Objective: To explore the interaction between school bullying and insomnia in relation to depression-anxiety-stress emotions among primary and secondary school students,so as to provide a basis for preventing negative emotional states in adolescents. Methods: In October 2024, a stratified cluster sampling method was used to select 3 058 students in grade 5-6 of primary, junior and senior high school in Sheyang County of Jiangsu Province. The Delaware Bullying Victimization Scale, Insomnia Severity Index, Depression-Anxiety-Stress Scale-21, and Study Condition Questionnaire were employed to investigate school bullying, insomnia, depression-anxiety-stress emotions, and academic performance. The χ 2 test and Logistic regression were used to analyze the association between school bullying and insomnia interactions and depression-anxiety-stress emotions among primary and secondary school students, multiplicative interaction analysis was conducted, and additive interaction analysis was performed using R software. Results: The detection rates of depression-anxiety-stress emotions among primary and secondary school students were 21.6%, 28.4% and 10.8%, respectively. The detection rates of physical bullying, relationship bullying, verbal bullying and cyberbullying in school bullying were 10.6%, 14.0%, 22.3%, and 6.2%, respectively. The detection rate for insomnia was 23.1%. Results from Logistic regression analysis showed that, after adjusting for relevant factors, physical, relational, verbal, and cyberbullying and insomnia were positively correlated with the detection rates of depression ( OR = 5.72- 10.93), anxiety ( OR =6.35-12.17), and stress emotions ( OR =5.97-14.52) among primary and secondary school students (all P <0.01). The multiplicative interaction between physical, relational, verbal, and cyberbullying and insomnia was positively correlated with the detection rates of depression ( OR =8.00-18.01), anxiety ( OR =11.35-17.76), and stress emotions ( OR =7.64-9.12) in primary and secondary school students (all P <0.01). Additive interactions were observed between physical, relational, verbal, and cyberbullying and insomnia in relation to the detection rates of depression, anxiety, and stress emotions among primary and secondary school students (both RERI and AP >0 and the credible interval excluded 0, SI >1 and the credible interval excluded 1). Conclusion School bullying and insomnia are associated with depression, anxiety, and stress emotions among primary and secondary school students, and they exhibit both multiplicative and additive interactions.

Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding developed a strategy for platelet and plasma infusion management in critically ill children based on systematic reviews and consensus meetings of international multidisciplinary experts. One good practice statement and six expert consensus statements were proposed for plasma and platelet transfusions in critically ill children following severe trauma, traumatic brain injury, and/or intracranial hemorrhage. This article introduces the specific methods and basis for the formation of recommendations in this part of the guide.

OBJECTIVE To establish the UPLC fingerprint and the method for multi-component content determination in Jingangteng capsules， and to evaluate its quality by combining chemical pattern recognition analysis. METHODS An UPLC method was established. Separation was performed on a Zorbax SB-C 18 Rapid Resolution HD column， with acetonitrile-0.1% formic acid as the mobile phase for gradient elution.Using the Similarity Evaluation System for Chromatographic Fingerprints of Traditional Chinese Medicines （2012 edition）， UPLC fi ngerprints were established for 10 batches of Jingangteng capsules， and similarity was evaluated. SPSS 22.0 and SIMCA 14.1 software were used to perform hierarchial-cluster analysis and orthogonal partial least squares discriminant analysis （OPLS-DA）， respectively. The same UPLC method was employed to determine the contents of chlorogenic acid， 3，5-dihydroxy-2-methylbenzoic acid-3- O -glucoside （M1）， caffeic acid， astilbin， oxyresveratrol， quercitrin and resveratrol in the 10 batches of samples. RESULTS A total of 17 common peaks were identified in UPLC fingerprints of the 10 batches of samples， of which 7 were identified as chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol. The similarities of 10 batches of samples ranged from 0.820 to 0.985. The results of hierarchial-cluster analysis showed that 10 batches of samples were grouped into four categories： S1-S4 formed one group， S5 and S6 formed another， S7， S8 and S10 formed a third， and S9 formed a fourth， consistent with the OPLS-DA results； the variable importance projection values for peaks 7， 10， 2， 16 （resveratrol）， 13 （oxyresveratrol）， 11， 6 （caffeic acid）， 5 （M1） and 15 （quercitrin） were ＞1. Quantitative analysis results showed that the contents of chlorogenic acid， M1， caffeic acid， astilbin， oxyresveratrol， quercitrin， and resveratrol were 1.650 8-4.213 7， 0.636 2-2.161 7， 0.031 0-0.086 5， 0.239 1-1.069 3， 0.211 9-1.104 0， 0.488 8-2.399 2， and 0.164 0-0.699 8 mg/g， respectively. CONCLUSIONS UPLC fingerprint and content determination methods established in this study are simple to operate， accurate， reliable and reproducible； when combined with chemical pattern recognition analysis， they can be used to evaluate the quality of Jingangteng capsules. Nine components， such as resveratrol， oxyresveratrol， caffeic acid， M1 and quercitrin， may serve as markers of quality variation.

