OBJECTIVE To explore the effect and potential mechanism of the compatibility of Astragali Radix-Puerariae Lobatae Radix on ferroptosis of liver cells in type 2 diabetes mellitus （T2DM） insulin resistance （IR） rats. METHODS Sixty male SD rats were randomly divided into control group （12 rats） and modeling group （48 rats）. The modeling group was fed with a high- fat diet for 4 consecutive weeks and then given a one-time tail vein injection of 1% streptozotocin to establish T2DM IR model. The model rats were randomly divided into model group， the compatibility of Astragali Radix-Puerariae Lobatae Radix group [QG group， 4.05 g/（kg·d）， intragastric administration]， ferroptosis inhibitor ferrostatin-1 group [Fer-1 group， 5 mg/kg by intraperitoneal injection， once every other day]， the compatibility of Astragali Radix-Puerariae Lobatae Radix+ferroptosis inducer erastin group [QG+erastin group， 4.05 g/（kg·d） by intragastric administration+erastin 10 mg/（kg·d）， intraperitoneal injection]. After 4 weeks of intervention， serum fasting blood glucose （FBG） and fasting insulin （FINS） were measured in each group of rats， and homeostasis model assessment of insulin resistance （HOMA-IR） and the natural logarithm of insulin action index（IAI） were calculated； the serum levels of total cholesterol （TC）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， high-density lipoprotein cholesterol （HDL-C）， aspartate transaminase （AST） and alanine transaminase （ALT）， Fe2+ and Fe content， glutathione （GSH）， malondialdehyde （MDA） and superoxide dismutase （SOD） levels， NADP+/NADPH ratio and reactive oxygen species （ROS） were determined. The pathological morphology of its liver tissue was observed； the protein expressions of glutathione peroxidase 4 （GPX4）， ferritin heavy chain 1 （FTH1）， long-chain acyl-CoA synthetase 3 （ACSL3）， ACSL4， ferritin mitochondrial （FTMT）， and cystine/glutamate anti-porter （xCT） in the liver tissue of rats were detected. RESULTS Compared with control group， the liver cells in the model group of rats showed disordered arrangement， swelling， deepened nuclear staining， and more infiltration of inflammatory cells， as well as a large number of hepatocyte vacuoles and steatosis； FBG （after medication）， the levels of TC， TG， LDL-C， AST， ALT， FINS， MDA and ROS， HOMA-IR， Fe2+ and Fe content， NADP+/NADPH ratio and protein expression of ACSL4 were significantly increased or up-regulated， while the levels of HDL-C， GSH and SOD， IAI， protein expressions of GPX4， FTH1， ACSL3， FTMT and xCT were significantly reduced or down-regulated （P＜0.01）. Compared with the model group， both QG group and Fer-1 group showed varying degrees of improvement in pathological damage of liver tissue and the levels of the above indicators， the differences in the changes of most indicators were statistically significant （P＜0.01 or P＜0.05）. Compared with QG group， the improvement of the above indexes of QG+erastin group had been reversed significantly （P＜0.01）. CONCLUSIONS The compatibility decoction of Astragali Radix-Puerariae Lobatae Radix can reduce the level of FBG in T2DM IR rats， and alleviate IR degree， ion overload and pathological damage of liver tissue. The above effects are related to the inhibition of ferroptosis.

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.

Piper nigrum L. is an evergreen climbing vine, which belongs to the genus Piperia in the Piperaceae family. Piper nigrum L., which known as the “king of spices”, is used as both food and medicine. The main active substances in Piper nigrum L. are alkaloids mainly composed of amides, and essential oil, as well as phenolic compounds. In this paper, the chemical compositions, especially amide alkaloids, and their biological activities of Piper nigrum L. were summarized. These studies showed that Piper nigrum L., as a medicinal and food plant, had a wide range of biological activities and was deserved further research and in-depth utilization.

