ABSTRACT
Significant attention is paid to the design of magnetoplasmonic nanohybrids, which exploit synergistic properties for biomedical applications. Here, a facile method was employed to prepare plasmonic magnetic Au-MnO heterostructured hybrid nanoparticles for imaging-guided photothermal therapy of cancers in vitro, with the view to reducing the serious drawbacks of chemotherapy and gadolinium-based contrast agents. The biocompatibility of the prepared Au-MnO nanocomposites was further enhanced by Food and Drug Administration (FDA)-approved triblock copolymers Pluronic® F-127 and chitosan oligosaccharide (COS), with complementary support to enhance the absorption in the near-infrared (NIR) region. In addition, synthesized COS-PF127@Au-MnO nanocomposites exhibited promising contrast enhancement in T1 MR imaging with a good r1 relaxivity value (1.2 mM-1 s-1), demonstrating a capable substitute to Gd-based toxic contrast agents. In addition, prepared COS-PF127@Au-MnO hybrid nanoparticles (HNPs) produced sufficient heat (62 °C at 200 µg/mL) to ablate cancerous cells upon 808 nm laser irradiation, inducing cell toxicity, and apoptosis. The promising diagnostic and photothermal therapeutic performance demonstrated the appropriateness of the COS-PF127@Au-MnO HNPs as a potential theranostic agent.
ABSTRACT
The presented study deals with the exploitation of the artificial intelligence knacks-based stochastic paradigm for the numerical treatment of the nonlinear delay differential system for dynamics of plant virus propagation with the impact of seasonality and delays (PVP-SD) model by implementing neural networks backpropagation with Bayesian regularization scheme (NNs-BBRS). The PVP-SD model is represented with five classes-based ODEs systems for the interaction between insects and plants. The nonlinear PVP-SD model governs with five populations: S(t) susceptible plants, I(t) infected plants, X(t) susceptible insect vectors, Y(t) infected insect vectors and P(t) predators. Adams numerical procedure is adopted to generate the reference solutions of the nonlinear PVP-SD model based on the variety of cases by varying the plants bite rate due to vectors, vector bite rate due to plants, plant's recovery rate, predator contact rate with healthy insects, predator contact rate with infected insects and death rate caused by insecticides. The approximate solutions of the nonlinear PVP-SD model are determined by executing the designed NNs-BBRS through different target and inputs arbitrary selected samples for the training and testing data. Validation of the consistent precision and convergence of the designed NNs-BBRS is efficaciously substantiated through exhaustive simulations and analyses on mean square error-based merit function, index of regression and error histogram illustrations.
ABSTRACT
The aim of this work is to design an intelligent computing paradigm through Levenberg-Marquardt artificial neural networks (LMANNs) for solving the mathematical model of Corona virus disease 19 (COVID-19) propagation via human to human interaction. The model is represented with systems of nonlinear ordinary differential equations represented with susceptible, exposed, symptomatic and infectious, super spreaders, infection but asymptomatic, hospitalized, recovery and fatality classes, and reference dataset of the COVID-19 model is generated by exploiting the strength of explicit Runge-Kutta numerical method for metropolitans of China and Pakistan including Wuhan, Karachi, Lahore, Rawalpindi and Faisalabad. The created dataset is arbitrary used for training, validation and testing processes for each cyclic update in Levenberg-Marquardt backpropagation for numerical treatment of the dynamics of COVID-19 model. The effectiveness and reliable performance of the design LMANNs are endorsed on the basis of assessments of achieved accuracy in terms of mean squared error based merit functions, error histograms and regression studies.
