ObjectiveTo investigate the mechanism of liver injury induced by tripterygium glycosides tablets （TG） and the molecular mechanism of compound glycyrrhizin tablets （CG） in alleviating the abnormalities of cholesterol metabolism caused by TG via cholesterol metabolism. MethodsAccording to the body weights， male Sprague-Dawley （SD） rats were randomly grouped as follows： control （pure water）， low-dose TG （TG-L， 189.0 mg·kg^-1·d^-1）， high-dose TG （TG-H， 472.5 mg·kg^-1·d^-1）， TG-L+CG （189.0 mg·kg^-1·d^-1 TG + 20.25 mg·kg^-1·d^-1 CG）， and TG-H+CG （472.5 mg·kg^-1·d^-1 TG + 20.25 mg·kg^-1·d^-1 CG）， with 6 rats in each group. Rats were administrated with corresponding drugs once daily for 3 weeks. At the end of the last administration， the mRNA and protein levels of liver X receptor-alpha （LXR-α）， low-density lipoprotein receptor （LDLR）， adenosine triphosphate-binding cassette transporter A1 （ABCA1）， adenosine triphosphate-binding cassette transporter G1 （ABCG1）， 3-hydroxy-3-methylglutaryl coenzyme A reductase （HMGCR）， cholesterol 7α-hydroxylase （CYP7A1）， cholesterol 12α-hydroxylase （CYP8B1）， and sterol 27-hydroxylase （CYP27A1） in the liver tissue were determined by Real-time PCR and Western blotting， respectively. The level of 3-hydroxy-3-methylglutaryl coenzyme A reductase （HMG-CoAR）， a regulatory enzyme of cholesterol synthesis， was measured by enzyme-linked immunosorbent assay （ELISA）. HepG2 cells were used to observe the effect of TG on the cell proliferation in vitro. Specifically， HepG2 cells were grouped as follows： Low-dose TG （TG-l， 15 mg·L^-1）， medium-dose TG （TG-m， 45 mg·L^-1）， high-dose TG （TG-h， 135 mg·L^-1）， fenofibrate （FB， 10 μmol·L^-1）， CG extract， TG-h+FB （135 mg·L^-1 TG + 10 μmol·L^-1 FB）， TG-m+FB （45 mg·L^-1 TG + 10 μmol·L^-1 FB）， TG-l+FB （15 mg·L^-1 TG + 10 μmol·L^-1 FB）， TG-h+CG （135 mg·L^-1 TG + 60 μmol·L^-1 CG）， TG-m+CG （45 mg·L^-1 TG + 60 μmol·L^-1 CG）， and TG-l+CG （15 mg·L^-1 TG + 60 μmol·L^-1 CG）. The mRNA and protein levels of LXR-α， ABCG1， LDLR， CYP7A1， CYP8B1， and CYP27A1 in HepG2 cells were determined by Real-time PCR and Western blotting， respectively. ResultsThe rat experiment showed that compared with the control group， the TG-H group showed down-regulated mRNA levels of CYP7A1， CYP8B1， and CYP27A1 in the liver tissue （P<0.05， P<0.01）， which were up-regulated by the application of CG （P<0.05， P<0.01）， and the TG-H+CG group showed up-regulated mRNA level of LDLR （P<0.01）. Compared with the control group， the TG-L and TG-H groups showed down-regulated protein levels of LDLR， CYP7A1， and CYP8B1 in the liver tissue （P<0.05， P<0.01）. In addition， the protein levels of ABCG1 and LXR-α were down-regulated in the TG-H and TG-L groups， respectively （P<0.05）. Compared with TG alone， TG+CG up-regulated the protein levels of ABCG1 and LDLR （P<0.05， P<0.01）， and the protein levels of CYP7A1 and CYP8B1 in the TG-H+CG group were up-regulated （P<0.05， P<0.01）. The cell experiment showed that compared with the control group， the TG-h group presented up-regulated mRNA level of LXR-α （P<0.01）， and the TG-m and TG-h groups showcased down-regulated mRNA levels of LDLR and CYP7A1 （P<0.01） and up-regulated mRNA level of CYP27A1 （P<0.01） in HepG2 cells. The combination of CG with TG restored the above changes （P<0.01）. Western blotting results showed that compared with the control group， the TG-m and TG-h groups showed down-regulated protein levels of LXR-α， ABCG1， LDLR， CYP7A1， CYP8B1， and CYP27A1 in HepG2 cells （P<0.01）. Compared with the TG-h group， the TG-h+CG group showed up-regulated protein level of LDLR （P<0.05）. Compared with the TG-m group， the TG-m+CG group showcased up-regulated protein levels of LDLR， ABCG1， CYP7A1， and CYP27A1 （P<0.05， P<0.01）. ConclusionThe administration of TG at 189.0， 472.5 mg·kg^-1 for 3 weeks could modulate the signaling pathways associated with cholesterol efflux， endocytosis， and cholesterol biotransformation in hepatocytes， leading to the accumulation of cholesterol and subsequent liver injury in rats. CG could ameliorate the liver injury induced by lipid metabolism disorders caused by TG by up-regulating the expression of LXR-α， LDLR， ABCG1， CYP7A1， CYP8B1， and CYP27A1 to promote cholesterol biotransformation.

