OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

Objective: To address systemic challenges in the blood system, such as supply-demand imbalance, inefficient allocation, and inappropriate clinical use, while bridging gaps in current theories regarding the integration of social mobilization, institutional practice, and policy coordination. It sought to construct a system dynamics model spanning from macro to micro levels to analyze the blood management system holistically. Methods: Key variables were identified by a systematic literature search in both Chinese and English databases. An improved Delphi method was then employed to conduct expert consultations across different fields, leading to the selection and determination of core variable sets for each sub-model. Furthermore, by defining their logical relationships, a system dynamics model was constructed to systematically analyze the operation mechanism of the blood management system. Results: Four core sub-models were developed: 1) A macro "Dynamic Balance" model quantifying regional supply-demand equilibrium and inventory control; 2) a mobilization "Three-Layer Funnel" model analyzing how socio-cultural factors, service channels, and policy incentives influence donation behavior; 3) an institutional "Dual-Cycle Regulation" model revealing hospital blood usage is driven by both disease burden (demand cycle) and management practices (regulation cycle); and 4) an individual "Three-Layer Filter" model standardizing clinical transfusion decisions based on necessity, risk-benefit, and context. These were integrated into a "Multi-Layer Linkage and Feedback" model, elucidating bidirectional interactions among five levels: policy environment, regional supply, blood station mobilization, hospital application, and clinical decision-making. Conclusion: This study constructed a system dynamics model for blood management. By defining key variables and their logical relationships, it systematically analyzes the system′s operational mechanism. The integrated framework connects multiple levels—regional supply, voluntary donation, hospital blood use, and clinical decision-making—revealing their intrinsic linkages. Future efforts should employ systems thinking to synergistically enhance supply-side mobilization, demand-side management, systemic regulation, and decision standardization to build a safe, efficient, and sustainable blood security system.

ObjectiveTo investigate whether the inhibitory effect of Panax quinquefolium saponins （PQS） on non-small cell lung cancer （NSCLC） is enhanced under graphene-mediated far-infrared therapy （GFIR）， and to analyze its relationships with mitochondrial dysfunction， mitochondrial DNA （mtDNA） leakage， and the activation of cyclic GMP-AMP synthase （cGAS）/stimulator of interferon genes （STING） signaling pathways. MethodsIn the cell experiments， the cell-counting kit 8 （CCK-8） was employed to assess the inhibitory effects of PQS alone or in combination with GFIR （PQS+GFIR） on A549 cell viability. The scratch assay and the colony formation assay were adopted to evaluate cell migration and colony formation capabilities， respectively. Flow cytometry was employed to assess apoptosis， mitochondrial membrane potential， and reactive oxygen species （ROS） levels. Immunofluorescence （IF） assay was performed to assess mitochondrial localization. Real-time polymerase China reaction（PCR） was used to detect mitochondrial DNA （mtDNA） leakage. A STING knockdown cell model was established by transfecting cells with small interfering RNA （siRNA）， and the knockdown efficiency was assessed by Real-time PCR and Western blot. The cell viability and apoptosis following gene knockdown were assessed through the CCK-8 assay and flow cytometry. In the animal experiments， an allogeneic xenograft mouse model was established with Lewis lung cancer cells. Mice were randomized into a control group， a model group， a cisplatin group， a low-dose PQS （PQS-Low， 90 mg·kg^-1） group， a high-dose PQS （PQS-High， 180 mg·kg^-1） group， a low-dose PQS combined with GFIR （PQS-Low+GFIR， 90 mg·kg^-1） group， and a high-dose PQS combined