Development of computational model for description of magnetic drug targeting for cancer therapy: Modeling and validation

IF 4.2 2区 工程技术 Q2 ENGINEERING, CHEMICAL Advanced Powder Technology Pub Date : 2024-07-30 DOI:10.1016/j.apt.2024.104577
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Abstract

Computation of blood flow containing ferrofluid would be useful for analysis of drug carrier motion for cancer therapy. A thorough understanding nanoparticles behavior is challenging and needs to be addressed by developing sophisticated theoretical methods. A hybrid modeling for analysis of blood motion containing ferrofluid was implemented via mechanistic modeling combined with artificial intelligence. The system of analysis also considered external magnetic force for control of nanoparticles motion in the blood vessel. This research focuses on the analysis of velocity field based on a dataset consisting of variables x(m), y(m), and U(m/s). The objective is to develop accurate predictive models using Gaussian Process Regression (GPR), Kernel ridge regression (KRR), and Polynomial Regression (PR). The Dragonfly Algorithm (DA) was employed for hyper-parameter optimizing. The results demonstrate the performance of these models in relation to R2 score, RMSE, and MAE. The GPR model achieves the highest score of 0.99603 in terms of R2, indicating excellent predictive accuracy. It also exhibits the lowest RMSE of 7.1443x10^-3 and MAE of 5.35436 x10^-3, suggesting minimal deviations between the expected and predicted velocity values. The PR model also has a significant performance with an R2 test score of 0.99348, RMSE of 9.1376 x10^-3, and MAE of 7.22828 x10^-3. The aforementioned results underscore the effectiveness of these models in accurately forecasting velocity based on the provided input variables.

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开发用于描述癌症治疗磁性药物靶向的计算模型:建模与验证
对含有铁流体的血流进行计算有助于分析用于癌症治疗的药物载体的运动。透彻理解纳米粒子的行为具有挑战性,需要通过开发复杂的理论方法来解决。通过机理建模与人工智能相结合,实现了分析含有铁流体的血液运动的混合建模。分析系统还考虑了控制纳米粒子在血管中运动的外部磁力。这项研究的重点是根据由变量 x(m)、y(m)和 U(m/s)组成的数据集分析速度场。目标是利用高斯过程回归(GPR)、核岭回归(KRR)和多项式回归(PR)建立精确的预测模型。超参数优化采用了蜻蜓算法(DA)。结果显示了这些模型在 R2 分数、RMSE 和 MAE 方面的性能。GPR 模型的 R2 得分最高,为 0.99603,表明其预测准确性极佳。它还显示出最低的 RMSE(7.1443x10^-3)和 MAE(5.35436 x10^-3),表明预期速度值和预测速度值之间的偏差极小。PR 模型的 R2 检验得分为 0.99348,RMSE 为 9.1376 x10^-3,MAE 为 7.22828 x10^-3,同样表现出色。上述结果凸显了这些模型在根据所提供的输入变量准确预测速度方面的有效性。
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来源期刊
Advanced Powder Technology
Advanced Powder Technology 工程技术-工程:化工
CiteScore
9.50
自引率
7.70%
发文量
424
审稿时长
55 days
期刊介绍: The aim of Advanced Powder Technology is to meet the demand for an international journal that integrates all aspects of science and technology research on powder and particulate materials. The journal fulfills this purpose by publishing original research papers, rapid communications, reviews, and translated articles by prominent researchers worldwide. The editorial work of Advanced Powder Technology, which was founded as the International Journal of the Society of Powder Technology, Japan, is now shared by distinguished board members, who operate in a unique framework designed to respond to the increasing global demand for articles on not only powder and particles, but also on various materials produced from them. Advanced Powder Technology covers various areas, but a discussion of powder and particles is required in articles. Topics include: Production of powder and particulate materials in gases and liquids(nanoparticles, fine ceramics, pharmaceuticals, novel functional materials, etc.); Aerosol and colloidal processing; Powder and particle characterization; Dynamics and phenomena; Calculation and simulation (CFD, DEM, Monte Carlo method, population balance, etc.); Measurement and control of powder processes; Particle modification; Comminution; Powder handling and operations (storage, transport, granulation, separation, fluidization, etc.)
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