Hematologic and molecular responses to ropeginterferon alfa-2b therapy of polycythemia vera: 48-week results from a prospective study.

To prevent thrombosis in patients with polycythemia vera (PV), achieving a complete hematologic response (CHR) is highly recommended in practice. In addition, a reduced JAK2 V617F mutation burden is expected to have a disease-modifying effect, and its molecular response (MR) is currently of significant interest. This study aimed to assess the association between CHR and MR in patients with PV following treatment with ropeginterferon alfa-2b.

Employing Automated Machine Learning (AutoML) Methods to Facilitate the ADMET Properties Prediction.

The rationale for using ADMET prediction tools in the early drug discovery paradigm is to guide the design of new compounds with favorable ADMET properties and ultimately minimize the attrition rates of drug failures. Artificial intelligence (AI) in ADMET modeling has gained momentum due to its high-throughput and low-cost attributes. In this study, we developed a machine learning model capable of predicting 11 ADMET properties of chemical compounds.

Association Between Levonorgestrel-Releasing Intrauterine System Exposure Duration and Breast Cancer Incidence.

Objective: To investigate the association between the duration of levonorgestrel-releasing intrauterine system (LNG-IUS) use and breast cancer risk in Korean women.

Methods: A retrospective cohort study was conducted using the Korean National Health Insurance Claims database from 2013 to 2022. A total of 2,094,029 women aged 30-49 years with initial diagnoses of endometriosis, uterine leiomyomas, or abnormal uterine bleeding between 2014 and 2017 were included in the study.

Clonal hematopoiesis: elements associated with clonal expansion and diseases.

Clonal hematopoiesis (CH), characterized by the expansion of hematopoietic stem and progenitor cells harboring somatic mutations, has emerged as a significant age-related phenomenon with profound implications for human health. While initially recognized in the 1960s, recent technological advances have revealed its complex nature and widespread prevalence, affecting up to 84% of individuals aged ≥ 70 years. The clinical significance of CH extends beyond its well-established role as a precursor to hematological malignancies, encompassing its association with cardiovascular diseases, chronic kidney disease, and other non-malignant disorders.

Head CT Scan Motion Artifact Correction via Diffusion-Based Generative Models.

Head motion is a major source of image artifacts in head computed tomography (CT), degrading the image quality and impacting diagnosis. Image-domain-based motion correction is practical for routine use since it doesn't rely on hard-to-obtain CT projection data. However, existing convolutional neural network (CNN)-based methods tend to over-smooth images, particularly in cases of moderate to severe 3D motion artifacts.