High expression of proviral integration site for Moloney murine leukemia virus-1 (PIM-1), a serine/threonine kinase, is associated with many cancers. The main purpose of this study were to investigate that the correlation between PIM-1 mRNA levels and clinicopathologic features and its clinical significance in acute myeloid leukemia (AML). qRT-PCR was performed for 118 de novo AML and 20 AML complete remission patients and 15 normal individuals. All statistical analysis were performed using Graphpad Prism5 software. We observed that expression of PIM-1 mRNA was higher in AML patients than in healthy individuals and in complete remission AML patients (P = 0.0177). Further, high PIM-1 mRNA levels were more associated with high-risk FLT3+ AML patients than the FLT3− group (P = 0.0001) and were also associated with clinical factors such as risk stratification (P = 0.0029) and vital status (P = 0.0322). Kaplan–Meier survival analysis indicated that PIM-1 mRNA expression correlated with overall survival (OS), disease free survival (DFS), and relapse rate (RR) in AML patients. Most importantly, the high PIM-1-expressing patients took longer to achieve complete remission than the low expression group (P = 0.001). In addition, the complete remission rate was significantly lower in the high PIM-1 group (P = 0.0277) after induction therapy. Above results suggest that PIM-1 mRNA levels may be an independent prognostic factor in AML.

Background: The impact of donor age on the immune reconstitution of patients with hematological malignancies who underwent hematopoietic cell transplantation (HCT) is unclear.Method: We retrospectively compared the outcomes of 381 patients who underwent allogeneic peripheral blood stem cell transplantation (PBSCT) from 308 donors under 50 years of age and 73 donors over 50 years of age. IVIG was regularly supplemented for patients in the first 3 months post‐HCT.Results: The counts of CD8+CD45RA+ naïve T cells were significantly lower in patients of the older donor group than in the younger donor group in the first year after PBSCT (190.6 cells/μl vs. 239.6 cells/μl, p = .018). Patients in the older donor group had significantly fewer CD19+ B cells on day +270 (123.4 cells/μl vs. 183.5 cells/μl, p = .021) and day +365 (169 cells/μl vs. 271.1 cells/μl, p = .01) after PBSCT. Serum IgA (.76 g/L vs. .97 g/L, p < .001) and IgM levels (.75 g/L vs. 1.04 g/L, p < .001) were significantly lower in patients in the older donor group from day +60 to +365 after PBSCT. The EBV reactivation rate within the first 3 months after PBSCT was significantly higher in patients in the older donor group (48.6% vs. 38.3%, p = .034). However, the incidences of CMV reactivation, II‐IV acute graft‐versus‐host disease (aGvHD), chronic GvHD (cGvHD), 3‐year relapse rate, 3‐year transplant‐related mortality (TRM) and 3‐year overall survival (OS) were not significantly different between the two groups.Conclusion: In conclusion, donors ≥50 years old were associated with inferior immune reconstitution and higher EBV reactivation in patients after PBSCT, but no change in OS.

Signal transducer and activator of transcription 3 (STAT3), a crucial transcription factor, exerts a notable influence by hyperactivating or acquiring functional mutations in the occurrence and progression of cancers. Hyperactive STAT3 is also implicated in a range of hematopoietic malignancies, especially acute myeloid leukemia (AML). The function of STAT3 is associated with the phosphorylated parallel dimer structure, enabling them to stimulate the transcription of specific genes. AML is a highly heterogeneous hematological malignancy, which is challenging in terms of therapy. The current efficacy of chemotherapy and targeted therapy remains suboptimal. Targeted inhibition of STAT3 has the potential to enhance the efficacy of AML treatment, thereby possibly improving the prognosis of individuals suffering from AML. The present review summarizes the development of inhibitors against STAT3 and discusses their applicability as AML therapeutics, which could inspire new possibilities for enhancing AML treatment strategies.

Acute leukemias (ALs) are a diverse group of hematological malignancies characterized by the abnormal proliferation of immature cells. Microscopic observation of cell morphology based on the French‑American‑British classification remains a fundamental diagnostic method for ALs. However, manual screening from bone marrow smear images is often inefficient, laborious and prone to subjective bias, leading to potential misdiagnosis or missed diagnosis. Artificial intelligence (AI), particularly machine learning (ML), has expanded human capabilities in analyzing complex datasets, leading to breakthroughs in multiple fields, including medical research and clinical practice. Increasingly, ML applications are being developed to diagnose hematological diseases by extracting and aggregating morphological characteristics from peripheral blood and bone marrow smears. However, applying ML methods to recognize cell morphology in hematological diseases presents unique challenges compared with other pathology subspecialties. The present review provided an overview of AI and ML applications in ALs diagnosis, focusing on cell segmentation and data mining methods from microscopy images, and highlights their advantages over manual microscopy.