Nearly 10% of newly diagnosed multiple myeloma (NDMM) patients develop a venous thromboembolism (VTE) episode during their disease course, despite current thromboprophylaxis strategies. Changes in hypercoagulability in these patients after treatment have been analyzed using the thrombin generation (TG) assay, the results being discrepant, probably due to the use of semi-automated techniques. This study aims to assess changes in TG measured by a fully automated analyzer. This prospective and multicentric study included NDMM patients from 8 centers (December 2018-September 2023). Fully automated TG was measured at baseline and after 1 and 4 cycles of treatment with ST Genesia® analyzer. Among 100 NDMM patients, a significant decrease was observed in velocity index (after 1 cycle) and peak height (after 4 cycles) and alongside increased sensitivity to thrombomodulin (after 4 cycles), indicating a reduction in hypercoagulability post-treatment. No differences in TG were observed according to the depth of response after 4 cycles. Patients on daratumumab-containing regimens experienced a transient increase in TG after cycle 1, whereas those on proteasome inhibitors (PI)-containing regimens showed a significant reduction in peak height and velocity index after 4 cycles. The development of a VTE event was associated with increased mortality, but there was no association between VTE development and TG results at baseline. Hypercoagulability decreases with anti-myeloma treatment, especially in those receiving PI-containing regimens. These changes in TG are not related to the depth of response to treatment.
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http://dx.doi.org/10.1007/s11239-025-03079-1 | DOI Listing |
J Med Internet Res
March 2025
Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Westmead, Australia.
Background: Conversational artificial intelligence (AI) allows for engaging interactions, however, its acceptability, barriers, and enablers to support patients with atrial fibrillation (AF) are unknown.
Objective: This work stems from the Coordinating Health care with AI-supported Technology for patients with AF (CHAT-AF) trial and aims to explore patient perspectives on receiving support from a conversational AI support program.
Methods: Patients with AF recruited for a randomized controlled trial who received the intervention were approached for semistructured interviews using purposive sampling.
J Biomol Struct Dyn
March 2025
School of Mechatronic Engineering and automation, Shanghai University, Shanghai, China.
Prediction of protein-ligand interactions is critical for drug discovery and repositioning. Traditional prediction methods are computationally intensive and limited in modeling structural changes. In contrast, data-driven deep learning methods significantly reduce computational costs and offer a more efficient approach for drug discovery.
View Article and Find Full Text PDFFront Robot AI
February 2025
Center for Robotics, University of Bonn, Bonn, Germany.
Robust perception systems allow farm robots to recognize weeds and vegetation, enabling the selective application of fertilizers and herbicides to mitigate the environmental impact of traditional agricultural practices. Today's perception systems typically rely on deep learning to interpret sensor data for tasks such as distinguishing soil, crops, and weeds. These approaches usually require substantial amounts of manually labeled training data, which is often time-consuming and requires domain expertise.
View Article and Find Full Text PDFLab Chip
March 2025
LAI, CNRS, INSERM, Turing Center for Living Systems, Aix Marseille Univ, Marseille, France.
Experiments with gradients of soluble bioactive species have significantly advanced with microfluidic developments that enable cell observation and stringent control of environmental conditions. While some methodologies rely on flow to establish gradients, others opt for flow-free conditions, which is particularly beneficial for studying non-adherent and/or shear-sensitive cells. In flow-free devices, bioactive species diffuse either through resistive microchannels in microchannel-based devices, through a porous membrane in membrane-based devices, or through a hydrogel in gel-based devices.
View Article and Find Full Text PDFJ Chromatogr A
March 2025
Vrije Universiteit Brussel, Department of Chemical Engineering, Pleinlaan 2, 1050 Brussel, Belgium. Electronic address:
Chromatographic problem solving, commonly referred to as method development (MD), is hugely complex, given the many operational parameters that must be optimized and their large effect on the elution times of individual sample compounds. Recently, the use of reinforcement learning has been proposed to automate and expedite this process for liquid chromatography (LC). This study further explores deep reinforcement learning (RL) for LC method development.
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