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Drug delivery to tumors suffers from poor solubility, specificity, diffusion through the tumor micro‐environment and nonoptimal interactions with components of the extracellular matrix and cell surface receptors. Nanoparticles and drug–polymer complexes address many of these problems. However, large size exasperates the problem of slow diffusion through the tumor. Three‐dimensional tumor spheroids are good models to evaluate approaches to mitigate these difficulties and aid in design strategies to improve the delivery of drugs to treat cancer effectively. Diffusion of drug carriers is highly dependent on cell uptake rate parameters (association/dissociation) and temperature. Hyperthermia increases molecular transport and is known to act synergistically with chemotherapy to improve treatment. This study presents a new inverse estimation approach based on Bayesian probability for estimating nanoparticle cell uptake rates from experiments. The parameters were combined with a finite element computational model of nanoparticle transport under hyperthermia conditions to explore its effect on tumor porosity, diffusion and particle binding (association and dissociation) at cell surfaces. Carboxy‐PEG‐silane (cPEGSi) nanoparticles showed higher cell uptake compared to methoxy‐PEG‐silane (mPEGSi) nanoparticles. Simulations were consistent with experimental results from Skov‐3 ovarian cancer spheroids. Amorphous silica (cPEGSi) nanoparticles (58 nm) concentrated at the periphery of the tumor spheroids at 37°C but mild hyperthermia (43°C) increased nanoparticle penetration. Thus, hyperthermia may enhance cancer treatment by improving blood delivery to tumors, enhancing extravasation and penetration into tumors, trigger release of drug from the carrier at the tumor site and possibly lead to synergistic anti‐cancer activity with the drug. [ABSTRACT FROM AUTHOR]

Inverse heat transfer problems deal with the estimation of parameters or functions appearing in the mathematical formulation of problems in thermal sciences, by utilizing measurements of dependent variables of the formulation. Inverse problems are extremely useful for the indirect measurement of thermophysical properties, in particular for challenging situations involving high temperatures, where coupled multi-physics phenomena and nonlinearities must be taken into account. In this paper, basic inverse problem concepts are reviewed. Solution techniques within the Bayesian framework of statistics are briefly described and applied to two inverse problems related to the authors' experience on the estimation of thermophysical properties at high temperatures. [ABSTRACT FROM AUTHOR]

In this work, we apply a Bayesian approach for the estimation of the thermal parameters appearing in the heat conduction problem in overhead power cables. The cables used for the transport of electric energy are composed by layers of stranded metallic wires, resulting in a heterogeneous media (wires and air voids). The inner core is made of steel wires, over which three aluminum wire layers are winded to form the conductor. The physical problem is formulated in terms of transient heat conduction in two homogeneous cylindrical regions. The Markov Chain Monte Carlo (MCMC) method is used to estimate the effective radial thermal conductivities and the volumetric heat capacities of both regions (steel core and aluminum conductor), the combined heat transfer coefficient at the cable surface, as well as the parameters related to the electrical current distribution in the conductor region. Temperature measurements were used in the inverse analysis, taken by thermocouples inserted in the cable together with the conductor wires during its manufacturing process, in experiments under controlled laboratory conditions. [ABSTRACT FROM AUTHOR]

In this work, we applied the Markov chain Monte Carlo (MCMC) method for the estimation of parameters appearing in the Pennes' formulation of the bioheat transfer equation. The inverse problem of parameter estimation was solved with the simulated transient temperature measurements. A one-dimensional (1D) test case was used to explore the capabilities of using the MCMC method in bioheat transfer problems, specifically for the detection of skin tumors by using surface temperature measurements. The analysis of the sensitivity coefficients was performed in order to examine linear dependence and low sensitivity of the model parameters. The solution of the direct problem was verified with a commercial code. The results obtained in this work show the ability of using inverse heat transfer analysis for the detection of skin tumors. [ABSTRACT FROM AUTHOR]