats， as well as its impact on relative indicators of lipid metabolism， inflammatory responses and oxidative stress， were investigated. RESULTS A total of 20 core targets for the treatment of NAFLD with Sanqi danshen tablets were screened， primarily involved in GO functions such as biological regulation， cellular membrane and binding， and enriched in signaling pathways related to inflammatory responses， oxidative stress and lipid metabolism. Compared with the model group， lipid droplet vacuoles were reduced significantly in low-dose， medium-dose， high-dose groups of Sanqi danshen tablets and positive control （simvastatin） group， the number of lipid droplets decreased significantly and the color became lighter. The contents of total cholesterol， triglyceride （except for medium- dose group of Sanqi danshen tablets）， aspartate transaminase， alanine transaminase， tumor necrosis factor-α （except for low-dose group of Sanqi danshen tablets）， interleukin-17 （except for Sanqi danshen tablets groups） and malondialdehyde （except for low- dose group of Sanqi danshen tablets） in liver tissue were significantly decreased， while the content of superoxide dismutase was significantly increased （P＜0.01 or P＜0.05）. CONCLUSIONS Sanqi danshen tablets exert anti-inflammatory， antioxidant and lipid metabolism regulating effects by influencing the levels of inflammation， oxidative stress and lipids metabolism-related indicators， thereby improving NAFLD in rats.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

To explore the variation laws of volatile oil during the extraction process of Artemisiae Argyi Folium and its impact on the quality of the medicinal solution, as well as to achieve precise control of the extraction process, this study employed headspace solid phase microextraction gas chromatography-mass spectrometry(HS-SPME-GC-MS) in combination with multiple light scattering techniques to conduct a comprehensive analysis, identification, and characterization of the changes in volatile components and the physical properties of the medicinal solution during the extraction process. A total of 82 volatile compounds were identified using the HS-SPME-GC-MS technique, including 21 alcohols, 15 alkenes, 14 ketones, 9 acids, 6 aldehydes, 5 phenols, 3 esters, and 9 other types of compounds. At different extraction time points(15, 30, 45, and 60 min), 71, 72, 64, and 44 compounds were identified in the medicinal solution, respectively. It was observed that the content of volatile components gradually decreased with the extension of extraction time. Through multivariate statistical analysis, four compounds with significant differences during different extraction time intervals were identified, namely 1,8-cineole, terpinen-4-ol, 3-octanone, and camphor. RESULTS:: from multiple light scattering techniques indicated that at 15 minutes of extraction, the transmittance of the medicinal solution was the lowest(25%), the particle size was the largest(0.325-0.350 nm), and the stability index(turbiscan stability index, TSI) was the highest(0-2.5). With the extension of extraction time, the light transmittance of the medicinal solution improved, stability was enhanced, and the particle size decreased. These laws of physicochemical property changes provide important basis for the control of Artemisiae Argyi Folium extraction process. In addition, the changes in the bioactivity of Artemisiae Argyi Folium extracts during the extraction process were investigated through mouse writhing tests and antimicrobial assays. The results indicated that the analgesic and antimicrobial effects of the medicinal solution were strongest at the 15-minute extracting point. In summary, the findings of this study demonstrate that the content of volatile oil in Artemisiae Argyi Folium extracts gradually decreases with the extension of extraction time, and the variation in volatile oil content directly influences the physicochemical properties and pharmacological efficacy of the medicinal solution. This discovery provides important scientific reference for the optimization of Artemisiae Argyi Folium extraction processes and the development and application of process analytical technologies.
Oils, Volatile/pharmacology* ; Artemisia/chemistry* ; Gas Chromatography-Mass Spectrometry ; Drugs, Chinese Herbal/pharmacology* ; Chlorogenic Acid/pharmacology* ; Solid Phase Microextraction ; Quality Control

OBJECTIVES: To investigate the efficacy and safety of empagliflozin in patients with glycogen storage disease (GSD)-associated inflammatory bowel disease (IBD). METHODS: A cross-sectional study was conducted, enrolling 25 patients with GSD-associated IBD who received empagliflozin treatment. General data, details of empagliflozin use, and adverse events were collected. Clinical symptoms and biochemical parameters before and after empagliflozin therapy were compared. RESULTS: Twenty-five patients with GSD-associated IBD were included, with a median age at diagnosis of 0.7 years, and a mean age at initiation of empagliflozin therapy of (11 ± 6) years. The initial dose of empagliflozin was (0.30 ± 0.13) mg/(kg·d), with a maintenance dose of (0.40 ± 0.21) mg/(kg·d), and a treatment duration of (34 ± 6) months. Seventy-eight percent (18/23) of patients' parents reported that empagliflozin therapy reduced the frequency of infections and oral ulcers, and increased neutrophil counts. Clinically, the number of patients with anorexia decreased from 12 to 5 after treatment, and 30% showed improved appetite (P<0.05). The numbers of patients with diarrhea, mucus/bloody stools, perianal disease, and oral ulcers decreased from 19, 9, 11, and 21 before treatment to 7, 1, 0, and 10 after treatment, respectively (P<0.05). Laboratory findings showed that absolute neutrophil counts increased, while platelet counts, lactate, and uric acid levels decreased significantly after empagliflozin treatment (P<0.05). Adverse reactions occurred in 7 patients (28%) during empagliflozin treatment. Two cases occurred in the treatment initiation phase, presenting as hypotension or profuse sweating with dehydration, along with urinary tract infections (UTIs); empagliflozin was discontinued in both cases. During the maintenance phase, 3 cases of UTIs and 2 cases of hypoglycemia (one with profuse sweating) were reported. CONCLUSIONS Empagliflozin therapy can increase neutrophil counts, reduce the incidence of infections and oral ulcers, alleviate diarrhea and abdominal pain, improve appetite, and ameliorate platelet count, lactate, and uric acid levels in patients with GSD-associated IBD, demonstrating significant clinical benefit. UTIs, hypoglycemia, hypotension, profuse sweating, and dehydration may be potential adverse reactions associated with empagliflozin therapy.
Humans ; Benzhydryl Compounds/adverse effects* ; Male ; Female ; Glucosides/adverse effects* ; Inflammatory Bowel Diseases/etiology* ; Child ; Child, Preschool ; Cross-Sectional Studies ; Adolescent ; Glycogen Storage Disease/drug therapy* ; Infant