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Ovchinnikova, O.

Publications and source records attributed to Ovchinnikova, O..

2 recordsLinked to original sources

Fgf23 expression increases atherosclerotic plaque burden in male ApoE deficient mice

IntroductionComponents of both the innate and adaptive immune system impact on arterial walls in atherosclerosis. Fibroblast growth factor-23 (FGF23) is a phosphate regulating hormone linked to cardiovascular disease (CVD) in patients with and without chronic renal disease. However, it remains controversial whether FGF23 is merely a biomarker or contributes to CVD. Here, we overexpressed FGF23 in ApoE-/- mice to delineate the role of FGF23 in atherogenesis. Methods and Results10-week old ApoE-/- mice received a hydrodynamic tail vein with a plasmid encoding for Fgf23, and were sacrificed 10 weeks later. Fgf23 concentrations increased more than 400-fold in the Fgf23 treated group, remaining high throughout the experiment. Mice in the Fgf23 group developed hypophosphatemia, secondary hyperparathyroidism and a moderate increase in plasma creatinine concentrations. Male ApoE-/- mice exposed to high Fgf23 developed larger atherosclerotic lesions compared to controls, in two different locations of aorta, whereas no differences in plaque burden were seen between female ApoE-/- mice and controls. Serum IL-6 concentrations were increased in the Fgf23 group, associated with a vascular inflammatory response of recruited macrophages and neutrophils, and with a shift of CD4+ T effector cells from Th1 to Th17 and migration of lymphocytes to the spleen. ConclusionFgf23 increases the atherosclerotic burden in male ApoE-/- mice and alters both the innate immune system and T cell subpopulations, generating an inflammatory environment that may promote adverse clinical outcomes associated with Fgf23 excess.

physiology↗

Deep Learning on Multimodal Chemical and Whole Slide Imaging Data for Predicting Prostate Cancer Directly from Tissue Images

Prostate cancer is one of the most common cancers globally and is the second most common cancer in the male population in the US. Here we develop a study based on correlating the H&E-stained biopsy data with MALDI mass-spectrometric imaging of the corresponding tissue to determine the cancerous regions and their unique chemical signatures, and variation of the predicted regions with original pathological annotations. We spatially register features obtained through deep learning from high-resolution optical micrographs of whole slide H&E stained data with MSI data to correlate the chemical signature with the tissue anatomy of the data, and then use the learned correlation to predict prostate cancer from observed H&E images using trained co-registered MSI data. We found that this system is more robust than predicting from a single imaging modality and can predict cancerous regions with[~] 80% accuracy. Two chemical biomarkers were also found to be predicting the ground truth cancerous regions. This will improve on generating patient treatment trajectories by more accurately predicting prostate cancer directly from H&E-stained biopsy images.

cancer biology↗