Real-world impact of luspatercept on red blood cell transfusions among patients with myelodysplastic syndromes: A United States healthcare claims database study.

Myelodysplastic syndromes (MDS) are associated with anemia and the need for blood transfusions. In clinical trials, luspatercept reduced transfusion dependency among patients with lower-risk MDS. This United States (US) study describes real-world clinical outcomes pre- and post-luspatercept initiation among patients with MDS.

Subtype-Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics.

This study explores the integration of Raman spectroscopy (RS) with machine learning for the early detection and subtyping of breast cancer using blood plasma samples. We performed detailed spectral analyses, identifying significant spectral patterns associated with cancer biomarkers. Our findings demonstrate the potential for classifying the four major subtypes of breast cancer at stage Ia with an average sensitivity and specificity of 90% and 95%, respectively, and a cross-validated macro-averaged area under the curve (AUC) of 0.

Facet-Dependent Adsorption of Rare Earth Elements (REEs) and Actinides onto Goethite: REE Pattern Variability and Cerium Anomaly.

Assessing the fate of contaminants in the environment requires a deep understanding of intrinsic adsorption mechanisms on natural minerals such as Fe-oxyhydroxides. In this study, we proposed an innovative approach to probe site heterogeneities on the goethite surface by comparing the adsorption behavior of rare earth elements (REEs, including Sc, Y, and all lanthanides; Ln) except Pm, as well as Th and U. A surface loading-dependent adsorption of Ln and Y was observed, with a shift from (i) preferential middle to heavy REE adsorption and (ii) limited to substantial fractionation between Y and Ho as the loading increased.

Artificial Intelligence-Prompted Explanations of Common Primary Care Diagnoses.

Background: Artificial intelligence (AI)-generated explanations about medical topics may be clearer and more accessible than traditional evidence-based sources, enhancing patient understanding and autonomy. We evaluated different AI explanations for patients about common diagnoses to aid in patient care.

Methods: We prompted ChatGPT 3.