OBJECTIVE To systematically review the key regulatory requirements for pharmacy compounding drug products in the United States， and to provide references for improving the management framework of pharmacy compounding drug products in China， thereby ensuring the safety and accessibility of medication use. METHODS A literature revi ew was conducted to summarize the regulatory requirements and practical characteristics of pharmacy compounding drug products under Section 503A of the United States Federal Food， Drug， and Cosmetic Act . Based on regulatory experience of the United States， current status and existing deficiencies in pharmacy compounding drug products in China were analyzed， and targeted recommendations were proposed. RESULTS &CONCLUSIONS The United States has established a systematic regulatory framework for pharmacy compounding drug products under Section 503A， covering prescription management， active pharmaceutical ingredient management， list management of withdrawn or removed drugs and drugs that present demonstrable difficulties for compounding， recordkeeping management， interstate distribution and so on. Through standardized regulation of the entire compounding process and collaborative governance with expert advisory committees， this framework effectively balances individualized medication needs with drug quality and safety. In contrast， pharmacy compounding drug products in China remain largely confined to traditional Chinese medicine and face several challenges， including the lack of legal and regulatory support， inadequate quality control， lagging development of industry standards， and shortages of specialized personnel. It is recommended that China should establish a tiered regulatory system， develop comprehensive technical standards， build a localized regulatory framework， promote technological innovation， and strengthen professional talent cultivation to better balance the legitimate rights and interests of public medication use with drug safety and efficacy， thereby enhancing public health.

ObjectiveTo verify the clinical efficacy of Qingfei Huatan Tongqiao Formula in treating children with adenoid hypertrophy （AH） of the lung-heat obstructing the nose syndrome， and to observe its effects on quality of life. MethodsA multicenter， double-blind， double-dummy， randomized controlled clinical trial was conducted. A total of 160 pediatric AH outpatients meeting the inclusion and exclusion criteria were enrolled between November 2022 and November 2024 from Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine， Shanghai Municipal Hospital of Traditional Chinese Medicine， Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine， and Yueyang Hospital of Integrated Traditional Chinese and Western Medicine. The children were randomly assigned to a treatment group （80 cases） or a control group 80 cases）. The treatment group received Qingfei Huatan Tongqiao Formula combined with a montelukast sodium placebo， while the control group received a Qingfei Huatan Tongqiao Formula placebo combined with montelukast sodium. The treatment course lasted 90 days. After treatment， therapeutic efficacy， sleep monitoring parameters， and Obstructive Sleep Apnea-18 （OSA-18） scores were compared between groups. ResultsCompared with pre-treatment values within each group， both groups showed significant decreases in the adenoid-to-nasopharyngeal ratio （A/N）， primary symptom scores， and TCM syndrome scores. OSA-18 scores were significantly reduced. Sleep monitoring showed decreases in the Apnea-Hypopnea Index （AHI） and Oxygen Desaturation Index （ODI）， and an increase in minimum SpO₂ （all P<0.05）. After treatment， compared with the control group， the treatment group had a lower A/N ratio and higher overall effectiveness rate and TCM syndrome effectiveness rate. The treatment group also had lower total OSA-18 scores and lower subscale scores （sleep disturbance， physical symptoms， emotional distress， and caregiver impact）， with statistically significant differences （all P<0.05）. There was no significant difference in daytime symptoms （P=0.201）. Regarding sleep monitoring indices， the treatment group had lower AHI and ODI and higher minimum SpO₂ （all P<0.05）， with no significant difference in average SpO₂ （P=0.116）. ConclusionQingfei Huatan Tongqiao formula can significantly reduce adenoid size in children with AH， alleviate clinical symptoms， improve quality of life， reduce sleep-disordered breathing events， and ameliorate hypoxia. Its therapeutic efficacy is significantly superior to that of montelukast sodium.

