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.

This study summarizes the preoperative and intraoperative nursing experience in 5 cases of bridge-to-transplant heart transplantation with left ventricular assist device(LVAD)explant.Key points of nursing include:preoperative care and assessment of LVAD patients,preoperative discussion of the multidisciplinary team,safe transfer of patients to surgical rooms and other preoperative preparation,cardiomyocardial protection and multidisciplinary team cooperation during bridging transplantation,and intra-operative patient safety management.All 5 patients in this group successfully completed the surgery and were discharged.Pressure sores,wound infections,and other postoperative complications have not occurred.Postoperative cardiac function of 5 patients in this group were classified as New York Heart Association class Ⅰ～Ⅱ.The follow-up period for the 5 patients in this group ranged from 6 months to 6 years.The results of the most recent echocardiography follow-up showed that the left ventricular ejection fraction of all patients was all above 65％,with well prognosis.

Objective To determine the chemical composition of TangNiaoLing Tablets by UHPLC-Q-Exactive-Orbitrap-MS/MS.Methods A Waters ACQUITY HSS T3 column(100 mm×2.1 mm,1.8 μm)was used for separation at a total flow rate of 0.2 mL/min.The mobile phase included an aqueous solution of 0.1%formic acid and acetonitrile mixed with 0.1%formic acid was supplied.The injection volume was set at 2 μL and the column oven temperature was 40℃.High-resolution mass spectrometric data were obtained by concurrently scanning the positive and negative ion modes.The identification was accomplished by inferring the empirical fragmentation patterns and comparing it with databases and references.Results 100 different chemical elements,including triterpenes,flavonoids,phenylpropanoids,phenylethanoid glycosides,iridoid glycosides,and phenols,among others were identified from the 50%methanol extract of TangNiaoLing pills.Conclusion The chemical contents of TangNiaoLing tablets were identified and analyzed using the UHPLC-Q-Exactive-Orbitrap-MS/MS method for the first time.This served as a foundation for future research into the tablets' effective components and quality control.

Objective:To compare the impact of the AlignRT-based optical guidance method with the traditional marker line method on the accuracy of positioning, and explore the effect of patient′s different thoracic characteristic parameters on the precision of optical guidance positioning.Methods:A total of sixty breast cancer patients who received post breast-conserving radiotherapy at Anhui Cancer Hospital from July 2022 to September 2023 were retrospectively selected. Then these patients were equally divided into traditional cross hairs set-up (marker line group) and surface-guided set-up (SGRT group). The cone-beam CT scanning results were used as the gold standard, the three-dimensional set-up errors and the CTV-PTV target area external radiotherapy boundaries of two groups were studied comparatively. Multiple patient′s thoracic characteristic parameters were defined and the correction between each parameter and set-up error of SGRT was analyzed.Results:The mean value of three-dimensional set-up errors in the SGRT group and the marker line group was within 0.2 cm and 0.5 cm, respectively. The differences in three-dimensional set-up errors and total displacements between two groups were statistically significant ( z=-2.93, -3.21, -2.59, -4.76, P<0.05). The SGRT group reduced the CTV-PTV boundary from 0.5 cm of conventional marker line group to 0.3 cm. The thoracic aspect ratio H/W, the affected side pinch angle α1, and the healthy side pinch angle α2 were positively correlated with the x-direction posing error ( r=0.49, 0.59, 0.71, P<0.05); whereas, the affected side and the healthy side mammary gland heights D1 and D2 were negatively correlated with the z-direction posing error ( r=-0.46, -0.49, P<0.05). Conclusions:For breast-conserving postoperative radiotherapy patients, SGRT can obtain a more accurate radiotherapy set-up than the traditional marker line method, and can effectively reduce the target area externally expanded range. Meanwhile, the monitoring accuracy of SGRT is affected by the patient′s thoracic characteristic parameters, and clinical attention should be paid to breast-conserving radiotherapy patients with thick body shape, narrow body width, and small breast glands.

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.

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*

Objective:To explore the risk factors for severe acute kidney injury (AKI) in children with sepsis in the pediatric intensive care unit (PICU) and construct a prediction model to assist early clinical identification.Methods:A retrospective analysis was performed on clinical data of 987 children with sepsis admitted to the PICU of Hunan Children′s Hospital from July 1, 2018 to January 31, 2021. Children who developed severe AKI during hospitalization were included in the AKI stage 2-3 group ( n=228), and the remaining were included in the No-AKI/AKI stage 1 group ( n=759). General information and biochemical indicators were compared between the two groups. Logistic regression analysis was used to identify risk factors for severe AKI in children with sepsis, and a prediction model and nomogram were established. Results:The mortality rate in the AKI stage 2-3 group was 2.49 times that of the No-AKI/AKI stage 1 group [31.1%(71/228) vs 12.5%(95/759), P<0.05]. Compared with the No-AKI/AKI stage 1 group, the AKI stage 2-3 group had lower levels of platelet count (PLT), total protein (TP), albumin (ALB), antithrombin Ⅲ (AT3), and fibrinogen (FIB), but higher levels of lactate dehydrogenase (LDH), serum creatinine (SCr), blood urea nitrogen (BUN), magnesium ion (Mg 2+ ), activated partial thromboplastin time (APTT), fibrinogen degradation products (FDP), and D-dimer (D-D) (all P<0.05), with no significant difference in total bile acid (TBAC) ( P>0.05). Multivariate logistic regression analysis showed that decreased AT3 ( OR=0.989, 95% CI: 0.980-0.997, P=0.007), increased LDH ( OR=1.001, 95% CI: 1.000-1.001, P<0.001), increased SCr ( OR=1.051, 95% CI: 1.037-1.066, P<0.001), and increased BUN ( OR=1.099, 95% CI: 1.028-1.174, P=0.005) were risk factors for severe AKI in children with sepsis. The prediction model was Logist Pr=-3.184-0.012 X1+ 0.001 X2+ 0.050 X3+ 0.094 X4 ( X1=AT3, X2=LDH, X3=SCr, X4=BUN), with the optimal cut-off value of 0.374 (Youden index=0.560). A nomogram was constructed by binary assignment of predictive variables, with an area under the curve of 0.826 (95% CI: 0.790-0.861, P<0.001). Conclusions:The mortality rate of septic children with severe AKI in PICU is significantly increased. Decreased AT3, and increased LDH, SCr, and BUN are risk factors for severe AKI in children with sepsis. Clinicians should be alert to severe AKI when the predicted probability of the early warning model exceeds 0.374.