Cryopreservation is the primary technique for in vitro preservation of allogeneic tissue. However, its success is often hindered by factors such as low temperature, ischemia, and hypoxia. This study investigated the potential of Sini Decoction, known for its antioxidant and anti-apoptotic properties, to reduce cryopreservation-induced injury in rats' sciatic nerves. Sini Decoction was prepared according to the Chinese Pharmacopoeia, and its cytotoxicity on Rsc96 cells was assessed by using the CCK-8 method. Sini Decoction at concentrations of 4, 8, and 16 mg·mL~(-1), termed as low-(SL), medium-(SM), and high-(SH) doses group, was used for cryopreservation of rats' sciatic nerves. A normal control(NC) group and a fresh nerve control(fresh) group were set. Flow cytometry and TUNEL staining were used to detect the apoptosis of neural tissue cells after cryopreservation. Western blot was used to detect the expression of apoptosis-related proteins(Bcl-2, Bax, caspase-3, and caspase-8) and nerve regeneration proteins(NGF and BDNF) in vitro after cryopreservation. Oxidative damage of neural tissue after cryopreservation was evaluated by measuring levels of GSH, SOD, MDA, ROS, and ATP. Cryopreserved nerves were then used for allogeneic transplantation. One week after transplantation, CD4~+ and CD8~+ fluorescent double staining assessed inflammatory cell invasion in the transplanted nerve segment, and ELISA evaluated the expression of serum inflammatory factors(IL-1, IFN-γ, and TNF-α) in recipients. Twenty weeks after transplantation, electrophysiology and NF200 neurofilament staining were used to evaluate nerve regeneration. RESULTS:: showed that Sini Decoction at concentrations of below 32 mg·mL~(-1) exhibited no cytotoxicity to Rsc96 cells. During in vitro nerve cryopreservation, Sini Decoction significantly reduced cell apoptosis, ROS, and MDA production compared to the NC group. In the SH group, the protein expression of NGF and BDNF in vitro, as well as ATP, SOD, and GSH production, were significantly increased. In the rejection reaction one week after transplantation, compared to the fresh nerve transplantation group, the SL and SM groups showed reduced CD4~+ and CD8~+ T cell invasion in the transplanted nerve segment and down-regulated IL-1, IFN-γ, and TNF-α expression in recipient serum. Twenty weeks after transplantation, the electrophysiological test results of CMAP, NCV, and NF200 neurofilament protein fluorescent staining in the SM and SH groups were superior to those in the NC and fresh groups. These findings indicate that Sini Decoction offers protective benefits in the cryopreservation of rats' sciatic nerves and holds significant potential for the in vitro preservation of tissue and organs.
Animals ; Apoptosis/drug effects* ; Rats ; Oxidative Stress/drug effects* ; Sciatic Nerve/cytology* ; Cryopreservation ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Rats, Sprague-Dawley ; Protective Agents/pharmacology*

