Combined information from Raman spectroscopy and optical coherence tomography for enhanced diagnostic accuracy in tissue discrimination

We thank the UK EPSRC for funding, the CR-UK/EPSRC/MRC/DoH (England) imaging programme, the European Union project FAMOS (FP7 ICT, contract no. 317744) and the European Union project IIIOS (FP7/2007-2013, contract no. 238802). We thank Tayside Tissue Bank for providing us with the tissue samples under request number TR000289. K.D. is a Royal Society-Wolfson Merit Award Holder.Optical spectroscopy and imaging methods have proved to have potential to discriminate between normal and abnormal tissue types through minimally invasive procedures. Raman spectroscopy and Optical Coherence Tomography (OCT) provides chemical and morphological information of tissues respectively, which are complementary to each other. When used individually they might not be able to obtain high enough sensitivity and specificity that is clinically relevant. In this study we combined Raman spectroscopy information with information obtained from OCT to enhance the sensitivity and specificity in discriminating between Colonic Adenocarcinoma from Normal Colon. OCT being an imaging technique, the information from this technique is conventionally analyzed qualitatively. To combine with Raman spectroscopy information, it was essential to quantify the morphological information obtained from OCT. Texture analysis was used to extract information from OCT images, which in-turn was combined with the information obtained from Raman spectroscopy. The sensitivity and specificity of the classifier was estimated using leave one out cross validation (LOOCV) method where support vector machine (SVM) was used for binary classification of the tissues. The sensitivity obtained using Raman spectroscopy and OCT individually was 89% and 78% respectively and the specificity was 77% and 74% respectively. Combining the information derived using the two techniques increased both sensitivity and specificity to 94% demonstrating that combining complementary optical information enhances diagnostic accuracy. These results demonstrate that a multimodal approach using Raman-OCT would be able to enhance the diagnostic accuracy for identifying normal and cancerous tissue types.Publisher PD

Impact of copper cyanide on the key metabolic enzymes of freshwater fish Catla catla (Hamilton)

Short term toxicity experiments were conducted to study the effect of metal cyanide complex (copper cyanide) on the key metabolic enzymes viz., lactate dehydrogenase (LDH), succinate dehydrogenase (SDH), glucose-6 phosphate dehydrogenase (G6PDH), aspartate amino transferase (AST) alanine amino transferase (ALT), acid phosphatase (AcP) and alkaline phosphatase (ALP) activity in Catla catla juveniles. A total of 60 fingerlings were (2±0.5 cm; 1.5±0.2 g) exposed to two sublethal concentrations (0.253 and 0.152 mg/L) for a period of 15 days. Copper cyanide had significant (P> 0.05) effect on the key metabolic enzymes, the highest activities were observed in the group exposed to 0.253 mg/L. Results suggest that metal cyanide complex significantly altered enzyme activities of fish in both the sublethal concentrations

Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset

The authors thank the UK EPSRC the CR-UK/EPSRC/MRC/DoH (England) imaging Program and the European Union project FAMOS (FP7 ICT, contract no. 317744) for funding, K.D. is a Royal Society-Wolfson Merit Award Holder.Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical.Publisher PD

Label-free haemogram using wavelength modulated Raman spectroscopy for identifying immune-cell subset

Leucocytes in the blood of mammals form a powerful protective system against a wide range of dangerous pathogens. There are several types of immune cells that has specific role in the whole immune system. The number and type of immune cells alter in the disease state and identifying the type of immune cell provides information about a person's state of health. There are several immune cell subsets that are essentially morphologically identical and require external labeling to enable discrimination. Here we demonstrate the feasibility of using Wavelength Modulated Raman Spectroscopy (WMRS) with suitable machine learning algorithms as a label-free method to distinguish between different closely lying immune cell subset. Principal Component Analysis (PCA) was performed on WMRS data from single cells, obtained using confocal Raman microscopy for feature reduction, followed by Support Vector Machine (SVM) for binary discrimination of various cell subset, which yielded an accuracy >85%. The method was successful in discriminating between untouched and unfixed purified populations of CD4+CD3+ and CD8+CD3+ T lymphocyte subsets, and CD56+CD3- natural killer cells with a high degree of specificity. It was also proved sensitive enough to identify unique Raman signatures that allow clear discrimination between dendritic cell subsets, comprising CD303+CD45+ plasmacytoid and CD1c+CD141+ myeloid dendritic cells. The results of this study clearly show that WMRS is highly sensitive and can distinguish between cell types that are morphologically identical

Classifying scotch whisky from near-infrared Raman spectra with a radial basis function network with relevance learning

The instantaneous assessment of high-priced liquor products with minimal sample volume and no special preparation is an important task for quality monitoring and fraud detection. In this contribution the automated classification of Raman spectra acquired with a special optofluidic chip is performed with the use of a number of Artificial Neural Networks. A standard Radial Basis Function Network is adopted to incorporate relevance learning and showed robust classification performance across classification tasks. The acquired relevance weighting per feature dimension can be used to reduce the number of features while retaining a high level of accuracy

Combined information from Raman spectroscopy and optical coherence tomography for enhanced diagnostic accuracy in tissue discrimination

Optical spectroscopy and imaging methods have proved to have potential to discriminate between normal and abnormal tissue types through minimally invasive procedures. Raman spectroscopy and Optical Coherence Tomography (OCT) provides chemical and morphological information of tissues respectively, which are complementary to each other. When used individually they might not be able to obtain high enough sensitivity and specificity that is clinically relevant. In this study we combined Raman spectroscopy information with information obtained from OCT to enhance the sensitivity and specificity in discriminating between Colonic Adenocarcinoma from Normal Colon. OCT being an imaging technique, the information from this technique is conventionally analyzed qualitatively. To combine with Raman spectroscopy information, it was essential to quantify the morphological information obtained from OCT. Texture analysis was used to extract information from OCT images, which in-turn was combined with the information obtained from Raman spectroscopy. The sensitivity and specificity of the classifier was estimated using leave one out cross validation (LOOCV) method where support vector machine (SVM) was used for binary classification of the tissues. The sensitivity obtained using Raman spectroscopy and OCT individually was 89% and 78% respectively and the specificity was 77% and 74% respectively. Combining the information derived using the two techniques increased both sensitivity and specificity to 94% demonstrating that combining complementary optical information enhances diagnostic accuracy. These results demonstrate that a multimodal approach using Raman-OCT would be able to enhance the diagnostic accuracy for identifying normal and cancerous tissue types

The role of LiO₂ solubility in O₂ reduction in aprotic solvents and its consequences for Li-O₂ batteries

When lithium–oxygen batteries discharge, ​O2 is reduced at the cathode to form solid ​Li2O2. Understanding the fundamental mechanism of ​O2 reduction in aprotic solvents is therefore essential to realizing their technological potential. Two different models have been proposed for ​Li2O2 formation, involving either solution or electrode surface routes. Here, we describe a single unified mechanism, which, unlike previous models, can explain ​O2 reduction across the whole range of solvents and for which the two previous models are limiting cases. We observe that the solvent influences ​O2 reduction through its effect on the solubility of LiO2, or, more precisely, the free energy of the reaction LiO2* ⇌ Li(sol)+ + O2−(sol) + ion pairs + higher aggregates (clusters). The unified mechanism shows that low-donor-number solvents are likely to lead to premature cell death, and that the future direction of research for lithium–oxygen batteries should focus on the search for new, stable, high-donor-number electrolytes, because they can support higher capacities and can better sustain discharge.</p