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 investigate whether electroacupuncture (EA) at sensitized acupoints could reduce sympathetic-sensory coupling (SSC) and neurogenic inflammatory response by interfering with 5-hydroxytryptamine (5-HT)ergic neural pathways to relieve colitis and somatic referred pain, and explore the underlying mechanisms. METHODS: Rats were treated with 5% dextran sodium sulfate (DSS) solution for 7 days to establish a colitis model. Twelve rats were randomly divided into the control and model groups according to a random number table (n=6). According to the "Research on Rat Acupoint Atlas", sensitized acupoints and non-sensitized acupoints were determined. Rats were randomly divided into the control, model, Zusanli-EA (ST 36), Dachangshu-EA (BL 25), and Xinshu (BL 15) groups (n=6), as well as the control, model, EA, and EA + GR113808 (a 5-HT inhibitor) groups (n=6). The rats in the control group received no treatment. Acupuncture was administered on 2 days after modeling using the stimulation pavameters: 1 mA, 2 Hz, for 30 min, with sparse and dense waves, for 14 consecutive days. GR113808 was injected into the tail vein at 5 mg/kg before EA for 10 min for 7 consecutive days. Mechanical sensitivity was assessed with von Frey filaments. Body weight and disease activity index (DAI) scores of rats were determined. Hematoxylin and eosin staining was performed to observe colon histopathology. SSC was analyzed by immunofluorescence staining. Immunohistochemical staining was performed to detect 5-HT and substance P (SP) expressions. The calcitonin gene-related peptide (CGRP) in skin tissue and tyrosine hydroxylase (TH) protein levels in DRG were detected by Western blot. The levels of hyaluronic acid (HA), bradykinin (BK), prostaglandin I2 (PGI2) in skin tissue, 5-HT, tryptophan hydroxylase 1 (TPH1), serotonin transporters (SERT), 5-HT 3 receptor (5-HT3R), and 5-HT 4 receptor (5-HT4R) in colon tissue were measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: BL 25 and ST 36 acupoints were determined as sensitized acupoints, and BL 15 acupoint was used as a non-sensitized acupoint. EA at sensitized acupoints improved the DAI score, increased mechanical withdrawal thresholds, and alleviated colonic pathological damage of rats. EA at sensitized acupoints reduced SSC structures and decreased TH and CGRP expression levels (P<0.05). Furthermore, EA at sensitized acupoints reduced BK, PGI2, 5-HT, 5-HT3R and TPH1 levels, and increased HA, 5-HT4R and SERT levels in colitis rats (P<0.05). GR113808 treatment diminished the protective effect of EA at sensitized acupoints in colitis rats (P<0.05). CONCLUSION EA at sensitized acupoints alleviated DSS-induced somatic referred pain in colitis rats by interfering with 5-HTergic neural pathway, and reducing SSC inflammatory response.
Rats ; Animals ; Electroacupuncture ; Rats, Sprague-Dawley ; Serotonin ; Acupuncture Points ; Pain, Referred ; Calcitonin Gene-Related Peptide ; Signal Transduction ; Colitis/therapy* ; Indoles ; Sulfonamides

italic>Salmonella has emerged as a promising tumor-targeting strategy in recent years due to its good tumor targeting ability and certain safety. In order to further optimize its therapeutic effect, scientists have tried to modify Salmonella, including its attenuation and drug loading. This paper summarizes the mechanism and research progress of Salmonella-mediated targeted tumor therapy, and introduces the strategies and related progress of its modification and optimization. At the same time, the advantages, current challenges and future development directions of Salmonella-mediated tumor therapy are summarized.

Circulating tumor DNA (ctDNA) comes from tumor, reflecting the genetic information of the tumor well, and will change with the progress of tumor. In recent years, the unique capabilities of ctDNA have attracted much attention and been widely studied. In this paper, based on the summary of the source, properties and sample processing of ctDNA, its detection technology and application in cancer diagnosis and treatment are reviewed. The roles and importance of ctDNA reference material in second-generation sequencing are described. The urgency of establishing uniform standards and specifications of ctDNA in various processes, such as samples collection, storage, quantitative testing and data analysis, has been pointed out.

