Background Asthma poses a serious threat to children's growth, development, and mental health, thus there has been an increasing focus on the control of asthma morbidity in children and the assessment of its risk factors. A growing body of research has found that exposure to ambient air pollutants an significatly increase the risk of childhood asthma. Objective To understand the changes of ambient air pollutant concentrations in Nanjing and asthma outpatient visits to Nanjing Children's Hospital, and to quantitatively analyze the effects of exposure to different ambient air pollutants on children's asthma outpatient visits. Methods Daily data of ambient air pollutants fine particulate matter (PM_2.5), inhalable particle (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon monoxide (CO), ozone (O₃), meteorological factors (air temperature & relative humidity), and outpatient visits due to asthma in the hospital from January 1, 2019 to December 31, 2023 were collected, and a generalized additive model based on quasi poisson distributions was used to quantitatively analyze the short-term effects of ambient air pollutant exposure on outpatient visits due to asthma in the hospital. Results The annual average concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ in Nanjing from 2019 to 2023 did not exceed the national limits. For single-day lagged effects, the single-pollutant model showed that the effects of PM_2.5, PM₁₀, NO₂, and CO on children's asthma outpatient visits were greatest for every 10 units increase at lag0, with excess risk (ER) of 1.39% (95%CI: 0.65%, 2.14%), 1.46% (95%CI: 0.97%, 1.95%), 5.46% (95%CI: 4.36%, 6.57%), and 0.18% (95%CI: 0.11%, 0.26%), respectively, and SO₂ reached the maximum effect at lag1, with an ER of 23.15% (95%CI: 13.57%, 33.53%) for each 10 units increase in concentration. Different pollutants reached their maximum cumulative lag effects at different time. The PM₁₀, PM_2.5, SO₂, NO₂, and CO showed the largest cumulative lag effects at lag01, lag01, lag02, lag02, and lag03, respectively, with ERs of 1.35% (95%CI: 0.77%, 1.92%), 0.96% (95%CI: 0.10%, 1.83%), 28.50% (95%CI: 15.49%, 42.98%), 6.92% (95%CI: 5.53%, 8.33%), and 0.31% (95%CI: 0.20%, 0.42%), respectively. The influences of PM_2.5 and PM₁₀ on outpatient visits due to asthma in the hospital became more pronounced with advancing age, while the associations with NO₂, SO₂, and CO were weakened as children grew older. Conclusion Ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, CO) can increase childhood asthma visits, and different pollutants have varied effects on the number of asthmatic children's visits at different ages.

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 investigate the feasibility of 3.0 T glutamate chemical exchange saturation transfer (GluCEST) imaging in evaluating renal redox metabolism in renal ischemia-reperfusion injury (IRI).Methods:Rabbits in the IRI group ( n=56) underwent surgery by clamping the left renal artery for 45 min and then releasing to establish IRI. Rabbits in the sham group ( n=8) underwent the same operation without clamping the left renal artery. GluCEST MRI was performed before and at 1 h, 12 h, 1 day, 3 days, 7 days, and 14 days after the operations, with eight rabbits in the IRI group sacrificed immediately after each scanning and eight in the sham group sacrificed at 14 days after scanning. The left kidneys were removed for histopathological examination and reactive oxygen species (ROS) fluorescence staining. Differences in the magnetic resonance ratio asymmetry (MTR asym) of the renal cortex and outer medulla among different groups were compared. Correlations between the MTR asym and ROS were analyzed. Results:The MTR asym of the renal cortex in the sham and IRI subgroups were higher than that of the outer medulla ( t=8.16, P<0.001; t=4.78, P=0.002; t=4.94, P=0.002; t=5.76, P=0.001, t=6.68, P<0.001; t=6.40, P<0.001; t=5.16, P=0.001; t=3.30, P=0.013). The MTR asym of the renal cortex and outer medulla in the IRI-1h, IRI-12h, IRI-1d, IRI-3d, IRI-7d, and IRI-14d groups were lower than in the sham and IRI-pre groups (all P<0.05). The MTR asym of the renal cortex and outer medulla in the IRI-1h group were lower than in the IRI-12h, IRI-1d, IRI-3d, IRI-7d, and IRI-14d groups (all P<0.05). The MTR asym of the renal cortex in the IRI-12h group was lower than in the IRI-7d and IRI-14d groups (1.84%±0.09% vs.2.42%±0.19%, 2.41%±0.31%, all P<0.05). The MTR asym of the renal cortex in the IRI-1d group was lower than in the IRI-7d group (1.99%±0.17% vs. 2.42%±0.19%, P=0.008). The MTR asym of the outer medulla in the IRI-12h group was lower than in the IRI-3d, IRI-7d, and IRI-14d groups (1.32%±0.27% vs. 1.79%±0.31%, 1.98%±0.18%, 1.66%±0.40%, respectively, all P<0.05]. The MTR asym of the outer medulla in the IRI-7d group was higher than in the IRI-1d and IRI-14d groups (1.98%±0.18% vs. 1.52%±0.31%, 1.66%±0.40%, all P<0.05). The MTR asym of the renal cortex and outer medulla had a strong negative correlation with the mean fluorescence intensity of ROS ( ρ=-0.889, P<0.001; ρ=-0.784, P<0.001). Conclusion:3.0 T GluCEST imaging can indirectly reflect the changes of renal redox metabolism in renal IRI.