with GFIR （PQS-High+GFIR， 180 mg·kg^-1） group. Treatment lasted for 14 days， and the fluorescence intensity was monitored at the tumor site by small-animal in vivo imaging during the treatment period. Flow cytometry was utilized to determine the proportions of T-lymphocyte subsets， B-lymphocyte levels， and peripheral blood NK cell levels in the spleens of mice. Immunohistochemical （IHC） staining was performed to assess the infiltration of CD3⁺， CD4⁺， and CD8⁺ T-lymphocytes in the tumor tissue. Enzyme-linked immunosorbent assay （ELISA） was conducted to measure the levels of interferon-α （IFN-α）， interferon-β （IFN-β）， tumor necrosis factor-α （TNF-α）， and interleukin-6 （IL-6） in mouse tumors. The mRNA levels of cGAS， STING， TANK-binding kinase 1 （TBK1）， interferon regulatory factor 3 （IRF3）， interferon beta 1 （IFNB1）， and nuclear factor κB p65 （NF-κB p65） were determined by Real-time PCR. The protein levels of cGAS， STING， p-STING， TBK1， p-TBK1， IRF3， p-IRF3， NF-κB p65， and p-NF-κB p65 were quantified via Western blot. ResultsThe cell experiment results showed that compared with the control group， PQS inhibited the viability and migration of A549 cells， promoted apoptosis， affected mitochondrial localization， reduced mitochondrial membrane potential， increased the ROS level， and promoted mtDNA leakage. Following STING knockdown， the reductions in cell viability and the apoptosis-inducing effects caused by PQS were partially reversed （P<0.05， P<0.01）. Compared with the PQS group， the PQS+GFIR group showed inhibited A549 cell migration and colony formation， exacerbated apoptosis， downregulated mitochondrial membrane potential， elevated ROS levels， and promoted mtDNA leakage. After STING knockdown， the reduced A549 cell viability and abnormal apoptosis phenotype were further reversed （P<0.05， P<0.01）. The animal experiment results showed that compared with the control group， the model group mice exhibited reduced proportions of T-lymphocyte subsets， CD19⁺ B-lymphocyte levels， and peripheral blood NK1.1⁺ cell levels in the spleen （P<0.01）. Compared with the model group， the PQS group showed increased immune cell parameters in mice （P<0.05， P<0.01）. Compared with the PQS group， the PQS+GFIR group exhibited enhanced levels of the aforementioned indicators （P<0.01）. Compared with the model group， the PQS group showed significantly reduced fluorescence intensity in transplanted tumors， significantly upregulated expression of CD3 and CD4 in the tumor tissue， elevated levels of IFN-α and IL-6 in tumors， and upregulated mRNA levels of cGAS， STING， TBK1， IRF3， IFNB1， and NF-κB p65 as well as upregulated protein level of cGAS and phosphorylation level of STING in tumors （P<0.05， P<0.01）. Compared with the PQS group， the PQS+GFIR group demonstrated further reduced tumor fluorescence intensity， elevated levels of CD3， CD4， and CD8 as well as cytokine levels in tumor tissue， and upregulated mRNA levels of genes related to the cGAS/STING pathway as well as upregulated protein level of cGAS and phosphorylation levels of STING， TBK1， IRF3， and NF-κB p65 （P<0.05， P<0.01） ConclusionGFIR enhances the inhibitory effect of PQS on NSCLC， potentially through mitochondrial dysfunction， mtDNA leakage， and activation of the cGAS/STING signaling pathway.

ObjectiveTo investigate whether the inhibitory effect of Panax quinquefolium saponins （PQS） on non-small cell lung cancer （NSCLC） is enhanced under graphene-mediated far-infrared therapy （GFIR）， and to analyze its relationships with mitochondrial dysfunction， mitochondrial DNA （mtDNA） leakage， and the activation of cyclic GMP-AMP synthase （cGAS）/stimulator of interferon genes （STING） signaling pathways. MethodsIn the cell experiments， the cell-counting kit 8 （CCK-8） was employed to assess the inhibitory effects of PQS alone or in combination with GFIR （PQS+GFIR） on A549 cell viability. The scratch assay and the colony formation assay were adopted to evaluate cell migration and colony formation capabilities， respectively. Flow cytometry was employed to assess apoptosis， mitochondrial membrane potential， and reactive oxygen species （ROS） levels. Immunofluorescence （IF） assay was performed to assess mitochondrial localization. Real-time polymerase China reaction（PCR） was used to detect mitochondrial DNA （mtDNA） leakage. A STING knockdown cell model was established by transfecting cells with small interfering RNA （siRNA）， and the knockdown efficiency was assessed by Real-time PCR and Western blot. The cell viability and apoptosis following gene knockdown were assessed through the CCK-8 assay and flow cytometry. In the animal experiments， an allogeneic xenograft mouse model was established with Lewis lung cancer cells. Mice were randomized into a control group， a model group， a cisplatin group， a low-dose PQS （PQS-Low， 90 mg·kg^-1） group， a high-dose PQS （PQS-High， 180 mg·kg^-1） group， a low-dose PQS combined with GFIR （PQS-Low+GFIR， 90 mg·kg^-1） group， and a high-dose PQS combined with GFIR （PQS-High+GFIR， 180 mg·kg^-1） group. Treatment lasted for 14 days， and the fluorescence intensity was monitored at the tumor site by small-animal in vivo imaging during the treatment period. Flow cytometry was utilized to determine the proportions of T-lymphocyte subsets， B-lymphocyte levels， and peripheral blood NK cell levels in the spleens of mice. Immunohistochemical （IHC） staining was performed to assess the infiltration of CD3⁺， CD4⁺， and CD8⁺ T-lymphocytes in the tumor tissue. Enzyme-linked immunosorbent assay （ELISA） was conducted to measure the levels of interferon-α （IFN-α）， interferon-β （IFN-β）， tumor necrosis factor-α （TNF-α）， and interleukin-6 （IL-6） in mouse tumors. The mRNA levels of cGAS， STING， TANK-binding kinase 1 （TBK1）， interferon regulatory factor 3 （IRF3）， interferon beta 1 （IFNB1）， and nuclear factor κB p65 （NF-κB p65） were determined by Real-time PCR. The protein levels of cGAS， STING， p-STING， TBK1， p-TBK1， IRF3， p-IRF3， NF-κB p65， and p-NF-κB p65 were quantified via Western blot. ResultsThe cell experiment results showed that compared with the control group， PQS inhibited the viability and migration of A549 cells， promoted apoptosis， affected mitochondrial localization， reduced mitochondrial membrane potential， increased the ROS level， and promoted mtDNA leakage. Following STING knockdown， the reductions in cell viability and the apoptosis-inducing effects caused by PQS were partially reversed （P<0.05， P<0.01）. Compared with the PQS group， the PQS+GFIR group showed inhibited A549 cell migration and colony formation， exacerbated apoptosis， downregulated mitochondrial membrane potential， elevated ROS levels， and promoted mtDNA leakage. After STING knockdown， the reduced A549 cell viability and abnormal apoptosis phenotype were further reversed （P<0.05， P<0.01）. The animal experiment results showed that compared with the control group， the model group mice exhibited reduced proportions of T-lymphocyte subsets， CD19⁺ B-lymphocyte levels， and peripheral blood NK1.1⁺ cell levels in the spleen （P<0.01）. Compared with the model group， the PQS group showed increased immune cell parameters in mice （P<0.05， P<0.01）. Compared with the PQS group， the PQS+GFIR group exhibited enhanced levels of the aforementioned indicators （P<0.01）. Compared with the model group， the PQS group showed significantly reduced fluorescence intensity in transplanted tumors， significantly upregulated expression of CD3 and CD4 in the tumor tissue， elevated levels of IFN-α and IL-6 in tumors， and upregulated mRNA levels of cGAS， STING， TBK1， IRF3， IFNB1， and NF-κB p65 as well as upregulated protein level of cGAS and phosphorylation level of STING in tumors （P<0.05， P<0.01）. Compared with the PQS group， the PQS+GFIR group demonstrated further reduced tumor fluorescence intensity， elevated levels of CD3， CD4， and CD8 as well as cytokine levels in tumor tissue， and upregulated mRNA levels of genes related to the cGAS/STING pathway as well as upregulated protein level of cGAS and phosphorylation levels of STING， TBK1， IRF3， and NF-κB p65 （P<0.05， P<0.01） ConclusionGFIR enhances the inhibitory effect of PQS on NSCLC， potentially through mitochondrial dysfunction， mtDNA leakage， and activation of the cGAS/STING signaling pathway.