Objective: To explore the influencing factors of incident caries at age 5 among caries-free 3-year-old children in Beijing, construct a machine learning prediction model based on oral health behavioral data, and provide a scientific basis and practical tool for early risk identification, stratified prevention and control, and home-kindergarten-medical collaborative intervention of incident dental caries in preschool children. Methods: This study has been reviewed and approved by the Medical Ethics Committee, and written informed consent was obtained from the guardians of the study participants and their guardians. Children with complete baseline data at age 3 and follow-up data at age 5 from Beijing public oral health programs were included. Oral examinations were conducted according to World Health Organization diagnostic criteria, and questionnaires were completed by guardians. Risk factors were screened by Logistic regression. The total sample was randomly divided into a test set (20%) and a validation set (80%). Five-fold cross-validation was performed on the validation set for model training and tuning, and the final model performance was evaluated on the test set. Four machine learning models—decision tree, random forest, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM)—were constructed. Model performance was evaluated using the area under the curve (AUC), accuracy, and specificity. Shapley Additive Explanations (SHAP) was used for model interpretation. Results: The incidence of incident caries for 3 372 5-year-old children in Beijing was 50.96%. Multivariate logistic regression showed that parental caries, sleeping with milk, frequent feeding at night, no postprandial cleaning, high sugar intake, insufficient bedtime tooth brushing, and non-fluoride toothpaste use were significant risk factors (P < 0.05). Although all four machine learning models performed well, the XGBoost model performed the best (AUC = 0.806). Feature importance and SHAP analysis indicated that the frequency of bedtime tooth brushing, the frequency of candy consumption, and parental caries status were the top three predictors. Conclusion The XGBoost model based on oral health behaviors at age 3 can accurately predict the risk of incident caries in 5-year-old children in Beijing. Bedtime tooth brushing, sugar control, and parental oral health management are key points for caries prevention in preschool children.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To constructCSF1R+/-mice and to analyze their genotypes, so as to provide animal model basis for disease pathological mechanism and drug target. Methods : A linearized targeting vector was designed according to Cre/Loxp system. A Loxp site was inserted upstream of the 5th exon of theCSF1Rgene, and a neomycin resistance box with Loxp sites on both sides was inserted downstream of the 5th exon. The linearized targeting vector was electroporated into embryonic stem cells. The correctly targeted embryonic stem cells were injected into the blastocysts of C57BL/6J mice to obtain chimeric mice, which were bred with Zp3-Cre mice. The newborn mice were numbered 9-14 days after birth and their tails were cut. The DNA of the mice was extracted, and the genotype of the mice was identified by polymerase chain reaction and agarose gel electrophoresis. The expression of CSF1R in mouse macrophages was detected by flow cytometry. The expression of CSF1R in mouse tissues was detected by Western blot. Results: The results of agarose gel electrophoresis showed that 453 bp bands were amplified in wild type mice, and 453 bp and 650 bp bands were amplified in heterozygous mice. The results of flow cytometry showed that the expression of CSF1R in peritoneal macrophages and bone marrow-derived macrophages of CSF1R heterozygous mice was lower than that of WT group(P<0.05). The results of Western blot showed that the expression of CSF1R in spleen, kidney and brain tissue of CSF1R heterozygous group was lower than that of WT group(P<0.05). Conclusion CSF1R+/-mice are successfully constructed, reproduced and identified, which provides an animal model basis for further revealing the potential mechanism of CSF1R in immune regulation.

Objective To investigate the relationship between between the levels of serum malondialdehyde(MDA)and superoxide dismutase(SOD),the balance of oxidative/antioxidant stress,and the prognostic nu-tritional index(PNI)and the risk of early neurological deterioration in patients with acute ischemic stroke(AIS).Methods A total of 95 patients with suspected AIS admitted to the Second People's Hospital of Fos-han,Foshan from January to April 2023 were selected as the research subjects.Among them,71 patients who were finally diagnosed with AIS were included in the stroke group,and the remaining 24 non-AIS patients were included in the control group.According to the National Institutes of Health Stroke Rating Scale(NIH-SS)score,the stroke group was divided into the moderate-severe stroke group and the mild stroke group.Ac-cording to whether early neurological deterioration(END)occurred,it was divided into the END group and the non-END group.To analyze the correlations between the levels of serum SOD and MDA,SOD/MDA,and the PNI and the severity of stroke.The receiver operating characteristic(ROC)curve was applied to evaluate the predictive value of each index for the risk of END.By fitting the indicators with high diagnostic efficacy,a Fisher discriminant function model for evaluating the risk of END was established to verify the overall accura-cy rate.Results The levels of serum MDA and SOD,SOD/MDA and the PNI in the moderate to severe stroke group were statistically different from those in the mild stroke group(P＜0.05).There were statistically sig-nificant differences in the levels of serum MDA and SOD,SOD/MDA and the PNI among the END group,the non-END group and the control group(P＜0.05).The areas under the curve(AUC)of SOD,MDA,SOD/MDA and PNI for evaluating END were 0.692,0.727,0.777 and 0.819,respectively.The Fisher discriminant function model established by fitting the NIHSS score,the SOD/MDA and the PNI has an overall accuracy rate of 85.9％.Conclusion The END risk prediction model established by applying the Fisher discriminant function can provide early and objective reference basis for clinical prediction of END risk and has certain practical value.