[Effect of CD8+ CD28- T Cells on Acute Graft-Versus-Host Disease after Haploidentical Hematopoietic Stem Cell Transplantation].

OBJECTIVE: To investigate the effect of CD8+ CD28- T cells on acute graft-versus-host disease(aGVHD) after haploidentical hematopoietic stem cell transplantation(haplo-HSCT). METHODS: The relationship between absolute count of CD8+ CD28- T cells and aGVHD in 60 patients with malignant hematological diseases was retrospectively analyzed after haplo-HSCT, and the differences in the incidence rate of chronic graft-versus host disease(cGVHD), infection and prognosis between different CD8+ CD28- T absolute cells count groups were compared. RESULTS: aGVHD occurred in 40 of 60 patients after haplo-HSCT, with an incidence rate of 66.67%. The median occurrence time of aGVHD was 32.5(20-100) days. At 30 days after the transplantation, the absolute count of CD8+ CD28- T cells of aGVHD group was significantly lower than that of non-aGVHD group (P =0.03). Thus the absolute count of CD8+ CD28- T cells at 30 days after transplantation can be used to predict the occurrence of aGVHD to some extent. At 30 days after transplantation, the incidence rate of aGVHD in the low cell count group (CD8+ CD28- T cells absolute count < 0.06/µl) was significantly higher than that in the high cell count group (CD8+ CD28- T cells absolute count ≥0.06/µl,P =0.011). Multivariate Cox regression analysis further confirmed that the absolute count of CD8+ CD28-T cells at 30 days after transplantation was an independent risk factor for aGVHD, and the risk of aGVHD in the low cell count group was 2.222 times higher than that in the high cell count group (P =0.015). The incidence of cGVHD, fungal infection, EBV infection and CMV infection were not significantly different between the two groups with different CD8+ CD28- T cells absolute count. The overall survival, non-recurrent mortality and relapse rates were not significantly different between different CD8+ CD28- T cells absolute count groups. CONCLUSION: Patients with delayed CD8+ CD28- T cells reconstitution after haplo-HSCT are more likely to develop aGVHD, and the absolute count of CD8+ CD28- T cells can be used to predict the incidence of aGVHD to some extent. The absolute count of CD8+ CD28- T cells after haplo-HSCT was not associated with cGVHD, fungal infection, EBV infection, and CMV infection, and was also not significantly associated with the prognosis after transplantation.

[Efficacy and Prognosis of Transplant-Associated Thrombotic Microangiopathy Based on Different Prognostic Risk Models].

OBJECTIVE: To investigate the clinical features of transplant-associated thrombotic microangiopathy (TA-TMA) and the prognostic value of different prognostic risk models for TA-TMA. METHODS: The clinical characteristics of 32 TA-TMA patients diagnosed at the First Medical Center of the PLA General Hospital from January 2018 to February 2022 in terms of short-term prognosis and influencing factors were retrospectively analyzed. In addition, the risk population composition ratio, treatment response, and overall survival between the BATAP risk model and the TMA index model were compared, as well as the efficacy of two prognostic risk models for predicting death in patients with TA-TMA. RESULTS: Independent risk factors affecting the short-term prognosis of TA-TMA include III-IV aGVHD prior to TA-TMA diagnosis (P=0.001), renal or neurological dysfunction (P=0.006), and Hb<70 g/L (P=0.043). In the TMA index model, treatment response was worst in the high-risk group (P=0.008), while there was no significant difference in treatment response between different risk groups in the BATAP model (P=0.105). In the BATAP model, there was a statistically significant difference in the OS between the three groups of low risk, intermediate risk, and high risk (87.5% vs 61.1% vs 16.7%, χ2＝6.7, P=0.014). In the TMA index model, there was a statistically significant difference in the OS between the three groups of low risk, intermediate risk, and high risk (77.8% vs 45.5% vs 0.0%, χ2＝7.3, P=0.017). The area under the ROC curve (AUC) of the TMA index model was 0.745 (95%CI: 0.56-0.88, P<0.05), and the AUC of the BATAP model was 0.743 (95%CI: 0.56-0.88, P<0.05), indicating that both prognostic risk models have good predictive value. CONCLUSION: The short-term prognosis of TA-TMA patients might be accurately determined using both the BATAP model and the TMA index model. When predicting the efficacy of TA-TMA in different risk groups, the TMA index model may perform better than the BATAP model.

Progress in Research on Antitumor Drugs and Dynamic Changes in Skeletal Muscles.

Objective: To review the research progress of reltionship between antitumor drugs and the dynamic changes of the skeletal muscles during treatment phase. Background: Sarcopenia is a common disease in patients with tumors, and it has been agreed that patients with tumors and sarcopenia experience more serious adverse reactions and have a shorter long-term survival after antitumor therapy than patients without sarcopenia. Antitumor drugs whilst beneficial for tumor regression, interferes and synergizes with cancer-induced muscle wasting/sarcopenia, induced myodemia or intramuscular fat and the two conditions often overlap making it difficult to drive conclusions. In recent years, increasing attention has been paid to the dynamic changes in skeletal muscles during antitumor drug therapy. Dynamic changes refer not only measurement skeletal muscle quantity at baseline level, but give more emphasis on the increasing or decreasing level during or end of the whole treatment course. Methods: We retrievaled published English-language original research articles via pubmed, those studies mainly focused on repeated measurements of skeletal muscle index using computed tomography (CT) in cancer patients who received antitumor drug treatment but not received interventions that produced muscle mass change (such as exercise and nutritional interventions). Conclusion: This article will summarize the research progress to date. Most of antineoplastic drug cause skeletal muscle loss during the treatment course, loss of L3 skeletal muscle index is always associated with poor clinical outcomes.

The application of 0-1 mixed integer nonlinear programming optimization model based on a surrogate model to identify the groundwater pollution source.

The optimization model is presently used for the identification of pollution sources and it is based on non-linear programming optimization. The decision variables in this model are continuous, resulting in a weak recognition of integer variables including pollution source location. In addition, as the number of pollution sources increase, so the calculated load increases exponentially and accuracy decreases. Compared with previous studies, this study makes a series of improvements by adopting a 0-1 mixed integer nonlinear programming optimization model to enable the simultaneous identification of both location (integer variable) and the release intensity (continuous variable) of the pollution source. One of the constraints in the optimization model is a simulation component which requires thousands of calls during the calculation process and therefore requires considerable computational load. To avoid this problem, the Kriging surrogate model is established in this study to reduce computational load, while at the same time ensuring the accuracy of the simulation results. The identification result is solved using a genetic algorithm (GA) and represents the real location of the pollution source, while release intensities are close to actual ones with small relative errors. The Kriging surrogate model is based on a 0-1 mixed integer nonlinear programming optimization model and can simultaneously identify both the location and the release intensity of the pollution source with a high degree of accuracy and by using short computational times.