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Hyperspectral interference tomography of nacre.

ABSTRACT: Structural characterization of biologically formed materials is essential for understanding biological phenomena and their enviro-nment, and for generating new bio-inspired engineering concepts. For example, nacre-the inner lining of some mollusk shells-encodes local environmental conditions throughout its formation and has exceptional strength due to its nanoscale brick-and-mortar structure. This layered structure, comprising alternating transparent aragonite (CaCO₃) tablets and thinner organic polymer layers, also results in stunning interference colors. Existing methods of structural characterization of nacre rely on some form of cross-sectional analysis, such as scanning or transmission electron microscopy or polarization-dependent imaging contrast (PIC) mapping. However, these techniques are destructive and too time- and resource-intensive to analyze large sample areas. Here, we present an all-optical, rapid, and nondestructive imaging technique-hyperspectral interference tomography (HIT)-to spatially map the structural parameters of nacre and other disordered layered materials. We combined hyperspectral imaging with optical-interference modeling to infer the mean tablet thickness and its disorder in nacre across entire mollusk shells from red and rainbow abalone (Haliotis rufescens and Haliotis iris) at various stages of development. We observed that in red abalone, unexpectedly, nacre tablet thickness decreases with age of the mollusk, despite roughly similar appearance of nacre at all ages and positions in the shell. Our rapid, inexpensive, and nondestructive method can be readily applied to in-field studies.

SUBMITTER: Salman J

PROVIDER: S-EPMC8053970 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionThe eye offers potential for the diagnosis of Alzheimer's disease (AD) with retinal imaging techniques being explored to quantify amyloid accumulation and aspects of neurodegeneration. To assess these changes, this proof-of-concept study combined hyperspectral imaging and optical coherence tomography to build a classification model to differentiate between AD patients and controls.MethodsIn a memory clinic setting, patients with a diagnosis of clinically probable AD (n = 10) or biomarker-proven AD (n = 7) and controls (n = 22) underwent non-invasive retinal imaging with an easy-to-use hyperspectral snapshot camera that collects information from 16 spectral bands (460-620 nm, 10-nm bandwidth) in one capture. The individuals were also imaged using optical coherence tomography for assessing retinal nerve fiber layer thickness (RNFL). Dedicated image preprocessing analysis was followed by machine learning to discriminate between both groups.ResultsHyperspectral data and retinal nerve fiber layer thickness data were used in a linear discriminant classification model to discriminate between AD patients and controls. Nested leave-one-out cross-validation resulted in a fair accuracy, providing an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [0.60-0.89]). Inner loop results showed that the inclusion of the RNFL features resulted in an improvement of the area under the receiver operating characteristic curve: for the most informative region assessed, the average area under the receiver operating characteristic curve was 0.70 (95% confidence interval [0.55, 0.86]) and 0.79 (95% confidence interval [0.65, 0.93]), respectively. The robust statistics used in this study reduces the risk of overfitting and partly compensates for the limited sample size.ConclusionsThis study in a memory-clinic-based cohort supports the potential of hyperspectral imaging and suggests an added value of combining retinal imaging modalities. Standardization and longitudinal data on fully amyloid-phenotyped cohorts are required to elucidate the relationship between retinal structure and cognitive function and to evaluate the robustness of the classification model.

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Hyperspectral interference tomography of nacre.

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