Publications by authors named "N M Kovalchuk"

This study evaluates the performance of continuous flow and drop-based microfluidic devices for the synthesis of silver nanoparticles (AgNPs) under identical hydrodynamic and chemical conditions. Flows at low values of Dean number (De < 1) were investigated, where the contribution of the vortices forming inside the drop to the additional mixing inside the reactor should be most noticeable. In the drop-based microfluidic device, discrete aqueous drops serving as reactors were generated by flow focusing using silicone oil as the continuous phase.

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Total body irradiation (TBI) has been an important component of myeloablative and nonmyeloablative conditioning regimens for allogeneic hematopoietic stem cell transplantation (HSCT) for decades. Playing a dual role, both cytotoxic and immuno-suppressive, TBI eliminates residual disease while also impairing the immune system from rejecting the foreign donor cells being transplanted. Unlike chemotherapy, radiotherapy is not hampered by perfusion, diffusion, or the blood-barrier effect and can effectively treat sanctuary sites.

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Introduction: Volumetric modulated arc therapy (VMAT) total body irradiation (TBI) allows for greater organ sparing with improved target coverage compared to 2D-TBI. However, there is limited evidence of whether improved organ sparing translates to decreases in toxicities and how its toxicities compare to those of the 2D technique. We aimed to compare differences in toxicities among patients treated with TBI utilizing VMAT and 2D techniques.

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Historically, total body irradiation (TBI) has been delivered using static, parallel opposed photon beams (2D-TBI). Recently, centers have increasingly used intensity-modulated radiation therapy (IMRT) techniques for TBI. Relative to 2D-TBI, IMRT can reduce doses to critical organs (i.

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Purpose: Artificial intelligence-aided methods have made significant progress in the auto-delineation of normal tissues. However, these approaches struggle with the auto-contouring of radiation therapy target volume. Our goal was to model the delineation of target volume as a clinical decision-making problem, resolved by leveraging large language model-aided multimodal learning approaches.

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