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 summarize the epidemiological and clinical features of infectious diseases of the central nervous system(CNS)by a single-center analysis.Methods A retrospective analysis was conducted on the data of 1247 cases of CNS infectious diseases diagnosed and treated in the First Medical Center of PLA General Hospital from 2001 to 2020.Results The data for this group of CNS infectious diseases by disease type in descending order of number of cases were viruses 743(59.6％),Mycobacterium tuberculosis 249(20.0％),other bacteria 150(12.0％),fungi 68(5.5％),parasites 18(1.4％),Treponema pallidum 18(1.4％)and rickettsia 1(0.1％).The number of cases increased by 177 cases(33.1％)in the latter 10 years compared to the previous 10 years(P<0.05).No significant difference in seasonal distribution pattern of data between disease types(P>0.05).Male to female ratio is 1.87︰1,mostly under 60 years of age.Viruses are more likely to infect students,most often at university/college level and above,farmers are overrepresented among bacteria and Mycobacterium tuberculosis,and more infections of Treponema pallidum in workers.CNS infectious diseases are characterized by fever,headache and signs of meningeal irritation,with the adductor nerve being the more commonly involved cranial nerve.Matagenomic next-generation sequencing improves clinical diagnostic capabilities.The median hospital days for CNS infectious diseases are 18.00(11.00,27.00)and median hospital costs are ￥29,500(￥16,000,￥59,200).The mortality rate from CNS infectious diseases is 1.6％.Conclusions The incidence of CNS infectious diseases is increasing last ten years,with complex clinical presentation,severe symptoms and poor prognosis.Early and accurate diagnosis and standardized clinical treatment can significantly reduce the morbidity and mortality rate and ease the burden of disease.

Objective:To investigate the predictive efficacy of brain natriuretic peptide(BNP),angiotensin Ⅱ(An-gⅡ),erythrocyte distribution width(RDW)and neutrophil/lymphocyte ratio(NLR)for postoperative left ventric-ular systolic dysfunction(LVSD)in patients with acute anterior myocardial infarction(AAMI).Methods:A total of 160 AAMI patients treated in our hospital from January 2018 to January 2022 were selected.According to left ventricular ejection fraction(LVEF)after percutaneous coronary intervention,they were divided into no LVSD group(LVEF＞40％,n=110)and LVSD group(LVEF≤40％,n=50).Baseline data were compared between two groups.Multivariate Logistic regression analysis was used to analyze influencing factors of postoperative LVSD in AAMI patients.Receiver operating characteristic(ROC)curve was used to analyze the predictive value of single and combined detection of BNP,AngⅡ,RDW and NLR for postoperative LVSD in AAMI patients.Results:Compared with no LVSD group,there were significant rise in levels of BNP[(347.52±82.66)pg/ml vs.(405.55±105.47)pg/ml],AngⅡ[(238.11±20.43)ng/ml vs.(254.58±22.53)ng/ml],RDW[(22.88±5.25)％vs.(25.52±5.58)％]and NLR[(4.34±1.09)vs.(5.31±1.50)]in LVSD group(P＜0.01 all);BNP,AngⅡ,RDW and NLR were independent risk factors for postoperative LVSD in AAMI patients(OR=2.002～3.692,P＜0.05 or＜0.01);predictive value of combined detection of BNP,AngⅡ,RDW and NLR for postoperative LVSD in AAMI patients was significantly higher than those of single detection[area under the curve(AUC):0.809 vs.0.650,0.696,0.641,0.694,P＜0.01 all].Conclusion:Left ventricular systolic function is closely associated with levels of BNP,AngⅡ,RDW and NLR in AAMI patients.Combined detection of the above indexes can effectively predict postoperative left ventricular systolic dysfunction in these patients.

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective To explore the risk factors related to afterload mismatch(AM)after transcatheter mitral valve repair(MitraClip).Methods This was a retrospective cohort study.48 patients hospitalized in the Department of Cardiovascular Medicine,the First Affiliated Hospital of Sun Yat-sen University from December 2021 to December 2023,who underwent MitraClip due to severe mitral regurgitation(MR)were included.Preoperative clinical data,laboratory tests,and preoperative and postoperative color Doppler echocardiographic examination results of surgical patients were collected.AM was defined as the left ventricular ejection fraction(LVEF)decreased by 15％or more after surgery compared with the one before(dLVEF≤-15％).Patients were divided into AM group and non-AM group according to whether afterload mismatch occurred.Univariate and multivariate logistic regression were used to analyze the risk factors of postoperative AM.Results Among 48 patients who underwent MitraClip,14 of them(29.2％)developed afterload-mismatched.For those without AM,their overall LVEF was improved after the operation;for patients in both AM group and non-AM group,their overall left ventricular end-diastolic diameter(LVEDd),left ventricular end-diastolic diameter volume index(LVEDVi)was reduced compared with the preoperative ones.Univariate regression analysis showed that C-reactive protein levels(OR 1.98,95％CI 1.02-3.83),platelets(OR 2.22,95％CI 1.08-4.53),systemic immune inflammation index(OR 1.96,95％CI 1.03-3.71)were associated with an increased risk of AM in patients undergoing MitraClip(all P＜0.05),while those with larger right atrial diameter(OR 0.35,95％CI 0.13-0.93)or moderate to severe tricuspid regurgitation(OR 0.19,95％CI 0.05-0.81)were less likely to develop into AM(both P＜0.05),which is still satisfied after adjustment.Conclusions For patients who underwent MitraClip,C-reactive protein levels,platelets and systemic immune inflammation index(SII)are associated with an increased risk of afterload mismatched,while those with larger right atrial diameter or moderate to severe tricuspid regurgitation were less likely to develop into AM.

Acute mitral regurgitation(MR)in the setting of myocardial infarction(MI)may be the result of papillary muscle rupture(PMR).The clinical presentation can be catastrophic,with refractory cardiogenic shock.This condition is associated with high morbidity and mortality.Transcatheter edge-to-edge repair(TEER)has become increasingly common in treating severe mitral regurgitation.This case details a successful TEER is feasible and safe in patients with acute MR following MI.TEER is an emerging treatment option in this clinical scenario that should be taken into consideration.