Aim To investigate the therapeutic effect of the MW-9 on ulcerative colitis(UC)and reveal the underlying mechanism, so as to provide a scientific guidance for the MW-9 treatment of UC. Methods The model of lipopolysaccharide(LPS)-stimulated RAW264.7 macrophage cells was established. The effect of MW-9 on RAW264.7 cells viability was detected by MTT assay. The levels of nitric oxide(NO)in RAW264.7 macrophages were measured by Griess assay. Cell supernatants and serum levels of inflammatory cytokines containing IL-6, TNF-α and IL-1β were determined by ELISA kits. Dextran sulfate sodium(DSS)-induced UC model in mice was established and body weight of mice in each group was measured. The histopathological damage degree of colonic tissue was assessed by HE staining. The protein expression of p-p38, p-ERK1/2 and p-JNK was detected by Western blot. Results MW-9 intervention significantly inhibited NO release in RAW264.7 macrophages with IC50 of 20.47 mg·L-1 and decreased the overproduction of inflammatory factors IL-6, IL-1β and TNF-α(P<0.05). MW-9 had no cytotoxicity at the concentrations below 6 mg·L-1. After MW-9 treatment, mouse body weight was gradually reduced, and the serum IL-6, IL-1β and TNF-α levels were significantly down-regulated. Compared with the model group, MW-9 significantly decreased the expression of p-p38 and p-ERK1/2 protein. Conclusions MW-9 has significant anti-inflammatory activities both in vitro and in vivo, and its underlying mechanism for the treatment of UC may be associated with the inhibition of MAPK signaling pathway.

Objective To comapre and analyze the differences and commonalities of expression profiles of serum exosomal microRNA between patients with thyroid nodules and healthy persons at different iodine levels,and then provide evidence for screening early diag-nostic markers of thyroid nodules at different iodine levels.Methods The peripheral blood samples from 10 patients with thyroid nod-ules and healthy volunteers at different iodine levels were collected.Their serum iodine levels were measured by the arsenic cerium cat-alytic spectrophotometry.Serum exosomal microRNA were extracted and the expression levels of microRNA were determined by the high-throughput sequencing technology.The differential target genes were predicted and further performed Gene ontology(GO)analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis.Results Compared with healthy volunteers,there were 6 downreg-ulated miRNAs in the patients with thyroid nodules at different iodine levels,namely miR-324-5p,miR-6511b-3p,miR-9903,miR-550a-3p,miR-5001-3p,and miR-3688-3p.Differentially expressed exosomal microRNA could regulate the MAPK signaling path-way,PI3K-AKT signaling pathway,VEGF signaling pathway,and NF-κB signaling pathway.Conclusion Six differentially expressed microRNAs is identified,which may serve as biological markers for the early diagnosis of thyroid nodules at different iodine levels.

Objective:To compare the clinical efficacy between a single palmar lateral transverse approach and the modified Henry approach in the locking plate fixation for distal radius fractures.Methods:A retrospective study was conducted to analyze the data of 82 patients with distal radius fracture who had undergone locking plate fixation through either a single palmar lateral approach or the modified Henry approach between January 2016 and December 2022 at Department of Traumatic Orthopaedics, Taihe Hospital, Affiliated to Hubei University of Medicine. There were 25 males and 57 females, with an age of (53.8±12.7) years. Based on the difference in surgical approach, the patients were divided into a single transverse approach group ( n=42) and a modified Henry approach group ( n=40). The 2 groups were compared in terms of injury cause, injury location, time from injury to surgery, AO fracture classification, tourniquet time during surgery, incision length, fracture reduction, Patient and Observer Scar Assessment Scale (PSAS & OSAS) and Disability of the Arm, Shoulder, and Hand (DASH) score at the last follow-up, and complications after surgery. Results:There were no statistically significant differences in the baseline characteristics between the 2 groups, indicating comparability ( P>0.05). All patients were followed up for (8.2±3.5) months. Follow-ups revealed one case of injury to the palmar cutaneous branch of the median nerve but no other complications like infection, non-union, internal fixation failure, or tendon injury in the single transverse approach group. The single transverse approach group was significantly superior over the modified Henry approach group in incision length [(2.4±0.9) cm versus (5.3±1.6) cm], OSAS (8.1±4.2 versus 10.3±5.7), and PSAS (10.1±5.8 versus 14.7±6.4) ( P<0.05). There were no significant differences between the 2 groups in tourniquet time, fracture reduction quality, and DASH score at the last follow-up ( P>0.05). Conclusion:In locking plate fixation for distal radius fractures, in comparison with the modified Henry approach, a single palmar lateral approach is more minimally invasive so that more aesthetically pleasing outcomes can be achieved to facilitate patients' rapid return to work and society.