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.

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.

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: To investigate the characteristics of hospitalized injury cases in Huangpu District, Guangzhou City in 2022, so as to provide evidence for optimizing injury prevention interventions. Methods: Data on hospitalized injury cases admitted between January to December 2022 were collected through the hospitalization registry system from 17 healthcare institutions in Huangpu District. The population distribution characteristics, causes of injury, injury sites, duration of hospital stay, and hospitalization costs were descriptively analyzed. Results: A total of 6 729 hospitalized injury cases were reported in Huangpu District in 2022, including 4 277 males and 2 452 females, with a male-to-female ratio of 1.74∶1. The average age was (49.57±19.82) years, with 2 064 cases (30.67%) aged 45 to <60 years and 1 921 cases (28.55%) aged ≥60 years. The median length of hospitalization was 9.00 (interquartile range, 11.00) days, with median hospitalization costs of 15 968.93 (interquartile range, 25 786.69) yuan. In the months of June to August, there were more cases of injury hospitalization,with 1 904 cases accounting for 28.30%. The top three causes of injury were falls (2 895 cases, 43.02%), transportation accidents (1 247 cases, 18.53%) and exposure to inanimate mechanical forces (1 104 cases, 16.41%). The top three injured sites were lower limb injuries (1 850 cases, 27.49%), upper limb injuries (1 596 cases, 23.72%) and other sites (1 178 cases, 17.51%). The three leading causes of injury with longest hospitalization duration were burns and scalds, transport accidents and falls, with the median duration being 12.00 (interquartile range, 8.00) days, 10.00 (interquartile range, 13.00) days and 10.00 (interquartile range, 11.00) days, respectively. The top three injury sites associated with the longest hospitalization duration were others, lower limb injuries, and head and neck injuries, with the median duration being 11.00 (interquartile range, 13.00) days, 11.00 (interquartile range, 11.00) days, and 10.00 (interquartile range, 12.00) days, respectively. The causes of injury associated with higher hospitalization costs were falls and transportation accidents, with the median hospitalization cost being 23 550.13 (interquartile range, 30 087.76) yuan for falls and 20 301.94 (interquartile range, 30 589.86) yuan for transportation accidents. The injury sites associated with higher hospitalization costs were lower limb injuries and upper limb injuries, with the median hospitalization cost being 24 257.32 (interquartile range, 34 145.54) yuan for lower limb injuries and 16 506.33 (interquartile range, 20 052.27) yuan for upper limb injuries. Conclusions In Huangpu District, hospitalized injury mainly occurred among males and individuals aged ≥45 years, with the higher incidence observed between June and August. Fall was the primary cause of injury, while lower limb injuries was the main injury sites. The injury resulted in substantially higher hospitalization costs.

Three carboline fluorescent probes F1-F3 were designed and synthesized, based on lead compound JYJ-19, an antifungal compound discovered previously by our group. The antifungal activity in vitro results showed that compound F1 had moderate antifungal activity (MIC₈₀ = 32 μg·mL^-1). The stokes shift of F1 is 70 nm. The fluorescent probe F1 has good optical properties and can be used for fluorescence imaging research. Subcellular localization experiments results showed that F1 was enriched in the mitochondria of fungal cells. The detection of intracellular reactive oxygen species levels shows that JYJ-19 enhances intracellular reactive oxygen species levels. The above results indicated that carboline compounds could exert antifungal effects by acting on fungal mitochondria.