Acupuncture is one of the most extensively used complementary and alternative medicine therapies worldwide. In this study, we explore the use of near-infrared light-emitting diodes (LEDs) to provide acupuncture-like physical stimulus to the skin tissue, but in a completely non-invasive way. A computational modeling framework has been developed to investigate the light-tissue interaction within a three-dimensional multi-layer model of skin tissue. Finite element-based analysis has been conducted, to obtain the spatiotemporal temperature distribution within the skin tissue, by solving Pennes\' bioheat transfer equation, coupled with the Beer-Lambert law. The irradiation profile of the LED has been experimentally characterized and imposed in the numerical model. The experimental validation of the developed model has been conducted through comparing the numerical model predictions with those obtained experimentally on the agar phantom. The effects of the LED power, treatment duration, LED distance from the skin surface, and usage of multiple LEDs on the temperature distribution attained within the skin tissue have been systematically investigated, highlighting the safe operating power of the selected LEDs. The presented information about the spatiotemporal temperature distribution, and critical factors affecting it, would assist in better optimizing the desired thermal dosage, thereby enabling a safe and effective LED-based photothermal therapy.

Focused Ultrasound (FUS)-triggered nano-sized drug delivery, as a smart stimuli-responsive system for treating solid tumors, is computationally investigated to enhance localized delivery of drug and treatment efficacy. Integration of thermosensitive liposome (TSL), as a doxorubicin (DOX)-loaded nanocarrier, and FUS, provides a promising drug delivery system. A fully coupled partial differential system of equations, including the Helmholtz equation for FUS propagation, bio-heat transfer, interstitial fluid flow, drug transport in tissue and cellular spaces, and a pharmacodynamic model is first presented for this treatment approach. Equations are then solved by finite element methods to calculate intracellular drug concentration and treatment efficacy. The main objective of this study is to present a multi-physics and multi-scale model to simulate drug release, transport, and delivery to solid tumors, followed by an analysis of how FUS exposure time and drug release rate affect these processes. Our findings not only show the capability of model to replicate this therapeutic approach, but also confirm the benefits of this treatment with an improvement of drug aggregation in tumor and reduction of drug delivery in healthy tissue. For instance, the survival fraction of tumor cells after this treatment dropped to 62.4%, because of a large amount of delivered drugs to cancer cells. Next, a combination of three release rates (ultrafast, fast, and slow) and FUS exposure times (10, 30, and 60 min) was examined. Area under curve (AUC) results show that the combination of 30 min FUS exposure and rapid drug release leads to a practical and effective therapeutic response.

Our core body temperature is held around [Formula: see text]C by an effective internal thermoregulatory system. However, various clinical scenarios have a more favorable outcome under external temperature regulation. Therapeutic hypothermia, for example, was found beneficial for the outcome of resuscitated cardiac arrest patients due to its protection against cerebral ischemia. Nonetheless, practice shows that outcomes of targeted temperature management vary considerably in dependence on individual tissue damage levels and differences in therapeutic strategies and protocols. Here, we address these differences in detail by means of computational modeling. We develop a multi-segment and multi-node thermoregulatory model that takes into account details related to specific post-cardiac arrest-related conditions, such as thermal imbalances due to sedation and anesthesia, increased metabolic rates induced by inflammatory processes, and various external cooling techniques. In our simulations, we track the evolution of the body temperature in patients subjected to post-resuscitation care, with particular emphasis on temperature regulation via an esophageal heat transfer device, on the examination of the alternative gastric cooling with ice slurry, and on how anesthesia and the level of inflammatory response influence thermal behavior. Our research provides a better understanding of the heat transfer processes and therapies used in post-cardiac arrest patients.

OBJECTIVE: The thermal therapy is a minimally invasive technique used as an alternative approach to conventional cancer treatments. There is an increasing concern about the accuracy of the thermal simulation during the process of tumor ablation. This study is aimed at investigating the effect of finite speed of heat propagation in the biological lung tissue, experimentally and numerically.
METHODS: In the experimental study, a boundary heat flux is applied to the lung tissue specimens and the temperature variation is measured during a transient heat transfer procedure. In the numerical study, a code is developed based on the finite volume method to solve the classical bio-heat transfer, the Cattaneo and Vernotte, and the Dual-phase-lag (DPL) equations. The thermal response of tissue during the experiments is compared with the predictions of the three heat transfer models.
RESULTS: It is found that the trend of temperature variation by the DPL model resembles the experimental results. The experimental observation in parallel with the numerical results reveals that the accumulated thermal energy diffuses to the surrounding tissue with a slower rate in comparison with the conventional bio-heat transfer model. The DPL model is implemented to study the temperature elevation in the laser irradiation to lung tissue in the presence of gold nanoparticles (GNPs). It is concluded that the extent of the necrotic tumoral region and the area of the damaged healthy tissue are reduced, when the non-Fourier heat transfer is taken into account.
CONCLUSIONS: Results show that considering the phase lags is crucial in planning for an effective thermal treatment, in which the cancerous tissue is ablated and the surrounding tissues are preserved from irreversible thermal damage.

