1.Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study
Xing-Xuan DONG ; Jian-Hua LIU ; Tian-Yang ZHANG ; Chen-Wei PAN ; Chun-Hua ZHAO ; Yi-Bo WU ; Dan-Dan CHEN
Psychiatry Investigation 2025;22(3):267-278
		                        		
		                        			 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.	 
		                        		
		                        		
		                        		
		                        	
2.Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study
Xing-Xuan DONG ; Jian-Hua LIU ; Tian-Yang ZHANG ; Chen-Wei PAN ; Chun-Hua ZHAO ; Yi-Bo WU ; Dan-Dan CHEN
Psychiatry Investigation 2025;22(3):267-278
		                        		
		                        			 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.	 
		                        		
		                        		
		                        		
		                        	
3.Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study
Xing-Xuan DONG ; Jian-Hua LIU ; Tian-Yang ZHANG ; Chen-Wei PAN ; Chun-Hua ZHAO ; Yi-Bo WU ; Dan-Dan CHEN
Psychiatry Investigation 2025;22(3):267-278
		                        		
		                        			 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.	 
		                        		
		                        		
		                        		
		                        	
4.Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study
Xing-Xuan DONG ; Jian-Hua LIU ; Tian-Yang ZHANG ; Chen-Wei PAN ; Chun-Hua ZHAO ; Yi-Bo WU ; Dan-Dan CHEN
Psychiatry Investigation 2025;22(3):267-278
		                        		
		                        			 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.	 
		                        		
		                        		
		                        		
		                        	
5.Comparison of Logistic Regression and Machine Learning Approaches in Predicting Depressive Symptoms: A National-Based Study
Xing-Xuan DONG ; Jian-Hua LIU ; Tian-Yang ZHANG ; Chen-Wei PAN ; Chun-Hua ZHAO ; Yi-Bo WU ; Dan-Dan CHEN
Psychiatry Investigation 2025;22(3):267-278
		                        		
		                        			 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.	 
		                        		
		                        		
		                        		
		                        	
6.Pharmacokinetics of wogonin-aloperine cocrystal in rats
Zhong-shui XIE ; Chun-xue JIA ; Yu-lu LIANG ; Xiao-jun ZHAO ; Bin-ran LI ; Jing-zhong HAN ; Hong-juan WANG ; Jian-mei HUANG
Acta Pharmaceutica Sinica 2024;59(9):2606-2611
		                        		
		                        			
		                        			 Pharmaceutical cocrystals is an advanced technology to improve the physicochemical and biological properties of drugs. However, there are few studies on the 
		                        		
		                        	
7.Radix Angelica Sinensis and Radix Astragalus ultrafiltration extract improves radiation-induced pulmonary fibrosis in rats by regulating NLRP3/caspase-1/GSDMD pyroptosis pathway
Chun-Zhen REN ; Jian-Fang YUAN ; Chun-Ling WANG ; Xiao-Dong ZHI ; Qi-Li ZHANG ; Qi-Lin CHEN ; Xin-Fang LYU ; Xiang GAO ; Xue WU ; Xin-Ke ZHAO ; Ying-Dong LI
Chinese Pharmacological Bulletin 2024;40(11):2124-2131
		                        		
		                        			
		                        			Aim To investigate the mechanism of py-roptosis mediated by the NLRP3/caspase-1/GSDMD signaling pathway and the intervention effect of Radix Angelica Sinensis and Radix Astragalus ultrafiltration extract(RAS-RA)in radiation-induced pulmonary fi-brosis.Methods Fifty Wistar rats were randomly di-vided into five groups,with ten rats in each group.Ex-cept for the blank control group,all other groups of rats were anesthetized and received a single dose of 40 Gy X-ray local chest radiation to establish a radiation-in-duced pulmonary fibrosis rat model.After radiation,the rats in the RAS-RA intervention groups were orally administered doses of 0.12,0.24 and 0.48 g·kg-1 once a day for 30 days.The average weight and lung index of the rats were observed after 30 days of contin-uous administration.Hydroxyproline(HYP)content in lung tissue was determined by hydrolysis method.The levels of IL-18 and IL-1 β in serum were detected by ELISA.Lung tissue pathological changes were ob-served by HE and Masson staining.Ultrastructural changes in lung tissue were observed by transmission e-lectron microscopy.The expression levels of NLRP3/caspase-1/GSDMD pyroptosis pathway-related proteins and fibrosis-related proteins in lung tissue were detec-ted by Western blot.Results Compared with the blank group,the HYP content in lung tissue and the levels of IL-18 and IL-1 β in serum significantly in-creased in the model group(P<0.01).HE and Mas-son staining showed inflammatory cell infiltration and collagen fiber deposition.Transmission electron mi-croscopy revealed increased damaged mitochondria,disordered arrangement,irregular morphology,shallow matrix,outer membrane rupture,mostly fractured and shortened cristae,mild expansion,increased electron density of individual mitochondrial matrix,mild sparse structure of lamellar bodies,partial disorder,unclear organelles,and characteristic changes of pyroptosis.Western blot analysis showed increased expression of caspase-1,GSDMD,NLRP3,CoL-Ⅰ,α-SMA,and CoL-Ⅲ proteins(P<0.01).Compared with the model group,the RAS-RA intervention group showed signifi-cant improvement in body mass index and lung index of rats,decreased levels of IL-18 and IL-1 β inflammatory factors(P<0.01),improved mitochondrial structure,reduced degree of fibrosis,and decreased expression of caspase-1,GSDMD,NLRP3,COL-Ⅰ,COL-Ⅲ,and α-SMA proteins in lung tissue(P<0.01).Conclusion RAS-RA has an inhibitory effect on radiation-in-duced pulmonary fibrosis,and its mechanism may be related to the inhibition of pyroptosis through the regu-lation of the NLRP3/caspase-1/GSDMD signaling pathway.
		                        		
