Critical reviews in biomedical engineering最新文献

Targeted drug delivery using nanoparticle-based technology represents an advance in tumor treatment aiming to improve drug retention in tumors and minimize side effects. This study explores nanoparticle aggregation as a mechanism of enhanced retention and controlled dispersion of therapeutic agents in tumor tissues. Unlike existing models that primarily focus on single-particle diffusion, this research investigates the aggregation dynamics of nanoparticles upon diffusion from capillaries into the surrounding tissue, using a fractal-based mathematical model. By incorporating fractal geometry, this model uniquely captures the complexity of nanoparticle interactions with heterogeneous tumor environments. The equations, solved using MATLAB, reveal that nanoparticles form aggregates of approximately 75 nm in the capillary, with an optimal fractal dimension of 2.8 promoting efficient aggregation and retention. These findings provide a new perspective on aggregation-controlled drug delivery systems, offering insights for enhancing nanoparticle bioavailability and therapeutic efficacy in tumors.

Burn injuries constitute a significant public health challenge, often necessitating the expertise of medical professionals for diagnosis. However, in scenarios where specialized facilities are unavailable, the utility of automated burn assessment tools becomes evident. Factors such as burn area, depth, and location play a pivotal role in determining burn severity. In this study, we present a classification model for burn diagnosis, leveraging automated machine learning techniques. Our approach includes an image reclamation system that incorporates the peak and valley algorithm, ensuring the removal of noise while consistently delivering high-quality results. By using skewness and kurtosis, we demonstrate substantial improvements in diagnostic accuracy. Our proposed system sources key features from enhanced grafting samples using peak valley transformation, enabling the computation of BQs and a unique bin analysis to enhance image reclamation. Our experimental results highlight efficiency gains, notably growing the matching features of graft samples for 14 matching images. The intended work involves the creation of a burn classification reclamation model. The proposed approach utilizes a support vector machine (SVM). The evaluation of the model will be conducted using an untrained catalogue, with a specific focus on its effectiveness in reclaiming images that necessitate grafts and distinguishing them from those that do not. Our approach holds promise in grafting sample reclamation in emergency settings, thereby expediting more accurate diagnoses and treatments for acute burn injuries. This work has the latent to save lives and improve patient upshots in burn traumas.

In this paper, we gave the numerical solution of the various population categories of susceptible, exposed, infected, and recovered (SEIR) mathematical models by using homotopy perturbation method, which is a technique that combines the perturbation and homotopy methods to solve nonlinear problems. Also, we discuss the susceptible population category and explore the graphical solution of all populations (SEIR) using the parameters α and β for both fractional and integer order. In the end, the stability analysis is also shown in the population graphs.

Population growth and its consequences remain one of the most pressing challenges of our time. The study of population dynamics, including factors like resource availability, disease, and environmental constraints, is fundamental for planning in various domains such as ecology, economics, and public health. One of the earliest models proposed to explain population growth was by Thomas Robert Malthus in the late 18th century. Malthus theorized that populations grow exponentially, while the food supply increases only in an arithmetic manner and that was explained by a mathematical model i.e. the population growth model. This imbalance, according to Malthus, could eventually lead to resource scarcity and population collapse. However, Malthus's model, though foundational, was simplistic in nature. Over time, a more refined and realistic model was developed by Pierre François Verhulst, a Belgian mathematician, which led to the formulation of the logistic growth model. This model involves a fractional differential equation (FDE) namely the logistic differential equation. Due to the significance of FDEs, several authors have proposed solutions for the model using different techniques. Our work finds this model's solution using the Laplace decomposition method (LDM) approach. The method represents a significant advancement in the tool case of applied mathematicians and scientists. Its ability to efficiently and accurately solve complex differential equations, especially FPDEs. The graphical interpretation of the behavior of the result is also mentioned and compare our results with exact solutions found in literature.

