Rahat Ullah

Analysis of dengue infection based on Raman spectroscopy and support vector machine (SVM).

The current study presents the use of Raman spectroscopy combined with support vector machine (SVM) for the classification of dengue suspected human blood sera. Raman spectra for 84 clinically dengue suspected patients acquired from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study.The spectral differences between dengue positive and normal sera have been exploited by using effective machine learning techniques. In this regard, SVM models built on the basis of three different kernel functions including Gaussian radial basis function (RBF), polynomial function and linear functionhave been employed to classify the human blood sera based on features obtained from Raman Spectra.The classification model have been evaluated with the 10-fold cross validation method. In the present study, the best performance has been achieved for the polynomial kernel of order 1. A diagnostic accuracy of about 85% with the precision of 90%, sensitivity of 73% and specificity of 93% has been achieved under these conditions.

Raman spectroscopy based discrimination of NS1 positive and negative dengue virus infected serum

This study is intended to develop a multivariate statistical model for the prediction of nonstructural protein 1 (NS1) in dengue virus (DENV) infected blood serum in humans. The model has been developed on the basis of partial least squares regression using the Raman spectra of NS1 positive and NS1 negative samples. Human blood sera of 218 subjects is included in this study, of which 95 were NS1 positive and 123 were NS1 negative, which was confirmed with the enzyme linked immunosorbent assay method. For model development, 80 NS1 positive and 98 NS1 negative samples were used, while 40 DENV suspected samples were used for double blind testing of the model. This selection of samples was performed by the code in an automatic manner to avoid biasing. A laser at 785 nm was used as the excitation source to acquire Raman spectra of samples with an integration time of 15 s. The multivariate model yields coefficients of regression at corresponding Raman shifts. These coefficients represent changes in the molecular structures associated with NS1 positive and negative samples. The analysis of the regression coefficients which differentiate NS1 positive and NS1 negative groups shows an increasing trend for phosphatidylinositol, ceramide, and amide-III, and a decreasing trend for thiocyanate in the DENV infected serum. The R-squared value of the model was found to be 0.91, which is clinically acceptable. The blind testing of 40 suspected samples yields an accuracy, sensitivity, and specificity of about 100% each.

Random Forest-Based Evaluation of Raman Spectroscopy for Dengue Fever Analysis:

This work presents the evaluation of Raman spectroscopy using random forest (RF) for the analysis of dengue fever in the infected human sera. A total of 100 dengue suspected blood samples, collected from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study. Out of these samples, 45 were dengue-positive based on immunoglobulin M (IgM) capture enzyme-linked immunosorbent assay (ELISA) tests. For highlighting the spectral differences between normal and infected samples, an effective machine learning system is developed that automatically learns the pattern of the shift in spectrum for the dengue compared to normal cases and thus is able to predict the unknown class based on the known example. In this connection, dimensionality reduction has been performed with the principal component analysis (PCA), while RF is used for automatic classification of dengue samples. For the determination of diagnostic capabilities of Raman spectroscopy based on RF, sensitivity, specificity, and accuracy have been calculated in comparison to normally performed IgM capture ELISA. According to the experiment, accuracy of 91%, sensitivity of 91%, and specificity of 91% were achieved for the proposed RF-based model.

Evaluation of Raman spectroscopy in comparison to commonly performed dengue diagnostic tests

Abstract. This study demonstrates the evaluation of Raman spectroscopy as a rapid diagnostic test in comparison to commonly performed tests for an accurate detection of dengue fever in human blood sera. Blood samples of 104 suspected dengue patients collected from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study. Out of 104 samples, 52 (50%) were positive based on immunoglobulin G (IgG), whereas 54 (52%) were positive based on immunoglobulin M (IgM) antibody tests. For the determination of the diagnostic capabilities of Raman spectroscopy, accuracy, sensitivity, specificity and false positive rate have been calculated in comparison to normally performed IgM and IgG captured enzyme-linked immunosorbent assay tests. Accuracy, precision, specificity, and sensitivity for Raman spectroscopy in comparison to IgM were found to be 66%, 70%, 72%, and 61%, whereas based on IgG they were 47%, 46%, 52%, and 43%, respectively.

Identification of cow and buffalo milk based on Beta carotene and vitamin-A concentration using fluorescence spectroscopy

The current study presents the application of fluorescence spectroscopy for the identification of cow and buffalo milk based on β-carotene and vitamin-A which is of prime importance from the nutritional point of view. All samples were collected from healthy animals of different breeds at the time of lactation in the vicinity of Islamabad, Pakistan. Cow and buffalo milk shows differences at fluorescence emission appeared at band position 382 nm, 440 nm, 505 nm and 525 nm both in classical geometry (right angle) setup as well as front face fluorescence setup. In front face fluorescence geometry, synchronous fluorescence emission shows clear differences at 410 nm and 440 nm between the milk samples of both these species. These fluorescence emissions correspond to fats, vitamin-A and β-carotene. Principal Component Analysis (PCA) further highlighted these differences by showing clear separation between the two data sets on the basis of features obtained from their fluorescence emission spectra. These results indicate that classical geometry (fixed excitation wavelength) as well as front face (synchronous fluorescence emission) of cow and buffalo milk nutrients could be used as fingerprint from identification point of view. This same approach can effectively be used for the determination of adulterants in the milk and other dairy products.