To guide transfusion practice in critically ill children who often need plasma and platelet transfusions, the Transfusion and Anemia Expertise Initiative-Control/Avoidance of Bleeding (TAXI-CAB) developed Recommendations and Expert Consensus for Plasma and Platelet Transfusion Practice in Critically Ill Children. This guideline addresses 53 recommendations related to plasma and platelet transfusion in critically ill children with 8 kinds of diseases, laboratory testing, selection/treatment of plasma and platelet components, and research priorities. This paper introduces the specific methods and results of the recommendation formation of the guideline.

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 analyze the distribution characteristics of fluoride and trichloromethane in drinking water in rural schools in Guizhou Province and assess their health risks, so as to provide a scientific basis for ensuring the safety of drinking water in rural schools. Methods: During the dry season (March to May) and wet season (July to September) of 2020 to 2022, 788 rural primary and secondary schools in agricultural counties (districts) in Guizhou Province were selected for investigation by using a direct sampling method. A total of 1 566 drinking water samples were collected from these schools, and the mass concentrations of fluoride and trichloromethane in the water samples were detected. The Mann-Whitney U test was used for intergroup comparison, and a health risk assessment model was employed to evaluate the health risks of students oral intake of fluoride and trichloromethane. Results: From 2020 to 2022, the mass concentrations of fluoride and trichloromethane in the drinking water of rural schools in Guizhou Province all met the standards, and the ranges were no detection to 0.99 mg/L and (no detection to 0.06)×10 -3 mg/L, respectively. The mass concentrations of fluoride in dry and wet seasons were 0.05(0.05,0.10), 0.05(0.05,0.10) mg/L, the mass concentrations of trichloromethane were [0.02(0.02,1.00)]×10 -3 , [0.02(0.02,1.00)]×10 -3 mg/L, the mass concentrations of fluoride in factory water and terminal water were 0.05(0.05,0.05), 0.05(0.05,0.10) mg/L, and the differences were not statistically significant ( Z=-0.04, -0.88, - 0.98 , P >0.05). There was a statistically significant difference in the mass concentration of trichloromethane between factory water and peripheral water [0.02(0.02,0.02)×10 -3 , 0.02(0.02,1.05)×10 -3 mg/L]( Z=-2.16, P < 0.05 ). The non-carcinogenic risk assessment values for students oral exposure to fluoride and trichloromethane were in the range of 0.01(0.01,0.03)-0.03(0.03,0.06) and [0.26( 0.26 ,14.54)]×10 -4 -[0.52(0.52,48.62)]×10 -4 , respectively, all of which were at acceptable levels; the carcinogenic risk assessment values for oral exposure to trichloromethane were in the range of [0.08(0.08, 4.51 )]×10 -7 -[0.16(0.16,15.07)]×10 -7 , indicating a low risk. Conclusions The health risks of students expore to fluoride and trichloromethane in drinking water in rural schools of Guizhou Province are low. It is necessary to strengthen the standardized management of disinfection in some rural drinking water projects and the monitoring of fluoride in water sources to reduce the exposure risk to children.

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 value of dual-energy CT parameters in the evaluation of pathological grade of pancreatic ductal adenocarcinoma.Methods:80 cases of pancreatic ductal adenocarcinoma confirmed by pathology were retrospectively analyzed and divided into high grade group (36 cases) and low grade group (44 cases) according to their differentiation degree. All 80 patients underwent SOMATOM Force DECT for arterial phase (AP) and pancreatic phase (PP) scanning, and measured dual-energy parameters including dual-phase iodine concentration (IC AP, IC PP) in tumors and normal pancreatic parenchyma, pancreatic phase and arterial phase iodine concentration difference (ICD PP-AP) in tumors, dual-phase iodine uptake ratio (IUR AP, IUR PP) , dual-phase tumor/normal pancreatic parenchyma fat fraction ratio, and dual-phase slope of energy spectrum curve. Differences between two groups were compared by two independent sample t-test or Mann-Whitney U test or chi-square test. The multivariate logistic regression model was used to analyze the influencing factors of pathological grading of PDAC. Results:There were statistically significant differences in gender, age, aspect ratio, positive lymph node, fat fraction ratio in pancreatic phase between the two groups ( P< 0.05) . The multivariate logistic regression analysis showed that fat fraction ratio in pancreatic phase ( OR=1.781, 95% CI 1.127-2.814, P=0.013) , positive lymph node ( OR=4.870, 95% CI 1.488-15.938, P=0.009) , aspect ratio ( OR=0.019, 95% CI 0.001-0.437, P=0.013) were independent factors influencing the pathologic grade of PDAC. Conclusion:Parameters of dual-energy CT are valuable in the evaluation of pathological grading of PDAC.

