Multidisciplinary evaluation of the remineralization potential of three fluoride-based toothpastes on natural white spot lesions.

OBJECTIVES This in vitro study aimed assessing the remineralization potential of three commercial fluoride-based toothpastes in permanent teeth with natural white spot lesions (WSLs). A multidisciplinary approach based on Raman microspectroscopy (RMS), Scanning electron microscopy (SEM), Energy-dispersive x-ray spectroscopy (EDS), and Vickers microhardness (VMH) was exploited. METHODS N = 12 human molars with natural WSLs in the proximal-vestibular zone were selected and divided into 4 groups (n = 3) according to the different treatments: HAF (hydroxyapatite with fluoride ions); SMF (sodium monofluorophosphate with arginine); SF (sodium fluoride with enzymes), and CTRL (untreated group). All toothpastes tested contained 1450 ppm of fluoride. Teeth samples were submitted to the following protocol: a 7-day pH cycling treatment, with two daily exposures (2 min each time) to the commercial toothpastes described above. The surface micromorphology (SEM), the chemical/elemental composition (RMS and EDS), and the Vickers microhardness (VMH) were evaluated. Statistical analysis was performed. RESULTS A remarkable remineralization of WSLs in SEM images was observed in all treated groups compared to CTRL. In particular, HAF and SF displayed higher values of VMH, phosphates amount (I960), crystallinity (FWHM960), and lower ones of C/P (I1070/I960) with respect to CTRL. Intermediate values were found in SMF, higher than CTRL but lower with respect to HAF and SF. As regards the Ca/P ratio, statistically significant differences (p < 0.05) were found between SF and the other groups. CONCLUSIONS All the tested dentifrices have shown to remineralize the WSLs. SF and HAF have comparable capability in hardness recovery and crystallinity; however, SF shows the best remineralizing potential according to both micromorphological and chemical analyses. Clinical relevance The daily use of toothpastes containing hydroxyapatite partially replaced with fluoride, sodium monofluorophosphate with arginine and sodium fluoride toothpaste associated with enzymes represents a preventive, therapeutic, effective, and non-invasive tool for remineralize WSLs

Unraveling the biomechanical properties of collagenous tissues pathologies using synchrotron-based phase-contrast microtomography with deep learning

Mechanical stimuli are regulators not only in cells but also of the extracellular matrix activity, with special reference to collagen bundles composition, amount and distribution. Synchrotron-based phase-contrast computed tomography was widely demonstrated to resolve collagen bundles in 3D in several body districts and in both pre-clinical and clinical contexts. In this perspective study we hypothesized, supporting the rationale with synchrotron imaging experimental examples, that deep learning semantic image segmentation can better identify and classify collagen bundles compared to common thresholding segmentation techniques. Indeed, with the support of neural networks and deep learning, it is possible to quantify structures in synchrotron phase-contrast images that were not distinguishable before. In particular, collagen bundles can be identified by their orientation and not only by their physical densities, as was made possible using conventional thresholding segmentation techniques. Indeed, localised changes in fiber orientation, curvature and strain may involve changes in regional strain transfer and mechanical function (e.g., tissue compliance), with consequent pathophysiological implications, including developmental of defects, fibrosis, inflammatory diseases, tumor growth and metastasis. Thus, the comprehension of these kinetics processes can foster and accelerate the discovery of therapeutic approaches for the maintaining or re-establishment of correct tissue tensions, as a key to successful and regulated tissues remodeling/repairing and wound healing

Deep Learning for Microstructural Characterization of Synchrotron Radiation-Based Collagen Bundle Imaging in Peri-Implant Soft Tissues

The study of the organizational kinetics in the area surrounding the transmucosal part of dental implants promises to ensure an accurate diagnosis of the healing process, in terms of osseointegration and long-term implant success. In this demonstrative work, the morphological, qualitative and quantitative characteristics of 3D images of collagen bundles obtained by synchrotron-based high-resolution X-ray tomography were analyzed. Data analysis was performed using deep learning algorithms, neural networks that were applied on multiple volumes extracted from connective portions of different patients. The neural network was trained with mutually consistent examples from different patients; in particular, we used a neural network model, U-Net, well established when applying deep learning to datasets of images. It was trained not only to distinguish the collagen fibers from the background, but also to subdivide the collagen bundles based on the orientation of the fibers. In fact, differently from conventional thresholding methods, deep learning semantic segmentation assigns a label to each pixel, not only relying on grey level distribution but also on the image morphometric (shape or direction) characteristics. With the exception of Pt2 biopsies that, as confirmed by the polarized light investigation, were shown to present an immature tissue condition, the quantity, the anisotropy degree and the connectivity density of transverse bundles were always demonstrated to be higher than for longitudinal ones. These are interesting and new data; indeed, as collagen bundles are organized in an intertwining pattern, these morphometric and 3D complexity parameters, distinguished in transversal and longitudinal directions, give precise indications on the amount and distribution of connective tissue forces exerted during the healing process

Influence of Trabecular Geometry on Scaffold Mechanical Behavior and MG-63 Cell Viability

In a scaffold-based approach for bone tissue regeneration, the control over morphometry allows for balancing scaffold biomechanical performances. In this experimental work, trabecular geometry was obtained by a generative design process, and scaffolds were manufactured by vat photopolymerization with 60% (P60), 70% (P70) and 80% (P80) total porosity. The mechanical and biological performances of the produced scaffolds were investigated, and the results were correlated with morphometric parameters, aiming to investigate the influence of trabecular geometry on the elastic modulus, the ultimate compressive strength of scaffolds and MG-63 human osteosarcoma cell viability. The results showed that P60 trabecular geometry allows for matching the mechanical requirements of human mandibular trabecular bone. From the statistical analysis, a general trend can be inferred, suggesting strut thickness, the degree of anisotropy, connectivity density and specific surface as the main morphometric parameters influencing the biomechanical behavior of trabecular scaffolds, in the perspective of tissue engineering applications