Dados do Trabalho

Título

Study of Multiple Sclerosis by Fourier-Transform Infrared vibrational spectroscopy technique

Resumo

Multiple Sclerosis (MS) is a chronic, immune-mediated, inflammatory and degenerative disease characterized by demyelinating lesions of the central nervous system. The development of new methods that can extract biochemical information from biological fluids and promote an effective and rapid diagnosis is of great importance to improve patient management and treatment. The vibrational infrared spectroscopy technique with Fourier transform (ATR-FTIR) has been approached as an alternative for the identification of biomarkers and diagnosis in several areas. The present study aims to distinguish individuals with MS from the control group by ATR-FTIR spectra through pattern recognition (PR). The sample consisted of serum obtained from fifteen control subjects (mean age 35.0±13.7; 11 females) and fifteen subjects with relapsing-remitting MS (mean age 32.0±11.7; 10 females), from the Neurology outpatient clinic of the Cassiano Antonio Moraes University Hospital, following the 2017 McDonald's criteria. The EDSS score in subjects with MS ranged from 1 to 7 (mean 1.8±1.5) and the number of outbreaks from 1 - 8 (mean 3± 1.5). FTIR spectra were obtained from 10 uL pipetted onto an aluminum plate and dried for at least 2 hours in triplicate. The spectra were preprocessed and normalized to selected regions. For PR, Principal Component Analysis (PCA) and Unsupervised Random Forest (URF) methods were used. Using the PCA method, it was possible to observe a tendency to distinguish between the groups, but with a greater number of combinations up to the main component 4 (total spectrum 89.22%; high number of waves 98.68%; fingerprint 86.46% ). Then, the URF method was performed, where it was possible to distinguish samples from the EM and control groups both in the regions of high number of waves (48.35%), fingerprint (47.84%) and total spectral (62.86%) with 3 main components. Combinations of regions (high number of waves and fingerprint) by the URF method were performed, with a distinction between the samples (50.42%). The results showed a distinction between the two groups, which suggests the presence of structures in the data capable of biologically distinguish between MS and the control group. This indicates a high potential of the ATR-FTIR technique associated with machine learning methods for the study and diagnosis of MS. Ethical approval: (CAAE n.44387521.8.0000.5071).

Palavras Chave

Multiple Sclerosis; ATR-FTIR; Serum

Área

Neuroimunologia