OBJECTIVE: To screen anti-tumor drugs that improve antigen processing and presentation in acute myeloid leukemia (AML) cells. METHODS: A TCR-like or TCR mimic antibody that can specifically recognize HLA-A*0201:WT1_126-134 ( RMFPNAPYL) complex (hereafter referred to as HLA-A2:WT1) was synthesized to evaluate the function of antigen processing and presentation machinery (APM) in AML cells. AML cell line THP1 was incubated with increasing concentrations of IFN-γ, hypomethylating agents (HMA), immunomodulatory drugs (IMiD), proteasome inhibitors (PI) and γ-secretase inhibitors (GSI), followed by measuring of HLA-ABC, HLA-A2 and HLA-A2:WT1 levels by flow cytometry at consecutive time points. RESULTS: The TCR-like antibody we generated only binds to HLA-A*0201⁺WT1⁺ cells, indicating the specificity of the antibody. HLA-A2:WT1 level of THP-1 cells detected with the TCR-like antibody was increased significantly after co-incubation with IFN-γ, showing that the HLA-A2:WT1 TCR like antibody could evaluate the function of APM. Among the anti-tumor agents screened in this study, GSI (LY-411575) and HMA (decitabine and azacitidine) could significantly increase the HLA-A2:WT1 level. The IMiD lenalidomide and pomalidomide could aslo upregulate the expression of HLA-A2:WT1 complex under certain concentrations of the drugs and incubation time. As proteasome inhibitors, carfilzomib could significantly decreased the expression of HLA-A2:WT1, while bortezomib had no significant effect on HLA-A2:WT1 expression. CONCLUSION HLA-A2:WT1 TCR-like antibody can effectively reflect the APM function. Some of the anti-tumor drugs can affect the APM function and immunogenicity of tumor cells.
Humans ; Leukemia, Myeloid, Acute/immunology* ; Antineoplastic Agents/pharmacology* ; Antigen Presentation/drug effects* ; HLA-A2 Antigen/immunology* ; Receptors, Antigen, T-Cell/immunology* ; Cell Line, Tumor ; Interferon-gamma

As a non-invasive imaging modality,cardiac magnetic resonance(CMR)enables a"one-stop"in vivo assessment of cardiac morphology,structure,functional status and histological features,plays an irreplaceable role in the diagnosis,prognosis and risk stratification of cardiovascular diseases.In 2024,CMR has made continuous progress towards precision medicine.Upgraded technologies such as tissue characterization imaging and myocardial strain analysis,are gradually transformed into standard clinical practice.Artificial intelligence and other new algorithms have improved the quality and efficiency of CMR.The application of CMR in non-ischemic heart disease,ischemic heart disease and other areas is highly valued in the new version of various guidelines,highlighting the importance of CMR in the clinical management of cardiovascular diseases.This article aims to systematically review representative achievements of CMR in 2024 from the perspectives of both technological innovation and clinical translation,providing the latest update in this field.

Objective To screen metabolic core genes in colon cancer based on machine learning algorithms and analyze their functional mechanisms.Methods Data were obtained from The Cancer Genome Atlas(TCGA)database and the Gene Expression Omnibus(GEO)database.The TCGA co-hort included 375 tumor samples and 32 adjacent normal tissue samples,while the GSE39582 cohort comprised 419 tumor samples.Univariate Cox regression analysis combined with random forest,sup-port vector machine recursive feature elimination(SVM-RFE),and least absolute shrinkage and selec-tion operator(LASSO)regression algorithms were employed to screen for metabolic core genes.Re-ceiver operating characteristic(ROC)curves were plotted,and the area under the curve(AUC)was used to evaluate the predictive efficacy of the core genes.Real-time fluorescent quantitative polymerase chain reaction(qRT-PCR)and immunohistochemistry(IHC)methods were adopted to detect the ex-pression of the core genes.The core genes were knocked down to explore their roles in colon cancer.Results Three core genes,namely CPT2,SCP2 and NR3C2,were screened based on machine learning algorithms.According to the comparison results of the AUCs of the ROC curves,NR3C2 exhibited the best predictive efficacy.qRT-PCR detection results showed that NR3C2 mRNA was lowly ex-pressed in colon cancer cell lines;IHC detection results revealed that NR3C2 was lowly expressed in colon cancer tissues.Knocking down NR3C2 significantly promoted the proliferation and migration of colon cancer cells.Conclusion NR3C2 is identified as a core metabolic inhibitory gene in colon cancer by cross-applying three machine learning algorithms,which may provide a new strategy for metabolic targeted therapy.

Objective To address personnel management challenges in large comprehensive hospitals by developing a comprehensive personnel information management system for refined multi-campus administration.Methods A centralized data-base was employed to construct a personnel information management system compatible with both"interactive management"and"independent management"modes.The system progressively implemented functions including personnel information manage-ment,meal card and subsidy administration,and shift scheduling.Results The system achieved effective interconnections be-tween subsystems,significantly improving personnel management efficiency,data governance,risk prevention capabilities,and operational decision-making.Personnel data were efficiently utilized across multiple scenarios.Conclusion The multi-campus comprehensive personnel information management system meets the refined requirements of multi-campus personnel administration and provides valuable experience for the development and expansion of subsequent hospital operation management information sys-tems.

