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.

ObjectiveTo investigate the contamination status, transmission risk and drug resistance of Klebsiella pneumoniae (KP) on the object surfaces in the surrounding environment of hospitalized patients infected with carbapenem-resistant Klebsiella pneumoniae (CRKP) , so as to provide a scientific guidance for the prevention and control of healthcare-associated infection. MethodsSamples from the surfaces of objects in the surrounding environment of CRKP infected patients living in the intensive care unit (ICU) and hand specimens from healthcare workers were collected for KP isolation and identification, as well as drug susceptible test in a medical institution located in Minhang District, Shanghai from 2021 to 2023. Additionally, both univariate and multivariate logistic regression analyses were used to identify the influencing factors associated with KP contamination in the hospital environment. ResultsA total of 546 surface samples were collected from the surrounding environment objects of 15 patients infected with CRKP, with a KP detection rate of 6.59% (36/546).The KP detection rate in the ICU of general ward (10.22%) was higher than that in the ICU of emergency department (2.94%) (χ²=12.142, P<0.001). Moreover, the KP detection rate on the surfaces of patient-contacted items (15.66%) was higher than that on shared-use items (6.25%), cleaning items (10.00%), and medical supplies (3.30%) (χ²=17.943, P<0.001). Besides, the detection rate of KP in items sent out of hospital for disinfection (15.38%) was higher than that in those self-disinfected (4.20%) (χ²=19.996, P<0.001).The highest detection rate of KP was observed in high-temperature washing (15.13%, 18/119) (χ²=21.219, P<0.001), while the lowest detection rate was observed in antibacterial hand sanitizer with trichlorohydroxydiphenyl ether sanitizing factor (0, 0/60) ( χ²=21.219, P<0.001).The detection rate of KP in samples taken more than 24 hours after the last disinfection (23.08%) was higher than that in those taken at 4 to24 hours (12.90%) and less than 4 hours (4.22%) (χ²=23.398,P<0.001).ICU of general ward (OR=4.045, 95%CI: 2.206‒7.416), patient-contacted items (OR=3.113, 95%CI: 1.191‒8.141), and self-disinfection ( OR=0.241, 95%CI:0.144‒0.402) were influencing factors for KP contamination in environmental surface. From 2021 to 2023, the drug resistance rates of hospital environmental KP isolates showed an upward trend (P<0.001) to antibiotics such as ceftazidime and gentamicin. Furthermore, high drug resistance rates of KP (>90%) were observed to ciprofloxacin, levofloxacin, cefotaxime, ceftriaxone, and cefepime. ConclusionCRKP can be transmitted outward through the surfaces of objects in the patients’ surroundings, and the drug resistance situation is severe. In clinical settings, it is necessary to implement isolation measures for CRKP infection patients, to increase the frequency of disinfection for objects in their surroundings, to strengthen hand hygiene practices, and to use antibiotics appropriately.

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 relationship between serum stem cell factor receptor(c-kit)and myocardial fibrosis,cardiac function and prognosis in patients with dilated cardiomyopathy(DCM)complicat-ed with heart failure.Methods A total of 77 patients with DCM complicated with heart failure who were trea-ted in 3201 Hospital from May 2020 to June 2022 were enrolled in the study as study group,and 70 DCM pa-tients without heart failure were enrolled as the control group.The levels of serum c-kit mRNA and three my-ocardial fibrosis markers[α-smooth muscle actin(α-SMA),collagen type Ⅰ and collagen type Ⅲ]were detec-ted in the two groups.Cardiac function parameters were obtained by echocardiography.The relationship be-tween serum c-kit mRNA level and myocardial fibrosis and cardiac function was analyzed.According to the oc-currence of major adverse cardiovascular events(MACE),the patients were divided into poor prognosis group and good prognosis group.Multivariate Logistic regression analysis was used to analyze the factors affecting the prognosis of DCM patients complicated with heart failure.Receiver operating characteristic(ROC)curve was used to analyze the efficacy of serum c-kit mRNA level in predicting the prognosis of DCM patients.Results The serum c-kit mRNA level in the study group was lower than that in the control group(P＜0.05).The levels of three myocardial fibrosis indicators in the study group were higher than those in the con-trol group(P＜0.05).The left ventricular ejection fraction(LVEF)in the study group was lower than that in the control group(P＜0.05),and the left ventricular end-diastolic volume(LVEDV)and left ventricular end-systolic volume(LVESV)in the study group were higher than those in the control group(P＜0.05).Pearson correlation analysis showed that serum c-kit mRNA level was positively correlated with LVEF(r=0.677,P＜0.05),while negatively correlated with α-SMA,collagen type Ⅰ,collagen type Ⅲ,LVEDV and LVESV(r=-0.725,-0.748,-0.744,-0.745,-0.662,P＜0.05).Multivariate Logistic regression analysis showed that serum c-kit mRNA level is an independent factor for the prognosis of DCM patients with heart failure.ROC curve analysis showed that serum c-kit mRNA level had a sensitivity of 82.80％,a specificity of 81.80％,and an area under the curve of 0.829(95％CI:0.745-0.912,P＜0.001)for evaluating the progno-sis of DCM patients complicated with heart failure.Conclusion The serum c-kit mRNA level is significantly decreased in patients with DCM complicated with heart failure,and the serum c-kit mRNA level is correlated with myocardial fibrosis and cardiac function.The detection of serum c-kit mRNA level has a high efficacy in evaluating the prognosis of patients with DCM complicated with heart failure.

