Blood Flow Mediated Hybrid Nanoparticles in Human Arterial System: Recent Research, Development and Applications
Bibliographic record
Abstract
Blood flow dynamics contributes an elemental part in the formation and expansion of cardiovascular diseases in human body. Computational simulation of blood flow in the human arterial system has been widely used in recent decades for better understanding the symptomatic spectrum of various diseases, in order to improve already existing or develop new therapeutic techniques. The characteristics of the blood flow in an artery can be changed significantly by arterial diseases, such as aneurysms and stenoses. The progress of atherosclerosis or stenosis in a blood vessel is quite common which may be caused due to the addition of lipids in the arterial wall. Nanofluid is a colloidal mixture of nanometer sized (which ranges from 10–100 m) metallic and non-metallic particles in conventional fluid (such as water, oil). The delivery of nanoparticles is an interesting and growing field in the development of diagnostics and remedies for blood flow complications. An enhancement of nano-drug delivery performance in biological systems, nanoparticles properties such as size, shape and surface characteristics can be regulated. Nanoparticle offers remarkably advantages over the traditional drug delivery in terms of high specificity, high stability, high drug carrying capacity, ability for controlled release. Highly dependency has been found for their behavior under blood flow while checking for their ability to target and penetrate tissues from the blood. In the field of nano-medicine, organic (including polymeric micelles and vesicles, liposomes) and inorganic (gold and mesoporous silica, copper) nanoparticles have been broadly studied as particular carriers because as drug delivery systems they delivered a surprising achievement as a result of their biocompatibility with tissue and cells, their subcellular size, decreased toxicity and sustained release properties. For the extension of nanofluids research, the researchers have also tried to use hybrid nanofluid recently, which is synthesized by suspending dissimilar nanoparticles either in mixture or composite form. The main idea behind using the hybrid nanofluid is to further improve the heat transfer and pressure drop characteristics. Nanoparticles are helpful as drug carriers to minimize the effects of resistance impedance to blood flow or coagulation factors due to stenosis. Discussed various robust approaches have been employed for the nanoparticle transport through blood in arterial system. The main objective of the paper is to provide a comprehensive review of computational simulations of blood flow containing hybrid-nanoparticles as drug carriers in the arterial system of the human body. The recent developments and analysis of convective flow of particle-fluid suspension models for the axi-symmetric arterial bodies in hemodynamics are summarized. Detailed existing mathematical models for simulating blood flow with nanoparticles in stenotic regions are reviewed. The review focuses on selected numerical simulations of physiological convective flows under various stenosis approximations and computation of the temperature, velocity, resistance impedance to flow, wall shear stress and the pressure gradient with the corresponding boundary conditions. The current review also highlights that the drug carrier nanoparticles are efficient mechanisms for reducing hemodynamics of stenosis and could be helpful for other biomedical applications. The review considers flows through various stenoses and the significances of numerical fluid mechanics in clinical medicine. The review examines nano-drug delivery systems, nanoparticles and describes recent computational simulations of nano-pharmacodynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".