Objective:To explore the relationship between life events, anxiety, depression, and behavior problems in adolescents.Methods:From September to October 2022, the cluster sampling method was used to select 5 341 adolescents from 4 middle schools in Xinxiang urban area.The subjects and their parents were investigated by the adolescent self-rating life events check list (ASLEC), generalized anxiety disorder scale (GAD-7), patient health questionnaire (PHQ-9), and child behavior checklist (CBCL). SPSS 27.0 software was used for Spearman correlation analysis, and AMOS 28.0 software was used to construct the structural equation model.Results:The scores of anxiety, depression, and behavioral problems were 1 (0, 4), 1 (0, 4), and 3 (0, 10). The total score of life events was 5 (1, 13), and the dimensions scored as follows: interpersonal conflict 1 (0, 4), academic pressure 2 (0, 5), punishment 0 (0, 2), loss 0 (0, 0), health and adaptation problem 0 (0, 1), and others 0 (0, 2). There were positive correlations between life events and its dimensions, depression, anxiety and behavioral problems ( r=0.28-0.69, all P<0.01). In the overall population, anxiety and depression played parallel mediating roles in the impact of life events on behavior problems. Life events could positively predict anxiety ( β=0.68, P<0.01), and anxiety could positively predict behavior problems ( β=0.04, P=0.02). Life events could positively predict depression ( β=0.77, P<0.01), and depression could positively predict behavior problems ( β=0.18, P<0.01). The standardized total effect size of the impact of life events on behavioral problems was 0.622 (95% CI=0.564-0.675). The standardized direct effect size and indirect effect size were 0.460 (95% CI=0.374-0.539) and 0.162 (95% CI=0.108-0.218), accounting for 74.0% and 26.0%of the total effect, respectively. After stratification by gender, the results for male adolescents were consistent with the overall population, while the mediating effect of anxiety was not significant in the female adolescents. Conclusion:Life events can lead to anxiety and depression in adolescents, thereby increasing the risk of behavior problems.

Objective To investigate and compare the epidemiological characteristics of common respiratory viruses among influenza-like illness(ILI)and severe acute respiratory infection(SARI)cases in Huzhou,Zhejiang Province before and after the COVID-19 pandemic,so as to provide a basis for formulating and adjusting the prevention and control strategies for viral respiratory infectious diseases.Methods ILI and SARI cases at two influenza surveillance sentinel hospitals in Huzhou and had throat swab samples collected during Nov 2017 to Feb 2020(pre-COVID-19 pandemic period)and Dec 2022 to Apr 2024(post-COVID-19 mitigation phase)were selected as the participants.Seven common viral respiratory pathogens were tested,including influenza A virus(H1N1 and H3N2 subtypes),influenza B virus(Victoria lineage,FluB),respiratory syncytial virus(RSV),rhinovirus(HRV),adenovirus(ADV),and severe acute respiratory syndrome coronavirus-2(SARS-CoV-2).The positive rates of respiratory pathogens before and after the COVID-19 pandemic were compared across different age groups and different time.Results A total of 7 948 ILI samples and 2 294 SARI samples were included.The overall positive rate of ILI samples increased from 33.6%to 47.1%,primarily due to the increase in influenza and COVID-19 infections;the overall positive rate of SARI samples decreased from 31.4%to 24.8%,mainly due to the reduction in HRV and ADV infections.During the post-COVID-19 mitigation phase,SARS-CoV-2(22.1%),H3N2(12.7%),and FluB(6.0%)were the primary pathogens in ILI samples,while RSV(7.1%),H3N2(5.3%),and HRV(4.5%)dominated in SARI samples.During the post-COVID-19 mitigation phase,the influenza virus circulation period was shortened.Before the COVID-19 pandemic,RSV was mainly detected in autumn and winter,while during the post-COVID-19 mitigation phase,out-of-season RSV epidemics were observed in spring and summer.Co-infection rate in ILI cases increased significantly in the post-COVID-19 mitigation phase,predominantly consisting of co-infections of COVID-19 and influenza A virus,while co-infection rate in SARI cases showed a decline.Conclusion We found important epidemiological changes in respiratory viruses in Huzhou during the post-COVID-19 mitigation phase compared to pre-COVID-19 period,including increased positive rates of influenza and COVID-19,and disruptions to the seasonal patterns of influenza and RSV.The prevention and control strategies should be adjusted in a timely manner based on the monitoring data.