Objective To explore the efficacy and safety of ticagrelor de-escalation and nicorandil therapy in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 300 elderly patients with ACS were selected from the Sixth and Seventh Medical Center of Chinese PLA General Hospital and Beijing Chaoyang Integrative Medicine Emergency Rescue and First Aid Hospital from November 2016 to June 2019,including 153 males and 147 females,aged>65 years old.All the patients received PCI,and all had double antiplatelet therapy(DAPT)scores≥2 and a new DAPT(PRECISE-DAPT)score of≥25.All patients were divided into two groups by random number table method before operation:ticagrelor group(n=146,ticagrelor 180 mg load dose followed by PCI,and ticagrelor 90 mg bid after surgery)and ticagrelor de-escalation + nicorandil group(n=154,ticagrelor 180 mg load dose followed by PCI,ticagrelor 90 mg bid+nicorandil 5 mg tid after surgery,changed to ticagrelor 60 mg bid+ nicorandil 5 mg tid 6 months later).Follow-up was 12 months.The composite end points of cardiovascular death,myocardial infarction and stroke,the composite end points of mild hemorrhage,minor hemorrhage,other major hemorrhage and major fatal/life-threatening hemorrhage as defined by the PLATO study,and the composite end points of cardiovascular death,myocardial infarction,stroke and bleeding within 12 months in the two groups were observed.Results The comparison of general baseline data between the two groups showed no statistically significant difference(P>0.05).There was also no significant difference in the composite end points of cardiovascular death,myocardial infarction and stroke between the two groups(P>0.05).The cumulative incidence of bleeding events in ticagrelor de-escalation + nicorandil group was significantly lower than that in ticagrelor group(P<0.05),while the composite end points of cardiovascular death,myocardial infarction,stroke and bleeding were also significantly lower than those in tecagrelor group(P<0.05).Conclusion In elderly patients with ACS,the treatment of ticagrelor de-escalation + nicorandil after PCI may not increase the incidence of ischemic events such as cardiovascular death,myocardial infarction or stroke,and it may reduce the incidence of hemorrhagic events.

