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 evaluate the safety and efficacy of splenectomy with distal pancreatectomy during cytoreductive surgery in epithelial ovarian cancer(EOC).Methods A total of 17 patients from Zhongshan Hospital,Fudan University and the First Affiliated Hospital of University of Science and Technology of China(Anhui Provincial Hospital)received splenectomy with distal pancreatectomy during cytoreductive surgery in EOC were recruited.Their clinicopathological characteristics,postoperative complications and survival situation were retrospective analyzed.Results Of the 17 patients,there were 13 primary cases and 4 recurrent cases.Eleven cases(64.7%)had preoperative imaging finding with metastatic lesions in the splenic hilum,among whom 6 cases had distal pancreas metastasis during the operation.The drainage was placed in the splenic fossa for the measurement of amylase levels in drain fluid and was removed after 8(3-12)days.There were 4 patients had postoperative pancreatic fistula(POPF)of grade A,3 patients had POPF of grade B and no POPF of grade C occurred.The 2 patients with POPF of grade B improved after percutaneous drainage,and the rest recovered with somatostatin,antibiotic drugs and medicines without perioperative mortality.The interval between surgery to chemotherapy was 17.5(13-37)days.The median follow-up time was 14(4-64)months and the median progression-free survival was 10(5-32)months.Conclusion Splenectomy with distal pancreatectomy as part of cytoreduction surgery in EOC is needed for optimal resection,and the complication of pancreatic fistula could be managed conservatively.

Nanozyme is novel nanoparticle with enzyme-like activity, which can be classified into peroxidase-like nanozyme, catalase-like nanozyme, superoxide dismutase-like nanozyme, oxidase-like nanozyme and hydrolase-like nanozyme according to the type of reaction they catalyze. Since researchers first discovered Fe₃O₄ nanoparticles with peroxidase-like activity in 2007, a variety of nanoparticles have been successively found to have catalytic activity and applied in bioassays, inflammation control, antioxidant damage and tumor therapy, playing a key role in disease diagnosis and treatment. We summarize the use of nanozymes with different classes of enzymatic activity in the diagnosis and treatment of diseases and describe the main factors influencing nanozyme activity. A Mn-based peroxidase-like nanozyme that induces the reduction of glutathione in tumors to produce glutathione disulfide and Mn²⁺, which induces the production of reative oxygen species (ROS) in tumor cells by breaking down H₂O₂ in physiological media through Fenton-like action, thereby inhibiting tumor cell growth. To address the limitation of tumor tissue hypoxia during photodynamic tumor therapy, the effect of photodynamic therapy is significantly enhanced by using hydrogen peroxide nanozymes to catalyze the production of oxygen from H₂O₂. In pathological states, where excess superoxide radicals are produced in the body, superoxide dismutase-like nanozymes are able to selectively regulate intracellular ROS levels, thereby protecting normal cells and slowing down the degradation of cellular function. Based on this principle, an engineered nanosponge has been designed to rapidly scavenge free radicals and deliver oxygen in time to save nerve cells before thrombolysis. Starvation therapy, in which glucose oxidase catalyzes the hydrolysis of glucose to gluconic acid and hydrogen peroxide in cancer cells with the involvement of oxygen, attenuates glycolysis and the production of intermediate metabolites such as nucleotides, lipids and amino acids, was used to synthesize an oxidase-like nanozyme that achieved effective inhibition of tumor growth. Furthermore, by fine-tuning the Lewis acidity of the metal cluster to improve the intrinsic activity of the hydrolase nanozyme and providing a shortened ligand length to increase the density of its active site, a hydrolase-like nanozyme was successfully synthesized that is capable of cleaving phosphate bonds, amide bonds, glycosidic bonds and even biofilms with high efficiency in hydrolyzing the substrate. All these effects depend on the size, morphology, composition, surface modification and environmental media of the nanozyme, which are important aspects to consider in order to improve the catalytic efficiency of the nanozyme and have important implications for the development of nanozyme. Although some progress has been made in the research of nanozymes in disease treatment and diagnosis, there are still some problems, for example, the catalytic rate of nanozymes is still difficult to reach the level of natural enzymes in vivo, and the toxic effects of some heavy metal nanozymes material itself. Therefore, the construction of nanozyme systems with multiple functions, good biocompatibility and high targeting efficiency, and their large-scale application in diagnosis and treatment is still an urgent problem to be solved. (1) To improve the selectivity and specificity of nanozymes. By using antibody coupling, the nanoparticles are able to specifically bind to antigens that are overexpressed in certain cancer cells. It also significantly improves cellular internalization through antigen-mediated endocytosis and enhances the enrichment of nanozymes in target tissues, thereby improving targeting during tumor therapy. Some exogenous stimuli such as laser and ultrasound are used as triggers to control the activation of nanozymes and achieve specific activation of nanozyme. (2) To explore more practical and safer nanozymes and their catalytic mechanisms: biocompatible, clinically proven material molecules can be used for the synthesis of nanoparticles. (3) To solve the problem of its standardization and promote the large-scale clinical application of nanozymes in biomonitoring. Thus, it can go out of the laboratory and face the market to serve human health in more fields, which is one of the future trends of nanozyme development.

