Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Following the evidence-based diagnosis and treatment guideline development method for common pediatric diseases,the The Guideline for the Diagnosis and Treatment of Pediatric Food Accumulation and Stagnation in Chinese Medicine was formed through literature retrieval,expert questionnaire survey,expert discussion meeting,and solicitation of opinions.The revised content includes the scope of the guideline,normative reference documents,terms and definitions,diagnosis,syndrome differentiation,treatment,pre-vention and nursing suggestions,etc.,aiming to update the clinical diagnosis and treatment plan of Chinese medicine for accumulation and provide important clinical decision-making references for clinical physicians to rationally apply the diagnosis and treatment plan of Chinese medicine in the prevention and treatment of accumulation and stagnation in Children.

Following the evidence-based diagnosis and treatment guideline development method for common pediatric diseases,the The Guideline for the Diagnosis and Treatment of Pediatric Food Accumulation and Stagnation in Chinese Medicine was formed through literature retrieval,expert questionnaire survey,expert discussion meeting,and solicitation of opinions.The revised content includes the scope of the guideline,normative reference documents,terms and definitions,diagnosis,syndrome differentiation,treatment,pre-vention and nursing suggestions,etc.,aiming to update the clinical diagnosis and treatment plan of Chinese medicine for accumulation and provide important clinical decision-making references for clinical physicians to rationally apply the diagnosis and treatment plan of Chinese medicine in the prevention and treatment of accumulation and stagnation in Children.

Objective:To establish a rapid quantitative model of sodium chloride content in Halitum of traditional Chinese medicine by using X-ray diffraction technology and machine learning algorithm.Methods:The data of X-ray diffraction patterns of 90 batches of Halitum samples were collected,and the rapid prediction models of X-ray diffraction were constructed by using partial least squares(PLS),support vector regression(SVR)long short-term memory(LSTM)according to the reference values determined by the content determination method of Halitum in the Chinese Pharmacopoeia 2020 edition.Results:The data preprocessed by multivariate scatter correction(MSC)and selected by competitive adaptive reweighted sampling(CARS)were better in PLS model and LSTM model.The data pretreated by standard normal transformation(SNV)and selected by CARS feature variable perform well in SVR.Conclusion:The three models show good prediction potential,which shows that the combination of X-ray diffraction technology and machine learning algorithm is feasible for accurate prediction of the content of Halitum in traditional Chinese medicine.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Based on the Global Burden of Disease (GBD) 2019, this study characterized the burden of dietary iron deficiency across 154 countries participating in the Belt and Road Initiative (BRI). A joinpoint regression model was employed to assess temporal trends in disease burden. Pearson correlation analysis and locally weighted regression were utilized to investigate the relationship between burden and the socio-demographic index (SDI). Slope indices and concentration indices were calculated to evaluate health inequalities, while frontier analysis explored disease burden benchmarks. Key metrics included prevalence and disability-adjusted life years (DALYs). Results revealed downward trends in age-standardized prevalence rates and age-standardized DALYs rates of dietary iron deficiency among children under 10 years old in 117 and 125 BRI countries, respectively, from 1990 to 2019. A significant negative correlation was observed between disease burden and SDI in 2019 ( R=-0.80, P<0.001). The slope indices decreased from -936 (95% CI:-1 006, -806) in 1990 to -1 128 (95% CI:-1 256, -999) in 2019, while the concentration indices declined from -0.24 (95% CI:-0.28, -0.20) to -0.35 (95% CI:-0.39, -0.30) during the same period. Frontier analysis further identified substantial gaps between observed outcomes and optimal performance thresholds in several countries.

Objective:To establish a rapid quantitative model of sodium chloride content in Halitum of traditional Chinese medicine by using X-ray diffraction technology and machine learning algorithm.Methods:The data of X-ray diffraction patterns of 90 batches of Halitum samples were collected,and the rapid prediction models of X-ray diffraction were constructed by using partial least squares(PLS),support vector regression(SVR)long short-term memory(LSTM)according to the reference values determined by the content determination method of Halitum in the Chinese Pharmacopoeia 2020 edition.Results:The data preprocessed by multivariate scatter correction(MSC)and selected by competitive adaptive reweighted sampling(CARS)were better in PLS model and LSTM model.The data pretreated by standard normal transformation(SNV)and selected by CARS feature variable perform well in SVR.Conclusion:The three models show good prediction potential,which shows that the combination of X-ray diffraction technology and machine learning algorithm is feasible for accurate prediction of the content of Halitum in traditional Chinese medicine.