Objective To investigate the effect of microglia activation regulated by C-X3-C motif chemokine ligand 1(CX3CL1)-C-X3-C motif chemokine receptor 1(CX3CR1)pathway on memory function in hemorrhagic shock/resuscitation rats.Methods The experiment was divided into two parts.In the first part,the rats were randomly divided into sham group,model-0.5 hour group,model-1.5 hour group,model-3 hour group,10 rats in each group.There were differences in the time of hemorrhagic shock among each group.In the second part,rats were randomly divided into control group and CX3CL1 group,10 rats in each group.The rats in CX3CL1 group were treated with CX3CL1 protein factor(intraventricular injection),and the rats in control group were treated with saline.All rats were trained in Morris water maze experiments before model construction,and tests of Morris water maze experiments were carried out after 4 days of model construction.After completion,the whole brains were taken for HE staining and immunohistochemical staining.Cerebrospinal fluid was taken for detection of inflammatory cytokines,and hippocampus tissues were taken for Real-time PCR detection and Western blotting detection.Results Compared with the sham group,the escape latency of rats in model group increased,the number of platform crossings and the resident time in the third quadrant decreased.The neuronal state was impaired in HE staining in model group.In addition,compared with the sham group,the expression of ionized calcium binding adaptor molecule-1(Iba1)in the brain of the rats in model group increased,the contents of tumor necrosis factor-α(TNF-α)and interleukin(IL)-6 in the cerebrospinal fluid increased,and the M1-type microglia markers CD16,TNF-α,IL-1β and inducible nitric oxide synthase(iNOS)mRNA content increased.At the same time,compared with the sham group,the expressions of CX3CL1 and CX3CR1 in the brain of model group decreased,and the expressions of phosphorylated nuclear factor-κB(p-NF-κB)and nucleotide binding oligomerization domain(NOD)-like receptor protein 3(NLRP3)increased.However,compared with the control group,rats in CX3CL1 group had reduced escape latency,increased platform crossing times and quadrantⅢresident time,and recovered neuronal states.In addition,the expression of Iba1 in the brain of CX3CL1 group decreased,the contents of TNF-α and IL-6 in the cerebrospinal fluid decreased,the mRNA contents of M1-type microglia markers like CD16,TNF-α,IL-1β and iNOS decreased,and the mRNA contents of markers of M2-type microglia glial like CD206,transforming growth factor-β(TGF-β),arginase-1(Arg1),Chitinase 3-like protein 1(Ym 1)increased.Conclusion CX3CL1 can help inhibit the excessive activation of microglia,induce the polarization of microglia to M2 type,inhibit the polarization of M1 type,reduce the release of inflammatory cytokines,and alleviate the memory function damage induced by hemorrhagic shock/resuscitation.

Objective:To investigate the current status of endoscopic treatment for gastroesophageal varices in portal hypertension in China, and to provide supporting data and reference for the development of endoscopic treatment.Methods:In this study, initiated by the Liver Health Consortium in China (CHESS), a questionnaire was designed and distributed online to investigate the basic condition of endoscopic treatment for gastroesophageal varices in portal hypertension in 2022 in China. Questions included annual number and indication of endoscopic procedures, adherence to guideline for preventing esophagogastric variceal bleeding (EGVB), management and timing of emergent EGVB, management of gastric and isolated varices, and improvement of endoscopic treatment. Proportions of hospitals concerning therapeutic choices to all participant hospitals were calculated. Guideline adherence between secondary and tertiary hospitals were compared by using Chi-square test.Results:A total of 836 hospitals from 31 provinces (anotomous regions and municipalities) participated in the survey. According to the survey, the control of acute EGVB (49.3%, 412/836) and the prevention of recurrent bleeding (38.3%, 320/836) were major indications of endoscopic treatment. For primary [non-selective β-blocker (NSBB) or endoscopic therapies] and secondary prophylaxis (NSBB and endoscopic therapies) of EGVB, adherence to domestic guideline was 72.5% (606/836) and 39.2% (328/836), respectively. There were significant differences in the adherence between secondary and tertiary hospitals in primary prophylaxis of EGVB [71.0% (495/697) VS 79.9% (111/139), χ2=4.11, P=0.033] and secondary prophylaxis of EGVB [41.6% (290/697) VS 27.3% (38/139), χ2=9.31, P=0.002]. A total of 78.2% (654/836) hospitals preferred endoscopic therapies treating acute EGVB, and endoscopic therapy was more likely to be the first choice for treating acute EGVB in tertiary hospitals (82.6%, 576/697) than secondary hospitals [56.1% (78/139), χ2=46.33, P<0.001]. The optimal timing was usually within 12 hours (48.5%, 317/654) and 12-24 hours (36.9%, 241/654) after the bleeding. Regarding the management of gastroesophageal varices type 2 and isolated gastric varices type 1, most hospitals used cyanoacrylate injection in combination with sclerotherapy [48.2% (403/836) and 29.9% (250/836), respectively], but substantial proportions of hospitals preferred clip-assisted therapies [12.4% (104/836) and 26.4% (221/836), respectively]. Improving the skills of endoscopic doctors (84.2%, 704/836), and enhancing the precision of pre-procedure evaluation and quality of multidisciplinary team (78.9%, 660/836) were considered urgent needs in the development of endoscopic treatment. Conclusion:A variety of endoscopic treatments for gastroesophageal varices in portal hypertension are implemented nationwide. Participant hospitals are active to perform emergent endoscopy for acute EGVB, but are inadequate in following recommendations regarding primary and secondary prophylaxis of EGVB. Moreover, the selection of endoscopic procedures for gastric varices differs greatly among hospitals.