AIM: To investigate the efficacy of lacrimal duct laser dacryoplasty combined with intubation and postoperative meibomian gland treatment in patients with chronic dacryocystitis complicated by meibomian gland dysfunction.METHODS: Data were collected from 128 patients with chronic dacryocystitis complicated by meibomian gland dysfunction treated at Xi'an No.1 Hospital from March 2021 to December 2022. All patients underwent lacrimal duct laser dacryoplasty combined with intubation. Postoperatively, those patients were randomly divided into two groups: group A(64 cases, without meibomian gland treatment)and group B(64 cases, with meibomian gland treatment). The lacrimal intubation was removed at 3 mo after surgery to evaluate the patency rate of lacrimal irrigation. Additionally, changes in the ocular surface disease index(OSDI)score, non-invasive tear film break-up time, tear meniscus height, conjunctival hyperemia analysis, meibomian gland analysis, tear lipid layer thickness, tear ferning test, and conjunctival impression cytology were compared between the two groups.RESULTS: The lacrimal irrigation patency rates in the group A and group B were 78.1% and 81.2% respectively, with no statistically significant difference between the two groups(P>0.05); compared with the group A, group B showed a significant extension in non-invasive tear breakup time at 3 mo after surgery, and the OSDI score, conjunctival hyperemia analysis, tear ferning test and conjunctival impression cytology grading were all significantly decreased(all P<0.05), while there was no significant difference in tear meniscus height, tear lipid layer thickness and meibomian gland loss score between the two groups(all P>0.05).CONCLUSION: Comprehensive treatment for patients with chronic dacryocystitis combined with meibomian gland dysfunction have improved patients' comfort, tear film stability, and reduces local inflammatory response. It is important to simultaneously address ocular surface microenvironment abnormalities during surgical treatment to achieve satisfactory efficacy.

ObjectiveTo study the bioactive secondary metabolites of Talaromyces sp. TP21 and their bioactivities. MethodThe secondary metabolites of Talaromyces sp. TP21 were isolated by high performance liquid chromatography （HPLC）， normal phase and reversed phase column chromatography combined with molecular networking and bioassay-guided fractionation， and their structures were determined by nuclear magnetic resonance （NMR） and high resolution mass spectrometry （HR MS）. The inhibitory effects of the compounds on the growth of the lung cancer cell line A549 and the liver cancer cell line Hep G2 were measured by themethyl thiazolyl tetrazolium （MTT） method. The antimicrobial activities of the compounds were measured with Staphylococcus aureus and human oral cavity-derived Saccharomyces cerevisiae as the indicator microorganisms. ResultSeventeen compounds were isolated from the secondary metabolites of Talaromyces sp. TP21 and identified as ergochrome C （1）， daldiniaeschsone A （2）， seco-blennolide B （3）， penitholabene （4）， penicichrysogene A （5）， orsellinic acid （6）， griseofulvin （7）， isorhodoptilometrin （8）， （+）-5-chloromitorubrinic acid （9）， penicillixanthone A （10）， pentacecilide B （11）， pentacecilide C （12）， chrodrimanin C （13）， chrodrimanin E （14）， chrodrimanin H （15）， chrodrimanin F （16）， and 3-hydroxypentacecilide A （17）. ConclusionCompounds 1-5， 11-12， and 17 were isolated from Talaromyces for the first time. Among them， compounds 4， 7， and 10-12 exhibited inhibitory activities against the growth of A549 and Hep G2 cells， with the median inhibitory concentration below 50 μmol·L^-1. Furthermore， compounds 9 and 10 showed inhibitory activities against S. aureus and human oral cavity-derived S. cerevisiae， with the minimum inhibitory concentration of 8-128 mg·L^-1.

G protein-coupled receptor (GPR) 40, as one of GPRs family, plays a potential role in regulating glucose and lipid metabolism. To study the effect of GPR40 novel agonist SZZ15-11 on hyperglycemia and hyperlipidemia and its potential mechanism, spontaneous type 2 diabetic KKA^y mice, human hepatocellular carcinoma HepG2 cells and murine mature adipocyte 3T3-L1 cells were used. KKA^y mice were divided into four groups, vehicle group, TAK group, SZZ (50 mg·kg^-1) group and SZZ (100 mg·kg^-1) group, with oral gavage of 0.5% sodium carboxymethylcellulose (CMC), 50 mg·kg^-1 TAK875, 50 and 100 mg·kg^-1 SZZ15-11 respectively for 45 days. Fasting blood glucose, blood triglyceride (TG) and total cholesterol (TC), non-fasting blood glucose were tested. Oral glucose tolerance test and insulin tolerance test were executed. Blood insulin and glucagon were measured via enzyme-linked immunosorbent assay (ELISA). After mice′s execution, liver tissue was harvested to test TG and TC content. Then pathological morphology of liver was observed through hematoxylin-eosin (HE) staining, and the lipid metabolism relative signal pathway was analyzed by Western blot and RT-PCR. The experiments were approved by the Institutional Animal Care and Use Committee of the Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College. At the same time, Akt phosphorylation level in HepG2 cells and adiponectin in 3T3-L1 cells treated with TNFα were measured with Western blot. The results show that SZZ15-11 not only decreased blood glucose and lipid, improved insulin sensitivity, but also increased fasting blood glucagon and promoted insulin secretion after glucose loading in KKA^y mice. Additionally, SZZ15-11 alleviated hepatic steatosis and liver dysfunction in KKA^y mice. In liver tissue, SZZ15-11 increased AMPKα phosphorylation level and cholesterol metabolism relative gene Abcg8 transcription. In HepG2 cells, SZZ15-11 increased Akt phosphorylation level. In adipocyte 3T3-L1, SZZ15-11 recovered the decreased adiponectin expression by TNFα. This study proved that GPR40 agonist SZZ15-11 could be a candidate compound for regulating glucolipid metabolic disorder.