Nuclear factor erythroid 2-related factor 2 (Nrf2) deficiency causes age-dependent progression of female osteoporosis.

BACKGROUND: Nuclear factor erythroid 2-related factor 2 (Nrf2) is a crucial transcription factor for cellular redox homeostasis. The association of Nrf2 with elderly female osteoporotic has yet to be fully described. The aim was to elucidate a potential age-dependent Nrf2 contribution to female osteoporosis in mice. METHODS: Eighteen female wild type (WT) and 16 Nrf2-knockout (KO) mice were sacrificed at different ages (12 weeks = young mature adult and 90 weeks = old) to analyze their femurs. The morphological properties (trabecular and cortical) were evaluated by micro-computed tomography (µCT) and compared to gold standard histochemistry analysis. The quasi-static compression tests were performed to calculate the mechanical properties of bones. Additionally, the population of bone resorbing cells and aromatase expression by osteocytes was immunohistochemically evaluated and empty osteocyte lacunae was counted in cortical bone. RESULTS: Old Nrf2-KO mice revealed a significantly reduced trabecular bone mineral density (BMD), cortical thickness, cortical area, and bone fraction compared to old WT mice, regardless of no significant difference in skeletally mature young adult mice between WT and KO. Specifically, while all old WT mice showed thin metaphyseal trabeculae, trabecular bone was completely absent in 60% of old KO mice. Additionally, old KO mice showed significantly more osteoclast-like cells and fewer aromatase-positive osteocytes than WT mice, whereas the occurrence of empty osteocyte lacunae did not differ between both groups. Nrf2-KO mice further showed an age-dependently reduced fracture resilience compared to age-matched WT mice. CONCLUSION: Our results suggest that chronic Nrf2 loss can lead to age-dependent progression of female osteoporosis.

Deep Learning for Size-Agnostic Inverse Design of Random-Network 3D Printed Mechanical Metamaterials.

Practical applications of mechanical metamaterials often involve solving inverse problems aimed at finding microarchitectures that give rise to certain properties. The limited resolution of additive manufacturing techniques often requires solving such inverse problems for specific specimen sizes. Moreover, the candidate microarchitectures should be resistant to fatigue and fracture. Such a multi-objective inverse design problem is formidably difficult to solve but its solution is the key to real-world applications of mechanical metamaterials. Here, a modular approach titled "Deep-DRAM" that combines four decoupled models is proposed, including two deep learning (DL) models, a deep generative model based on conditional variational autoencoders, and direct finite element (FE) simulations. Deep-DRAM integrates these models into a framework capable of finding many solutions to the posed multi-objective inverse design problem based on random-network unit cells. Using an extensive set of simulations as well as experiments performed on 3D printed specimens, it is demonstrate that: 1) the predictions of the DL models are in agreement with FE simulations and experimental observations, 2) an enlarged envelope of achievable elastic properties (e.g., rare combinations of double auxeticity and high stiffness) is realized using the proposed approach, and 3) Deep-DRAM can provide many solutions to the considered multi-objective inverse design problem.

Spiral Honeycomb Microstructured Bacterial Cellulose for Increased Strength and Toughness.

Natural materials, such as nacre and silk, exhibit both high strength and toughness due to their hierarchical structures highly organized at the nano-, micro-, and macroscales. Bacterial cellulose (BC) presents a hierarchical fibril structure at the nanoscale. At the microscale, however, BC nanofibers are distributed randomly. Here, BC self-assembles into a highly organized spiral honeycomb microstructure giving rise to a high tensile strength (315 MPa) and a high toughness value (17.8 MJ m-3), with pull-out and de-spiral morphologies observed during failure. Both experiments and finite-element simulations indicate improved mechanical properties resulting from the honeycomb structure. The mild fabrication process consists of an in situ fermentation step utilizing poly(vinyl alcohol), followed by a post-treatment including freezing-thawing and boiling. This simple self-assembly production process is highly scalable, does not require any toxic chemicals, and enables the fabrication of light, strong, and tough hierarchical composite materials with tunable shape and size.