Lessons from mouse models of MPN

Assessing serum thrombopoietin for enhanced diagnosis of ITP, AA, and MDS using machine learning: A retrospective cohort study

Differentiating between immune thrombocytopenia (ITP), aplastic anemia (AA), and myelodysplastic syndromes (MDS) is critical due to the distinct treatment approaches required for each condition. This study investigates the role of serum thrombopoietin (TPO) levels as a potential biomarker to aid in the diagnosis of these hematological disorders. This retrospective cohort study analyzed serum TPO levels in patients diagnosed with ITP, AA, and MDS, using clinical records and stored serum samples collected from patients treated between September 2023 and May 2024. Statistical analyses were performed to determine cut-off values for TPO levels that effectively differentiate between these conditions. Additionally, machine learning models were utilized to enhance diagnostic accuracy based on clinical indicators, including TPO levels. Serum TPO levels were markedly elevated in AA (1369.19 ± 751.26 pg/ml) compared to ITP (263.57 ± 355.91 pg/ml), MDS (434.55 ± 551.56 pg/ml), and health control (71.64 ± 30.32 pg/ml) (P < 0.0001). Correlation analysis revealed a significant positive correlation between TPO levels and ITP, AA, and MDS (P < 0.0001), Linear regression analysis indicated that age was a significant predictor of TPO levels (P < 0.0001). The optimal cut-off value for TPO levels distinguishing ITP from AA was 302.43 pg/mL, yielding an AUC of 0.925 (sensitivity with 80.75%, specificity with 94.06%). Machine learning models demonstrated that Logistic Regression, XGBoost, and LightGBM performed best, with the Logistic Regression achieving an accuracy of 86.3% and an AUC of 0.910. Serum TPO levels are a promising non-invasive biomarker for distinguishing between ITP, AA, and MDS. Incorporating TPO measurements into clinical practice may enhance diagnostic accuracy and improve patient management strategies.

Exploring the Molecular Aspects of Myeloproliferative Neoplasms Associated with Unusual Site Vein Thrombosis: Review of the Literature and Latest Insights

Myeloproliferative neoplasms (MPNs) are the leading causes of unusual site thrombosis, affecting nearly 40% of individuals with conditions like Budd-Chiari syndrome or portal vein thrombosis. Diagnosing MPNs in these cases is challenging because common indicators, such as spleen enlargement and elevated blood cell counts, can be obscured by portal hypertension or bleeding issues. Recent advancements in diagnostic tools have enhanced the accuracy of MPN diagnosis and classification. While bone marrow biopsies remain significant diagnostic criteria, molecular markers now play a pivotal role in both diagnosis and prognosis assessment. Hence, it is essential to initiate the diagnostic process for splanchnic vein thrombosis with a JAK2 V617F mutation screening, but a comprehensive approach is necessary. A multidisciplinary strategy is vital to accurately determine the specific subtype of MPNs, recommend additional tests, and propose the most effective treatment plan. Establishing specialized care pathways for patients with splanchnic vein thrombosis and underlying MPNs is crucial to tailor management approaches that reduce the risk of hematological outcomes and hepatic complications.

JAK2 V617F allele burden in polycythemia vera: burden of proof

Polycythemia vera (PV) is a hematopoietic stem cell neoplasm defined by activating somatic mutations in the JAK2 gene and characterized clinically by overproduction of red blood cells, platelets, and neutrophils; a significant burden of disease-specific symptoms; high rates of vascular events; and evolution to a myelofibrosis phase or acute leukemia. The JAK2V617F variant allele frequency (VAF) is a key determinant of outcomes in PV, including thrombosis and myelofibrotic progression. Here, we critically review the dynamic role of JAK2V617F mutation burden in the pathogenesis and natural history of PV, the suitability of JAK2V617F VAF as a diagnostic and prognostic biomarker, and the utility of JAK2V617F VAF reduction in PV treatment.