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective To construct an in vitro"metabolic memory"cell model of HT-22 mouse hippocampal neurons induced by high glucose,and to investigate the effect of"metabolic memory"on apoptosis and histone acetylation in HT-22 cells.Methods HT-22 cells were cultured in high glucose medium(glucose concentration was 55 mmol/L)and conventional glucose medium(glucose concentration was 25 mmol/L),and cells were divided into the control group(NG 4,6 and 8 groups,25 mmol/L glucose was cultured for 4,6 and 8 days,respectively),the high glucose group(HG 4,6 and 8 groups,respectively)and the metabolic memory group(HG2NG2,HG2NG4,HG2NG6,HG4NG2 and HG4NG4 groups,high glucose culture for 2 days to 25 mmol/L glucose culture for 2,4 or 6 days,high glucose culture for 4 days to 25 mmol/L glucose culture for 2 or 4 days).Cell viability was detected by CCK-8 method.The release of lactate dehydrogenase(LDH)in cell culture supernatant was detected,and the optimal time to establish a"metabolic memory"model was selected.Subsequently,cells were divided into the NG4 group,the NG8 group,the HG4 group,the HG4NG4 group and the HG8 group,and the cell morphology of each group was observed by optical microscope.The apoptosis rate was detected by flow cytometry.The activities of deacetylase(HDAC)and histone acetyltransferase(HAT)were detected by enzyme-linked immunosorbent assay(ELISA).Western blot assay was used to detect expression levels of histone deacetylase 4(HDAC4),B lymphocyte tumor 2(Bcl-2),Bcl-2 related X protein(Bax)and Caspase-3 protein.Results The HG4NG4 group was the ideal cell model with high glucose metabolic memory.Cells of the NG4 group and the NG8 group were interwoven into a dense network,growing well,with spindle shaped cells and distinct synaptic structures.However,in the HG4 group and the HG8 group,the cell body became round,synaptic structure disappeared and growth was inhibited.In the HG4NG4 group,the number of cells increased but their morphology was damaged.Results of flow cytometry showed that compared with the NG8 group,the apoptosis rates were significantly increased in the HG8 group and the HG4NG4 group(P＜0.05).ELISA results showed that compared with the NG8 group,the expression levels of HDAC4,Bax,and Caspase-3 proteins increased in the HG8 group and the HG4NG4 group,while the expression level of Bcl-2 protein significantly decreased(P＜0.05).Compared with the HG8 group,there were no significant differences in protein expression levels of HAT and HDAC in the HG4NG4 group.Western blot reslts showed that compared with the NG8 group,the levels of HDAC4,Bax and Caspase-3 protein increased in the HG8 group and the HG4NG4 group(P＜0.05).Compared with the HG8 group,there were no significant differences in protein expression levels in the HG4NG4 group.Conclusion HT-22 mouse hippocampal neurons cultured with 55mmol/L high glucose for 4 days,and then cultured with 25 mmol/L glucose for 4 days are the ideal"metabolic memory"cell model.The mechanism may be related to the increased activity of HDAC,HAT and HDAC4 expression in the hyperglycemic model.

Objective:To explore the feasibility of chemical exchange saturation transfer (CEST) imaging at 3.0 T MRI in quantifying renal redox metabolism in vitro models and experimental animals.Methods:Redox metabolites in vitro models with physiological concentrations were prepared, including reduced metabolites (glutamate, alanine, glutathione) and oxidized metabolites (2-ketoglutarate, pyruvate, glutathione disulfide, ammonium hydroxide). CEST examinations were performed at 3.0 T MRI. The imaging parameters were as follows: CEST images with different saturation pulse intensity (B 1) (1, 2, 3, 4 μT) and a fixed radio frequency (RF) duration of 2 000 ms; CEST images with different RF durations (1 500 and 2 000 ms) were acquired with a fixed B 1 value of 2 μT to obtain the optimal scanning parameters. CEST examinations with optimized parameters were performed on the left kidneys of seven healthy rabbits, and the differences in magnetic resonance ratio asymmetry (MTR asym) between rabbit renal cortex and outer medulla were measured. A paired t-test was used to compare the differences. Results:The optimal B 1 for CEST examination of redox metabolites was 2 μT, and the optimal RF duration was 2 000 ms. The MTR asym peaks of glutathione disulfide, glutathione, glutamic acid, and alanine were at 3.75, 3.5, 3, and 1.5 ppm, respectively. The MTR asym peaks of pyruvate, 2-ketoglutarate, and ammonium hydroxide were at 1 ppm. The MTR asym peak values of reduced metabolites were higher than those of oxidized metabolites. When the B 1 value was 2 μT and the RF duration was 2 000 ms, the MTR asym signal of the renal cortex was (2.60±1.10) %, (2.86±1.32) %, (3.04±1.06) %, and (2.98±0.91) % at 1, 3, 3.5, and 3.75 ppm, respectively. The MTR asym signal of the outer medulla was (1.00±0.56) %, (2.43±0.94) %, (2.29±0.88) % and (1.98±0.58) %, respectively. The MTR asym signal of the renal cortex was higher than that of the outer medulla, and the differences were statistically significant ( t=3.04, P=0.023; t=2.56, P=0.043; t=3.50, P=0.013; t=3.45, P=0.014). Conclusion:CEST imaging at 3.0 T MRI can be used to quantitatively evaluate redox metabolism of healthy rabbit kidneys in vitro model and normal experimental rabbits.