ObjectiveTo investigate the regulatory effect of overexpressed protein tyrosine phosphatase non-receptor type 2 （Ptpn2） on the inflammatory response of mouse alveolar macrophages （MH-S） induced by SiO₂. MethodsCells with overexpressed Ptpn2 were constructed and induced by SiO₂. The experimental groups were divided into four groups： the negative control group with an empty vector （NC）， the overexpressed Ptpn2 group （P）， the negative control group with an empty vector + SiO₂ induction （NS）， and the overexpressed Ptpn2 + SiO₂ induction group （PS）. Isobaric tags for relative and absolute quantification （iTRAQ） combined with liquid chromatography-tandem mass spectrometry （LC-MS/MS） were used to screen differential proteins， followed by Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） database analyses. Immunofluorescence staining was used to detect the expressions of Tumor necrosis factor （TNF） α， Gasdermin D （GSDMD）， and Transforming growth factor （TGF）-β1. Western blot was used to detect the protein expression levels of PTPN2， Toll-like receptor 4 （TLR4）， tumor necrosis factor-α （TNF-α）， nucleotide-binding oligomerization domain-like receptor protein 3 （NLRP3）， and proteins related to the TGF-β1 signaling pathway in the cells of each group. ResultsiTRAQ results identified 144 differential proteins among the four groups. GO analysis showed that in biological processes （BP）， these differential proteins were mainly enriched in IκB kinase/nuclear factor-κB （NF-κB） signaling， cell activation and signal transduction involved in immune responses， and regulation of receptor signaling pathways by signal transducer and activator of transcription （STAT）， etc. KEGG analysis revealed that the differential proteins were mainly enriched in Toll-like receptor signaling pathway， NF-κB signaling pathway， NOD-like receptor signaling pathway， TGF-β signaling pathway， and TNF signaling pathway. The results of immunofluorescence staining showed that compared with the NC group， the expressions of TNF α， GSDMD， and TGF-β1 in the cells of the NS group increased （P < 0.05）； compared to the NS group， the expression of the aforementioned proteins in the PS group decreased in cellular proteins（P < 0.05）. The results of Western blot showed that compared with the NC group， the protein expression levels of PTPN2， p-NF-κB，MyD88，TLR4，NLRP3，GSDMD，Caspase-1，IL-1β， TGF-βR1， TGF-βR，p-Smad2/3 in the NS group were significantly upregulated （P < 0.05）； compared with the NS group， the expression levels of the aforementioned proteins in the PS group were significantly downregulated （P < 0.05）. ConclusionOverexpression of Ptpn2 can inhibit the protein expressions of TLR4-TNF-α signaling， NLRP3 signaling， and TGF-β1 signaling closely related to inflammatory response in SiO₂-mediated MH-S macrophages.

To investigate the clinical efficacy of neoadjuvant imatinib in the treatment of rectal gastrointestinal stromal tumor (GIST).

As people's attention to health continues to increase,the market demand for traditional Chinese medi-cine(TCM)is growing steadily.The quality and safety of Chinese medicinal materials have attracted unprecedent-ed social attention.In particular,the issue of exogenous harmful residue pollution in TCM has become a hot topic of concern for both regulatory authorities and society.The Chinese Pharmacopoeia 2025 Edition further refines the detection methods and limit standards for exogenous harmful residues in TCM .This not only reflects China's high-level emphasis on the quality and safety of TCM but also demonstrates the continuous progress made by China in the field of TCM safety supervision.Basis on this study,by systematically reviewing the development history of the detection standards for exogenous harmful residues in TCM and analyzing the revisions and updates of these detec-tion standards in the Chinese Pharmacopoeia 2025 Edition,deeply explores the key points of the changes in the monitoring standards for exogenous harmful residues in TCM in the Chinese Pharmacopoeia 2025 Edition.Moreo-ver,it interprets the future development directions of the detection of exogenous residues in TCM ,aiming to provide a reference for the formulation of TCM safety supervision policies.

OBJECTIVE To provide medication reference for duloxetine use in clinical settings， particularly for patients with depression in primary medical institutions in Xinjiang that lack therapeutic drug monitoring conditions. METHODS The medical records of 281 depression inpatients taking duloxetine in the First Affiliated Hospital of Xinjiang Medical University from January 2022 to December 2023 were retrospectively collected. They were divided into training set （196 cases） and test set （85 cases） in the ratio of 7∶3. Feature selection was performed by encapsulating random forests （RF） with recursive feature elimination. Four machine learning algorithms， namely support vector machine， RF， extreme gradient boosting （XGBoost） and artificial neural network， were used to construct duloxetine blood concentration prediction model. The prediction performance of the models was evaluated and compared by coefficient of determination （R2）， mean absolute error （MAE） and root mean squared error （RMSE）. The feature of the selected optimal model was explained by Shapley additive explanation method， and the importance ranking of the features and the influence on the prediction results of duloxetine blood concentration were determined. RESULTS A total of 29 characteristic variables were selected， including age， ethnicity， body mass index（BMI）， etc. XGBoost showed the highest R2 （0.808）， and the lowest MAE （7.644） and RMSE （10.808）. The ranking of feature importance for predicting the blood concentration of duloxetine was as follows： BMI＞age＞other 20 feature sets （including liver and kidney function and biochemical indicators）＞daily dosage＞comorbidities＞combination therapy＞ethnicity＞white blood cell count＞hemoglobin＞height. CONCLUSIONS XGBoost model possesses the best prediction performance of duloxetine blood concentration； BMI and age have a greater impact on the prediction of duloxetine blood concentration.

