Showing papers by "Ilangko Balasingham published in 2023"
26 Mar 2023
TL;DR: In this article , the design steps for miniaturized implant antennas, the frequency dependency of biological tissues, and EM modeling and simulation tools are provided for application in the cardiac, gastric, and brain.
Abstract: Antenna miniaturization for integration with small implants for sensing, wireless powering, and communication is a multi-parameter design task. The implanted antenna performance is governed by the antenna size, operating frequency, antenna surrounding tissues, subsequent biological tissues, antenna encapsulation, the electronics and metal objects nearby the antenna, implant depth, and the electromagnetic radiation source defined by the antenna geometry. The antenna performance can be characterized by impedance matching, bandwidth, antenna near-field, far-field radiation pattern, efficiency and gain, and the specific absorption rate (SAR). This paper reviews the design steps for miniaturized implant antennas, the frequency dependency of biological tissues, and EM modeling and simulation tools to support the design. Examples of miniaturized implant antennas are provided for application in the cardiac, gastric, and brain.
TL;DR: In this article , the authors proposed a comprehensive deep learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies.
Abstract: Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma by integrating CT imaging and clinical data and addressing the limitations observed in prior studies. The aim is to facilitate the identification of patients requiring urgent treatment. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction. The feature extractor module, based on the 3D CNN architecture, predicts the ISUP grade of renal cell carcinoma tumors linked to mortality rates from CT images. A selection of clinical variables is systematically chosen using the Spearman score and random forest importance score as criteria. A deep learning-based network, trained with discrete LogisticHazard-based loss, performs the survival prediction. Nine distinct experiments are performed, with varying numbers of clinical variables determined by different thresholds of the Spearman and importance scores. Our findings demonstrate that the proposed strategy surpasses the current literature on renal cancer prognosis based on CT scans and clinical factors. The best-performing experiment yielded a concordance index of 0.84 and an area under the curve value of 0.8 on the test cohort, which suggests strong predictive power. The multimodal deep-learning approach developed in this study shows promising results in estimating survival probabilities for renal cell carcinoma patients using CT imaging and clinical data. This may have potential implications in identifying patients who require urgent treatment, potentially improving patient outcomes. The code created for this project is available for the public on: \href{https://github.com/Balasingham-AI-Group/Survival_CTplusClinical}{GitHub}
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TL;DR: In this paper , a model of the anomalous diffusion of extracellular vesicles (EVs) based on a 3-dimensional partial differential equation from the molecular communications theory is presented.
Abstract: Anomalous diffusion of extracellular vesicles (EVs) occurs because of the natural stiffness and stress relaxation of the extracellular matrix (ECM). This phenomenon has not been considered so far in attempts of computational modeling of the biodistribution of EVs, which is used as a powerful tool in pre-clinical and clinical practice. Here we present a novel model of the anomalous EV diffusion based on a 3-dimensional partial differential equation from the molecular communications theory, and solve it using the Green’s function theorem. We also verify our analytical results using a particle-based simulation (PBS). The model encompasses a source function for the EV release from cells, their degradation through natural half-life, and extracellular binding. Our findings reveal that different anomalous schemes lead to various propagation patterns and can be used for providing insights into designing EV-based drug delivery systems.
TL;DR: In this paper , the authors derived the frequency response of internalization function in the framework of a unilateral communication link and adapted it to a closed-loop system to find a bilateral system, where the transmitter is affected by the induced release initiated by the target cell due to the extracellular vesicles received from the donor cell.
Abstract: Interactions of cells via extracellular vesicles (EVs) manipulate various actions, including cancer initiation and progression, inflammation, anti-tumor signaling and cell migration, proliferation and apoptosis in the tumor microenvironment. EVs as the external stimulus can activate or inhibit some receptor pathways in a way that amplify or attenuate a kind of particle release at target cells. This can also be carried out in a biological feedback-loop where the transmitter is affected by the induced release initiated by the target cell due to the EVs received from the donor cell, to create a bilateral process. In this paper, at first we derive the frequency response of internalization function in the framework of a unilateral communication link. This solution is adapted to a closed-loop system to find the frequency response of a bilateral system. The overall releases of the cells, given by the combination of the natural release and the induced release, are reported at the end of this paper and the results are compared in terms of distance between the cells and reaction rates of EVs at the cell membranes.