Acanthamoeba are widely distributed in the environment and are known to cause blinding keratitis and brain infections with greater than 90% mortality rate. Currently, polymerase chain reaction (PCR) is a highly sensitive and promising technique in Acanthamoeba detection. Remarkably, the rate of heating-cooling and convective heat transfer of the PCR tube is limited by low thermal conductivity of the reagents mixture. The addition of nanoparticles to the reaction has been an interesting approach that could augment the thermal conductivity of the mixture and subsequently enhance heat transfer through the PCR tube. Here, we have developed hexagonal boron nitride (hBN) nanoparticle-based PCR assay for the rapid detection of Acanthamoeba to amplify DNA from low amoeba cell density. As low as 1 × 10-4 wt % was determined as the optimum concentration of hBN nanoparticles, which increased Acanthamoeba DNA yield up to ~16%. Further, it was able to reduce PCR temperature that led to a ~2.0-fold increase in Acanthamoeba DNA yield at an improved PCR specificity at 46.2 °C low annealing temperature. hBN nanoparticles further reduced standard PCR step time by 10 min and cycles by eight; thus, enhancing Acanthamoeba detection rapidly. Enhancement of Acanthamoeba PCR DNA yield is possibly due to the high adsorption affinity of hBN nanoparticles to purine (Guanine-G) due to the higher thermal conductivity achieved in the PCR mixture due to the addition of hBN. Although further research is needed to demonstrate these findings in clinical application, we propose that the interfacial layers, Brownian motion, and percolation network contribute to the enhanced thermal conductivity effect.

OBJECTIVE: During thermal heating surgical procedures such as electrosurgery, thermal ablative treatment and hyperthermia, soft tissue deformation due to surgical tool-tissue interaction and patient movement can affect the distribution of thermal energy induced. Soft tissue temperature must be obtained from the deformed tissue for precise delivery of thermal energy. However, the classical Pennes bio-heat transfer model can handle only the static non-moving state of tissue. In addition, in order to enable a surgeon to visualise the simulated results immediately, the solution procedure must be suitable for real-time thermal applications.
METHODS: This paper presents a formulation of bio-heat transfer under the effect of soft tissue deformation for fast or near real-time tissue temperature prediction, based on fast explicit dynamics finite element algorithm (FED-FEM) for transient heat transfer. The proposed thermal analysis under deformation is achieved by transformation of the unknown deformed tissue state to the known initial static state via a mapping function. The appropriateness and effectiveness of the proposed formulation are evaluated on a realistic virtual human liver model with blood vessels to demonstrate a clinically relevant scenario of thermal ablation of hepatic cancer.
RESULTS: For numerical accuracy, the proposed formulation can achieve a typical 10-3 level of normalised relative error at nodes and between 10-4 and 10-5 level of total errors for the simulation, by comparing solutions against the commercial finite element analysis package. For computation time, the proposed formulation under tissue deformation with anisotropic temperature-dependent properties consumes 2.518 × 10-4 ms for one element thermal loads computation, compared to 2.237 × 10-4 ms for the formulation without deformation which is 0.89 times of the former. Comparisons with three other formulations for isotropic and temperature-independent properties are also presented.
CONCLUSIONS: Compared to conventional methods focusing on numerical accuracy, convergence and stability, the proposed formulation focuses on computational performance for fast tissue thermal analysis. Compared to the classical Pennes model that handles only the static state of tissue, the proposed formulation can achieve fast thermal analysis on deformed states of tissue and can be applied in addition to tissue deformable models for non-linear heating analysis at even large deformation of soft tissue, leading to great translational potential in dynamic tissue temperature analysis and thermal dosimetry computation for computer-integrated medical education and personalised treatment.

