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Ayesha Sohail

Bio: Ayesha Sohail is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Finite element method & Nonlinear system. The author has an hindex of 12, co-authored 95 publications receiving 666 citations. Previous affiliations of Ayesha Sohail include Islamia College University & University of Sheffield.

Papers published on a yearly basis

Papers
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TL;DR: A state-of-the-art review focused on progress in nanoparticle induced hyperthermia treatments that have several potential advantages over both global and local hyperThermia treatments achieved without nanoparticles.
Abstract: Hyperthermia treatment, generated by magnetic nanoparticles (MNPs) is promising since it is tumour-focused, minimally invasive and uniform. The most unique feature of magnetic nanoparticles is its reaction and modulation by a magnetic force basically responsible for enabling its potential as heating mediators for cancer therapy. In magnetic nanoparticle hyperthermia, a tumour is preferentially loaded with systemically administered nanoparticles with high-absorption cross-section for transduction of an extrinsic energy source to heat. To maximize the energy deposited in the tumour while limiting the exposure to healthy tissues, the heating is achieved by exposing the region of tissue containing magnetic nanoparticles to an alternating magnetic field. The magnetic nanoparticles dissipate heat from relaxation losses thereby heating localized tissue above normal physiological ranges. Besides thermal efficiency, the biocompatibility of magnetite nanoparticles assisted its deployment as efficient drug carrier for targeted therapeutic regimes. In the present article, we provide a state-of-the-art review focused on progress in nanoparticle induced hyperthermia treatments that have several potential advantages over both global and local hyperthermia treatments achieved without nanoparticles. Green bio-nanotechnology has attracted substantial attention and has demonstrable abilities to improve cancer therapy. Furthermore, we have listed the challenges associated with this treatment along with future prospective that could attract the interest of biomedical engineers, biomaterials scientists, medical researchers and pharmacological research groups.

79 citations

Journal ArticleDOI
TL;DR: In this paper, an experimental study of the rheology and lubricity properties of a drilling fluid is reported, motivated by applications in highly deviated and extended reach wells, with the potential to reduce costs via a decrease in drag and torque during the construction of highly deviating and ERD wells.
Abstract: An experimental study of the rheology and lubricity properties of a drilling fluid is reported, motivated by applications in highly deviated and extended reach wells. Recent developments in nanofluids have identified that the judicious injection of nano-particles into working drilling fluids may resolve a number of issues including borehole instability, lost circulation, torque and drag, pipe sticking problems, bit balling and reduction in drilling speed. The aim of this article is, therefore, to evaluate the rheological characteristics and lubricity of different nano-particles in water-based mud, with the potential to reduce costs via a decrease in drag and torque during the construction of highly deviated and ERD wells. Extensive results are presented for percentage in torque variation and coefficient of friction before and after aging. Rheology is evaluated via apparent viscosity, plastic viscosity and gel strength variation before and after aging for water-based muds (WBM). Results are included for silica and titanium nano-particles at different concentrations. These properties were measured before and after aging the mud samples at 80 °C during 16 h at static conditions. The best performance was shown with titanium nano-particles at a concentration of 0.60% (w/w) before aging.

72 citations

Journal ArticleDOI
TL;DR: In this article, an artificial neural networks (ANNs) model based on experimental data was developed to predict the catalytic performance of nanosilver, and good correlation between ANN model based results and experimental data indicated that it could be used to forecast the catalyst performance and hence extent of pollutant reduction at various catalyst concentrations.

67 citations

Journal ArticleDOI
TL;DR: The proposed model mathematical framework for the magnetic drug targeting is adopted while the flow of the ferrofluid, with different concentrations is taken into account, and the flow without any obstruction is compared with the flow having obstruction.

57 citations

Journal ArticleDOI
TL;DR: The work includes a significant hypothesis that quantifies the complex dynamics of the infection, by relating it to the effect of the inflammatory syndrome generated by IL-6, which can help in understanding the SARS-CoV2 virus infection and treatment model.

42 citations


Cited by
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[...]

08 Dec 2001-BMJ
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

33,785 citations

Book
30 Apr 2000
TL;DR: In this article, the authors present a survey of charging mechanisms and experiments, including mass and size distributions, and other modes of charge, such as self-gravitation, self-charge, and fluxuating dust charges.
Abstract: Preface. 1. Plasmas and Dust. 2. Charging Mechanisms and Experiments. 3. Space Observations. 4. Multispecies Formalism and Waves. 5. Electrostatic Modes. 6. Electromagnetic Modes. 7. Fluctuating Dust Charges. 8. Self-Gravitation. 9. Mass and Size Distributions. 10. Other Modes. 11. Conclusions and Outlook. Bibliography. Index.

425 citations

Journal Article
TL;DR: Ferroelectricity in BaTiO3 crystals is used to tune the sharp metamagnetic transition temperature of epitaxially grown FeRh films and electrically drive a transition between antiferromagnetic and ferromagnetic order with only a few volts, just above room temperature, correspond to a magnetoelectric coupling larger than previous reports by at least one order of magnitude.
Abstract: Controlling magnetism by means of electric fields is a key issue for the future development of low-power spintronics1. Progress has been made in the electrical control of magnetic anisotropy2, domain structure3,4, spin polarization5,6 or critical temperatures7,8. However, the ability to turn on and o robust ferromagnetism at room temperature and above has remained elusive. Here we use ferroelectricity in BaTiO3 crystals to tune the sharp metamagnetic transition temperature of epitaxially grown FeRh films and electrically drive a transition between antiferromagnetic and ferromagnetic order with only a few volts, just above room temperature. The detailed analysis of the data in the light of first-principles calculations indicate that the phenomenon is mediated by both strain and field e ects from the BaTiO3. Our results correspond to a magnetoelectric coupling larger than previous reports by at least one order of magnitude and open new perspectives for the use of ferroelectrics in magnetic storage and spintronics.

371 citations

Journal ArticleDOI
TL;DR: Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings.
Abstract: INTRODUCTION: Artificial intelligence is a branch of computer science capable of analysing complex medical data. Their potential to exploit meaningful relationship with in a data set can be used in the diagnosis, treatment and predicting outcome in many clinical scenarios. METHODS: Medline and internet searches were carried out using the keywords 'artificial intelligence' and 'neural networks (computer)'. Further references were obtained by cross-referencing from key articles. An overview of different artificial intelligent techniques is presented in this paper along with the review of important clinical applications. RESULTS: The proficiency of artificial intelligent techniques has been explored in almost every field of medicine. Artificial neural network was the most commonly used analytical tool whilst other artificial intelligent techniques such as fuzzy expert systems, evolutionary computation and hybrid intelligent systems have all been used in different clinical settings. DISCUSSION: Artificial intelligence techniques have the potential to be applied in almost every field of medicine. There is need for further clinical trials which are appropriately designed before these emergent techniques find application in the real clinical setting.

269 citations