A reliable method for real-time blood flow monitoring in vivo is critical for several medical applications, including monitoring cardiovascular diseases, evaluating interventional procedures and surgeries, and increasing the safety and efficacy of neuromodulation procedures. High-speed methods are particularly necessary for neural monitoring, due to the brain's heightened sensitivity to hypoxic and ischemic conditions. High-speed CBF monitoring methods may also provide a useful biomarker for the development of a closed-loop deep brain stimulation (DBS) system. Current methods such as laser Doppler, bold fMRI, and positron emission tomography (PET) often involve cumbersome instrumentation and are therefore not well- suited for chronic microvasculature monitoring. The purpose of this study is to develop a method for real-time measurement of blood flow changes using electrochemical impedance spectra (EIS). Utilizing EIS to measure CBF has the potential to be included in a chronic, closed-loop DBS system that is modulated by fluctuations in CBF, using minimal additional instrumentation. Five experiments in rodents were conducted, with the objective of 1) determining whether electrochemical impedance spectra showed impedance changes correlated with changes in blood flow, assessing the sensitivity, specificity, and limitations of detection of this method, and 2) determining whether cyclic voltammetry-based method could be used to produce EIS more rapidly than current methods. The experimental set-up included electrodes in the femoral artery with the administration of endothelin (ET-1) to induce blood flow changes (N=1), electrodes in the motor cortex using isoflurane variation to induce blood flow changes (N=3), and electrodes in the femoral artery with the administration of nitroglycerin (NTG) to induce blood flow changes (N=1). Preliminary results suggest that impedance changes in the higher frequencies (over 160 Hz) demonstrated higher sensitivity to blood flow changes in the femoral artery model compared to <100 Hz frequencies, with inconclusive results in the motor cortex model. Future in vivo experiments will be conducted using endothelin-1 to further establish the relationship between impedance and cerebral blood flow in the brain.

Traumatic brain injury (TBI) poses a significant global health concern with substantial health and economic consequences. Patients often face significant consequences after injury, notably persistent cognitive changes and an increased risk of developing neurodegenerative disease later in life. Apart from the immediate insult, the resulting inflammatory response can lead to neuroinflammation, oxidative stress, tissue death, and long-term neurodegeneration. Microglia and astrocytes play critical roles in these inflammatory processes, emphasizing the unmet need for targeted therapies. Vaccine formulations consisting of poly (a-ketoglutarate) (paKG) microparticles (MPs) encapsulating PFK15 (1-(4-pyridinyl)-3-(2-quinolinyl)-2-propen-1-one) and myelin proteolipid protein (PLP) were developed for prior studies and have demonstrated the production of antigen-specific adaptive T-cell responses in the brain, spleen, and lymph nodes of mice, suggesting that these formulations may be able to prevent neuronal inflammation in mice after TBI. The vaccine efficacy was further evaluated through the image analysis of immunohistochemically stained brain tissue sections from naive, saline, and paKG(PFK15+PLP) MPs or paKG(PFK15) MPs treated mice. Though microglia (Iba1), astrocytes (GFAP) and CD86 were visualized in this method, only Iba1 was found to be significantly reduced in the contralateral hemisphere for paKG(PFK15+PLP) MPs and paKG(PFK15) MPs groups when compared to naive (p=0.0373 and p=0.0186, respectively). However, the naive group also showed an unexpectedly high level of CD86 after thresholding (compared to the TBI groups), indicating flaws were present in the analysis pipeline. Challenges of the image analysis process included thresholding setting optimization, folded tissues, bubbles, and saturated punctate signal. These issues may have impacted data accuracy, underscoring the need for rigorous optimization of experimental techniques and imaging methodologies when evaluating the therapeutic potential of the vaccines in mitigating TBI-induced neuroinflammation. Thus, future analyses should consider microglial morphology and employ more accurate thresholding in FIJI/ImageJ to better measure cellular activation and the overall positive signal.

Artificial intelligence (AI) and machine learning (ML) algorithms are revolutionizing the field of healthcare by offering new opportunities for improved diagnosis and treatment planning. These technologies have the potential to transform the way medical professionals approach patient care by analyzing vast amounts of data, identifying patterns, and making predictions. This overview highlights the current state of research and development in the field of AI and ML for diagnosis and treatment planning, as well as explore the ethical benefits and challenges associated with their implementation.

In this comprehensive research, we have pursued a dual investigation within the scope of tissue engineering: firstly, to investigate the retention of nanoprobe siloxane emulsions in bio-compatible hydrogel matrices in order to be able to measure oxygen saturation within the hydrogel; secondly, to refine the design of 3D printed hydrogel molds to enhance structural integrity of hydrogels for cell encapsulation. We evaluated the retention capabilities of these nanoemulsions, tagged with fluorescent dyes, across varying concentrations, and further advanced the mold design to prevent hydrogel unraveling and ensure complete filling. The findings suggest pivotal implications for the application of these hydrogels in cell transplantation and set a methodological precedent for future empirical studies.