Bone continuously adapts to its mechanical environment by structural reorganization to maintain mechanical strength. As the adaptive capabilities of bone are portrayed in its nano- and microstructure, the existence of dark and bright osteons with contrasting preferential collagen fiber orientation (longitudinal and oblique-angled, respectively) points at a required tissue heterogeneity that contributes to the excellent fracture resistance mechanisms in bone. Dark and bright osteons provide an exceptional opportunity to deepen our understanding of how nanoscale tissue properties influence and guide fracture mechanisms at larger length scales. To this end, a comprehensive structural, compositional, and mechanical assessment is performed using circularly polarized light microscopy, synchrotron nanocomputed tomography, focused ion beam/scanning electron microscopy, quantitative backscattered electron imaging, Fourier transform infrared spectroscopy, and nanoindentation testing. To predict how the mechanical behavior of osteons is affected by shifts in collagen fiber orientation, finite element models are generated. Fundamental disparities between both osteon types are observed: dark osteons are characterized by a higher degree of mineralization along with a higher ratio of inorganic to organic matrix components that lead to higher stiffness and the ability to resist plastic deformation under compression. On the contrary, bright osteons contain a higher fraction of collagen and provide enhanced ductility and energy dissipation due to lower stiffness and hardness.

We report in this study the in vivo biodistribution of ultra-small luminescent gold (Au) particles (∼1.5 nm core size; 17 kDa), so-called nanoclusters (NCs), stabilized by bidentate zwitterionic molecules in subcutaneous (s.c.) and orthotopic glioblastoma mice models. Particular investigations on renal clearance and tumor uptake were performed using highly sensitive advanced imaging techniques such as multi-elemental Laser-Induced Breakdown Spectroscopy (LIBS) imaging and in-line X-ray Synchrotron Phase Contrast Tomography (XSPCT). Results show a blood circulation time of 6.5 ± 1.3 min accompanied by an efficient and fast renal clearance through the cortex of the kidney with a 66% drop between 1 h and 5 h. With a similar size range, these Au NCs are 5 times more fluorescent than the well-described Au₂₅GSH₁₈ NCs in the near-infrared (NIR) region and present significantly stronger tumor uptake and retention illustrated by an in vivo s.c. tumor-to-skin ratio of 1.8 measured by non-invasive optical imaging and an ex vivo tumor-to-muscle of 6.1. This work highlights the pivotal role of surface coating in designing optimum Au NC candidates for cancer treatment.

Hard X-ray nanotomography enables 3D investigations of a wide range of samples with high resolution (<100 nm) with both synchrotron-based and laboratory-based setups. However, the advantage of synchrotron-based setups is the high flux, enabling time resolution, which cannot be achieved at laboratory sources. Here, the nanotomography setup at the imaging beamline P05 at PETRA III is presented, which offers high time resolution not only in absorption but for the first time also in Zernike phase contrast. Two test samples are used to evaluate the image quality in both contrast modalities based on the quantitative analysis of contrast-to-noise ratio (CNR) and spatial resolution. High-quality scans can be recorded in 15 min and fast scans down to 3 min are also possible without significant loss of image quality. At scan times well below 3 min, the CNR values decrease significantly and classical image-filtering techniques reach their limitation. A machine-learning approach shows promising results, enabling acquisition of a full tomography in only 6 s. Overall, the transmission X-ray microscopy instrument offers high temporal resolution in absorption and Zernike phase contrast, enabling in situ experiments at the beamline.

High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic segmentation. Filtering methods are often required for a detailed quantitative analysis. However, most filters induce blurring in the reconstructed tomograms. Here, machine learning (ML) techniques offer a powerful alternative to conventional filtering methods. In this article, we verify that a self-supervised denoising ML technique can be used in a very efficient way for eliminating noise from nanotomography data. The technique presented is applied to high-resolution nanotomography data and compared to conventional filters, such as a median filter and a nonlocal means filter, optimized for tomographic data sets. The ML approach proves to be a very powerful tool that outperforms conventional filters by eliminating noise without blurring relevant structural features, thus enabling efficient quantitative analysis in different scientific fields.

While advanced imaging strategies have improved the diagnosis of bone-related pathologies, early signs of bone alterations remain difficult to detect. The Covid-19 pandemic has brought attention to the need for a better understanding of bone micro-scale toughening and weakening phenomena. This study used an artificial intelligence-based tool to automatically investigate and validate four clinical hypotheses by examining osteocyte lacunae on a large scale with synchrotron image-guided failure assessment. The findings indicate that trabecular bone features exhibit intrinsic variability related to external loading, micro-scale bone characteristics affect fracture initiation and propagation, osteoporosis signs can be detected at the micro-scale through changes in osteocyte lacunar features, and Covid-19 worsens micro-scale porosities in a statistically significant manner similar to the osteoporotic condition. Incorporating these findings with existing clinical and diagnostic tools could prevent micro-scale damages from progressing into critical fractures.

Vivid structural colours in butterflies are caused by photonic nanostructures scattering light. Structural colours evolved for numerous biological signalling functions and have important technological applications. Optically, such structures are well understood, however insight into their development in vivo remains scarce. We show that actin is intimately involved in structural colour formation in butterfly wing scales. Using comparisons between iridescent (structurally coloured) and non-iridescent scales in adult and developing H. sara, we show that iridescent scales have more densely packed actin bundles leading to an increased density of reflective ridges. Super-resolution microscopy across three distantly related butterfly species reveals that actin is repeatedly re-arranged during scale development and crucially when the optical nanostructures are forming. Furthermore, actin perturbation experiments at these later developmental stages resulted in near total loss of structural colour in H. sara. Overall, this shows that actin plays a vital and direct templating role during structural colour formation in butterfly scales, providing ridge patterning mechanisms that are likely universal across lepidoptera.