Raman Spectroscopy for Adipose Tissue Assessment in Rat Models of Obesity and Type 1 Diabetes

Adipose tissue presents structural and functional changes in obesity and type 1 diabetes mellitus (T1DM). In obesity, the size and number of adipocytes and adipokine secretion increases. In T1DM, a loss of adipose tissue suggests changes in the metabolic activity of this tissue. A significant challenge is to find alternative noninvasive methods to evaluate molecular changes in adipose tissue related to obesity and T1DM. Recently, Raman spectroscopy and chemometrics techniques have emerged as a tool for biological tissue analysis. In this work, we propose the use of Raman spectroscopy to characterize spectral differences in adipose tissue from different rat groups (control, obese, and T1DM). The Raman spectra were analyzed using direct band analysis, ratiometric analysis, and chemometric methods (principal component analysis (PCA) and support vector machines (SVMs)). We found that the Raman spectra of obese rats showed significant spectral differences compared to control and diabetic groups related to fatty acids Raman bands. Also, the obese group has a significant decrease in the degree of unsaturation of lipids. The PCA–SVM models showed classification performance ranging from 71.43% to 71.79% accuracy for brown and white adipose tissue samples, respectively. In conclusion, the results demonstrate that Raman spectroscopy can be used as a nondestructive method to assess adipose tissue according to a metabolic condition.

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Possibility of Human Gender Recognition Using Raman Spectra of Teeth

Molecules ◽

10.3390/molecules26133983 ◽

2021 ◽

Vol 26 (13) ◽

pp. 3983

Author(s):

Ozren Gamulin ◽

Marko Škrabić ◽

Kristina Serec ◽

Matej Par ◽

Marija Baković ◽

...

Keyword(s):

Raman Spectra ◽

Principal Component ◽

Support Vector ◽

Gender Recognition ◽

Proof Of Concept ◽

Male And Female ◽

Tooth Type ◽

Tooth Apex ◽

The Difference

Gender determination of the human remains can be very challenging, especially in the case of incomplete ones. Herein, we report a proof-of-concept experiment where the possibility of gender recognition using Raman spectroscopy of teeth is investigated. Raman spectra were recorded from male and female molars and premolars on two distinct sites, tooth apex and anatomical neck. Recorded spectra were sorted into suitable datasets and initially analyzed with principal component analysis, which showed a distinction between spectra of male and female teeth. Then, reduced datasets with scores of the first 20 principal components were formed and two classification algorithms, support vector machine and artificial neural networks, were applied to form classification models for gender recognition. The obtained results showed that gender recognition with Raman spectra of teeth is possible but strongly depends both on the tooth type and spectrum recording site. The difference in classification accuracy between different tooth types and recording sites are discussed in terms of the molecular structure difference caused by the influence of masticatory loading or gender-dependent life events.

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A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

Applied Sciences ◽

10.3390/app11041742 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1742

Author(s):

Ignacio Rodríguez-Rodríguez ◽

José-Víctor Rodríguez ◽

Wai Lok Woo ◽

Bo Wei ◽

Domingo-Javier Pardo-Quiles

Keyword(s):

Diabetes Mellitus ◽

Machine Learning ◽

Type 1 Diabetes ◽

Feature Selection ◽

Blood Glucose ◽

Type 1 Diabetes Mellitus ◽

Support Vector ◽

Chronic Hyperglycemia ◽

Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

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Raman spectroscopy-based label-free cell identification using wavelet transform and support vector machine

RSC Advances ◽

10.1039/c6ra01004k ◽

2016 ◽

Vol 6 (55) ◽

pp. 50027-50033 ◽

Cited By ~ 3

Author(s):

S. Bakhtiaridoost ◽

H. Habibiyan ◽

S. Muhammadnejad ◽

M. Haddadi ◽

H. Ghafoorifard ◽

...

Keyword(s):

Support Vector Machine ◽

Raman Spectroscopy ◽

Wavelet Transform ◽

Raman Spectra ◽

Free Cell ◽

Support Vector ◽

Label Free ◽

Cell Identification ◽

Rare Cells

Wavelet transform and SVM applied to Raman spectra makes a powerful and accurate tool for identification of rare cells such as CTCs.

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Analysis of immune cell components and immune-related gene expression profiles in peripheral blood of patients with type 1 diabetes mellitus

10.21203/rs.3.rs-512609/v1 ◽

2021 ◽

Author(s):

Jian Lin ◽

Yuanhua Lu ◽

Bizhou Wang ◽

Ping Jiao ◽

Jie Ma

Keyword(s):

Gene Expression ◽

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Type 1 Diabetes Mellitus ◽

Nk Cells ◽

Immune Cells ◽

Support Vector ◽

Hub Genes ◽

Immune Related Genes

Abstract Background Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM. Methods The raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO) - support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM. Results The 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM. Conclusions The immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

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Immunomodulatory and protective effects of adipose tissue-derived mesenchymal stem cells in an allograft islet composite transplantation for experimental autoimmune type 1 diabetes

Immunology Letters ◽

10.1016/j.imlet.2017.05.006 ◽

2017 ◽

Vol 188 ◽

pp. 21-31 ◽

Cited By ~ 16

Author(s):

Jamal Mohammadi Ayenehdeh ◽

Bahare Niknam ◽

Shima Rasouli ◽

Seyed Mahmoud Hashemi ◽

Hossein Rahavi ◽

...

Keyword(s):

Stem Cells ◽

Type 1 Diabetes ◽

Mesenchymal Stem Cells ◽

Adipose Tissue ◽

Protective Effects ◽

Experimental Autoimmune

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Reduced brown adipose tissue‐associated skin temperature following cold stimulation in children and adolescents with type 1 diabetes

Pediatric Diabetes ◽

10.1111/pedi.13163 ◽

2020 ◽

Author(s):

James M. Law ◽

David E. Morris ◽

Lindsay Robinson ◽

Tabitha Randell ◽

Louise Denvir ◽

...

Keyword(s):

Type 1 Diabetes ◽

Adipose Tissue ◽

Brown Adipose Tissue ◽

Children And Adolescents ◽

Skin Temperature ◽

Cold Stimulation ◽

Brown Adipose

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On predicting glycaemia in type 1 diabetes mellitus patients by using support vector machines

Proceedings of the 1st International Conference on Internet of Things and Machine Learning - IML '17 ◽

10.1145/3109761.3158404 ◽

2017 ◽

Author(s):

Ignacio Rodríguez-Rodríguez ◽

Miguel Ángel Zamora ◽

José-Victor Rodríguez

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Support Vector Machines ◽

Type 1 Diabetes Mellitus ◽

Support Vector ◽

Vector Machines

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Cultivar Discrimination of Single Alfalfa (Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis

Sensors ◽

10.3390/s20226575 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6575

Author(s):

Lingjie Yang ◽

Zuxin Zhang ◽

Xiaowen Hu

Keyword(s):

Multivariate Analysis ◽

Multispectral Imaging ◽

Principal Component ◽

Nondestructive Method ◽

Morphological Features ◽

Support Vector ◽

Linear Discrimination ◽

Vector Machines ◽

Alfalfa Seed ◽

Medicago Sativa L

Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.

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