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Mitochondrial oxidative stress is a hallmark of cerebrovascular aging, leading to endothelial dysfunction. This study tests the hypothesis that increased mitochondrial oxidative stress contributes to age-related CMH susceptibility and evaluates the mitochondrial-targeted antioxidative peptide SS-31 (elamipretide) as a potential protective agent in an aged, hypertensive mouse model. Concurrently, we developed a high-throughput, machine learning\u2013driven imaging pipeline to enhance CMH quantification and facilitate the screening of anti-aging vasoprotective interventions. To detect CMHs, brain sections were labeled with diaminobenzidine (DAB) and digitized using a slide scanner-based imaging platform. We developed multiple quantification tools, including color space transformation for enhanced contrast separation and a supervised machine-learning approach utilizing a random forest algorithm to generate whole-brain 3D reconstructions and precisely localize CMHs. We optimized a semi-automated detection method integrating color space transformation and machine learning, benchmarking it against traditional manual counting and color deconvolution-based approaches. While SS-31 treatment did not significantly mitigate hypertension-induced CMH burden in aged mice, our high-throughput imaging pipeline provided a reliable, scalable, and unbiased approach to CMH detection, reducing processing time while improving accuracy. This methodological advancement paves the way for future preclinical studies evaluating therapeutic strategies for cerebrovascular protection in aging. Our findings underscore the need for multi-targeted interventions to mitigate CMH-related neurovascular impairments and prevent VCID.<\/jats:p>","DOI":"10.1007\/s11357-025-01634-5","type":"journal-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T01:24:09Z","timestamp":1743729849000},"page":"4871-4887","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Aging, mitochondrial dysfunction, and cerebral microhemorrhages: a preclinical evaluation of SS-31 (elamipretide) and development of a high-throughput machine learning-driven imaging pipeline for cerebromicrovascular protection therapeutic screening"],"prefix":"10.1007","volume":"47","author":[{"given":"Roland","family":"Patai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krish","family":"Patel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boglarka","family":"Csik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rafal","family":"Gulej","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghavendra Y.","family":"Nagaraja","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dorina","family":"Nagy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siva Sai","family":"Chandragiri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santny","family":"Shanmugarama","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kiana Vali","family":"Kordestan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Nagykaldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shoba","family":"Ekambaram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5053-2828","authenticated-orcid":false,"given":"Anna","family":"Ungvari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andriy","family":"Yabluchanskiy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Tarantini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoltan","family":"Benyo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Csiszar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zoltan","family":"Ungvari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Nyul-Toth","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"1634_CR1","doi-asserted-by":"publisher","first-page":"934","DOI":"10.1001\/jamaneurol.2016.1017","volume":"73","author":"S Akoudad","year":"2016","unstructured":"Akoudad S, Wolters FJ, Viswanathan A, de Bruijn RF, van der Lugt A, Hofman A, Koudstaal PJ, Ikram MA, Vernooij MW. 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