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 the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

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 validate the diagnostic value of soluble scavenger receptor class A (sSRA) in rheumatoid arthritis (RA) and evaluate the clinical utility of enzyme-linked immunosorbent assay (ELISA) and chemiluminescence immunoassay (CLIA) for sSR-A detection.Methods:A total of 200 RA patients (including 37 seronegative RA cases), 19 osteoarthritis (OA) patients, 45 gouty arthritis (GA) patients, and 86 healthy controls (HC) were enrolled from the Department of Rheumatology and Immunology, Northern Jiangsu People′s Hospital, between July 2023 and December 2023. Serum sSR-A levels were measured using ELISA and CLIA, and clinical data were collected. Statistical analyses included t-tests, Mann-Whitney U tests, chi-square tests, Fisher′s exact tests, Kruskal-Wallis tests with Dunn′s post hoc analysis, Spearman correlation, and receiver operating characteristic (ROC) curve analysis. Results:ELISA Detection of sSR-A, RA patients exhibited significantly higher sSR-A levels [0.46(0.25, 0.78) ng/ml] than OA [0.15(0.08, 0.30) ng/ml; Z=4.11, P<0.001], GA [0.22(0.14, 0.44) ng/ml; Z=3.51, P<0.01], and HC [0.15(0.06, 0.24) ng/ml; Z=9.10, P<0.001]. ROC curve analysis revealed that sSR-A had a sensitivity of 68.00% and specificity of 89.41% for diagnosing RA, with an area under the curve (AUC)(95% CI) of 0.834 2(0.787 5, 0.880 8) ( P<0.001), and an optimal cut-off value of 0.291 7 ng/ml. The sSR-A positivity rates in RF/ACPA double-positive RA, RF/ACPA single-negative RA, and seronegative RA subgroups were 74.4% (99/133), 56.7% (17/30), and 54.1% (20/37), respectively, with all subgroups showing significantly higher sSR-A levels than HC ( P<0.001). sSR-A correlated positively with DAS28 ( r=0.60, P<0.001), ESR ( r=0.29, P<0.001), globulin ( r=0.25, P<0.001), IgM ( r=0.26, P=0.044), IgG ( r=0.39, P=0.002), IgA ( r=0.31, P=0.017), RF-IgG ( r=0.47, P<0.001), RF-IgA ( r=0.46, P<0.001), RF-IgM ( r=0.50, P <0.001), and ACPA ( r=0.26, P <0.001). sSR-A levels in high-activity [0.85 (0.65, 1.33) ng/ml], moderate-activity [0.46 (0.27, 0.68) ng/ml], and low-activity RA groups [0.24 (0.11, 0.40) ng/ml] were significantly higher than HC [0.15(0.06, 0.24) ng/ml, Z=7.08, P<0.001; Z=7.45, P<0.001; Z=2.68, P<0.001], with levels increasing with disease activity. RA patients were divided into three groups based on ESR and CRP levels including ESR +CRP + [0.54(0.26, 0.87) ng/ml], ESR/CRP - [0.53(0.25, 0.89) ng/ml], and ESR -/CRP - [0.36(0.18, 0.53) ng/ml], All three groups showed significantly higher sSR-A levels compared to the HC group [0.15(0.06, 0.24) ng/ml, Z=8.02, P<0.001; Z=7.61, P<0.001; Z=5.06, P<0.001]. Moreover, sSR-A levels were significantly positively correlated with grayscale synovitis scores ( r=0.30, P<0.001), power Doppler blood flow scores ( r=0.26, P<0.001), and total semi-quantitative ultrasound scores ( r=0.32, P<0.001), patients who were tested positive for sSR-A exhibited significantly higher grayscale synovitis, power Doppler blood flow, and total ultrasound scores compared to those who were negative for sSR-A ( Z=-2.90, P=0.004; Z=-2.37, P=0.018; Z=-3.09, P=0.002). CLIA detection of sSR-A, the RA group [6.83(3.22, 10.65) ng/ml] demonstrated significantly elevated sSR-A concentrations compared to both the OA+GA [1.99(1.27, 4.18) ng/ml] and HC groups [1.03 (0.50, 1.73) ng/ml] ( Z=4.20, P<0.001; Z=9.62, P<0.001). ROC curve analysis revealed AUC (95% CI) of 0.924 6(0.787 5, 0.880 8), with a sensitivity of 86.92% and a specificity of 93.10% for the diagnosis of RA. Conclusion:sSR-A is a reliable biomarker for diagnosing RA, particularly in seronegative patients, and is closely associated with disease activity. CLIA offers superior diagnostic performance over ELISA in detecting sSR-A in RA patients.

