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Significance: People with Parkinson's disease (PD) experience changes in fine motor skills, which is viewed as one of the hallmark signs of this disease. Due to its non-invasive nature and portability, functional near-infrared spectroscopy (fNIRS) is a promising tool for assessing changes related to fine motor skills. Aim: We aim to compare activation patterns in the primary motor cortex using fNIRS, comparing volunteers with PD and sex- and age-matched control participants during a fine motor task and walking. Moreover, inter and intrahemispheric functional connectivity (FC) was investigated during the resting state. Approach: We used fNIRS to measure the hemodynamic changes in the primary motor cortex elicited by a finger-tapping task in 20 PD patients and 20 controls matched for age, sex, education, and body mass index. In addition, a two-minute walking task was carried out. Resting-state FC was also assessed. Results: Patients with PD showed delayed hypoactivation in the motor cortex during the fine motor task with the dominant hand and delayed hyperactivation with the non-dominant hand. The findings also revealed significant correlations among various measures of hemodynamic activity in the motor cortex using fNIRS and different cognitive and clinical variables. There were no significant differences between patients with PD and controls during the walking task. However, there were significant differences in interhemispheric connectivity between PD patients and control participants, with a statistically significant decrease in PD patients compared with control participants. Conclusions: Decreased interhemispheric FC and delayed activity in the primary motor cortex elicited by a fine motor task may one day serve as one of the many potential neuroimaging biomarkers for diagnosing PD.
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Excess fat in abdominal deposits is a risk factor for multiple conditions, including metabolic syndrome (MetS); lipid metabolism plays an essential role in these pathologies; fatty acid-binding proteins (FABPs) are dedicated to the cytosolic transport of fat. FABP4, whose primary source is adipose tissue, is released into the circulation, acting as an adipokine, while FABP5 also accompanies the adverse effects of MetS. FABP4 and 5 are potential biomarkers of MetS, but their behavior during syndrome evolution has not been determined. Raman spectroscopy has been applied as an alternative method to disease biomarker detection. In this work, we detected spectral changes related to FABP4 and 5 in the serum at different points of time, using an animal model of a high-fat diet-induced MetS. FABP4 and 5 spectral changes show a contribution during the evolution of MetS, which indicates alteration to a molecular level that predisposes to established MetS. These findings place FABPs as potential biomarkers of MetS and Raman spectroscopy as an alternative method for MetS assessment.
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Síndrome Metabólica , Animais , Síndrome Metabólica/metabolismo , Análise Espectral Raman , Fatores de Risco , Proteínas de Ligação a Ácido Graxo/metabolismo , BiomarcadoresRESUMO
This letter aims to reply to Bratchenko and Bratchenko's comment on our paper "Feasibility of Raman spectroscopy as a potential in vivo tool to screen for pre-diabetes and diabetes." Our paper analyzed the feasibility of using in vivo Raman measurements combined with machine learning techniques to screen diabetic and prediabetic patients. We argued that this approach yields high overall accuracy (94.3%) while retaining a good capacity to distinguish between diabetic (area under the receiver-operating curve [AUC] = 0.86) and control classes (AUC = 0.97) and a moderate performance for the prediabetic class (AUC = 0.76). Bratchenko and Bratchenko's comment focuses on the possible overestimation of the proposed classification models and the absence of information on the age of participants. In this reply, we address their main concerns regarding our previous manuscript.
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Diabetes Mellitus , Estado Pré-Diabético , Humanos , Estado Pré-Diabético/diagnóstico , Análise Espectral Raman/métodos , Estudos de Viabilidade , Diabetes Mellitus/diagnóstico , Aprendizado de MáquinaRESUMO
In this article, we investigated the feasibility of using Raman spectroscopy and multivariate analysis method to noninvasively screen for prediabetes and diabetes in vivo. Raman measurements were performed on the skin from 56 patients with diabetes, 19 prediabetic patients and 32 healthy volunteers. These spectra were collected along with reference values provided by the standard glycated hemoglobin (HbA1c) assay. A multiclass principal component analysis and support vector machine (PCA-SVM) model was created from the labeled Raman spectra and was validated through a two-layer cross-validation scheme. Classification accuracy of the model was 94.3% with an area under the receiver operating characteristic curve AUC of 0.76 (0.65-0.84) for the prediabetic group, 0.86 (0.71-0.93) for the diabetic group and 0.97(0.93-0.99) for the control group. Our results suggest the feasibility of using Raman spectroscopy for the classification of prediabetes and diabetes in vivo.
