OBJECTIVE To analyze the characteristics of pediatric medicines with priority review and approval for marketing in China， providing a reference for promoting enterprise R&D and production， as well as improving the supply guarantee mechanism for pediatric medicines. METHODS Based on publicly available data sources such as List of Approved Information for Pediatric Medications Subject to Priority Review and Approval， Pharnexcloud biomedical database， and National Medical Insurance Drug Directory， this study conducted a comprehensive analysis of the main characteristics of pediatric medicines with priority review and approval for marketing. RESULTS As of June 30， 2024， a total of 68 pediatric medicines had been approved through the priority review and approval process， covering 12 therapeutic areas， with oral dosage forms accounting for 64.71%. The median time from application to inclusion in priority review was 35.50 days， with an average of 41.69 days. The median time from inclusion in priority review to market approval was 1.24 years， with an average of 1.42 years. This included 12 domestic new medicines， 21 domestic generic medicines， 35 imported medicines， as well as 29 pediatric-specific medicines and 21 orphan medicines. Additionally， 31 of these medicines had been included in the medical insurance catalog， representing a proportion of 45.59%. CONCLUSIONS Currently， a trend of differentiated competition is emerging between domestic and imported pediatric medicines. The therapeutic areas for pediatric medicines are continuously expanding， and the dosage forms are becoming more tailored to children’s needs. However， there are still issues such as slow progress in new medicine development， insufficient stability in the medicine review and approval process， and a need to increase the proportion of medicines included in medical insurance.

This study aims to explore the effects and action mechanisms of the active ingredients in Buyang Huanwu Decoction(BYHWD), namely tetramethylpyrazine(TMP) and hydroxy-safflor yellow A(HSYA), on oxygen-glucose deprivation/reglucose-reoxygenation(OGD/R)-induced inflammation and oxidative stress of microglia(MG). Network pharmacology was used to screen the effective monomer ingredients of BYHWD and determine the safe concentration range for each component. Inflammation and oxidative stress models were established to further screen the best ingredient combination and optimal concentration ratio with the most effective anti-inflammatory and antioxidant effects. OGD/R BV2 cell models were constructed, and BV2 cells in the logarithmic growth phase were divided into a normal group, a model group, an HSYA group, a TMP group, and an HSYA + TMP group. Enzyme-linked immunosorbent assay(ELISA) was used to detect the levels of inflammatory cytokines such as interleukin-1β(IL-1β), tumor necrosis factor-α(TNF-α), and interleukin-6(IL-6). Oxidative stress markers, including superoxide dismutase(SOD), nitric oxide(NO), and malondialdehyde(MDA), were also measured. Western blot was used to analyze the protein expression of both inflammation-related pathway [Toll-like receptor 4(TLR4)/nuclear factor-kappa B(NF-κB)] and oxidative stress-related pathway [nuclear factor erythroid 2-related factor 2(Nrf2)/heme oxygenase-1(HO-1)]. Immunofluorescence was used to assess the expression of proteins such as inducible nitric oxide synthase(iNOS) and arginase-1(Arg-1). The most effective ingredients for anti-inflammatory and antioxidant effects in BYHWD were TMP and HSYA. Compared to the normal group, the model group showed significantly increased levels of IL-1β, TNF-α, IL-6, NO, and MDA, along with significantly higher protein expression of NF-κB, TLR4, Nrf2, and HO-1 and significantly lower SOD levels. The differences between the two groups were statistically significant. Compared to the model group, both the HSYA group and the TMP group showed significantly reduced levels of IL-1β, TNF-α, IL-6, NO, and MDA, lower expression of NF-κB and TLR4 proteins, higher levels of SOD, and significantly increased protein expression of Nrf2 and HO-1. Additionally, the expression of the M1-type MG marker iNOS was significantly reduced, while the expression of the M2-type MG marker Arg-1 was significantly increased. The results of the HSYA group and the TMP group had statistically significant differences from those of the model group. Compared to the HSYA group and the TMP group, the HSYA + TMP group showed further significant reductions in IL-1β, TNF-α, IL-6, NO, and MDA levels, along with significant reductions in NF-κB and TLR4 protein expression, an increase in SOD levels, and elevated Nrf2 and HO-1 protein expression. Additionally, the expression of the M1-type MG marker iNOS was reduced, while the M2-type MG marker Arg-1 expression increased significantly in the HSYA + TMP group compared to the TMP or HSYA group. The differences in the results were statistically significant between the HSYA + TMP group and the TMP or HSYA group. The findings indicated that the combined use of HSYA and TMP, the active ingredients of BYHWD, can effectively inhibit OGD/R-induced inflammation and oxidative stress of MG, showing superior effects compared to the individual use of either component.
Oxidative Stress/drug effects* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Mice ; Glucose/metabolism* ; Cell Line ; Inflammation/genetics* ; Oxygen/metabolism* ; Pyrazines/pharmacology* ; Microglia/metabolism* ; NF-E2-Related Factor 2/immunology* ; NF-kappa B/immunology* ; Toll-Like Receptor 4/immunology* ; Anti-Inflammatory Agents/pharmacology* ; Humans

