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Objectives Investigate the biochemistry of in vivo healthy oral tissues through Raman spectroscopy. We aimed to characterize the biochemical features of healthy condition in oral subsites (buccal mucosa, lip, tongue, and gingiva) of healthy subjects. More specifically, we investigated Raman spectral characteristics and biochemical content of in vivo healthy tissues on Brazilian population. This characterization can be used to better define normal tissue and improve the detection of oral premalignant conditions in future studies. Materials and methods For spectroscopic analysis a Raman spectrometer (Kaiser Optical Systems imaging spectrograph Holospec, f / 1.8i-NIR) coupled with a laser 785 nm, 60 mW was used. Raman measurements were obtained by means of an optical fiber (EMVision fiber optic probe) coupled between the laser and the spectrometer. Three spectra per site were acquired from the lip, buccal mucosa, tongue, and gingiva of ten healthy volunteers. This resulted in 30 spectra per oral sub-site and in total 120 spectra.Results We report detailed biochemical information on these subsites and their relative composition based on deconvolution studies of their spectra. Finally, we also report classification efficiency of 61, 83, 41, and 93% for buccal, gingiva, lip, and tongue respectively after applying multivariate statistical tools. Conclusions We quantitated the contribution of various biochemicals in terms of percentage, and this will enable comparison not only across anatomical sites but also across studies. Raman spectroscopy can rapidly probe tissue biochemistry of healthy oral regions. Moreover, the study suggests the possibility of using Raman spectroscopy combined with signal processing and multivariate analysis methods to differentiate the oral sites in healthy conditions and compare with pathological conditions in future studies. Clinical relevance The spectral characterization of the healthy condition of oral tissues by a noninvasive, label-free, and real-time analytical techniques is important to create a spectral reference for future diagnosis of pathological conditions.

Ischemic stroke is a neurovascular disorder caused by reduced or blockage of blood flow to the brain, which may permanently affect motor and cognitive abilities. The diagnostic of stroke is performed using imaging technologies, clinical evaluation, and neuropsychological protocols, but no blood test is available yet. In this work, we analyzed amino acid concentrations in blood plasma from poststroke patients in order to identify differences that could characterize the stroke etiology. Plasma concentrations of sixteen amino acids from patients with chronic ischemic stroke (n = 73) and the control group (n = 16) were determined using gas chromatography coupled to mass spectrometry (GC-MS). The concentration data was processed by Partial Least Squares-Discriminant Analysis (PLS-DA) to classify patients with stroke and control. The amino acid analysis generated a first model able to discriminate ischemic stroke patients from control group. Proline was the most important amino acid for classification of the stroke samples in PLS-DA, followed by lysine, phenylalanine, leucine, and glycine, and while higher levels of methionine and alanine were mostly related to the control samples. The second model was able to discriminate the stroke subtypes like atherothrombotic etiology from cardioembolic and lacunar etiologies, with lysine, leucine, and cysteine plasmatic concentrations being the most important metabolites. Our results suggest an amino acid biosignature for patients with chronic stroke in plasma samples, which can be helpful in diagnosis, prognosis, and therapeutics of these patients.

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