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 explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.

Objective:To explore the strategy of prostate biopsy in patients with prostate specific antigen(PSA)gray zone based on prostate imaging reporting and data system (PI-RADS).Methods:The clinical data of 427 patients who underwent transperineal prostate biopsy in the First Affiliated Hospital of Nanjing Medical University from January 2020 to December 2022 were retrospectively analyzed. The median age was 66 (61, 72) years old. The median PSA was 6.62 (5.46, 8.19) ng/ml. The median PSA density (PSAD) was 0.15 (0.11, 0.21) ng/ml 2. The median prostate volume (PV) was 43.68 (31.12, 56.82) ml. PSA velocity (PSAV) data were available in 65 patients with negative MRI examination(PI-RADS <3), and the median PSAV was 1.40 (0.69, 2.89) ng/(ml· year). Among the patients with positive MRI(PI-RADS≥3), there were 174 patients with only 1 lesion and 83 patients with ≥2 lesions. A total of 170 patients with negative MRI underwent systematic biopsy, and 257 patients with positive MRI underwent systematic combined targeted biopsy. The PI-RADS score, regions of interest(ROI), PSAD, f/tPSA and PSAV were analyzed to explore the biopsy strategy for patients with PSA gray area based on bpMRI imaging. Results:Of the 427 patients included in the study, 194 were positive and 233 were negative. Among the patients with positive biopsy pathology, 140 cases were clinically significant prostate cancer (CsPCa). Among the MRI-negative patients, there were 33 cases with PSAV ≥1.4 ng/(ml·year), and 10 cases of prostate cancer and 6 cases of CsPCa were detected by systematic biopsy.In 32 cases with PSAV <1.4 ng/(ml·year), 3 cases of prostate cancer and 0 case of CsPCa were detected by systematic biopsy. The sensitivity of systematic biopsy for the diagnosis of prostate cancer and CsPCa in patients with PSAV≥1.4 ng/(ml·year) were 76.9% (10/13) and 100.0% (6/6) respectively, the specificity were 55.8% (29/52) and 54.2% (32/59) respectively, the negative predictive value were 90.6% (29/32) and 100.0% (32/32) respectively, and the positive predictive value were 30.3% (10/33) and 18.2% (6/33) respectively. In MRI-positive patients with PI-RADS 3, the prostate cancer detection rates of targeted biopsy combined with systematic biopsy, systematic biopsy and targeted biopsy were 41.7% (45/108), 32.4% (35/108) and 35.2% (38/108), respectively ( P=0.349). The detection rates of CsPCa were 27.8% (30/108), 21.3% (23/108) and 25.0% (27/108), respectively ( P=0.541). In patients with PI-RADS 4-5 and PSAD > 0.15 ng/ml 2, the detection rates of CsPCa in targeted biopsy combined with systematic biopsy, systematic biopsy and targeted biopsy were 67.8% (61/90), 58.9% (53/90) and 67.8% (61/90), respectively ( P=0.354). Conclusions:For MRI-negative patients, all CsPCa could be detected by perineal systematic biopsy when PSAV ≥1.4 ng/(ml·year), and active observation could be performed when PSAV <1.4 ng/(ml·year). For MRI-positive patients, targeted combined systemic biopsy was required when PI-RADS score was 3, and targeted biopsy only could be performed when PI-RADS score ≥4 and PSAD >0.15 ng/ml 2, otherwise targeted combined systemic biopsy was required.

OBJECTIVE To study on the identification of traditional Chinese medicine of Macleaya cordata(M. cordata) and its similar varieties. METHODS By consulting the ancient herbal books and modern literature, this paper systematically combs and studies the M. cordata. The morphological identification, microscopic identification, physiochemical identification, molecular identification were used to identify M. cordata and its similar varieties. RESULTS Obtained M. cordata herbal textual research data. There were some differences between M. cordata and Macleaya microcarpa(M. microcarpa) and other similar varieties in traits, microscopic, physicochemical and molecular characteristics. Molecular identification results showed that the length of the rbcL gene of M. cordata were 600 bp to 603 bp, with the average GC content ranging from 43.95% to 44.28%. There were significant differences in the variation sites between M. cordata and other similar varieties, and the variation sites with M. microcarpa were the least. The interspecific genetic distance between M. cordata and its similar varieties was greater than its maximum intraspecific genetic distance. NJ analysis results of rbcL could effectively distinguish M. cordata from other similar varieties accurately and quickly. There were significant differences in the secondary structure of rbcL between M. cordata and its similar varieties. CONCLUSION The traditional Chinese medicine identification methods of M. cordata, M. microcarpa and other similar varieties are constructed, which provides experimental basis for the variety identification of M. cordata and the subsequent development of traditional Chinese medicine resources.

Objective To assess the feasibility and imaging outcomes of unilateral biportal endoscopic technique in the treatment of lumbar foraminal stenosis through contralateral approach.Methods The clinical data of 33 patients with lumbar foraminal stenosis treated with unilateral biportal endoscopic technique from January 2021 to July 2022 were retrospectively analyzed.There were 17 males and 16 females;age ranging from 34 to 72 years old with an average of(56.00±7.89)years old;operation time and perioperative complications were recorded;visual analogue scale(VAS)of pain was recorded,to evaluate the degree of low back pain and lower extremity pain,and Oswestry disability index(ODI)to evaluate the lumbar spine func-tion.At the latest follow-up,the modified Macnab score was used to evaluate the clinical efficacy.Results All patients success-fully completed the operation.The operation time ranged from 47 to 65 minutes,with an average of(56.10±5.19)minutes.The postoperative follow-up ranged from 12 to 18 months,with an average of(14.9±2.3)months.The VAS of low back and lower extermity pain before operation were(7.273±1.442)and(7.697±1.447)scores,ODI was(69.182±9.740)％.Postoperative lumbocrural pain VAS were(3.394±0.966)and(2.818±0.727)scores,ODI was(17.30±4.78)％.At the latest follow-up,VAS of back and lower extermity pain was(2.788±0.650)and(2.394±0.704)scores,ODI was(14.33±350)％.There were signifi-cant differences in VAS of low back and lower extremity pain and ODI before and after operation(P＜0.05).At the latest follow-up,according to the modified Macnab criteria,24 patients got excellent result,5 as good,2 as fair,and 2 as poor.Conclusion Unilateral biportal endoscopic treatment of lumbar foraminal stenosis through the contralateral approach is a safe and efficient method,with few complications,quick postoperative recovery,and satisfactory clinical outcomes.During the follow-up period,no iatrogenic lumbar instability was observed.

Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5％were gram-positive and 70.5％were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3％,76.7％and 77.9％,respectively.Overall,94.0％of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8％of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2％in the isolates from children and 95.7％in the isolates from adults.The resistance rate to carbapenems was lower than 13.1％in most Enterobacterales species except for Klebsiella,21.7％-23.1％of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1％to 13.3％.The prevalence of meropenem-resistant strains decreased from 23.5％in 2019 to 18.0％in 2022 in Pseudomonas aeruginosa,and decreased from 79.0％in 2019 to 72.5％in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.