The object of this paper is in vivo study of skin spectral‐characteristics in patients with kidney failure by conventional Raman spectroscopy in near infrared region. The experimental dataset was subjected to discriminant analysis with the projection on latent structures (PLS‐DA). Application of Raman spectroscopy to investigate the forearm skin in 85 adult patients with kidney failure (90 spectra) and 40 healthy adult volunteers (80 spectra) has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS‐DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm−1. In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost‐effective and accurate detection of kidney failure and associated metabolic changes in the skin.
Changes in the lens appear by the age of 2 months in early aging OXYS rats, at the age of 6 months these changes are detected in 100% animals, and by 12 months both eyes are involved. According to biomicroscopy findings, predominant forms of cataract are vesicular, annular, and dendritic, located in the cortical layer and/or nucleus of the lens. Light and electron microscopy showed morphological changes involving all structural components of the lens: epithelial degeneration progressing with age, deformation of fiber architectonics, and appearance of uneven condensations. Cataract in OXYS rats corresponds to senile cataract, which makes this rat strain a unique model for studies of the pathogenesis of involutive cataract and elaboration of methods for its prevention and correction.
The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm-1, 724 cm-1, 1094 cm-1 and 1393 cm-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.
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