ObjectiveTo investigate the regulatory effect of icariin （ICA） on transforming growth factor-β₁ （TGF-β₁）/Smad pathway in bone microvascular endothelial cells （BMECs） and the effect on autophagy in BMECs. MethodsBMECs were isolated and cultured， and the cell types were identified by immunofluorescence. Cells were divided into the control group， model group （0.1 g·L^-1 methyl prednisolone）， ICA group （0.1 g·L^-1 methyl prednisolone +1×10^-5 mol·L^-1 ICA）， and TGF-β inhibitor group （0.1 g·L^-1 methyl prednisolone +1×10^-5 mol·L^-1 ICA +1×10^-5 mol·L^-1 LY2157299）. Transmission electron microscopy was used to observe the ultrastructure and autophagosome number of BMECs. Autophagy double-standard adenovirus was used to monitor the confocal autophagy flow generation of each cell. Real-time quantitative polymerase chain reaction （Real-time PCR） and Western blot were used to detect the gene and protein expression of autophagy in the TGF-β₁/ Smad pathway. ResultsAfter cell separation culture， platelet endothelial cell adhesion molecule （CD31） and von willebrand factor （vWF） immunofluorescence identified BMECs. Transmission electron microscopy showed that the cell membrane was damaged， and the nucleus was pyknotic and broken in the model group. Compared with the model group， the ICA group had complete cell membranes， clear structures， with autophagy-lysosome sparsely distributed. The confocal photo showed that BMECs had autophagosomes and autophagy-lysosomes， and the autophagy expression of the ICA group was similar to that of the blank group. Compared with the blank group， in the model group and the LY2157299 group， autophagosomes and autophagy-lysosomes were barely seen in the autophagy flow. Compared with the blank group， the mRNA and protein expressions of autophagy effector protein 1 （Beclin1） and microtubule-associated protein 1 light chain 3B （LC3B） in the model group were significantly decreased （P<0.01）， and those of ubiquitin-binding protein （p62） were significantly increased （P<0.01）. The mRNA expression of TGF-β₁， Smad homolog 2 （Smad2）， and Smad homolog 3 （Smad3） decreased （P<0.05， P<0.01）. The protein expressions of TGF-β₁， p-Smad2， and p-Smad3 were significantly decreased （P<0.01）. Compared with those of the model group， the mRNA and protein expression of Beclin1 and LC3B in BMECs of the ICA group increased （P<0.01）， and those of p62 significantly reduced （P<0.01）. The mRNA expression of TGF-β₁， Smad2， and Smad3 increased significantly （P<0.01）. The protein expression of TGF-β₁， p-Smad2， and p-Smad3 increased significantly （P<0.01）. Compared with those in the model group， the mRNA and protein expressions of Beclin1， LC3B， and p62 in the inhibitor group were not statistically significant. The expression of key genes and proteins of the TGF-β₁ pathway in the inhibitor group was not statistically significant. ConclusionICA can promote glucocorticoid-induced autophagy expression of BMECs， and its mechanism may be related to activating the TGF-β₁/Smad signaling pathway.

Steroid-induced osteonecrosis of the femoral head （SONFH） is a severe musculoskeletal disorder often induced by the prolonged or excessive use of glucocorticoids. Characterized by ischemia of bone cells， necrosis， and trabecular fractures， SONFH is accompanied by pain， femoral head collapse， and joint dysfunction， which can lead to disability in severe cases. The pathogenesis of SONFH involves hormone-induced osteoblast apoptosis， bone microvascular endothelial cell （BMEC） apoptosis， oxidative stress， and inflammatory responses. The phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） signaling pathway plays a pivotal role in the development of the disease. Modulating the PI3K/Akt signaling pathway can promote Akt phosphorylation， thereby stimulating the osteogenic differentiation of bone marrow mesenchymal stem cells and osteoblasts， promoting angiogenesis in BMECs， and inhibiting osteoclastogenesis. The research on the treatment of SONFH with traditional Chinese medicine （TCM） has gained increasing attention. Recent studies have shown that TCM monomers and compounds have potential therapeutic effect on SONFH by intervening in the PI3K/Akt signaling pathway. These studies not only provide a scientific basis for the application of TCM in the treatment of SONFH but also offer new ideas for the development of new therapeutic strategies. This review summarized the progress in Chinese and international research on the PI3K/Akt signaling pathway in SONFH over the past five years. It involved the composition and transmission mechanisms of the signaling pathway， as well as its regulatory effects on osteoblasts， mesenchymal stem cells， osteoclasts， BMECs， and other cells. Additionally， the review explored the TCM understanding of SONFH and the application of TCM monomers and compounds in the intervention of the PI3K/Akt pathway. By systematically analyzing and organizing these research findings， this article aimed to provide references and point out directions for the clinical prevention and treatment of SONFH and promote further development of TCM in this field. With in-depth research on the PI3K/Akt pathway and the modern application of TCM， it is expected to bring safer and more effective treatment options for patients with SONFH.

Objective: To systematically analyze the revisions content and technological development trends of microbiological standards in the Chinese Pharmacopoeia (ChP) 2025 Edition, and explore its novel requirements in risk-based pharmaceutical product lifecycle management.
Methods: A comprehensive review was conducted on 26 microbiological-related standards to summarize the revision directions and scientific implications from perspectives including the revision overview, international harmonization of microbiological standards, risk-based quality management system, and novel tools and methods with Chinese characteristics.
Results: The ChP 2025 edition demonstrates three prominent features in microbiological-related standards: enhanced international harmonization, introduced emerging molecular biological technologies, and established a risk-based microbiological quality control system.
Conclusion: The new edition of the Pharmacopoeia has systematically constructed a microbiological standard system, which significantly improves the scientificity, standardization and applicability of the standards, providing a crucial support for advancing the microbiological quality control in pharmaceutical industries of China.