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

The differences in chemical quality characteristics between Xinjiang Rehmannia glutinosa and R. glutinosa were analyzed to provide a theoretical basis for the introduction and quality control of R. glutinosa. In this study, the high performance liquid chromatography(HPLC) fingerprints of 6 batches of Xinjiang R. glutinosa and 10 batches of R. glutinosa samples were established. The content of iridoid glycosides, phenylethanoid glycosides, monosaccharides, oligosaccharides, and polysaccharides in Xinjiang R. glutinosa and R. glutinosa was determined by high performance liquid chromatography-diode array detection(HPLC-DAD), high performance liquid chromatography-evaporative light scattering detection(HPLC-ELSD), and ultraviolet-visible spectroscopy(UV-Vis). The determination results were analyzed with by chemical pattern recognition and entropy weight TOPSIS method. The results showed that there were 19 common peaks in the HPLC fingerprints of the 16 batches of R. glutinosa, and catalpol, aucubin, rehmannioside D, rehmannioside A, hydroxytyrosol, leonuride, salidroside, cistanoside A, and verbascoside were identified. Hierarchical cluster analysis(HCA) and principal component analysis(PCA) showed that Qinyang R. glutinosa, Mengzhou R. glutinosa, and Xinjiang R. glutinosa were grouped into three different categories, and eight common components causing the chemical quality difference between Xinjiang R. glutinosa and R. glutinosa in Mengzhou and Qinyang of Henan province were screened out by orthogonal partial least squares discriminant analysis(OPLS-DA). The results of content determination showed that there were glucose, sucrose, raffinose, stachyose, polysaccharides, and nine glycosides in Xinjiang R. glutinosa and R. glutinosa samples, and the content of catalpol, rehmannioside A, leonuride, cistanoside A, verbascoside, sucrose, and glucose was significantly different between Xinjiang R. glutinosa and R. glutinosa. The analysis with entropy weight TOPSIS method showed that the comprehensive quality of R. glutinosa in Mengzhou and Qinyang of Henan province was better than that of Xinjiang R. glutinosa. In conclusion, the types of main chemical components of R. glutinosa and Xinjiang R. glutinosa were the same, but their content was different. The chemical quality of R. glutinosa was better than Xinjiang R. glutinosa, and other components in R. glutinosa from two producing areas and their effects need further study.
Rehmannia/classification* ; Drugs, Chinese Herbal/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Quality Control

OBJECTIVE To compare the clinical efficacy and safety of remimazolam and propofol in general anesthesia induction and maintenance for elderly patients undergoing thoracoscopic lobectomy. METHODS A total of 86 elderly lung cancer patients who underwent thoracoscopic lobectomy at Chongqing University Three Gorges Hospital from February to July 2024 were selected and divided into the propofol group and the remimazolam group according to the randomized numerical table method， with 43 cases in each group. During anesthesia induction， patients in the propofol group and the remimazolam group were intravenously administered 2 mg/kg of Propofol medium- and long-chain fat emulsion injection or 0.25 mg/kg of Remimazolam tosilate for injection， respectively； during anesthesia maintenance， the two groups received intravenous infusion of 6-10 mg/（kg·h） of Propofol medium- and long- chain fat emulsion injection or 1-3 mg/（kg·h） of Remimazolam tosilate for injection， respectively. The anesthesia effects， anesthesia-related indicators， intraoperative opioid and muscle relaxant dosages， Ramsay sedation score， numerical rating scale （NRS） score， and hemodynamic parameters were compared between the two groups， and the occurrence of adverse drug reactions was recorded. RESULTS A total of 41 patients in the propofol group and 43 patients in the remimazolam group completed the trial. The proportion of patients with grade Ⅰ anesthesia effect in the remimazolam group was significantly higher than that in the propofol group， while the proportion of patients with grade Ⅱ anesthesia effect was significantly lower than that in the propofol group （P＜0.05）. In this group， the disappearance time of eyelash reflex， the time taken for the bispectral index to drop to 60， and the Ramsay sedation scores （2 and 6 hours after operation） were all significantly prolonged or increased， while the recovery time， NRS scores （2 and 6 hours after operation）， and the incidence of intraoperative hypotension were all significantly shortened or reduced； moreover， the improvements of the above sedation/NRS scores exhibited a time-dependent pattern within 2 to 24 hours after operation （P＜0.05）. Compared with before anesthesia induction （T0）， the heart rate [except at 2 min after medication （T1）， 60 min after anesthesia （T4）， and at the end of surgery （T5） in the remimazolam group] and mean arterial pressure [except at T1 in the remimazolam group] of patients in both groups significantly decreased at T1， 5 min after medication （T2）， at the start of surgery （T3）， T4， and T5 （P＜0.05）. Meanwhile， regional cerebral oxygen saturation significantly increased in both groups. Furthermore， the heart rate and mean arterial pressure of patients in the remimazolam group were significantly higher than those in the propofol group at T1， T2 and T4 （P＜0.05）. No statistically significant differences were observed between the two groups in terms of postanesthesia care unit stay time， dosage of opioids and muscle relaxants， regional cerebral oxygen saturation， or peripheral oxygen saturation at various time points （P＞0.05）. CONCLUSIONS Compared to propofol， remimazolam demonstrates superior anesthesia effects when used for the induction and maintenance of general anesthesia in elderly patients undergoing thoracoscopic lobectomy. It not only provides more stable intraoperative hemodynamics and shortens the postoperative recovery time but also effectively reduces the incidence of intraoperative hypotension.

Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

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.