A Prognostic Model Based on Clinical Biomarkers for Heart Failure in Adult Patients Following Allogeneic Hematopoietic Stem Cell Transplantation.

Heart failure (HF) is an uncommon but serious cardiovascular complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Unfortunately, knowledge about early mortality prognostic factors in patients with HF after allo-HSCT is limited, and an easy-to-use prognostic model is not available. This study aimed to develop and validate a clinical-biomarker prognostic model capable of predicting HF mortality following allo-HSCT that uses a combination of variables readily available in clinical practice.

Machine learning algorithm as a prognostic tool for venous thromboembolism in allogeneic transplant patients.

As a serious complication after allogenic hematopoietic stem cell transplantation (allo-HSCT), venous thromboembolism (VTE) is significantly related to increased nonrelapse mortality. Therefore distinguishing patients at high risk of death who should receive specific therapeutic management is key to improving survival. This study aimed to establish a machine learning-based prognostic model for the identification of post-transplantation VTE patients who have a high risk of death.

Two smart coumarin-based fluorescent probes with AIE effect for sensing ClO and imaging in living cells.

It's worth noting that detect effective methods for tracking ClO could help us uncover the function of ClO in living systems. Here, two coumarin-based probes, named (E)-3-(1-hydrazonoethyl)-2H-chromen-2-one (1A) and 3-((E)-1-(((E)-(2,3-dihydro-1H-imidazol-4-yl)methylene)-hydrazono)ethyl)- 2H-chromen-2-one (1B) with aggregation-induced emission (AIE) effect in Tris-HCl (pH = 7.2) buffer solution were synthesized and used for sensing ClO selectivity.