A Raman algorithm to estimate human age from protein structural variations in autopsy skin samples: a protein biological clock

Abstract The recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spect...

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Auteurs principaux: Daisuke Miyamori, Takeshi Uemura, Wenliang Zhu, Kei Fujikawa, Takaaki Nakaya, Satoshi Teramukai, Giuseppe Pezzotti, Hiroshi Ikegaya
Format: article
Langue:EN
Publié: Nature Portfolio 2021
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Accès en ligne:https://doaj.org/article/b3fd73f0f9dc45eab487f1b41e9ddbca
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Résumé:Abstract The recent increase of the number of unidentified cadavers has become a serious problem throughout the world. As a simple and objective method for age estimation, we attempted to utilize Raman spectrometry for forensic identification. Raman spectroscopy is an optical-based vibrational spectroscopic technique that provides detailed information regarding a sample’s molecular composition and structures. Building upon our previous proof-of-concept study, we measured the Raman spectra of abdominal skin samples from 132 autopsy cases and the protein-folding intensity ratio, RPF, defined as the ratio between the Raman signals from a random coil an α-helix. There was a strong negative correlation between age and RPF with a Pearson correlation coefficient of r = 0.878. Four models, based on linear (RPF), squared (RPF 2), sex, and RPF by sex interaction terms, were examined. The results of cross validation suggested that the second model including linear and squared terms was the best model with the lowest root mean squared error (11.3 years of age) and the highest coefficient of determination (0.743). Our results indicate that the there was a high correlation between the age and RPF and the Raman biological clock of protein folding can be used as a simple and objective forensic age estimation method for unidentified cadavers.