VEXAS syndrome (vacuoles in myeloid progenitors, E1 ubiquitin activating enzyme, X-linked, autoinflammatory manifestations and somatic) is an autoinflammatory condition caused by somatically acquired UBA1 mutations. Heiblig et al report on an international retrospective analysis of 30 patients with VEXAS syndrome treated with different Janus kinase (JAK) inhibitors, finding encouraging evidence supporting the use of the JAK1/2 inhibitor ruxolitinib with clinical remissions and reductions in steroid use seen in the majority of patients.

B-cell prolymphocytic leukemia (B-PLL) is a rare lymphoid neoplasm with an aggressive clinical course. Treatment strategies for B-PLL remain to be established, and, until recently, alemtuzumab was the only effective therapeutic option in patients harboring 17p deletions. Herein, we describe, for the first time, a case of B-cell prolymphocytic leukemia harboring a 17p deletion in a 48-year-old man that was successfully treated sequentially with idelalisib-rituximab/ibrutinib followed by allogeneic hematopoietic stem cell transplant (allo-HSCT). After 5 months of therapy with idelalisib-rituximab, clinical remission was achieved, but the development of severe diarrhea led to its discontinuation. Subsequently, the patient was treated for 2 months with ibrutinib and the quality of the response was maintained with no severe adverse effects reported. A reduced-intensity conditioning allo-HSCT from a HLA-matched unrelated donor was performed, and, thereafter, the patient has been in complete remission for 10 months now. In conclusion, given the poor prognosis of B-PLL and the lack of effective treatment modalities, the findings here suggest that both ibrutinib and idelalisib should be considered as upfront therapy of B-PLL and as a bridge to allo-HSCT.

Background: Patient blood management (PBM) is a patient-centered, evidence-based approach for optimizing anemia management, minimizing blood loss, and ensuring appropriate transfusion. Artificial intelligence (AI) provides powerful tools for prediction, diagnosis, and decision support across PBM, but current evidence remains emerging and not yet consolidated. Objectives: This review synthesizes AI applications in PBM, summarizing predictive, diagnostic, and decision support models; highlighting methodological trends; and discussing challenges for clinical translation. Methods: PubMed, Scopus, and Web of Science were searched from inception to 31 March 2025. Eligible studies reported AI models addressing the three established PBM pillars. Studies on transfusion safety and blood bank operations relevant to PBM were also included. Extracted data covered study characteristics, predictors, models, validation strategies, and performance. The findings were narratively synthesized given study heterogeneity. Results: A total of 338 studies were included, spanning anemia detection, bleeding risk stratification, transfusion prediction, transfusion safety, and inventory management. Deep learning (DL) predominated in image-based anemia detection, while ensemble and gradient boosting methods frequently outperformed baselines in bleeding and transfusion risk prediction. Recurrent and hybrid architectures proved effective for blood supply forecasting. Across domains, machine learning and DL models generally surpassed logistic regression, clinical scores, and expert judgment. Despite strong internal performance, external validation and clinical deployment remain limited. Conclusions: AI is advancing PBM by enabling earlier anemia detection, more accurate bleeding and transfusion prediction, and smarter resource allocation. Translation into practice requires standardized reporting, robust external validation, explainability, and workflow integration. Future work should emphasize multimodal learning, prospective evaluation, and cost-effectiveness.

Multiple myeloma is a clonal plasma cell malignancy with a highly variable range of clinical manifestations. Over recent decades, substantial progress has been made in laboratory diagnostics, which has deepened our understanding of disease biology, improved risk stratification, and informed treatment strategies. In an era of transformation and innovation, conventional laboratory methods remain essential, as cutting-edge technologies might not be immediately accessible to all laboratories. Nonetheless, even widely used laboratory methodologies present many challenges, such as variability in assay performance, interpretative criteria, and standardization. This review by the Portuguese Multiple Myeloma Group of the Portuguese Society of Hematology provides a comprehensive overview and practical appraisal of current conventional laboratory methods employed for multiple myeloma diagnosis.