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In adults with acute lymphoblastic leukemia (ALL), post-transplant relapse is a major risk factor for mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Our study investigated the efficacy and safety of decitabine (dec) with ALL patients post-transplantation. We performed a retrospective cohort study to assess the efficacy of decitabine (dec) with post-transplant ALL at the First Affiliated Hospital of Zhengzhou University from February 2016 to September 2021. A total of 141 consecutive ALL patients were analyzed and divided into decitabine (dec, n = 65) and control (ctrl, n = 76) groups based on whether they were treated with decitabine after allo-HSCT. The 3-year cumulative incidence of relapse (CIR) rate in the dec group was lower than that in the ctrl group (19.6% vs. 36.1%, p = 0.031), with a hazard ratio of 0.491 (95% confidence interval [CI], 0.257–0.936). Additionally, subgroup analyses revealed that the 3-year CIR rate of T-ALL and Ph-negative B-ALL patients in the dec and ctrl groups was 11.7% vs. 35.9% and 19.5% vs. 42.2% (p = 0.035, p = 0.068) respectively. In summary, ALL patients, especially those with T-ALL and Ph-negative B-ALL, may benefit from decitabine as maintenance therapy following allo-HSCT.

Purpose: Blinatumomab has promising applications in treating relapsed or refractory B-cell acute lymphoblastic leukemia (R/R B-ALL). However, there are few studies on using blinatumomab for treating R/R B-ALL patients in China. Therefore, we evaluated the efficacy and safety of blinatumomab among Chinese R/R B-ALL patients. Methods: In total, 39 R/R B-ALL patients who received blinatumomab between October 2021 and August 2022 were selected as the study subjects. The primary endpoints include overall response rate (ORR) and complete minimal residual disease (MRD) response. Secondary endpoints included overall survival (OS) and adverse events (AEs). Results: The ORR and OS for 19 patients with less than 5% bone marrow (BM) blasts were 63.2% and 11 months (7.3–14.7), respectively. The median OS for the 20 patients was unavailable, and 17 (or 85%) had a full MRD response. Twenty-seven patients (69.2%) reported having at least 1 AE. Hematologic toxicity and infections were the most common AEs. Allogeneic hematopoietic stem cell transplantation (allo-HSCT) was performed on nine patients who had been remission-free for a long time. In the first month, one patient (11.1%) developed thrombotic microangiopathy associated with HSCT (TA-TMA) following transplantation. Conclusion: In R/R B-ALL patients, blinatumomab is an effective option in China.

Myeloid sarcoma is a rare manifestation of acute myeloid leukemia (AML) and is associated with poor overall survival (OS). The optimal treatment remains unclear. The study retrospectively evaluated 118 patients with myeloid sarcoma who were treated at the First Affiliated Hospital of Zhengzhou University from January 2010 to July 2021. All cases were diagnosed by tissue biopsy. 41 patients underwent genetic mutation analysis. The most frequent genetic mutations were KIT (16.6%), followed by TET2 (14.6%), and NRAS (14.6%). The median survival time of 118 patients was 4 months (range, 1–51 months), while the median survival time of 11 patients who received allogeneic hematopoietic stem cell transplantation (allo-HSCT) was 19 months (range, 8–51 months). 4 (36.4%) of the 11 patients experienced relapse within 1 year after transplantation. 1 patient died from a severe infection. Of the 6 surviving patients, 5 patients have received maintenance treatment with decitabine after transplantation, and all remained in a state of recurrence-free survival. Patients with myeloid sarcoma have a very unfavorable outcome. Allo-HSCT is an effective treatment option. Recurrence remains the main cause of transplant failure. Maintenance treatment with decitabine after transplantation can prolong the recurrence-free survival time, although these results must be verified in a study with expanded sample size.

Collapsibility of loess is a significant factor affecting engineering construction in loess area, and testing the collapsibility of loess is costly. In this study, A total of 4,256 loess samples are collected from the north, east, west and middle regions of Xining. 70% of the samples are used to generate training data set, and the rest are used to generate verification data set, so as to construct and validate the machine learning models. The most important six factors are selected from thirteen factors by using Grey Relational analysis and multicollinearity analysis: burial depth、water content、specific gravity of soil particles、void rate、geostatic stress and plasticity limit. In order to predict the collapsibility of loess, four machine learning methods: Support Vector Machine (SVM), Random Subspace Based Support Vector Machine (RSSVM), Random Forest (RF) and Naïve Bayes Tree (NBTree), are studied and compared. The receiver operating characteristic (ROC) curve indicators, standard error (SD) and 95% confidence interval (CI) are used to verify and compare the models in different research areas. The results show that: RF model is the most efficient in predicting the collapsibility of loess in Xining, and its AUC average is above 80%, which can be used in engineering practice.