Objective:To establish a rapid quantitative model for the determination of moisture,extractives,and content in Pollen Typhae.Methods:Near-infrared spectra of 91 batches of Pollen Typhae samples were collected.Spectral preprocessing was performed using S-G,MSC,SNV,and CWT methods.Variable selection was conducted using CARS,SPA,and VIP methods,and compared with full-spectrum modeling.Partial least squares(PLS)mod-els were established for the quantitative determination of moisture,total ash,extractives,and content.The model performance was evaluated by calculating the coefficient of determination for the calibration set and validation set(R2 c,R2v),root mean square error of calibration and validation(RMSEc,RMSEv),and residual prediction devia-tion(RPD).Results:The PLS models for moisture,extractives,and content in Pollen Typhae showed R2c and R2v values greater than 0.9,RMSEc and RMSEv values approaching 0,and RPD values greater than 3.Conclusion:In this study,near-infrared spectroscopy was used to construct quantitative prediction models for moisture,extractives,typhaneoside,and isorhamnetin-3-O-neohesperidoside content in Pollen Typhae.This method enables rapid detection of the main quality control indicators of Pollen Typhae,providing strong technical support for its quality supervision.

Objective:To evaluate the association between pre-pregnancy urolithiasis and pre-eclampsia and to further explore the mediating effect of hyperuricemia in early pregnancy on the relationship between urolithiasis and pre-eclampsia.Methods:Pregnant women attending prenatal care in early pregnancy at 7 Maternal and Child Health Hospitals in Hunan Province from August 2014 to December 2019 were recruited to construct a cohort of early pregnancy. The paper questionnaire collected demographic data on pregnant women, pre-pregnancy disease history, and living habits, etc. Besides, the early pregnancy laboratory examination and pregnancy outcome for this pregnancy were derived from the hospital's electronic medical record system. Logistic regression models were used to analyze the association between pre-pregnancy urolithiasis and pre-eclampsia, and causal mediation analysis was used to investigate the mediating role and magnitude of hyperuricemia in early pregnancy in the association pathway between pre-pregnancy urolithiasis and pre-eclampsia. Results:A total of 33 579 naturally conceived singleton pregnant women were included in the analysis, of which 3 230 cases (9.6%) had hyperuricemia in early pregnancy, and 666 cases (2.0%) had pre-eclampsia. The multivariate logistic regression analysis indicated that pre-pregnancy urolithiasis increased the risk of pre-eclampsia ( OR=2.65, 95% CI: 1.56-4.51). Mediation analysis showed that after controlling for confounders, hyperuricemia in early pregnancy could mediate the association between pre-pregnancy urolithiasis and pre-eclampsia, with a mediation effect proportion of 46% ( P<0.05). Conclusions:Pre-pregnancy urolithiasis is an independent risk factor for pre-eclampsia, and early pregnancy hyperuricemia has a certain mediating effect between urolithiasis and pre-eclampsia.

Objective:To evaluate the efficacy and safety of combining third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKI) with brain radiotherapy in patients with newly diagnosed EGFR mutation-positive non-small cell lung cancer (NSCLC) with brain metastases. Methods:A retrospective analysis was performed on the clinical data of patients with newly diagnosed EGFR-mutant NSCLC with brain metastases who received first-line treatment with third-generation EGFR-TKI with or without brain radiotherapy at the Fourth Affiliated Hospital of Guangxi Medical University between January 2018 and December 2022. Patients treated with EGFR-TKI plus brain radiotherapy were assigned to the combination group, while those treated with EGFR-TKI alone were assigned to the monotherapy group. Intracranial progression-free survival (iPFS), progression-free survival (PFS), overall survival (OS), intracranial disease control rate (iDCR), intracranial objective response rate (iORR), and adverse events were compared between groups. Subgroup analyses were performed according to EGFR exon 19 deletion (19del) and exon 21L858R mutation status. Survival was estimated using the Kaplan-Meier method, with the log-rank test applied for group comparisons and univariate analysis, while multivariate analysis was conducted using Cox proportional hazards regression model. Results:A total of 107 patients were included: 57 (53%) in the monotherapy group and 50 (47%) in the combination group. The combination therapy significantly improved iORR (80% vs. 60%, P=0.023), prolonged median OS (37.7 vs. 31.6 months, P=0.004), and extended median iPFS (21.8 vs. 16.7 months, P=0.018). The iDCR was 100% in both groups, and the difference in median PFS was not statistically significant (18.6 vs. 15.2 months, P=0.086). In the 19del subgroup ( n=53), patients in the combination group had longer OS ( P=0.028) and iPFS ( P=0.028). In the 21L858R subgroup ( n=54), the median OS was also longer in the combination group ( P=0.050). Multivariate analysis identified TKI monotherapy and an Eastern Cooperative Oncology Group (ECOG) performance status score=2 as independent adverse prognostic factors for iPFS, while TKI monotherapy, age ≥65 years, ECOG score=2, and >3 brain metastases were the independent adverse prognostic factors for OS. The incidence of adverse events did not differ significantly between groups (all P>0.05). Conclusions:First-line combination therapy with third-generation EGFR-TKI and cranial radiotherapy provides superior efficacy and acceptable safety compared with EGFR-TKI monotherapy in patients with EGFR-mutant lung adenocarcinoma and brain metastases. Both EGFR 19del and 21L858R mutation subgroups benefit from the combined treatment approach.