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.

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.

Objective To evaluate the clinical effect of a new three-phase Chinese medicine(CM)external treatment for acute lateral ankle ligament injuries.Methods From July to December 2023,64 patients with acute lateral ankle ligament in-juries were randomly assigned to receive either the new three-phase CM external treatment combined with the POLICE(pro-tect,optimal loading,ice,compression,elevation)treatment(observation group)or the POLICE treatment(control group),with 32 cases in each group.The observation group consisted of 17 males and 15 females,with an average age of(30.59±3.10)years old ranging from 25 to 36 years old,while the control group included 14 males and 18 females,with an average age of(30.03±3.19)years old ranging from 24 to 37 years old.Visual analogue scale(VAS)evaluation and Figure of 8 measurement were used to evaluate the degree of ankle joint pain and swelling of the subjects at the initial enrollment and after 1 week and sixth weeks of treatment.At the same time,the American Orthopaedic Foot and Ankle Society(AOFAS)and Karlsson Ankle Function Score System were used to evaluate the improvement of ankle joint function in patients at all stages.MRI imaging was employed to observe the degree of biological healing of the anterior talofibular ligament,with the signal to noise ratio(SNR)in-dicating the level of healing.A lower SNR suggests better ligament healing,as it represents lower water content in the ligament.Results All patients completed a 6-week follow-up.There was no significant difference in VAS,AOFAS score and Karlsson score between the two groups before treatment(P＞0.05).After 1 week and 6 weeks of treatment,the VAS,AOFAS score and Karlsson score of the two groups were significantly improved(P＜0.05).After 1 week of treatment,the VAS score of the obser-vation group(3.21±0.87)was lower than that of the control group(4.21±1.50),and the difference was statistically significant(P＜0.05).After 1 weeks of treatment,the AOFAS and Karlsson scores[(50.84±4.70)points,(49.97±4.00)points]of the ob-servation group were higher than those[(46.91±5.56)points,(46.66±5.36)points]of the control group(P＜0.05).MRI images showed that after 6 weeks of treatment,the SNR value of the observation group was significantly lower than that of the control group,and the difference was statistically significant(SNR of the observation group was 75.25±16.59,the contral gruop was 85.81±15.55),(P＜0.05).Conclusion Compared with the control group,the new three-phase CM external treatment is signifi-cantly effective in reducing pain and swelling,enhancing ligament repair quality,and promoting functional recovery of the an-kle joint in patients with acute lateral malleolar ligament injuries.

This paper applied gas chromatography-mass spectrometry (GC-MS), network pharmacology and nuclear magnetic resonance hydrogen spectroscopy (¹H NMR) metabolomics techniques to study the material basis and mechanism of action of Ning Shen Essential Oil in anti-insomnia. The main volatile components of Ning Shen Essential Oil were analyzed by gas chromatography-mass spectrometry (GC-MS), and the insomnia-related targets were predicted using the Traditional Chinese Medicine Systematic Pharmacology Database and Analytical Platform (TCMSP) and the databases of GeneCards, OMIM and Drugbank. The insomnia model of rats was replicated by intraperitoneal injection of 4-chloro-DL-phenylalanine (PCPA). Animal experiments were approved by the Animal Ethics Committee of Shandong University of Traditional Chinese Medicine (Ethics No.: SDUTCM20221025010). The modulating effect of Compound Ning Shen Essential Oil on anxiety behavior of rats was evaluated by behavioral related indexes. The serum levels of corticotropin-releasing hormone (CRH), adrenotropic corticotropic hormone (ACTH) and melatonin (MT) were measured by enzyme-linked immunoassay (ELISA). Rat serum and hippocampus were taken for nuclear magnetic resonance (¹H NMR) metabolomics to detect the changes of endogenous metabolites in rat serum hippocampus, to designate the differential metabolites and to construct metabolic pathways. The results showed that the exercise distance in the open field experiment and the number of times and time to enter the open arm in the elevated cross maze experiment of the rats in the model group were significantly reduced (P < 0.05, P < 0.01). The behavioral indexes of rats improved to different degrees after the administration of Ning Shen Essential Oil. The serum CRH and ACTH levels of rats in the model group increased significantly (P < 0.05, P < 0.01), and the MT level decreased significantly (P < 0.01); After the intervention, serum CRH and ACTH levels were reduced to different degrees, and MT levels could be regressed. ¹H NMR metabolomics screened 10 potential biomarkers related to insomnia, which involved in 6 potential metabolic pathways. A total of 35 components of Ning Shen Essential Oil were detected by GC-MS, the main component targets of Ning Shen Essential Oil and insomnia disease targets were intersected, a total of 172 intersecting genes were screened, and 26 core targets were identified. The study demonstrated that Ning Shen Essential Oil had protective effects against PCPA-induced insomnia in rats, which was probably correlated with regulation of the hypothalamic-pituitary-adrenal axis (HPA) related hormones and metabolism of amino acids, lipids and choline.

