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- Kontos, C, et al.
(författare)
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Designed CXCR4 mimic acts as a soluble chemokine receptor that blocks atherogenic inflammation by agonist-specific targeting
- 2020
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 11:1, s. 5981-
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Tidskriftsartikel (refereegranskat)abstract
- Targeting a specific chemokine/receptor axis in atherosclerosis remains challenging. Soluble receptor-based strategies are not established for chemokine receptors due to their discontinuous architecture. Macrophage migration-inhibitory factor (MIF) is an atypical chemokine that promotes atherosclerosis through CXC-motif chemokine receptor-4 (CXCR4). However, CXCR4/CXCL12 interactions also mediate atheroprotection. Here, we show that constrained 31-residue-peptides (‘msR4Ms’) designed to mimic the CXCR4-binding site to MIF, selectively bind MIF with nanomolar affinity and block MIF/CXCR4 without affecting CXCL12/CXCR4. We identify msR4M-L1, which blocks MIF- but not CXCL12-elicited CXCR4 vascular cell activities. Its potency compares well with established MIF inhibitors, whereas msR4M-L1 does not interfere with cardioprotective MIF/CD74 signaling. In vivo-administered msR4M-L1 enriches in atherosclerotic plaques, blocks arterial leukocyte adhesion, and inhibits atherosclerosis and inflammation in hyperlipidemic Apoe−/− mice in vivo. Finally, msR4M-L1 binds to MIF in plaques from human carotid-endarterectomy specimens. Together, we establish an engineered GPCR-ectodomain-based mimicry principle that differentiates between disease-exacerbating and -protective pathways and chemokine-selectively interferes with atherosclerosis.
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- Li, W, et al.
(författare)
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Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL
- 2020
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Ingår i: NPJ breast cancer. - : Springer Science and Business Media LLC. - 2374-4677. ; 6:1, s. 63-
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Tidskriftsartikel (refereegranskat)abstract
- Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.
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- Gastounioti, A, et al.
(författare)
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External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women
- 2022
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:19
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Tidskriftsartikel (refereegranskat)abstract
- Despite the demonstrated potential of artificial intelligence (AI) in breast cancer risk assessment for personalizing screening recommendations, further validation is required regarding AI model bias and generalizability. We performed external validation on a U.S. screening cohort of a mammography-derived AI breast cancer risk model originally developed for European screening cohorts. We retrospectively identified 176 breast cancers with exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4963 controls from women with at least one-year negative follow-up. A risk score for each woman was calculated via the AI risk model. Age-adjusted areas under the ROC curves (AUCs) were estimated for the entire cohort and separately for White and Black women. The Gail 5-year risk model was also evaluated for comparison. The overall AUC was 0.68 (95% CIs 0.64–0.72) for all women, 0.67 (0.61–0.72) for White women, and 0.70 (0.65–0.76) for Black women. The AI risk model significantly outperformed the Gail risk model for all women p < 0.01 and for Black women p < 0.01, but not for White women p = 0.38. The performance of the mammography-derived AI risk model was comparable to previously reported European validation results; non-significantly different when comparing White and Black women; and overall, significantly higher than that of the Gail model.
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