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 effect and mechanism of Jingangteng capsules in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Thirty-two SD rats were randomly divided into normal group and modeling group. The modeling group was fed a high-fat diet to establish a NAFLD model. The successfully modeled rats were then randomly divided into model group， atorvastatin group[positive control， 2 mg/（kg·d）]， and Jingangteng capsules low- and high-dose groups [0.63 and 2.52 mg/（kg·d）]， with 6 rats in each group. The pathological changes of the liver were observed by hematoxylin-eosin staining and oil red O staining. Enzyme-linked immunosorbent assay was performed to determine the serum levels of triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine transaminase （ALT）， aspartate transaminase （AST）， tumour necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， IL-6， IL-18. 16S rDNA amplicon sequencing and metabolomics techniques were applied to explore the effects of Jingangteng capsules on gut microbiota and metabolisms in NAFLD rats. Based on the E-mail：591146765@qq.com metabolomics results， Western blot analysis was performed to detect proteins related to the nuclear factor kappa-B （NF-κB）/NOD-like receptor family protein 3 （NLRP3） signaling pathway in the livers of NAFLD rats. RESULTS The experimental results showed that Jingangteng capsules could significantly reduce the serum levels of TG， TC， LDL-C， AST， ALT， TNF-α， IL-1β， IL-6， IL-18， while increased the level of HDL-C， and alleviated the hepatic cellular steatosis and inflammatory infiltration in NAFLD rats. They could regulate the gut microbiota disorders in NAFLD rats， significantly increased the relative abundance of Romboutsia and Oscillospira， and significantly decreased the relative abundance of Blautia （P＜0.05）. They also regulated metabolic disorders primarily by affecting secondary bile acid biosynthesis， fatty acid degradation， O-antigen nucleotide sugar biosynthesis， etc. Results of Western blot assay showed that they significantly reduced the phosphorylation levels of NF-κB p65 and NF-κB inhibitor α， and the protein expression levels of NLRP3， caspase-1 and ASC （P＜0.05 or P＜0.01）. CONCLUSIONS Jingangteng capsules could improve inflammation， lipid accumulation and liver injury in NAFLD rats， regulate the disorders of gut microbiota and metabolisms， and inhibit NF-κB/NLRP3 signaling pathway. Their therapeutic effects against NAFLD are mediated through the inhibition of the NF-κB/NLRP3 signaling pathway.

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.

ObjectiveTo investigate the pharmacodynamic effects of Houshihei San （HSHS） recorded with the effects of treating wind and limb heaviness on muscle tissue injury after middle cerebral artery occlusion （MCAO） in rats through the ferroptosis pathway. MethodsThirty SD male rats were selected and randomly grouped as follows： sham， MCAO， deferoxamine mesylate， high-dose HSHS （HSHS-H， 0.54 g·kg^-1）， and low-dose HSHS （HSHS-L， 0.27 g·kg^-1）， with 6 rats in each group. A laser scattering system was used to evaluate the stability of the MCAO model， and rats were administrated with corresponding agents by gavage for 7 days. During the administration period， behavioral， imaging and other methods were used to systematically evaluate the skeletal muscle tissue injury after MCAO and the therapeutic effect in each administration group. Hematoxylin-eosin staining was employed to evaluate the cross-section of muscle cells. Subsequently， immunohistochemistry was used to detect tumor suppressor p53 and glutathione peroxidase 4 （GPX4） in the soleus tissue. Western blot was employed to determine the protein levels of p53， GPX4， myogenic differentiation 1 （MyoD1）， nuclear factor E₂-related factor 2 （Nrf2）， Myostatin， solute carrier family 7 member 11 （SLC7A11）， muscle ring-finger protein-1 （MuRF1）， and muscle atrophy F-box protein （MAFbx） to verify the therapeutic effect in each group. ResultsCompared with the MCAO group， HSHS enhanced the locomotor ability and promoted muscle regeneration， which suggested that the pharmacological effects of HSHS were related to the inhibition of muscle tissue ferroptosis to reduce the expression of muscle atrophy factors. Behavioral and imaging results suggested that compared with the MCAO group， HSHS ameliorated neurological impairments in rats on day 7 （P<0.01）， enhanced 5-min locomotor distance and postural control （P<0.01）， strengthened grasping power and promoted muscle growth （P<0.01）， stabilized skeletal muscle length and weight （P<0.01）， and increased the cross-section of muscle cells （P<0.01）. Compared with the MCAO group， HSHS promoted the increases in glutathione and superoxide dismutase content and inhibited the increase in malondialdehyde content （P<0.05，P<0.01）. Ferroptosis pathway-related assays suggested that HSHS reduced the p53-positive cells and increased the GPX4-positive cells （P<0.01）. HSHS ameliorated muscle function decline after stroke by promoting the expression of GPX4， Nrf2， SLC7A11， and MyoD1 and inhibiting the expression of p53， Myostatin， MurRF1， and MAFbx to reduce ferroptosis in the muscle （P<0.01）. ConclusionHSHS， prepared with reference to the method in the Synopsis of Golden Chamber， can simultaneously reduce the myolysis and increase the protein synthesis in the skeletal muscle tissue after ischemic stroke by regulating the ferroptosis pathway.

Implementation Science is an interdisciplinary field dedicated to systematically studying how to effectively translate evidence-based research findings into practical application and implementation. In the health-related context, it focuses on enhancing the efficiency and quality of healthcare services, thereby facilitating the transition from scientific evidence to real-world practice. This article elaborates on Theories, Models, and Frameworks (TMF) within health-related Implementation Science, clarifying their basic concepts and classifications, and discussing their roles in guiding implementation processes. Furthermore, it reviews and prospects current research from three aspects: the constituent elements of TMF, their practical applications, and future directions. Five representative frameworks are emphasized, including the Consolidated Framework for Implementation Research (CFIR), the Practical Robust Implementation and Sustainability Model (PRISM), the Exploration, Preparation, Implementation, Sustainment (EPIS)framework, the Behavior Change Wheel (BCW), and the Normalization Process Theory (NPT). Additionally, resources such as the Dissemination & Implementation Models Webtool and the T-CaST tool are introduced to assist researchers in selecting appropriate TMFs based on project-specific needs.