This study aims to compare the efficacy of magnetic nanoparticle hyperthermia for treating cancerous tissues in two distinct scenarios: breast and muscle/prostate tumors. Heat transfer dynamics during magnetic hyperthermia cancer therapy are explored using intravenously administered nanoparticles to a muscle/prostate tumor and intratumoral injection into a breast tumor. Utilizing non-Newtonian blood rheological models, we analyze a complex geometric domain for both tumor types and apply the mixed finite element technique for solving the governing equations. The impact of varying magnetic field frequencies and injected nanoparticle concentrations on heat transfer and nanoparticle transport within muscle/prostate, and breast tissues are examined numerically. Higher magnetic field frequencies and injected nanoparticle concentrations were found to increase localized heating in tumor regions, reduce therapy duration, and maximize thermal damage to cancer cells for both tumor configurations. This research provides valuable insights for optimizing magnetic hyperthermia parameters for different tumor types and also highlights the potential for personalized treatment strategies.

A concise overview of three major advancements in fast magnetic resonance imagine (MRI) reconstruction techniques is presented, focusing on their roles in enhancing image quality and reducing acquisition times. The first set of methods, parallel imaging techniques, includes sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE utilizes spatial sensitivity information from multiple receiver coils to accelerate image acquisition by undersampling k-space data and reconstructing images using coil sensitivity profiles, allowing for faster scans. GRAPPA, another parallel imaging method, uses estimated weights from a calibration scan to fill in missing data in undersampled k-space and then reconstructs unaliased images. Additionally, this review explores sparse reconstruction techniques such as compressed sensing, which leverages the sparsity of images in a transformed domain to reconstruct high quality images from significantly fewer measurements, thus reducing scan times. The latest developments in machine learning applications for MRI acquisition are also discussed, highlighting how advanced algorithms are being used to improve image reconstruction, enhance diagnostic accuracy, and simplify workflow processes.

Heart diseases which can lead to stroke and heart attacks, affect numerous individuals worldwide due to disruptions in blood flow within the body. A common underlying cause for such hemodynamic disorders is a constriction in the artery, which is known as a stenosis, which is attributable to a range of causes including atherosclerosis or plaque accumulation. Many theoretical and computational studies have been presented in this area providing a useful compliment to experimental (clinical) studies. These studies have benefited clinical practice by providing insights into complex fluid dynamics associated with cardiovascular disease and identifying robust methodologies for mitigating such diseases. This review therefore aims to provide an overview of recent mathematical and numerical modelling advancements in understanding blood flow in stenosed arteries which have served to expand the current understanding of disease onset and mitigation for patients. Many diverse aspects of stenotic hemodynamics have been addressed in a large body of literature under various assumptions, such as different fluid material models, artery channel characteristics and diverse analytical and numerical solution techniques. These studies have also considered a variety of multi-physical effects including heat transfer, mass diffusion, nanoparticle effects in actual clinical treatments. In this review, over 100 recent articles from reputable journals are appraised. The primary objectives of this review paper are to emphasize the methodologies used for modelling, numerical simulation, and robust evaluation of hemodynamic characteristics in arterial blood flow which provide a more sophisticated insight into hemodynamics associated with diseases and possible mitigation strategies. The tabular format outlines different aspects of geometries and blood behavior (fluids) examined in the period 2015-2025. This organized presentation and crystallization of key contributions in a single article will also serve as a valuable resource for multi-disciplinary researchers including mathematicians, bioengineers, computer scientists in addition to medical researchers. Future pathways are also outlined.

Nanomedicine has revolutionized the pharmaceutical and biological sciences industry by providing new frontiers and targets on the diagnosis and treatment of diseases. One promising development is the integration of natural products with nanoformulations, is emerging as a novel strategy in antitumor therapy. This combination has opened up new avenues for primary intervention and treatment due to targeted delivery of therapeutic agents against cancer. Researchers are trying to enhance and establish the safety and efficacy of naturally occurring bioactive compounds characterised by their anticancer potential. Polyphenols and other phytochemicals, along with functional foods, have high potency in circumventing tumors by the effective inhibition of the expansion of cancerous cells and induction of apoptosis. Incorporation of such natural products natural products through nanotechnology is therefore meant to enhance therapeutic efficacy with an improved safety profile. The aim will be to formulate these bioactive compounds as nanoformulations to achieve higher tumor site concentration, which would result in maximum anticancer effects and perhaps reduce the possible side effects associated with conventional therapies. This calls for collaboration among researchers and clinicians in establishing evidence-based guidelines and standardized protocols that ensure safety and efficacy in the use of such therapies.