Karmakar Poly, Das Sukanya, Das Sanatan
Department of Mathematics, Gour Mahavidyalaya, Malda, India.
Department of Biotechnology, Saint Xavier's College, Kolkata, India.
Electromagn Biol Med. 2025;44(3):294-324. doi: 10.1080/15368378.2025.2501733. Epub 2025 May 13.
In cardiovascular research, electromagnetic fields (EMFs) induced by Riga plates are applied to study and potentially manipulate blood flow dynamics, offering insights for therapies against arterial plaque deposition and for understanding varied blood flow behaviors. This research focuses on predicting the flow patterns of blood infused with gold and maghemite nanoparticles (gold-maghemite/blood) inside an EM microchannel under these electromagnetic influences and abruptly change in pressure gradient. The study models these flows by considering radiation heat emission and Darcy drag forces within porous media. Mathematical representation involves time-variant partial differential equations, resolved through Laplace transform (LT) to yield compact-form expressions for the model variables. The outcomes, including shear stress (SS) and rate of heat transfer (RHT) across the microchannel, are analyzed and displayed graphically, highlighting the effects of modified Hartmann number and electrode width on these parameters. Hybrid nano-blood (HNB) and nano-blood (NB) exhibit distinct thermal characteristics, with HNB transferring more heat within the blood flow. These study implements a cutting-edge AI-powered approach for high-fidelity evaluation of critical flow parameters, achieving unprecedented prediction accuracy. Validation results confirm the algorithm's excellence, with SS predictions reaching 99.552% (testing) and 97.019% (cross-validation) accuracy, while RHT predictions show 100% testing accuracy and 97.987% cross-validation reliability. This convergence of nanotechnology with advanced machine learning paves the way for transformative clinical applications that could redefine standards of care in surgical oncology, interventional cardiology, and therapeutic radiology. This model underpins potential applications such as controlled drug release and magnetic fluid hyperthermia, enhancing procedures like cardiopulmonary bypass, vascular surgery, and diagnostic imaging.
在心血管研究中，利用 Riga 板产生的电磁场（EMF）来研究并潜在地操控血流动力学，为抗动脉斑块沉积的治疗以及理解各种血流行为提供见解。本研究聚焦于预测在这些电磁影响以及压力梯度突然变化的情况下，电磁微通道内注入金和磁赤铁矿纳米颗粒的血液（金 - 磁赤铁矿/血液）的流动模式。该研究通过考虑多孔介质内的辐射热发射和达西阻力来对这些流动进行建模。数学表示涉及时变偏微分方程，通过拉普拉斯变换（LT）求解以得出模型变量的紧凑形式表达式。分析了包括微通道内的剪切应力（SS）和传热速率（RHT）在内的结果，并以图形方式展示，突出了修正的哈特曼数和电极宽度对这些参数的影响。混合纳米血液（HNB）和纳米血液（NB）表现出不同的热特性，HNB 在血流中传递更多热量。本研究采用前沿的人工智能驱动方法对关键流动参数进行高保真评估，实现了前所未有的预测精度。验证结果证实了该算法的卓越性，SS 预测的测试准确率达到 99.552%，交叉验证准确率达到 97.019%，而 RHT 预测的测试准确率为 100%，交叉验证可靠性为 97.987%。纳米技术与先进机器学习的这种融合为变革性临床应用铺平了道路，这些应用可能会重新定义外科肿瘤学、介入心脏病学和治疗放射学的护理标准。该模型为诸如控释药物和磁流体热疗等潜在应用提供了支持，增强了诸如体外循环、血管手术和诊断成像等程序。