Based on the interleukin-17(IL-17) signaling pathway, this study explores the effect and mechanism of Bufei Decoction on Klebsiella pneumoniae pneumonia in rats. SD rats were randomly divided into the control group, model group, Bufei Decoction low-dose group(6.68 g·kg~(-1)·d~(-1)), Bufei Decoction high-dose group(13.36 g·kg~(-1)·d~(-1)), and dexamethasone group(1.04 mg·kg~(-1)·d~(-1)), with 10 rats in each group. A pneumonia model was established by tracheal drip injection of K. pneumoniae. After successful model establishment, the improvement in lung tissue damage was observed following drug administration. Core targets and signaling pathways were screened using transcriptomics techniques. Real-time fluorescence quantitative polymerase chain reaction was used to detect the mRNA expression of core targets interleukin-6(IL-6), interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and chemokine CXC ligand 6(CXCL6). Western blot was used to assess key proteins in the IL-17 signaling pathway, including interleukin-17A(IL-17A), nuclear transcription factor-κB activator 1(Act1), tumor necrosis factor receptor-associated factor 6(TRAF6), and downstream phosphorylated p38 mitogen-activated protein kinase(p-p38 MAPK), and phosphorylated nuclear factor-κB p65(p-NF-κB p65). Apoptosis of lung tissue cells was detected by terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling(TUNEL). The results showed that, compared with the control group, the model group exhibited significant pathological damage in lung tissue. The mRNA expression of IL-6, IL-1β, TNF-α, and CXCL6, as well as the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly increased, and the number of apoptotic cells was notably higher, indicating successful model establishment. Compared with the model group, both low-and high-dose groups of Bufei Decoction showed reduced pathological damage in lung tissue. The mRNA expression levels of IL-6, IL-1β, TNF-α, and CXCL6, and the protein levels of IL-17A, Act1, TRAF6, p-p38 MAPK/p38 MAPK, and p-NF-κB p65/NF-κB p65, were significantly decreased, with a significant reduction in apoptotic cells in the high-dose group. In conclusion, Bufei Decoction can effectively improve lung tissue damage and reduce inflammation in rats with K. pneumoniae. The mechanism may involve the regulation of the IL-17 signaling pathway and the reduction of apoptosis.
Animals ; Interleukin-17/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Signal Transduction/drug effects* ; Rats ; Male ; Klebsiella pneumoniae/physiology* ; Klebsiella Infections/immunology* ; Humans ; Lung/drug effects*

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 working principle of Bausch&Lomb BL11110 phacoemulsification system was described.Three cases of typical faults of the phacoemulsification system were introduced,and the causes were analyzed,then the maintenance measures were given accordingly.References were provided for diagnosing and eliminating the faults of the phacoemulsification system.[Chinese Medical Equipment Journal,2025,46(4):118-120]

Objective:To evaluate the efficacy and safety of the Chi-BEAM regimen (chidamide combined with carmustine, etoposide, cytarabine, and melphalan) followed by autologous hematopoietic stem cell transplantation (ASCT) in patients with high-risk or relapsed/refractory lymphoma.Methods:This retrospective case series included 78 patients with newly treated high-risk or relapsed/refractory lymphoma who underwent ASCT with the Chi-BEAM conditioning regimen in the Department of Hematology, the First Affiliated Hospital of Nanjing Medical University (Jiangsu Province Hospital), from June 2021 to May 2024. Descriptive statistics were employed to evaluate clinical characteristics, efficacy, and adverse events. The Kaplan-Meier method was applied to calculate cumulative progression-free survival (PFS) and overall survival (OS) rates.Results:The median age of the 78 evaluable patients was 47 years (range 16-68), with 8 patients (10.3%) aged ≥60 years. At the first post-transplant assessment (3 months), the objective response rate was 94.9% (74/78). The median follow-up was 20.1 months (range 2.9-44.9). The median PFS time was 20.1 months (range 1.6-45.1), with a 2-year cumulative PFS rate of 81.8%. The median OS time was 20.6 months (range 3.1-45.1), with a cumulative 2-year OS rate of 93.2%. The regimen was well-tolerated; mild-to-moderate hypocalcemia within 1 week post-infusion and transient mild erythrocyturia on the infusion day were the primary adverse reactions.Conclusion:The Chi-BEAM regimen combined with ASCT demonstrates both safety and clinical benefit in patients with high-risk or relapsed/refractory lymphoma.

With the rapid development of competitive sports, the incidence of anterior cruciate ligament (ACL) injury is on the rise. Such injuries may shorten athletes′ career and lead to other long-term adverse consequences. Although athletes generally recover well after ACL reconstruction, many still struggle to return to their pre-injury performance levels. Advances in the understanding of ACL anatomy and injury mechanisms, along with the evolution of surgical techniques and rehabilitation methods, have provided more individualized and tailored options for athletes following ACL injuries. However, there is currently no consensus in China regarding surgical and rehabilitation strategies for competitive athletes aiming to return to sports after ACL injuries. To this end, the Sports Medicine Committee of the Chinese Research Hospital Association and the Editorial Board of the Chinese Journal of Trauma jointly formulated the Expert consensus on surgical treatment and rehabilitation for competitive sports athletes returning to sports after anterior cruciate ligament injury ( version 2025), and presented 14 recommendations covering surgical indications, preoperative rehabilitation, surgical timing, surgical strategies and postoperative rehabilitation strategies, aiming to improve the surgical treatment and rehabilitation system for ACL injuries in competitive athletes and facilitate their return to high-level sports performance after injury.