Lactate based optical screening of dengue virus infection in human sera using Raman spectroscopy

This study presents the screening of dengue virus (DENV) infection in human blood sera based on lactate concentration using Raman spectroscopy. A total of 70 samples, 50 from confirmed DENV infected patients and 20 from healthy volunteers have been used in this study. Raman spectra of all these samples have been acquired in the spectral range from 600 cm-1 to 1800 cm-1 using a 532 nm laser as an excitation source. Spectra of all these samples have been analyzed for assessing the biochemical changes resulting from infection. In DENV infected samples three prominent Raman peaks have been found at 750, 830 and 1450 cm-1. These peaks are most probably attributed to an elevated level of lactate due to an impaired function of different body organs in dengue infected patients. This has been proven by an addition of lactic acid solution to the healthy serum in a controlled manner. By the addition of lactic acid solution, the intense Raman bands at 1003, 1156 and 1516 cm-1 found in the spectrum of healthy serum got suppressed when the new peaks appeared around 750, 830, 925, 950, 1123, 1333, 1450, 1580 and 1730 cm-1. The current study predicts that lactate may possibly be a potential biomarker for the diagnosis of DENV infection.

Raman Spectroscopy Combined with Principal Component Analysis for Screening Nasopharyngeal Cancer in Human Blood Sera

This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220–1275, and 1524 cm−1, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.

Computer assisted optical screening of human ovarian cancer using Raman spectroscopy

Conventional screening tools for ovarian cancer such as cancer antigen (CA-125) and trans-pelvic ultrasound have poor sensitivity and specificity, indicating the need for better and more reliable screening methodologies. Here, we investigate the capability of Raman spectroscopy as a screening technique for ovarian cancer. Raman spectra from the blood serum of healthy control and ovarian cancer subjects were measured. Highly significant Raman shifts (p<0.0001) and intensity variations were observed in the cancer group as compared to the healthy group. These spectral differences were exploited by support vector machine classifier towards computer assisted classification. Calculated evaluation metrics such as sensitivity (=90), specificity (=100), positive predictive value (=100) and negative predictive value (=87.5) for such classification indicated that these results are promising, with potential future application of Raman spectroscopy for ovarian cancer screening.

Raman spectroscopy-based screening of IgM positive and negative sera for dengue virus infection

A statistical method based on Raman spectroscopy for the screening of immunoglobulin M (IgM) in dengue virus (DENV) infected human sera is presented. In total, 108 sera samples were collected and their antibody indexes (AI) for IgM were determined through enzyme-linked immunosorbent assay (ELISA). Raman spectra of these samples were acquired using a 785 nm wavelength excitation laser. Seventy-eight Raman spectra were selected randomly and unbiasedly for the development of a statistical model using partial least square (PLS) regression, while the remaining 30 were used for testing the developed model. An R-square (r 2) value of 0.929 was determined using the leave-one-sample-out (LOO) cross validation method, showing the validity of this model. It considers all molecular changes related to IgM concentration, and describes their role in infection. A graphical user interface (GUI) platform has been developed to run a developed multivariate model for the prediction of AI of IgM for blindly tested samples, and an excellent agreement has been found between model predicted and clinically determined values. Parameters like sensitivity, specificity, accuracy, and area under receiver operator characteristic (ROC) curve for these tested samples are also reported to visualize model performance.

Analysis of Tuberculosis Disease through Raman Spectroscopy and Machine Learning

We present the effectiveness of Raman spectroscopy (RS) in combination with machine learning for screening and analysis of blood sera collected from tuberculosis patients. Blood samples of 60 patients have confirmed active pulmonary tuberculosis and 14 samples of healthy age matched control were used in the current study. Spectra from entire sera samples were acquired using 785 nm laser Raman system. Support Vector Machine (SVM) together with Principal Component Analysis (PCA) has been used for highlighting variations spectral intensities between healthy and pathological samples. SVM model using Gaussian radial basis is able to discriminate between healthy and diseased patients based on the differences in the concentration of essential biomolecules such as lactate, β-carotene, and amide-I. Diagnostic accuracy of 92%, with precision, specificity and sensitivity of 95%, 98% and 81%, respectively, were achieved considering PC3 and PC4. Automatic analysis of the variations in the concentration of these molecules together with chemometrics can effectively be utilized for an early screening of tuberculosis through minimum invasion.