The accumulation of amyloid-β peptide (Aβ) in the brain is a critical hallmark of Alzheimer's disease. This high cerebral Aβ concentration may be partly caused by impaired clearance of Aβ across the blood-brain barrier (BBB). The low-density lipoprotein receptor-related protein-1 (LRP-1) and the ATP-binding cassette (ABC) protein ABCB1 (P-glycoprotein) are involved in the efflux of Aβ across the BBB. We hypothesized that other ABC proteins, such as members of the G subfamily, are also involved in the BBB clearance of Aβ. We therefore investigated the roles of ABCG2 (BCRP) and ABCG4 in the efflux of [3H] Aβ1-40 from HEK293 cells stably transfected with human ABCG2 or mouse abcg4. We showed that ABCG2 and Abcg4 mediate the cellular efflux of [3H] Aβ1-40. In addition, probucol fully inhibited the efflux of [3H] Aβ1-40 from HEK293-abcg4 cells. Using the in situ brain perfusion technique, we showed that GF120918 (dual inhibitor of Abcb1 and Abcg2) strongly enhanced the uptake (Clup, μl/g/s) of [3H] Aβ1-40 by the brains of Abcb1-deficient mice, but not by the brains of Abcb1/Abcg2-deficient mice, suggesting that Abcg2 is involved in the transport of Aβ at the mouse BBB. Perfusing the brains of Abcb1/Abcg2- and Abca1-deficient mice with [3H] Aβ1-40 plus probucol significantly increased the Clup of Aβ. This suggests that a probucol-sensitive transporter that is different from Abca1, Abcb1, and Abcg2 is involved in the brain efflux of Aβ. We suggest that this probucol-sensitive transporter is Abcg4. We conclude that Abcg4 acts in concert with Abcg2 to efflux Aβ from the brain across the BBB.

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This article details the ESAFORM Benchmark 2021. The deep drawing cup of a 1 mm thick, AA 6016-T4 sheet with a strong cube texture was simulated by 11 teams relying on phenomenological or crystal plasticity approaches, using commercial or self-developed Finite Element (FE) codes, with solid, continuum or classical shell elements and different contact models. The material characterization (tensile tests, biaxial tensile tests, monotonic and reverse shear tests, EBSD measurements) and the cup forming steps were performed with care (redundancy of measurements). The Benchmark organizers identified some constitutive laws but each team could perform its own identification. The methodology to reach material data is systematically described as well as the final data set. The ability of the constitutive law and of the FE model to predict Lankford and yield stress in different directions is verified. Then, the simulation results such as the earing (number and average height and amplitude), the punch force evolution and thickness in the cup wall are evaluated and analysed. The CPU time, the manpower for each step as well as the required tests versus the final prediction accuracy of more than 20 FE simulations are commented. The article aims to guide students and engineers in their choice of a constitutive law (yield locus, hardening law or plasticity approach) and data set used in the identification, without neglecting the other FE features, such as software, explicit or implicit strategy, element type and contact model.