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Diabetes Mellitus , Estado Pré-Diabético , Diabetes Mellitus/diagnóstico , Estudos de Viabilidade , Humanos , Estado Pré-Diabético/diagnóstico , Análise de Componente Principal , Análise Espectral Raman/métodos , Máquina de Vetores de SuporteRESUMO
We show the spectra of advanced glycation products in response to recent comments made by Bratchenko et al. Our results suggest that information retrieved by Raman spectroscopy is relevant to screening diabetic patients, however, the comparison carried out in our paper, between ANN and SVM, was not fair, because of the erroneous PCA selection procedure and different sources of variation present in the analysis.
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Type 2 diabetes mellitus (DM2) is one of the most widely prevalent diseases worldwide and is currently screened by invasive techniques based on enzymatic assays that measure plasma glucose concentration in a laboratory setting. A promising plan of action for screening DM2 is to identify molecular signatures in a non-invasive fashion. This work describes the application of portable Raman spectroscopy coupled with several supervised machine-learning techniques, to discern between diabetic patients and healthy controls (Ctrl), with a high degree of accuracy. Using artificial neural networks (ANN), we accurately discriminated between DM2 and Ctrl groups with 88.9-90.9% accuracy, depending on the sampling site. In order to compare the ANN performance to more traditional methods used in spectroscopy, principal component analysis (PCA) was carried out. A subset of features from PCA was used to generate a support vector machine (SVM) model, albeit with decreased accuracy (76.0-82.5%). The 10-fold cross-validation model was performed to validate both classifiers. This technique is relatively low-cost, harmless, simple and comfortable for the patient, yielding rapid diagnosis. Furthermore, the performance of the ANN-based method was better than the typical performance of the invasive measurement of capillary blood glucose. These characteristics make our method a promising screening tool for identifying DM2 in a non-invasive and automated fashion.
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A four stage semi-pilot scale RFR reactor with ceramic disks as support for TiO2 modified with silver particles was developed for the removal of organic pollutants. The design presented in this article is an adaptation of the rotating biological reactors (RBR) and its coupling with the modified catalyst provides additional advantages to designs where a catalyst in suspension is used. The optimal parameter of rotation was 54 rpm and the submerged surface of the disks offer a total contact area of 387 M². The modified solid showed a decrease in the value of its bandgap compared to commercial titanium. The system has a semi-automatic operation with a maximum reaction time of 50 h. Photo-activity tests show high conversion rates at low concentrations. The results conform to the Langmuir heterogeneous catalysis model.
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Prata/química , Titânio/química , Purificação da Água/instrumentação , Catálise , Cinética , Luz , Oxirredução , Processos Fotoquímicos , Propriedades de Superfície , Termodinâmica , Eliminação de Resíduos Líquidos/instrumentaçãoRESUMO
Liver fibrosis is a pathological process that can escalate to cirrhosis and then liver failure, a major public health concern that affect hundreds of millions of people in both developed and developing countries. Detection of liver fibrosis during its earlier stages is a matter of great importance which may allow prevention of development of cirrhosis in patients with chronic liver disease. In this work, Raman spectroscopy and thermography were evaluated to detect early pathological signs of liver fibrosis in rats in which liver fibrosis was induced using carbon tetrachloride. Results show that Raman spectra of healthy and fibrotic livers significantly differ among each other and can be classified by principal component analysis and discriminant analysis. The PCA-LDA method has a sensitivity of 100%, specificity 85% and diagnostic accuracy of 93.5%. Thermography also revealed characteristic temperature patterns for fibrotic livers compared to healthy livers. Current data suggest that Raman spectroscopy and thermography could be used to detect fibrosis in ex vivo liver samples.