The incidence of intrahepatic cholangiocarcinoma (ICC) continues to rise, and there are no effective drugs to treat it. The immune microenvironment plays an important role in the development of ICC and is currently a research hotspot. Icaritin (ICA) is an innovative traditional Chinese medicine for the treatment of advanced hepatocellular carcinoma. It is considered to have potential immunoregulatory and anti-tumor effects, which is potentially consistent with the understanding of "Fuzheng" in the treatment of tumor in traditional Chinese medicine. However, whether ICA can be used to treat ICC has not been reported. Therefore, in this study, sgp19/kRas, an in situ ICC mouse model with intact immune system, was selected to evaluate the efficacy of ICA in the treatment of ICC in vivo for the first time, and the effects of ICA on the tumor immune microenvironment of ICC mice were analyzed by flow cytometry. This experiment was approved by the Experimental Animal Ethics Committee of Capital Medical University (approval number: AEEI-2023-138). In this study, sgp19/kRas ICC mouse model was treated with oral gavage of 100 mg·kg^-1 ICA. We found that ICA significantly inhibited tumor growth and tumor cell proliferation in the sgp19/kRas ICC mouse model after 3 weeks of treatment. Flow cytometry analysis indicated that ICA treatment markedly reduced the proportion of M2 macrophages and increased the number of CD3⁺CD4⁺ T cells. In vitro experiments demonstrated that ICA promoted macrophage polarization towards the M1 phenotype while inhibiting polarization towards the M2 phenotype. Furthermore, transcriptomic analysis suggested that ICA enhanced Toll-like receptor 9 (TLR9) expression, influencing macrophage nitric oxide metabolism synthesis pathways and receptor-related activities, thereby regulating macrophage polarization. In summary, this study demonstrates that ICA treatment significantly delays tumor progression in sgp19/kRas ICC mouse models. Mechanistically, ICA may achieve its anti-ICC effects by upregulating TLR9 receptor expression levels, promoting macrophage polarization towards the M1 phenotype, and altering the tumor immune microenvironment.

Objective Recombinase-aided polymerase chain reaction(RAP)is a sensitive,single-tube,two-stage nucleic acid amplification method.This study aimed to develop an assay that can be used for the early diagnosis of three types of bacteremia caused by Staphylococcus aureus(SA),Pseudomonas aeruginosa(PA),and Acinetobacter baumannii(AB)in the bloodstream based on recombinant human mannan-binding lectin protein(M1 protein)-conjugated magnetic bead(M1 bead)enrichment of pathogens combined with RAP. Methods Recombinant plasmids were used to evaluate the assay sensitivity.Common blood influenza bacteria were used for the specific detection.Simulated and clinical plasma samples were enriched with M1 beads and then subjected to multiple recombinase-aided PCR(M-RAP)and quantitative PCR(qPCR)assays.Kappa analysis was used to evaluate the consistency between the two assays. Results The M-RAP method had sensitivity rates of 1,10,and 1 copies/μL for the detection of SA,PA,and AB plasmids,respectively,without cross-reaction to other bacterial species.The M-RAP assay obtained results for<10 CFU/mL pathogens in the blood within 4 h,with higher sensitivity than qPCR.M-RAP and qPCR for SA,PA,and AB yielded Kappa values of 0.839,0.815,and 0.856,respectively(P<0.05). Conclusion An M-RAP assay for SA,PA,and AB in blood samples utilizing M1 bead enrichment has been developed and can be potentially used for the early detection of bacteremia.