In this work, the new concept of \"memory dependent derivative\" in the Pennes\' bio-heat transfer process of skin tissues is employed to investigate the one-dimensional problem of a skin tissue under sinusoidal heat flux conditions. Laplace transform technique is utilized to solve the problem. We investigate, numerically, the bio-heat transfer equation with memory-dependent derivative to find the effect on the tissue temperature of the kernel function and the time-delay parameter which are characteristic of memory dependent derivative heat transfer. Correlations are made with the results obtained in the case of the absence of memory-dependent derivative parameters. The effects of the time-delay on the temperature distribution in skin tissue for different forms of kernel functions are examined.

Focused ultrasound surgery (FUS) is a non-invasive thermal therapeutic method which has been emerged in the field of brain tumors treatment. During intraoperative brain surgery, application of FUS can significantly increase the accuracy of thermal ablation of tumor while reducing undesirable damage to healthy brain tissue. The main objective of this study is acquiring acoustic transducer specifications to achieve optimum thermal treatment in the tumoral tissue. 2D and 3D models are constructed from patient-specific brain MRI images which consist of a malignant vascular tumor. Acoustic pressure and temperature are obtained by using homogenous Helmholtz and bio-heat transfer equations according to insignificant nonlinear effect. Besides that, thermal lesion induced by FUS is obtained by the thermal dose function. Results show the significance of blood vessels\' cooling effect on the temperature profile. Moreover, correlation between temperature profile and transducer\'s operating parameter including power, frequency and duty cycle is obtained. Artificial neural network analysis is conducted to estimate required transducer parameters for optimum temperature rise.

Thermography is a developing and noninvasive medical imaging technique that can be used for diagnosis of body disorders based on temperature deviation from normal body temperature. This research investigates the feasibility of thermography method in conjunction with artificial neural networks (ANNs) for detection of thyroid tumors. For this purpose, first, a 3-D model of the healthy human neck is constructed based on patient-specific computed tomography (CT) images. This model is used for analyzing bio-heat transfer in the human neck. The healthy thyroid gland is considered as a heat source and generates heat according to its temporal temperature. Finite element results verify the thermography potential for detection of thyroid gland location and estimation of its butterfly shape on the neck thermogram. The numerical analysis is carried out on 35 models with varying thermo-physical parameters of the healthy thyroid gland, including heat generation and blood perfusion. The acquired thermograms are used to develop an ANN for correlating the thermo-physical parameters of the gland and temperature profile on the neck surface. In the next stage, dynamic thermal images are captured from 10 healthy and three cancerous human cases. The experimental thermal images are analyzed by the developed ANN and the corresponding thermo-physical parameters are obtained. Results show that the estimated heat generation values for the healthy cases are about 3000 W m 3 while it increases to more than 12 000 W m 3 for the cases with tumors. This significant variation confirms the potential of dynamic thermography in diagnosis of thyroid tumors.

The present work is concerned with the numerical investigation of the thermal response of tissue-mimicking biological phantom(s) subjected to high intensity focused ultrasound (HIFU). Simulations have been performed on the 3-dimensional physical domain for two-layered as well as multi-layered medium consisting of water and liver tissue. Local pressure distribution within the body of the phantom has been calculated by solving the complete full-wave nonlinear form of Westervelt equation. The solution of the pressure wave equation has been coupled with Pennes bioheat transfer equation to determine the full field temperature distribution. Results in the form of pressure fields, temperature distributions and the corresponding thermal dosage in the targeted region of the tissue domain have been presented. Magnitudes of the maximum pressure (and hence the resultant temperature levels) in the focal region as obtained using the nonlinear form of Westervelt equation are found to be significantly higher than that determined based on the linear form of the equation. Compared to water, wherein the acoustic intensity is maximum, the addition of sub-layers of skin, fat, and muscle into water resulted in the reduction of the peak intensity and also shifted the intensity profiles along the direction of propagation of the acoustic waves. However, addition of liver tissue into water led to the shifting of intensity profile in the opposite direction i.e., towards the transducer. The results further reveal that due to the dependence of attenuation coefficient on the source frequency, the temperature at the focal region increases with an increase in the transducer frequency. The work is further extended from single lesion to multiple lesion generation through controlled movement of the transducer and the resultant transient full field temperature distribution has been presented. The concerned observations highlight the need of optimizing the thermal energy for each lesion, the inter spatial distance between different lesions and the delay time so as to ensure minimal thermal damage to the surrounding healthy cells as well as to reduce the total treatment duration.