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- Huffman, JE, et al.
(författare)
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Rare and low-frequency variants and their association with plasma levels of fibrinogen, FVII, FVIII, and vWF
- 2015
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Ingår i: Blood. - : American Society of Hematology. - 1528-0020 .- 0006-4971. ; 126:11, s. E19-E29
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Tidskriftsartikel (refereegranskat)abstract
- Twelve independent, novel, low-frequency (n = 2) and rare (n = 10) genetic variants were associated with fibrinogen, FVII, FVIII, or vWF. Nine were within previously associated genes, and 3 novel candidate genes (KCNT1, HID1, and KATNB1) were confined to cohorts of African ancestry.
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- Lu, HX, et al.
(författare)
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Combinatorial protein therapy of angiogenic and arteriogenic factors remarkably improves collaterogenesis and cardiac function in pigs
- 2007
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424. ; 104:29, s. 12140-12145
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Tidskriftsartikel (refereegranskat)abstract
- Establishment of functional and stable collaterals in the ischemic myocardium is crucial to restoring cardiac function after myocardial infarction. Here, we show that only dual delivery of a combination of angiogenic and arteriogenic factors to the ischemic myocardium could significantly reestablish stable collateral networks and improve myocardial perfusion and function. A combination of FGF-2 with PDGF-BB, two factors primarily targeting endothelial cells and vascular smooth muscle cells, remarkably promotes myocardial collateral growth and stabilizes the newly formed collateral networks, which significantly restore myocardial perfusion and function. Using various members of the PDGF family together with FGF-2 in an angiogenesis assay, we demonstrate that PDGFR-α is mainly involved in angiogenic synergism, whereas PDGFR-β mediates vessel stability signals. Our findings provide conceptual guidelines for the clinical development of proangiogenic/arteriogenic factors for the treatment of ischemic heart disease.
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- Sun, MY, et al.
(författare)
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Prediction of BAP1 Expression in Uveal Melanoma Using Densely-Connected Deep Classification Networks
- 2019
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 11:10
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Tidskriftsartikel (refereegranskat)abstract
- Uveal melanoma is the most common primary intraocular malignancy in adults, with nearly half of all patients eventually developing metastases, which are invariably fatal. Manual assessment of the level of expression of the tumor suppressor BRCA1-associated protein 1 (BAP1) in tumor cell nuclei can identify patients with a high risk of developing metastases, but may suffer from poor reproducibility. In this study, we verified whether artificial intelligence could predict manual assessments of BAP1 expression in 47 enucleated eyes with uveal melanoma, collected from one European and one American referral center. Digitally scanned pathology slides were divided into 8176 patches, each with a size of 256 × 256 pixels. These were in turn divided into a training cohort of 6800 patches and a validation cohort of 1376 patches. A densely-connected classification network based on deep learning was then applied to each patch. This achieved a sensitivity of 97.1%, a specificity of 98.1%, an overall diagnostic accuracy of 97.1%, and an F1-score of 97.8% for the prediction of BAP1 expression in individual high resolution patches, and slightly less with lower resolution. The area under the receiver operating characteristic (ROC) curves of the deep learning model achieved an average of 0.99. On a full tumor level, our network classified all 47 tumors identically with an ophthalmic pathologist. We conclude that this deep learning model provides an accurate and reproducible method for the prediction of BAP1 expression in uveal melanoma.
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