Objective To explore the clinical characteristics and treatment scheme of patients with spinal infection caused by Prevotella intermedia(P.intermedia).Methods Clinical diagnosis and treatment processes of a patient with spinal infection caused by P.intermedia admitted to the spinal surgery department of a hospital were summa-rized,and relevant literature was retrieved from database for reviewing.Results The patient,a 50 year old male,was admitted to the hospital due to"lumbago pain complicated with pain in double lower extremities for 2 months".The lesion tissue was taken for metagenomic next-generation sequencing(mNGS)detection,which detected P.in-termedia,and the patient was diagnosed with P.intermedia spondylitis.After treatments with open lesion clea-rance,tube rinsing+autologous bone transplantation fusion internal fixation,intravenous drip of ceftriaxone sodium and metronidazole,as well as metronidazole rinsing,infection was under control.A total of 16 available papers were retrieved,together with this case,a total of 17 patients were included,with 7 males and 10 females.The main risk factors were diabetes and history of corticosteroid use(35.3％).The most common invasion sites were lumbar ver-tebra(n=12)and thoracic vertebra(n=6).13 cases were positive for pathogen culture,3 cases were positive for molecular detection,and 1 case was positive for staining microscopy.17 patients received anti-anaerobic bacteria treatment,with 14 cases receiving combined surgical treatment.One case died,with a mortality of 5.9％;5 cases had partial neurological impairment,with a disability rate of 29.4％.The survival rate of patients who received treatment of anti-anaerobic bacteria combined with surgery was 92.8％,3 patients only with anti-anaerobic bacteria treatment but without surgery were all cured.Conclusion P.intermedia is an opportunistic pathogeanic bacteria which often causes infection in immunocomprised individuals and is prone to be misdiagnosed.It is recommended to perform mNGS detection to identify the pathogen as early as possible and seize the opportunity for treatment to reduce mortality.

Objective To establish the method for content determination of three lignans of Dendrobium Fimbriatum Hook..Methods The lignans in Dendrobium tasselii were identified by high-performance liquid chromatography/multi-stage mass spectrometry(HPLC-ESI/MSn)coupled with ultraviolet absorption spectrometry(UV)coupled with retention time localization of high-performance liquid chromatography(HPLC).The separation was carried out on a Kromasil 100-5 C18 column(4.6 mm×250 mm,5 μm)using a gradient elution of acetonitrile-0.1%formic acid solution as the mobile phase,the volume flow rate was 0.8 mL·min-1 and the column temperature was 35℃,and the mass spectrometry was performed using an ESI ion source with the data collected in the negative ion mode.The HPLC content was determined on the same column as that of MS analysis,with the mobile phase methanol + acetonitrile(V/V=1∶1)-0.01 mol/L ammonium acetate solution,gradient elution,flow rate of 0.8 mL·min-1,column temperature of 40℃,and detection wavelength of 215 nm.Results Syringaresinol di-O-glucoside and(-)-Syringaresinol 4-O-β-D-glucopyranoside and DL-Syringaresinol were identified from Dendrobium fimbriatum Hook.,and the results of content determination showed that the linear ranges of above three components were respectively 0.1701-3.4020,0.1020-2.0400,0.0403-0.8060 μg(r≥0.9995),the average recoveries were in the range of 97.71%-101.67%,and the relative standard deviations(RSDs)were all less than 3.0%.The contents of Syringaresinol di-O-glucoside and(-)-Syringaresinol 4-O-β-D-glucopyranoside and DL-Syringaresinol in the 10 batches of samples were 0.7779-1.3852,0.0734-0.1966,0.0295-0.1882 mg·g-1.Conclusion This research method can provide a reference basis for the quality evaluation method of Dendrobium fimbriatum Hook..