BACKGROUND:Preliminary research by our group suggests that targeting fibroblast growth factor receptor 1(FGFR1)may be an effective strategy for treating RA. OBJECTIVE:To investigate the effects of an FGFR1 inhibitor(PD173074)on bone destruction in rats with collagen-induced arthritis. METHODS:Twenty-five female Sprague-Dawley rats were randomly divided into five groups:normal control group,model group,methotrexate group,low-dose PD173074 group,and high-dose PD173074 group.Except for the normal control group,rat models of type Ⅱ collagen-induced arthritis were made in each group.After successful modeling,rats were injected intraperitoneally with sterile PBS in the normal and model groups,1.04 mg/kg methotrexate in the methotrexate group,and 5 and 20 mg/kg in the low-dose group and high-dose PD173074 groups,once a week.After 4 weeks of drug administration,clinical symptoms and joint swelling in rats were observed.Micro-CT was used for three-dimensional reconstruction and analysis of the ankle joints.Pathological changes in the ankle joints were observed.Periarticular angiogenesis and the expression of receptor activator of nuclear factor-Κb ligand were detected.The expression levels of p-FGFR1,vascular endothelial growth factor A,and tartrate-resistant acid phosphatase in the synovial membrane were measured.Pathological changes in the liver,spleen,and kidney were observed and liver,spleen,and kidney indices were calculated. RESULTS AND CONCLUSION:PD173074 could alleviate clinical symptoms and joint swelling,delay bone loss,improve bone structure,reduce synovial invasion and cartilage bone erosion,reduce the number of periarticular osteoclasts,inhibit angiogenesis in synovial tissues,reduce the expression of receptor activator of nuclear factor-Κb ligand,and inhibit the expression of FGFR1 phosphorylated protein,tartrate-resistant acid phosphatase and vascular endothelial growth factor A.Pathologic observation of the liver,spleen and kidney in rats showed no obvious toxic side effects after PD173074 treatment.To conclude,the FGFR1 inhibitor can delay the progression of joint inflammation and bone destruction and inhibit angiogenesis in the rat model of type Ⅱ collagen-induced arthritis.The therapeutic effect of PD173074 has been preliminarily validated in the type Ⅱ collagen-induced arthritis model and may act by inhibiting FGFR1 phosphorylation,which provides a direction for the search of new therapeutic targets for rheumatoid arthritis.

ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.

Background: The aim of this study is to evaluate the prevalence and associated psychosocial factors of behavioral addictions among school adolescents living in the Gwangju and Jeonnam regions in Korea. Methods: A self-reported survey was conducted from December 4, 2023, to January 31, 2024, including 855 middle and high school students residing in the Gwangju-Jeonnam regions. Aside from the information on demographic characteristics, data on depression, anxiety, Internet gaming addiction, gambling problems, and resilience was obtained. Results: The prevalence of Internet gaming addiction among adolescents was 5.4%, while the prevalence of gambling problems was 3.3%. The male adolescents had a significantly higher risk of behavioral addiction compared with the female adolescents. The logistic regression analysis revealed that male and depression were significant risk factors for Internet gaming addiction. For gambling problems, male was identified as a significant risk factor. Conclusion The findings of this study suggested that the prevalence of behavioral addiction among school adolescents has been relatively higher than that of previous studies, emphasizing the need for community-based prevention and intervention strategies tailored to the sex difference and psychological factors associated with adolescent behavioral addictions.

Objective: To test the performance of an artificial intelligence-based computer-aided diagnosis (AI-CAD) designed for fullfield digital mammography (FFDM) when applied to synthetic mammography (SM). Materials and Methods: We analyzed 501 women (mean age, 57 ± 11 years) who underwent preoperative mammography and breast cancer surgery. This cohort consisted of 1002 breasts, comprising 517 with cancer and 485 without. All patients underwent digital breast tomosynthesis (DBT) and FFDM during the preoperative workup. The SM is routinely reconstructed using DBT. Commercial AI-CAD (Lunit Insight MMG, version 1.1.7.2) was retrospectively applied to SM and FFDM to calculate the abnormality scores for each breast. The median abnormality scores were compared for the 517 breasts with cancer using the Wilcoxon signed-rank test. Calibration curves of abnormality scores were evaluated. The discrimination performance was analyzed using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity using a 10% preset threshold. Sensitivity and specificity were further analyzed according to the mammographic and pathological characteristics.The results of SM and FFDM were compared. Results: AI-CAD demonstrated a significantly lower median abnormality score (71% vs. 96%, P < 0.001) and poorer calibration performance for SM than for FFDM. SM exhibited lower sensitivity (76.2% vs. 82.8%, P < 0.001), higher specificity (95.5% vs.91.8%, P < 0.001), and comparable AUC (0.86 vs. 0.87, P = 0.127) than FFDM. SM showed lower sensitivity than FFDM in asymptomatic breasts, dense breasts, ductal carcinoma in situ, T1, N0, and hormone receptor-positive/human epidermal growth factor receptor 2-negative cancers but showed higher specificity in non-cancerous dense breasts. Conclusion AI-CAD showed lower abnormality scores and reduced calibration performance for SM than for FFDM.Furthermore, the 10% preset threshold resulted in different discrimination performances for the SM. Given these limitations, off-label application of the current AI-CAD to SM should be avoided.