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.

Aim To investigate the role of metabolites of eicosapentaenoic acid (EPA) in promoting the transdifferentiation of pancreatic α cells to β cells. Methods Male C57BL/6J mice were injected intraperitoneally with 60 mg/kg streptozocin (STZ) for five consecutive days to establish a type 1 diabetes (T1DM) mouse model. After two weeks, they were randomly divided into model groups and 97% EPA diet intervention group, 75% fish oil (50% EPA +25% DHA) diet intervention group, and random blood glucose was detected every week; after the model expired, the regeneration of pancreatic β cells in mouse pancreas was observed by immunofluorescence staining. The islets of mice (obtained by crossing GCG

The gene GeDRP1E encoding dynamin-related protein 1E in Gastrodia elata was cloned by specific primers which were designed based on the transcriptome data of G. elata. Bioinformatics analysis on GeDRP1E gene was carried out by using ExPASy, ClustalW, MEGA, etc. Positive transgenic Arabidopsis plant and potato minituber were obtained with the genetic transformation system of Arabidopsis and potato. The plant height and seed setting rate of transgenic Arabidopsis, and agronomic characters, such as size, weight and starch content of potato minituber of transgenic potato were tested and analyzed. And GeDRP1E gene function was preliminarily investigated. The results showed that the open reading frame of GeDRP1E gene was 1 899 bp in length and 632 amino acids residues were encoded, with a relative molecular weight of 69.90 kDa and a molecular formula of C₃₀₇₉H₄₉₇₃N₈₈₃O₉₃₃S₁₉. It was predicted that the theoretical isoelectric point was 7.27, the instability coefficient was 43.34, and the average hydrophilicity index was -0.259, which was indicative of an unstable hydrophilic protein. GeDRP1E has no transmembrane structure and signal peptide, and was localized in the cytoplasm. The phylogenetic tree showed that GeDRP1E was highly homologous with DRP1E proteins of other plant species, among which GeDRP1E had the highest homology with DcDRP1E (XP_020689662.1) in Dendrobium candidum, reaching 90.05%. GeDRP1E plant expression vector pCambia1300-35Spro-GeDRP1E was constructed by double digests, and Arabidopsis complementary mutant and potato overexpression strain of GeDRP1E gene were obtained by Agrobacterium-mediated gene transformation. Compared with the Arabidopsis AtDRP1E mutant, the height and seed setting rate of the GeDRP1E complementation mutant were rescued. The minituber of GeDRP1E overexpression potato had larger size, heavier weight and higher starch content, comparing to wild-type potato. It was preliminarily induced that GeDRP1E was involved in mitochondrial morphology regulation, which related to the growth and development of Arabidopsis plants and potato miniature. The research results laid a foundation for further elucidating the molecular mechanisms underlying the growth and development of G. elata tuber development.

Twelve compounds were isolated from the ethyl acetate fraction of the 80% aqueous ethanol extract of the roots and stems of Dalbergia rimosa Roxb. by silica gel, MCI, Sephadex LH-20 column chromatography, and semi-preparative HPLC. Their structures were identified by spectral analysis such as UV, IR, MS, 1D/2D NMR and by comparison with literature information as dalbergiquinol A (1), dalbergiquinol B (2), R-(-)-3′-hydroxy-2,4,5-trimethoxydalbergiquinol (3), neokhriol A (4), mucronulatol (5), (3R)-7,2′,3′-trihydroxy-4′-methoxy-isoflavane (6), isomucronulatol (7), (3S)-violanone (8), 3′-O-methylviolanone (9), eryvarin M (10), (±)-α,3,4,2′,4′-pentahydroxydihydrochalcone (11) and (-)-butin (12). Compound 1 and 2 are new compounds, and compounds 3-12 were isolated from this plant for the first time. Compounds 1, 2, 4, 6, 8, 11, 12 showed good scavenging effect on DPPH free radical.