TL;DR: In this article , the authors proposed a wireless passive video transmission system for capsule endoscopy, in which the power consumption is reduced by using analog camera sensor, and implementing an innovative radar technique for remote reading of the analog video signal using radio frequency backscattering.
Abstract: Wireless capsule endoscopy is a fast-growing technology in healthcare systems. Due to using battery for powering the camera, light source, wireless communication, and other electronics, it has substantial limitations with the image quality, frame rate, and operating time. In this work, we propose a wireless passive video transmission system for capsule endoscopy, in which the power consumption is reduced by using analog camera sensor, and implementing an innovative radar technique for remote reading of the analog video signal using radio frequency backscattering. The power consumption of the capsule communication system tends to zero. The communication electronics system is minimized to a single Varactor diode with appropriate matching circuits and the image sensor power consumption is reduced by eliminating the camera sensor’s analog to digital converter. With these improvements the capsule system can operate for a longer period of time which enables the feasibility of continuous video streaming during the gastric tract screening. The design feasibility is demonstrated in a phantom experiment, and validated in an animal experiment for depths 6–11 cm using a bi-static radar system at 400 MHz, implemented using software defined radio platform.
TL;DR: In this paper , a dynamic lower body negative pressure (LBNP) model was used to diagnose levels of ongoing hypovolemia among healthy volunteers. But, the authors used a dynamic LBNP protocol as opposed to the traditional model, which is applied in a predictable step-wise, progressively descending manner.
TL;DR: In this article , an analytical solution for the controlled release of drugs carried by extracellular vesicles was derived and numerically verified for the end-to-end system model.
Abstract: Targeted drug delivery is a promising approach for many serious diseases, such as glioblastoma multiforme, one of the most common and devastating brain tumor. In this context, this work addresses the optimization of the controlled release of drugs which are carried by extracellular vesicles. Towards this goal, we derive and numerically verify an analytical solution for the end-to-end system model. We then apply the analytical solution either to reduce the disease treatment time or to reduce the amount of required drugs. The latter is formulated as a bilevel optimization problem, whose quasiconvex/quasiconcave property is proved here. For solving the optimization problem, we propose and utilize a combination of bisection method and golden-section search. The numerical results demonstrate that the optimization can significantly reduce the treatment time and/or the required drugs carried by extracellular vesicles for a therapy compared to the steady state solution.
07 Apr 2023
TL;DR: In this article , a CycleGAN-based framework is proposed to convert images captured with regular WLI to synthetic NBI (SNBI) as a pre-processing method for improving object detection on WLI when NBI is unavailable.
Abstract: To cope with the growing prevalence of colorectal cancer (CRC), screening programs for polyp detection and removal have proven their usefulness. Colonoscopy is considered the best-performing procedure for CRC screening. To ease the examination, deep learning based methods for automatic polyp detection have been developed for conventional white-light imaging (WLI). Compared with WLI, narrow-band imaging (NBI) can improve polyp classification during colonoscopy but requires special equipment. We propose a CycleGAN-based framework to convert images captured with regular WLI to synthetic NBI (SNBI) as a pre-processing method for improving object detection on WLI when NBI is unavailable. This paper first shows that better results for polyp detection can be achieved on NBI compared to a relatively similar dataset of WLI. Secondly, experimental results demonstrate that our proposed modality translation can achieve improved polyp detection on SNBI images generated from WLI compared to the original WLI. This is because our WLI-to-SNBI translation model can enhance the observation of polyp surface patterns in the generated SNBI images.