As the world's second largest vector of pathogens,ticks can spread a variety of pathogens by sucking the host's blood.Ticks not only threaten human life and health,but also cause great economic losses in animal husbandry.Artificial breeding of ticks can provide a stable environment for the growth and reproduction of ticks,thereby generating sufficient exper-imental materials for understanding ticks'biological characteristics,studying tick-borne pathogens,and developing anti-tick drugs and vaccines.Current methods of breeding ticks in the laboratory can be roughly divided into two categories:breeding methods using host animals or artificial membranes.The selection of breeding method must be comprehensively considered,ac-cording to tick types,blood-sucking habits,living environments,and other aspects.The development processes of the two methods,and their respective advantages and disadvantages,are described and discussed,to assist laboratories in artificial breeding of ticks.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

Objective To address personnel management challenges in large comprehensive hospitals by developing a comprehensive personnel information management system for refined multi-campus administration.Methods A centralized data-base was employed to construct a personnel information management system compatible with both"interactive management"and"independent management"modes.The system progressively implemented functions including personnel information manage-ment,meal card and subsidy administration,and shift scheduling.Results The system achieved effective interconnections be-tween subsystems,significantly improving personnel management efficiency,data governance,risk prevention capabilities,and operational decision-making.Personnel data were efficiently utilized across multiple scenarios.Conclusion The multi-campus comprehensive personnel information management system meets the refined requirements of multi-campus personnel administration and provides valuable experience for the development and expansion of subsequent hospital operation management information sys-tems.

As a non-invasive imaging modality,cardiac magnetic resonance(CMR)enables a"one-stop"in vivo assessment of cardiac morphology,structure,functional status and histological features,plays an irreplaceable role in the diagnosis,prognosis and risk stratification of cardiovascular diseases.In 2024,CMR has made continuous progress towards precision medicine.Upgraded technologies such as tissue characterization imaging and myocardial strain analysis,are gradually transformed into standard clinical practice.Artificial intelligence and other new algorithms have improved the quality and efficiency of CMR.The application of CMR in non-ischemic heart disease,ischemic heart disease and other areas is highly valued in the new version of various guidelines,highlighting the importance of CMR in the clinical management of cardiovascular diseases.This article aims to systematically review representative achievements of CMR in 2024 from the perspectives of both technological innovation and clinical translation,providing the latest update in this field.

Objective To propose a multiple regression-variational auto-encoders(MR-VAE)model to realize precise and non-invasive prediction of intra-abdominal pressure(IAP)in critically ill patients.Methods At first,a dataset was constructed by retrospectively analysing baseline characteristics and clinical indicators of 100 critically ill patients admitted to the Intensive Care Unit of Daping Hospital of Army Medical University between 30 August 2019 and 30 March 2021.Then,a MR-VAE prediction model was developed by integrating a feedforward neural network for supervised regression onto a variational autoencoder(VAE)framework and incorporating multiple regression strategies to mitigate feature interference.Finally,the MR-VAE model had its performance evaluated by its comparison with five classical models including support vector machines(SVM),convolutional neural networks(CNN),Scikit-learn integrated model(SIM),multi-layer perceptron(MLP)and K-nearest neighbors(KNN),and its prediction accuracy verified by testing the data of 10 randomly selected patients.Results The MR-VAE model behaved the best when compared with the five classical models,with a mean squared error(MSE)of 0.207,a root mean square error(RMSE)of 0.454,a mean absolute error(MAE)of 0.361,a median absolute deviation(MAD)of 0.243,an explained variance score(EVS)of 0.814 and a R2of 0.823,which also outperformed the five models in fitting performance,convergence and final loss.In random sample testing,the MR-VAE model exhibited high consistency between predicted and actual values.Conclusion The MR-VAE model proposed can accurately predict IAP,which has great potential in reducing the repeated measurements of IAP in critically ill patients and providing new ideas for the early diagnosis and treatment of IAH.

As the world's second largest vector of pathogens,ticks can spread a variety of pathogens by sucking the host's blood.Ticks not only threaten human life and health,but also cause great economic losses in animal husbandry.Artificial breeding of ticks can provide a stable environment for the growth and reproduction of ticks,thereby generating sufficient exper-imental materials for understanding ticks'biological characteristics,studying tick-borne pathogens,and developing anti-tick drugs and vaccines.Current methods of breeding ticks in the laboratory can be roughly divided into two categories:breeding methods using host animals or artificial membranes.The selection of breeding method must be comprehensively considered,ac-cording to tick types,blood-sucking habits,living environments,and other aspects.The development processes of the two methods,and their respective advantages and disadvantages,are described and discussed,to assist laboratories in artificial breeding of ticks.