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 clinical efficacy of Yiqi Huayu Decoction(mainly composed of Astragali Radix,Dioscoreae Rhizoma,Poria,fried Euryales Semen,Ecliptae Herba,Rosae Laevigatae Fructus,charred Crataegi Fructus,Ligustri Lucidi Fructus,Salviae Miltiorrhizae Radix et Rhizoma,and Leonuri Herba)combined with Calcium Dobesilate in the treatment of diabetic nephropathy(DN)with qi deficiency and blood stasis syndrome,and to observe the effect of the therapy on vascular endothelial growth factor(VEGF)and insulin-like growth factor 1(IGF-1).Methods Ninety patients with DN of qi deficiency and blood stasis type were randomly divided into an observation group and a control group,with 45 patients in each group.All patients received basic hypoglycemic therapy and treatment for controlling blood pressure and regulating lipid metabolism disorders.Moreover,the patients in the control group were given Calcium Dobesilate orally,and the patients in the observation group were given Yiqi Huayu Decoction combined with Calcium Dobesilate.The course of treatment lasted for 3 months.The changes of traditional Chinese medicine(TCM)syndrome scores,renal function parameters and serum VEGF and IGF-1 levels in the two groups of patients were observed before and after the treatment,and the clinical efficacy of the two groups was evaluated after treatment.Results(1)After 3 months of treatment,the total effective rate of the observation group was 91.11%(41/45),and that of the control group was 75.56%(34/45).The intergroup comparison(tested by chi-square test)showed that the therapeutic effect of the observation group was significantly superior to that of the control group(P＜0.05).(2)After one month and 3 months of treatment,the TCM syndrome scores of both groups were significantly lower than those before treatment(P＜0.05),and the scores after 3 months of treatment in the two groups were significantly lower than those after one month of treatment(P＜0.05).The intergroup comparison showed that the reduction of TCM syndrome scores of the observation group was significantly superior to that of the control group after one month and 3 months of treatment(P＜0.01).(3)After treatment,the levels of renal function parameters such as serum creatinine(Scr),blood urea nitrogen(BUN),and glomerular filtration rate(GFR)in the two groups of patients were significantly improved compared with those before treatment(P＜0.05),and the observation group's effect on the improvement of all renal function parameters was significantly superior to that of the control group(P＜0.01).(4)After treatment,the serum VEGF and IGF-1 levels in the two groups of patients were significantly lower than those before treatment(P＜0.05),and the observation group's effect on the decrease of serum VEGF and IGF-1 levels was significantly superior to that of the control group(P＜0.01).(5)In the course of treatment,no significant adverse reactions occurred in the two groups of patients,with a high degree of safety.Conclusion Yiqi Huayu Decoction combined with Calcium Dobesilate exerts certain therapeutic effect in treating DN patients with qi deficiency and blood stasis syndrome.The combined therapy can effectively down-regulate the serum levels of VEGF and IGF-1,significantly improve the renal function,and alleviate the clinical symptoms of the patients,with a high degree of safety.

Objective To explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.

Stroke is one of the main diseases endangering the health and life of the middle-aged and elderly,and in recent years it shows a younger trend.It has the characteristics of high incidence,mortality and disability rate.Atherosclerosis is the main intervention target for stroke prevention and treatment.CircRNA is highly expressed in the cerebrovascular system and plays an important regulatory role in the pathogenesis of ischemia-reperfusion and ischemic stroke.This paper reviews the mechanism of circRNA and miRNA molecular network in the pathophysiological process of atherosclerosis,ischemic stroke and ischemia-reperfusion injury,so as to provide theoretical support for the application of circRNA in gene diagnosis and drug treatment of atherosclerotic ischemic stroke.

Celastrol and wilforlide A are the main active triterpenoids of the traditional Chinese medicine Lei Gong Teng, which have anti-tumour, anti-inflammatory and immunosuppressive activities, and are the material basis for the clinical efficacy of Lei Gong Teng-related Chinese medicinal preparations. By analysing the biosynthetic pathway of active ingredients, optimizing genetic elements and utilizing "cell factory" to produce triterpenoids heterologously will be an effective way to obtain from Tripterygium wilfordii in the future in a "low-cost and high-efficient" manner. CYP712Ks are the first cytochrome P450s involved in the skeleton modification of friedelane-type and oleanane-type triterpenoids in T. wilfordii, and they can catalyse the generation of polpunonic acid from friedelin and 3-epi-katonic acid from β-amyrin by carboxylation at C-29 position. In this study, four multifunctional TwCYP712K1/2/3/5 were used to clarify the catalytic function and substrate selection preference using in vivo functional characterization in Saccharomyces cerevisiae. The spatial structure of the protein-substrate binding was clarified through homology modeling and molecular docking, and then the differential amino acids in the active pocket of the protein were mutated to clarify the crucial amino acids determining the catalytic function and the selection of substrate structure. A total of 63 mutant elements were constructed, and the amino acid sites affecting the carboxylation function of TwCYP712Ks were analyzed. In particular, the key amino acids affecting the substrate selectivity of TwCYP712K2 towards oleanane-type and friedelane-type triterpenoids were revealed and the TwCYP712K2^F127I and TwCYP712K2^A227T mutants would result in a reversal of the product ratio. In conclusion, four proteins of TwCYP712Ks were semi-rationally designed by homologous protein alignment and mutual mutation to elucidate multiple amino acid sites determining the catalytic function of the proteins, and a series of activity-enhancing or altering mutants were obtained, which provide abundant catalytic elements for the biosynthesis of active triterpenoids from T. wilfordii, and the mechanism of carboxylation in the C-29 position was initially elucidated.