Neuro-molecular perspectives on long COVID-19 impacted cerebrovascular diseases - a role for dipeptidyl peptidase IV.

The coronavirus disease 2019 (COVID-19) has caused immense devastation globally with many outcomes that are now extending to its long-term sequel called long COVID. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infects not only lungs, but also the brain and heart in association with endothelial cell dysfunction, coagulation abnormalities, and thrombosis leading to cardio-cerebrovascular health issues. Fatigue, cognitive decline, and brain fog are common neurological symptoms in persisting long COVID. Neurodegenerative processes and SARS-CoV-2 infection manifest overlapping molecular mechanisms, such as cytokine dysregulation, inflammation, protein aggregation, mitochondrial dysfunction, and oxidative stress. Identifying the key molecules in these processes is of importance for prevention and treatment of this disease. In particular, Dipeptidyl peptidase IV (DPPIV), a multifunctional peptidase has recently drawn attention as a potential co-receptor for SARS-CoV-2 infection and cellular entry. DPPIV is a known co-receptor for some other COVID viruses including MERS-Co-V. DPPIV regulates the immune responses, obesity, glucose metabolism, diabetes, and hypertension that are associated with cerebrovascular manifestations including stroke. DPPIV likely worsens persisting COVID-19 by disrupting inflammatory signaling pathways and the neurovascular system. This review highlights the neurological, cellular and molecular processes concerning long COVID, and DPPIV as a potential key factor contributing to cerebrovascular dysfunctions following SARS-CoV-2 infection.

A Novel Approach for Free, Affordable, and Sustainable Microsurgery Laboratory Training for Low- and Middle-Income Countries: University of Wisconsin-Madison Microneurosurgery Laboratory Experience.

BACKGROUND AND OBJECTIVES: In low- and middle-income countries (LMICs), approximately 5 million essential neurosurgical operations per year remain unaddressed. When compared with high-income countries, one of the reasons for this disparity is the lack of microsurgery training laboratories and neurosurgeons trained in microsurgical techniques. In 2020, we founded the Madison Microneurosurgery Initiative to provide no-cost, accessible, and sustainable microsurgery training opportunities to health care professionals from LMICs in their respective countries. METHODS: We initially focused on enhancing our expertise in microsurgery laboratory training requirements. Subsequently, we procured a wide range of stereo microscopes, light sources, and surgical instrument sets, aiming to develop affordable, high-quality, and long-lasting microsurgery training kits. We then donated those kits to neurosurgeons across LMICs. After successfully delivering the kits to designated locations in LMICs, we have planned to initiate microsurgery laboratory training in these centers by providing a combination of live-streamed, offline, and in-person training assistance in their institutions. RESULTS: We established basic microsurgery laboratory training centers in 28 institutions across 18 LMICs. This was made possible through donations of 57 microsurgery training kits, including 57 stereo microscopes, 2 surgical microscopes, and several advanced surgical instrument sets. Thereafter, we organized 10 live-streamed microanastomosis training sessions in 4 countries: Lebanon, Paraguay, Türkiye, and Bangladesh. Along with distributing the recordings from our live-streamed training sessions with these centers, we also granted them access to our microsurgery training resource library. We thus equipped these institutions with the necessary resources to enable continued learning and hands-on training. Moreover, we organized 7 in-person no-cost hands-on microanastomosis courses in different institutions across Türkiye, Georgia, Azerbaijan, and Paraguay. A total of 113 surgical specialists successfully completed these courses. CONCLUSION: Our novel approach of providing microsurgery training kits in combination with live-streamed, offline, and in-person training assistance enables sustainable microsurgery laboratory training in LMICs.

Methods in Stroke Prevention in the Wisconsin Native American Population.

Native American individuals are more frequently affected by cerebrovascular diseases including stroke and vascular cognitive decline. The aim of this study was to determine stroke risk factors that are most prevalent in Wisconsin Native Americans and to examine how education at the community and individual level as well as intensive health wellness coaching may influence modification of stroke risk factors. Additionally, we will investigate the role novel stroke biomarkers may play in stroke risk in this population. This paper details the aims and methods employed in the "Stroke Prevention in the Wisconsin Native American Population" (clinicaltrials.gov identifier: NCT04382963) study including participant health assessments, clinical ultrasound exam of the carotid arteries, cognitive testing battery, and structure and execution of the coaching program.

Lumen segmentation using a Mask R-CNN in carotid arteries with stenotic atherosclerotic plaque.

In patients at high risk for ischemic stroke, clinical carotid ultrasound is often used to grade stenosis, determine plaque burden and assess stroke risk. Analysis currently requires a trained sonographer to manually identify vessel and plaque regions, which is time and labor intensive. We present a method for automatically determining bounding boxes and lumen segmentation using a Mask R-CNN network trained on sonographer assisted ground-truth carotid lumen segmentations. Automatic lumen segmentation also lays the groundwork for developing methods for accurate plaque segmentation, and wall thickness measurements in cases with no plaque. Different training schemes are used to identify the Mask R-CNN model with the highest accuracy. Utilizing a single-channel B-mode training input, our model produces a mean bounding box intersection over union (IoU) of 0.81 and a mean lumen segmentation IoU of 0.75. However, we encountered errors in prediction when the jugular vein is the most prominently visualized vessel in the B-mode image. This was due to the fact that our dataset has limited instances of B-mode images with both the jugular vein and carotid artery where the vein is dominantly visualized. Additional training datasets are anticipated to mitigate this issue.