Objective:To investigate the incidence of postoperative complications in Chinese patients with gastric or colorectal cancer, and to evaluate the risk factors for postoperative complications.Methods:This was a national, multicenter, prospective, registry-based, cohort study of data obtained from the database of the Prevalence of Abdominal Complications After Gastro- enterological Surgery (PACAGE) study sponsored by the China Gastrointestinal Cancer Surgical Union. The PACAGE database prospectively collected general demographic characteristics, protocols for perioperative treatment, and variables associated with postoperative complications in patients treated for gastric or colorectal cancer in 20 medical centers from December 2018 to December 2020. The patients were grouped according to the presence or absence of postoperative complications. Postoperative complications were categorized and graded in accordance with the expert consensus on postoperative complications in gastrointestinal oncology surgery and Clavien-Dindo grading criteria. The incidence of postoperative complications of different grades are presented as bar charts. Independent risk factors for occurrence of postoperative complications were identified by multifactorial unconditional logistic regression.Results:The study cohort comprised 3926 patients with gastric or colorectal cancer, 657 (16.7%) of whom had a total of 876 postoperative complications. Serious complications (Grade III and above) occurred in 4.0% of patients (156/3926). The rate of Grade V complications was 0.2% (7/3926). The cohort included 2271 patients with gastric cancer with a postoperative complication rate of 18.1% (412/2271) and serious complication rate of 4.7% (106/2271); and 1655 with colorectal cancer, with a postoperative complication rate of 14.8% (245/1655) and serious complication rate of 3.0% (50/1655). The incidences of anastomotic leakage in patients with gastric and colorectal cancer were 3.3% (74/2271) and 3.4% (56/1655), respectively. Abdominal infection was the most frequently occurring complication, accounting for 28.7% (164/572) and 39.5% (120/304) of postoperative complications in patients with gastric and colorectal cancer, respectively. The most frequently occurring grade of postoperative complication was Grade II, accounting for 65.4% (374/572) and 56.6% (172/304) of complications in patients with gastric and colorectal cancers, respectively. Multifactorial analysis identified (1) the following independent risk factors for postoperative complications in patients in the gastric cancer group: preoperative comorbidities (OR=2.54, 95%CI: 1.51-4.28, P<0.001), neoadjuvant therapy (OR=1.42, 95%CI:1.06-1.89, P=0.020), high American Society of Anesthesiologists (ASA) scores (ASA score 2 points:OR=1.60, 95% CI: 1.23-2.07, P<0.001, ASA score ≥3 points:OR=0.43, 95% CI: 0.25-0.73, P=0.002), operative time >180 minutes (OR=1.81, 95% CI: 1.42-2.31, P<0.001), intraoperative bleeding >50 mL (OR=1.29,95%CI: 1.01-1.63, P=0.038), and distal gastrectomy compared with total gastrectomy (OR=0.65,95%CI: 0.51-0.83, P<0.001); and (2) the following independent risk factors for postoperative complications in patients in the colorectal cancer group: female (OR=0.60, 95%CI: 0.44-0.80, P<0.001), preoperative comorbidities (OR=2.73, 95%CI: 1.25-5.99, P=0.030), neoadjuvant therapy (OR=1.83, 95%CI:1.23-2.72, P=0.008), laparoscopic surgery (OR=0.47, 95%CI: 0.30-0.72, P=0.022), and abdominoperineal resection compared with low anterior resection (OR=2.74, 95%CI: 1.71-4.41, P<0.001). Conclusion:Postoperative complications associated with various types of infection were the most frequent complications in patients with gastric or colorectal cancer. Although the risk factors for postoperative complications differed between patients with gastric cancer and those with colorectal cancer, the presence of preoperative comorbidities, administration of neoadjuvant therapy, and extent of surgical resection, were the commonest factors associated with postoperative complications in patients of both categories.

Diabetes mellitus(DM)is a disease syndrome characterized by chronic hyperglycaemia.A long-term high-glucose environment leads to reactive oxygen species(ROS)production and nuclear DNA damage.Human umbilical cord mesenchymal stem cell(HUcMSC)infusion induces significant antidiabetic effects in type 2 diabetes mellitus(T2DM)rats.Insulin-like growth factor 1(IGF1)receptor(IGF1R)is important in promoting glucose metabolism in diabetes;however,the mechanism by which HUcMSC can treat diabetes through IGF1R and DNA damage repair remains unclear.In this study,a DM rat model was induced with high-fat diet feeding and streptozotocin(STZ)administration and rats were infused four times with HUcMSC.Blood glucose,interleukin-6(IL-6),IL-10,glomerular basement membrane,and renal function were examined.Proteins that interacted with IGF1R were determined through coimmunoprecipitation assays.The expression of IGF1R,phosphorylated checkpoint kinase 2(p-CHK2),and phosphorylated protein 53(p-p53)was examined using immunohistochemistry(IHC)and western blot analysis.Enzyme-linked immunosorbent assay(ELISA)was used to determine the serum levels of 8-hydroxydeoxyguanosine(8-OHdG).Flow cytometry experiments were used to detect the surface markers of HUcMSC.The identification of the morphology and phenotype of HUcMSC was performed by way of oil red"O"staining and Alizarin red staining.DM rats exhibited abnormal blood glucose and IL-6/10 levels and renal function changes in the glomerular basement membrane,increased the expression of IGF1 and IGF1R.IGF1R interacted with CHK2,and the expression of p-CHK2 was significantly decreased in IGF1R-knockdown cells.When cisplatin was used to induce DNA damage,the expression of p-CHK2 was higher than that in the IGF1R-knockdown group without cisplatin treatment.HUcMSC infusion ameliorated abnormalities and preserved kidney structure and function in DM rats.The expression of IGF1,IGF1R,p-CHK2,and p-p53,and the level of 8-OHdG in the DM group increased significantly compared with those in the control group,and decreased after HUcMSC treatment.Our results suggested that IGF1R could interact with CHK2 and mediate DNA damage.HUcMSC infusion protected against kidney injury in DM rats.The underlying mechanisms may include HUcMSC-mediated enhancement of diabetes treatment via the IGF1R-CHK2-p53 signalling pathway.