The development of bone in human body and the maintenance of bone mass in adulthood are regulated by a variety of biological factors. Myocyte enhancer factor 2C (MEF2C), as one of the many factors regulating bone tissue development and balance, has been shown to play a key role in bone development and metabolism. However, there is limited systematic analysis on the effects of MEF2C on bone tissue. This article reviews the role of MEF2C in bone development and metabolism. During bone development, MEF2C promotes the development of neural crest cells (NC) into craniofacial cartilage and directly promotes cartilage hypertrophy. In terms of bone metabolism, MEF2C exhibits a differentiated regulatory model across different types of osteocytes, demonstrating both promoting and other potential regulatory effects on bone formation, with its stimulating effect on osteoclasts being determined. In view of the complex roles of MEF2C in bone tissue, this paper also discusses its effects on some bone diseases, providing valuable insights for the physiological study of bone tissue and strategies for the prevention of bone diseases.
Humans ; MEF2 Transcription Factors/physiology* ; Bone and Bones/metabolism* ; Animals ; Bone Development/physiology* ; Osteogenesis/physiology* ; Myogenic Regulatory Factors/physiology*

Breast cancer is the most common malignant tumor in women worldwide,and its incidence rate is increasing annually.Early dia-gnosis is essential for improving patient prognosis.In recent years,artificial intelligence(AI)technology has emerged as a transformative tool,providing new directions for accurate diagnostic and prognostic assessments.This article systematically reviews research progress in the ap-plication of AI to breast cancer screening,multimodal imaging techniques for diagnosis,pathological diagnosis,treatment,and prognostic modeling.Through machine learning and deep learning algorithms,AI has demonstrated significant advantages in the enhancement of dia-gnostic accuracy,efficiency,and accessibility,including improvement of the detection rate of small lesions,reduction in the radiologists'workload,and optimization of treatment decisions.Despite challenges,including the integration of explainable technology in clinical trans-formation,lack of data standardization,complexity of multimodal data,and risk of model generalization,AI is expected to play a more prom-inent role in the precise diagnosis and intelligent therapy for breast cancer,thereby contributing to the full realization of the goal of preci-sion medicine.

Objective:This study aimed to investigate the morphological and functional changes of jejunal and ileal epithelium in aged mice using 3D organoid culture, to elucidate the age-related alterations in epithelial function across different regions of the intestine.Methods:The C57BL/6J male mice were divided into two groups: a young group(5 months)and an old group(24 months).Epithelial tissues from the jejunum and ileum were harvested, and the jejunal and ileal epithelial crypt units were separated for 3D organoid culture in vitro.Histological analyses, including H&E and PAS staining, immunofluorescence staining, and quantitative PCR, were employed to assess changes in crypt depth, villi length, goblet cell numbers, inflammation, and mucosal barrier function in the aged mice.Results:With aging, the crypt of jejunum became deeper and the ratio of villi length to crypt depth decreased, while the villi length of ileum became longer and the ratio of villi length to crypt depth increased.The rate of organoid formation in jejunal and ileal crypts and the number of crypto-like regions of organoid formation were significantly decreased in the aged mice, along with a decrease in the expression of the stem cell proliferation marker Ki67.The inflammation of the jejunal and ileal epithelium increased in the aged mice, and the mechanical barrier function of the epithelial mucosa was enhanced.Conclusions:In aged mice, both the jejunal and ileal epithelium exhibited similar changes in stem cell proliferation, inflammation, and mucosal mechanical barrier function, though the changes in villus length and crypt depth ratio were opposite.This study provides a foundation for further investigation of the differences in jejunal and ileal epithelial function and its potential mechanisms during aging.

The advent of bruton tyrosine kinase inhibitors (BTKi) has offered more therapeutic choices for patients with B-cell malignancies. However, with its widespread application in recent years, the use of BTKi increases the risk of severe infections in patients. Here, we report three cases of B-cell malignancies, including one case of chronic lymphocytic leukemia and two cases of diffuse large B-cell lymphoma. During treatment with BTKi zanubrutinib, different degrees of invasive fungal disease (IFD) of the central nervous system occurred in these patients. All patients fully recovered following antifungal therapy. With a median follow-up of 35 months, all patients remained in a continuous remission state, and none of them had a recurrence of IFD.

Objective:To identify the relevant factors for the first-course remission of acute myeloid leukemia (AML) and to develop a predictive model as well as assess its predictive capability.Methods:Clinical data of 749 patients newly diagnosed with AML admitted to the Department of Hematology, the First Affiliated Hospital, Zhejiang University, School of Medicine from January 1, 2019, to April 30, 2023, were collected and randomly divided into training and validation sets. Multivariate logistic regression analysis was conducted to determine variables associated with complete remission in the first course of induction therapy, and a predictive model was established based on these variables. The receiver operating characteristic (ROC) curve of the predictive model was plotted, and the area under the curve (AUC) was calculated.Results:The indicators predicting the first remission course included peripheral blood white blood cell count during onset, CBF::MYH11 fusion gene, CEBPA bZIP region mutation, myelodysplastic syndrome-related gene mutation, and induction chemotherapy regimen selection as independent factors for the first remission course. The model’s area under the training and validation curves was 0.738 (95% CI: 0.696-0.780) and 0.726 (95% CI: 0.650-0.801), respectively. The Hosmer-Lemeshow test results yielded P-values of 0.993 and 0.335, respectively. Conclusion:In this study, the developed model demonstrates a strong predictive capability for the efficacy of the first course of patients with AML, providing valuable guidance to clinicians in assessing patient prognosis and selecting appropriate treatment strategies.