Caio Fonseca is a PhD Student at the Walton Institute for Information and Communication Systems Science, Waterford Institute of Technology - WIT, working within the EU-H2020-FET GLADIATOR research project. His PhD work is supervised by Dr. Sasi Balasubramaniam and Dr. Michael Barros.
Caio’s research is currently related to mathematical and computational modeling of molecular communications systems in the brain for the treatment of brain pathologies, especifically, glioblastoma multiform. Also to develop evolutionary algorithms for the programming and design of brain organoids.
He is an Electrical and Electronics Engineer and a Computational Scientist. He received his Bachelor’s degree in Electrical Engineering with a concentration in Power Engineering from the Federal University of Campina Grande - UFCG in 2019.
In his undergraduate studies he was an exchange student with a scholarship from the Brazilian Scientific Mobility Program, studying at the Western New England University, Springfield, MA - USA in 2014 - 2015. He was a Visiting Undergraduate Researcher at the University of California at Los Angeles - UCLA in 2015. In 2017, he was also a Visiting Researcher at Edmond & Lily Safra International Institute of Neurosciences working within a group of engineers and physicians.
PhD in Computer Science, 2023
Waterford Institute of Technology - WIT, Ireland
BEng in Electrical Engineering, 2019
Federal University of Campina Grande - UFCG, Brazil
Exchange Program in Electrical Engineering, 2014-2015
Western New England University - WNE, USA
Glioblastoma Multiform (GBM) is known as one of the most malignant tumours in the brain, and challenges remain in developing effective therapeutic solutions. This paper addresses an open-loop control molecular communication system using an adaptive algorithm that controls engineered induced Neural Stem Cells (iNSCs) to release therapeutic exosomes for treating GBM. The adaptive algorithm is based on the Lotka-Volterra Predator-Prey model, and virtually monitors the tumour growth from an external Brain-Machine Interface to control the release of the exosomes for the treatment. We developed the model to incorporate the control from an external RF signal that controls the production of exosomes as well as the diffusion propagation of exosomes through a 3D simulated Extracellular Space tissue. Based on numerical analysis coupled with simulations, we found that factors such as stochastic propagation of exosomes influence the aggressiveness of the model to tackle the tumour. This work can lay the foundation for future adaptive Brain-Machine Interface that controls molecular communication system for GBM treatment.
The recent COVID-19 pandemic has resulted in high fatality rates, especially for patients who suffer from underlying health issues. One of the more serious symptoms exhibited from patients suffering from an acute COVID-19 infection is breathing difficulties and shortness of breath, which is largely due to the excessive fluid (cellular leakage and cytokine storm) and mucoid debris that have filled lung alveoli, and reduced the surfactant tension resulting in heavy and stiff lungs. In this paper we propose the use of micro-bubbles filled with exosomes that can be released upon exposure to ultrasound signals as a possible rescue therapy in deteriorating COVID-19 patients. Recent studies have shown that exosomes can be used to repair and treat lung damage for patients who have suffered from the viral infection. We have conducted simulations to show the efficacy of the ultrasound signals that will penetrate through layers of tissues reaching the alveoli that contains the micro-bubbles. Our results have shown that ultrasound signals with low frequencies are required to oscillate and rupture the polymerbased micro-bubbles. Our proposed system can be used for patients who require immediate rescue treatments for lung damage, as well as for recovered patients who may suffer from viral relapse infection, where the micro-bubbles will remain dormant for a temporary therapeutic window until they are exposed to the ultrasound signals.
Autism spectrum disorder (ASD) is a neuropsychiatric disorder characterized by the impairment in the social reciprocity, interaction/language, and behavior, with stereotypes and signs of sensory function deficits. Electroencephalography (EEG) is a well-established and noninvasive tool for neurophysiological characterization and monitoring of the brain electrical activity, able to identify abnormalities related to frequency range, connectivity, and lateralization of brain functions. This research aims to evidence quantitative differences in the frequency spectrum pattern between EEG signals of children with and without ASD during visualization of human faces in three different expressions: neutral, happy, and angry. Quantitative clinical evaluations, neuropsychological evaluation, and EEG of children with and without ASD were analyzed paired by age and gender. The results showed stronger activation in higher frequencies (above 30 Hz) in frontal, central, parietal, and occipital regions in the ASD group. This pattern of activation may correlate with developmental characteristics in the children with ASD.