		                        		
		                        		
		                        	
8.A multi-center epidemiological study on pneumococcal meningitis in children from 2019 to 2020
Cai-Yun WANG ; Hong-Mei XU ; Gang LIU ; Jing LIU ; Hui YU ; Bi-Quan CHEN ; Guo ZHENG ; Min SHU ; Li-Jun DU ; Zhi-Wei XU ; Li-Su HUANG ; Hai-Bo LI ; Dong WANG ; Song-Ting BAI ; Qing-Wen SHAN ; Chun-Hui ZHU ; Jian-Mei TIAN ; Jian-Hua HAO ; Ai-Wei LIN ; Dao-Jiong LIN ; Jin-Zhun WU ; Xin-Hua ZHANG ; Qing CAO ; Zhong-Bin TAO ; Yuan CHEN ; Guo-Long ZHU ; Ping XUE ; Zheng-Zhen TANG ; Xue-Wen SU ; Zheng-Hai QU ; Shi-Yong ZHAO ; Lin PANG ; Hui-Ling DENG ; Sai-Nan SHU ; Ying-Hu CHEN
Chinese Journal of Contemporary Pediatrics 2024;26(2):131-138
		                        		
		                        			
		                        			Objective To investigate the clinical characteristics and prognosis of pneumococcal meningitis(PM),and drug sensitivity of Streptococcus pneumoniae(SP)isolates in Chinese children.Methods A retrospective analysis was conducted on clinical information,laboratory data,and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country.Results Among the 160 children with PM,there were 103 males and 57 females.The age ranged from 15 days to 15 years,with 109 cases(68.1% )aged 3 months to under 3 years.SP strains were isolated from 95 cases(59.4% )in cerebrospinal fluid cultures and from 57 cases(35.6% )in blood cultures.The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87)and 27% (21/78),respectively.Fifty-five cases(34.4% )had one or more risk factors for purulent meningitis,113 cases(70.6% )had one or more extra-cranial infectious foci,and 18 cases(11.3% )had underlying diseases.The most common clinical symptoms were fever(147 cases,91.9% ),followed by lethargy(98 cases,61.3% )and vomiting(61 cases,38.1% ).Sixty-nine cases(43.1% )experienced intracranial complications during hospitalization,with subdural effusion and/or empyema being the most common complication[43 cases(26.9% )],followed by hydrocephalus in 24 cases(15.0% ),brain abscess in 23 cases(14.4% ),and cerebral hemorrhage in 8 cases(5.0% ).Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old,with rates of 91% (39/43)and 83% (20/24),respectively.SP strains exhibited complete sensitivity to vancomycin(100% ,75/75),linezolid(100% ,56/56),and meropenem(100% ,6/6).High sensitivity rates were also observed for levofloxacin(81% ,22/27),moxifloxacin(82% ,14/17),rifampicin(96% ,25/26),and chloramphenicol(91% ,21/23).However,low sensitivity rates were found for penicillin(16% ,11/68)and clindamycin(6% ,1/17),and SP strains were completely resistant to erythromycin(100% ,31/31).The rates of discharge with cure and improvement were 22.5% (36/160)and 66.2% (106/160),respectively,while 18 cases(11.3% )had adverse outcomes.Conclusions Pediatric PM is more common in children aged 3 months to under 3 years.Intracranial complications are more frequently observed in children under 1 year old.Fever is the most common clinical manifestation of PM,and subdural effusion/emphysema and hydrocephalus are the most frequent complications.Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates.Adverse outcomes can be noted in more than 10% of PM cases.SP strains are high sensitivity to vancomycin,linezolid,meropenem,levofloxacin,moxifloxacin,rifampicin,and chloramphenicol.[Chinese Journal of Contemporary Pediatrics,2024,26(2):131-138]
		                        		
		                        		
		                        		
		                        	
9.Bioequivalence study of ezetimibe tablets in Chinese healthy subjects
Pei-Yue ZHAO ; Tian-Cai ZHANG ; Yu-Ning ZHANG ; Ya-Fei LI ; Shou-Ren ZHAO ; Jian-Chang HE ; Li-Chun DONG ; Min SUN ; Yan-Jun HU ; Jing LAN ; Wen-Zhong LIANG
The Chinese Journal of Clinical Pharmacology 2024;40(16):2378-2382
		                        		