Objective: To investigate the stability of plasma from blood donors cryopreserved at-80℃for two months in ELISA and NAT detection (HBV, HCV, and HIV), and to provide a reference for the preservation, transportation, and retrospective testing of samples collected in remote areas or during emergency blood collection. Methods: Three sets of experimental designs were adopted, each including ELISA (HBsAg, anti-HCV, HIV Ag-Ab) and NAT (HBV DNA, HCV RNA, HIV-1 RNA) testing. Stability validation test: 136 positive and 150 negative donor samples were selected, retested with the original reagents after cryopreservation at-80℃for two months, and the ELISA S/CO values, NAT Ct values, and result concordance rates before and after freezing were compared. Limit of detection validation test: Standard substances with known concentrations were diluted into three gradients of weakly reactive samples (ELISA S/CO values of 0.8-5, NAT Ct values of 35-42). Each was tested six times before and after freezing, the mean values were calculated, and the ELISA S/CO values and NAT Ct values were compared. Real-world validation test: 300 randomly selected donor samples were subjected to plasma separation after initial testing and cryopreserved for two months. The reactive detection rates of ELISA and NAT before and after freezing were compared. Results: Stability validation test: In ELISA testing, 3 samples converted from weak-positive to negative, and 3 samples converted from weak-positive to gray zone, yielding a concordance rate of 97.9%. In NAT testing, 2 samples converted from weak-positive to gray zone, yielding a concordance rate of 99.30%. Significant differences were observed in both ELISA S/CO values and NAT Ct values after freezing (P<0.05). Limit of detection validation test: No significant differences were found in ELISA S/CO values or NAT Ct values for the three concentrations of weakly reactive samples before and after freezing (P>0.05); only the anti-HCV ELISA result for the 0.5 NCU/mL sample converted from positive to the gray zone. Real-world validation test: The ELISA reactive detection rates before and after freezing were 1.67% vs 1.00%, and the NAT reactive detection rates were 0.33% vs 0.33%, with no significant differences (P≥0.05). Conclusion: After cryopreservation of donor plasma at-80℃for two months, the overall concordance rates for ELISA and NAT testing are extremely high. Although weak-positive samples are susceptible to freezing effects, the overall qualitative results remain stable, demonstrating that deep-low temperature cryopreservation can effectively maintain the stability of viral marker detection.

Purpose: We used bioinformatics methods and Mendelian randomization (MR) analysis to investigate the hub genes involved in acute myeloid leukemia (AML) and their causal relationship with hemolysis, to explore a new direction for molecular biology research of AML. Methods: We first differentially analyzed peripheral blood samples from 62 healthy volunteers and 65 patients with AML from the Gene Expression Omnibus database to obtain differentially expressed genes (DEGs), and intersected them with genes sourced from weighted gene co-expression network analysis (WGCNA) and the GeneCards database to obtain target genes. Target genes were screened using protein–protein interaction (PPI) network analysis and ROC curves to identify genes associated with AML. Finally, we analyzed the correlation between genes and immune cells and the relationship between toll-like receptor 4 (TLR4) and AML using MR. Results: We compared peripheral blood expression profiles using an array of 62 healthy volunteers (GSE164191) and 65 patients with AML (GSE89565) (M0:25; M1:11; M2:10; M3:1; M4:7; M4 eo t [16;16] ou inv [16]:4; M5:6; M6:1) and obtained 7,339 DEGs (3,733 upregulated and 3,606 downregulated). We intersected these DEGs with 4,724 genes from WGCNA and 1,330 genes related to hemolysis that were identified in the GeneCards database to obtain 190 target genes. After further screening these genes using the PPI network, we identified TLR4, PTPRC, FCGR3B, STAT1, and APOE, which are closely associated with hemolysis in patients with AML. Finally, we found a causal relationship between TLR4 and AML occurrence using MR analysis (p < 0.05). Conclusion We constructed a WGCNA-based co-expression network and identified hemolysis-associated AML genes.

