My co-authored research article titled – Critical Volume of Human Myocardium Necessary to Maintain Ventricular Fibrillation was published in the peer review journal – Circulation. Arrhythmia and Electrophysiology. The article is available in print, in the December 2018 issue of the journal.
Briefly, the manuscript documents evidence using donor human hearts that ventricular arrhythmias (e.g., ventricular fibrillation) is only sustained if the cardiac tissue volume exceeds the cardiac wavelength volume . The article can be accessed here
It has been a long journey that started during the pandemic. Over the last two years, I have experienced my share of emotional peaks (excitement and joy) and valleys (disappointments, anxiety, and exhaustion), sometimes during the same day :). I am happy and a little relieved that this leg of journey is over.
At some point in the future, I will share my experience in more detail in the hopes that the insights I gained along the way may prove beneficial to those who are already looking for positions and/or may start the process soon. That said, I’d still like to share with you a couple of takeaways, that in my opinion is critical for successfully navigating the faculty interview process.
First and probably the most important is to shift your mindset to believe that you can become a successful TT faculty and not only survive but thrive in that academic setting. Transitioning from trainee to a faculty is big leap and comes with additional responsibilities beyond just research. You will need to effectively communicate that vision to the search committee.
Second and equally important is to be yourself. As you go through the process of myriad screening interviews, campus visits and one-on-one meetings in your quest to land that dream job, the one constant through all of it will be you. They should celebrate you for who you are and what you bring to table relative to your intellect, personality, and research strengths.
I will be looking for graduate student(s), post-doc(s), and a research technician to join my team at UB with focus on cardiac obesity and arrhythmias. We will use a combination of cardiac electrophysiology, bioinformatics, adipose biology and bioelectronics to advance our understanding of how obesity and metabolic syndrome mediate cardiac arrhythmias. Moreover, we will also develop new diagnostics and therapeutics for obesity mediated arrhythmias. If you are interested, send me an email (firstname.lastname@example.org) with your CV and a brief statement about yourself including research interests and goals. I look forward to hearing from you.
Here’s a recruiting pitch of UB facilities and I can say with certainty that the facilities are even more impressive in person.
Chapter 36– Innovation in Cardiovascular Bioelectronics
Rose T. Yin, Yeon Sik Choi, Kedar K. Aras, Helen S. Knight, Alana N. Miniovich, and Igor R. Efimov
Advances in materials science have enabled new bioelectronics platforms for novel approaches to medicine. Bioelectronics for disease diagnosis and treatment that were once bulky have become miniaturized and lightweight. The rigid geometries that were previously incompatible with tissues and organs are now flexible and stretchable to conform to organ curvatures. Energy sources dependent on batteries can now harvest energy from mechanical motion, static electricity, light, ultrasound, and electromagnetic fields.
Materials at the tissue – bioelectronics interface inducing significant foreign body responses have been replaced by materials such as hydrogels and graphene that are much more biocompatible. These innovations have enabled the development of bioelectronics for the treatment of cardiovascular diseases, such as monitors, ablation, pacemaker, and implantable cardioverter defibrillator (ICD) therapy.
This portfolio of bioelectronic devices collects high-resolution data across multiple parameters and can deliver the pertinent electrotherapy. The bioelectronic conformal devices serve as the foundation of the medical internet-of-things, which will ultimately improve the accessibility of medicine, the efficiency of the healthcare system, and enhance human health.
Bioresorbable steroid-eluting interface that minimizes local inflammation and fibrosis
Subcutaneous, bioresorbable power harvesting unit
Set of soft, skin-interfaced sensors that capture ECG, HR etc., to track patient physiology.
Wireless RF module that transfers power to the harvesting unit
Soft skin-interfaced haptic actuator that communicates via mechanical vibrations
Handheld device with software module for real time data visualization and automated adaptive control
The bioresorbable module for cardiac pacing undergoes complete dissolution by natural biological processes after a defined operating time frame. Moreover, the wireless battery recharge through the skin eliminates the need for transcutaneous wires.
LINK TO THE ARTICLE
Choi YS, Jeong H, Yin RT, Avila R, Pfenniger A, Yoo J, Lee JY, Tzavelis A, Lee YJ, Chen SW, Knight HS, Kim S, Ahn HY, Wickerson G, Vázquez-Guardado A, Higbee-Dempsey E, Russo BA, Napolitano MA, Holleran TJ, Razzak LA, Miniovich AN, Lee G, Geist B, Kim B, Han S, Brennan JA, Aras K, Kwak SS, Kim J, Waters EA, Yang X, Burrell A, San Chun K, Liu C, Wu C, Rwei AY, Spann AN, Banks A, Johnson D, Zhang ZJ, Haney CR, Jin SH, Sahakian AV, Huang Y, Trachiotis GD, Knight BP, Arora RK, Efimov IR, Rogers JA. A transient, closed-loop network of wireless, body-integrated devices for autonomous electrotherapy. Science. 2022 May 27;376(6596):1006-1012. doi: 10.1126/science.abm1703. Epub 2022 May 26. PMID: 35617386.
Organ conformal bioelectronics platform have enabled high-definition ventricular arrhythmia sensing, coupled with electrotherapy.
However, current conformal electronics platforms do not have the ability to perform real time computing to detect arrhythmia rotors and and subsequently deliver appropriate therapy.
WHAT THIS STUDY ADDS?
We propose the use of distributed computing algorithm based on cellular neural networks to provide high classification sensitivity, specificity, accuracy, and precision in detecting arrhythmia rotors and wavefronts.
The compact and efficient computing solution is readily mappable to a memristor based hardware circuitry and could enable a closed-loop solution for smart arrhythmia detection and real-time therapy.