		                        			
		                        			Objective To evaluate the bioequivalence and safety of ezetimibe tablets in healthy Chinese subjects.Methods The study was designed as a single-center,randomized,open-label,two-period,two-way crossover,single-dose trail.Subjects who met the enrollment criteria were randomized into fasting administration group and postprandial administration group and received a single oral dose of 10 mg of the subject presparation of ezetimibe tablets or the reference presparation per cycle.The blood concentrations of ezetimibe and ezetimibe-glucuronide conjugate were measured by high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS),and the bioequivalence of the 2 preparations was evaluated using the WinNonlin 7.0 software.Pharmacokinetic parameters were calculated to evaluate the bioequivalence of the 2 preparations.The occurrence of all adverse events was also recorded to evaluate the safety.Results The main pharmacokinetic parameters of total ezetimibe in the plasma of the test and the reference after a single fasted administration:Cmax were(118.79±35.30)and(180.79±51.78)nmol·mL-1;tmax were 1.40 and 1.04 h;t1/2 were(15.33±5.57)and(17.38±7.24)h;AUC0-t were(1 523.90±371.21)and(1 690.99±553.40)nmol·mL-1·h;AUC0-∞ were(1 608.70±441.28),(1 807.15±630.00)nmol·mL-1·h.The main pharmacokinetic parameters of total ezetimibe in plasma of test and reference after a single meal:Cmax were(269.18±82.94)and(273.93±87.78)nmol·mL-1;Tmax were 1.15 and 1.08 h;t1/2 were(22.53±16.33)and(16.02±5.84)h;AUC0_twere(1 463.37±366.03),(1 263.96±271.01)nmol·mL-1·h;AUC0-∞ were(1 639.01±466.53),(1 349.97±281.39)nmol·mL-1·h.The main pharmacokinetic parameters Cmax,AUC0-tand AUC0-∞ of the two preparations were analyzed by variance analysis after logarithmic transformation.In the fasting administration group,the 90%CI of the log-transformed geometric mean ratios were within the bioequivalent range for the remaining parameters in the fasting dosing group,except for the Cmax of ezetimibe and total ezetimibe,which were below the lower bioequivalent range.The Cmax of ezetimibe,ezetimibe-glucuronide,and total ezetimibe in the postprandial dosing group was within the equivalence range,and the 90%CI of the remaining parameters were not within the equivalence range for bioequivalence.Conclusion This test can not determine whether the test preparation and the reference preparation of ezetimibe tablets have bioequivalence,and further clinical trials are needed to verify it.
		                        		
		                        		
		                        		
		                        	
10.Treatment of male immune infertility by traditional Chinese medicine:A meta-analysis
Chun-Mei FAN ; Si-Qi MA ; Ke-Fan DING ; Yi-Jian YANG ; Xin-Bang WEN ; Zi-Qin ZHAO ; Shu-Hui CHEN ; Guo-Zheng QIN
National Journal of Andrology 2024;30(6):547-563
		                        		
		                        			
		                        			Objective:To evaluate the efficacy and safety of traditional Chinese medicine(TCM)in the treatment of male im-mune infertility(MII)by meta-analysis.Methods:We retrieved randomized controlled trial(RCT)on the treatment of male im-mune infertility with traditional Chinese medicine from the databases of WanFang,Chinese Biomedical Literature,Cochrane Library,Weipu,PubMed and CNKI,and performed methodological quality assessment of the RCTs identified and statistical analysis and evalua-tion of the publication bias using the RevMan5.4 software.Results:Totally,25 RCTs(2 563 cases)were included in this study.Compared with Western medicine alone in the treatment of MII,TCM achieved a significantly higher total effectiveness rate(OR=6.35,95% CI:4.96-8.13,P<0.000 01),negative conversion rate of seminal plasma anti-sperm antibodies(OR=4.52,95% CI:2.72-7.51,P<0.000 01),negative rate of serum anti-sperm antibodies(OR=2.98,95% CI:2.23-3.96,P<0.000 01),sperm concentration(MD=15.56,95% CI:11.32-19.79,P<0.000 01),grade a sperm motility(MD=3.85,95% CI:1.91-5.79,P=0.000 01),grade a+b sperm motility(MD=13.77,95% CI:7.06-20.48,P<0.000 1),sperm viability(MD=10.32,95% CI:6.78-13.86,P<0.000 01)and pregnancy rate(OR=3.53,95% CI:2.68-4.63,P<0.000 01),but a lower rate of adverse reactions(OR=0.06,95% CI:0.01-0.23,P<0.000 01).There was no statistically significant difference in the percentage of morphologically abnormal sperm between TCM and Western medicine alone in the treatment of MII(MD=-7.53,95% CI:-15.50-0.44,P=0.06).Conclusion:TCM has a definite effectiveness and high safe in the treatment of male immune infertility.
		                        		
		                        		
		                        		
		                        	
            
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