Purpose: We used bioinformatics methods and Mendelian randomization (MR) analysis to investigate the hub genes involved in acute myeloid leukemia (AML) and their causal relationship with hemolysis, to explore a new direction for molecular biology research of AML. Methods: We first differentially analyzed peripheral blood samples from 62 healthy volunteers and 65 patients with AML from the Gene Expression Omnibus database to obtain differentially expressed genes (DEGs), and intersected them with genes sourced from weighted gene co-expression network analysis (WGCNA) and the GeneCards database to obtain target genes. Target genes were screened using protein–protein interaction (PPI) network analysis and ROC curves to identify genes associated with AML. Finally, we analyzed the correlation between genes and immune cells and the relationship between toll-like receptor 4 (TLR4) and AML using MR. Results: We compared peripheral blood expression profiles using an array of 62 healthy volunteers (GSE164191) and 65 patients with AML (GSE89565) (M0:25; M1:11; M2:10; M3:1; M4:7; M4 eo t [16;16] ou inv [16]:4; M5:6; M6:1) and obtained 7,339 DEGs (3,733 upregulated and 3,606 downregulated). We intersected these DEGs with 4,724 genes from WGCNA and 1,330 genes related to hemolysis that were identified in the GeneCards database to obtain 190 target genes. After further screening these genes using the PPI network, we identified TLR4, PTPRC, FCGR3B, STAT1, and APOE, which are closely associated with hemolysis in patients with AML. Finally, we found a causal relationship between TLR4 and AML occurrence using MR analysis (p < 0.05). Conclusion We constructed a WGCNA-based co-expression network and identified hemolysis-associated AML genes.

Accurate timing of myelination is crucial for the proper functioning of the central nervous system. Here, we identified a de novo heterozygous mutation in TMEM63A (c.1894G>A; p. Ala632Thr) in a 7-year-old boy exhibiting hypomyelination. A Ca²⁺ influx assay suggested that this is a loss-of-function mutation. To explore how TMEM63A deficiency causes hypomyelination, we generated Tmem63a knockout mice. Genetic deletion of TMEM63A resulted in hypomyelination at postnatal day 14 (P14) arising from impaired differentiation of oligodendrocyte precursor cells (OPCs). Notably, the myelin dysplasia was transient, returning to normal levels by P28. Primary cultures of Tmem63a^-/- OPCs presented delayed differentiation. Lentivirus-based expression of TMEM63A but not TMEM63A_A632T rescued the differentiation of Tmem63a^-/- OPCs in vitro and myelination in Tmem63a^-/- mice. These data thus support the conclusion that the mutation in TMEM63A is the pathogenesis of the hypomyelination in the patient. Our study further demonstrated that TMEM63A-mediated Ca²⁺ influx plays critical roles in the early development of myelin and oligodendrocyte differentiation.
Animals ; Cell Differentiation/physiology* ; Oligodendroglia/metabolism* ; Mice, Knockout ; Mice ; Male ; Myelin Sheath/metabolism* ; Humans ; Child ; Cells, Cultured ; Oligodendrocyte Precursor Cells/metabolism*

Type 2 diabetes mellitus (T2DM) is a highly prevalent chronic metabolic disease with an increasing incidence worldwide, that poses a significant risk to public health. In many current clinical practices for diabetes management, conventional Western treatments, including oral or injectable hypoglycemic agents, have serious side effects. Given that traditional Chinese medicine (TCM) is characterized by a multi-component, multi-target and multi-pathway approach, its combination with Western medicine could enhance efficacy and reduce adverse effects. Consequently, the use of TCM as a potential auxiliary or alternative treatment for the prevention and/or management of T2DM has emerged as a research hotspot. This article reviews existing reports on TCM in the treatment of T2DM and provides a detailed discussion of its applications. By integrating relevant clinical evidence, this review summarizes the clinical data on 23 TCM formulas and Chinese patent medicines, comprehensively describing their efficacy and potential pharmacological mechanisms in the treatment of T2DM. This includes an exploration of the impacts of TCM-based therapeutic interventions on T2DM-related microRNAs and their target genes. We hope this review not only offers new insights for future research directions but also enhances the understanding of the scientific value of TCM. Please cite this article as: Ni HX, Cao LH, Gong XX, Zang ZY, Chang H. Traditional Chinese medicine for treatment of type 2 diabetes mellitus: Clinical evidence and pharmacological mechanisms. J Integr Med. 2025; 23(6):605-622.
Humans ; Diabetes Mellitus, Type 2/drug therapy* ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/pharmacology* ; Hypoglycemic Agents/pharmacology*