Multivariate Statistical Analysis Tools Research Articles

A statistical method for source approtionment of soil pollution

The present study was conducted to evaluate the source apportionment of soil pollution and its impact on soil quality using multivariate statistical analysis tools. Multivariable statistical techniques such as cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and correlation offers superior interpretation of complicated data sets to better understand the soil quality. Soil samples were analyzed for physicochemical parameters (pH, temperature, electrical conductivity, organic carbon, moisture content, phosphate, potassium and sulphar) and for some important heavy metals (zinc, iron, manganese and copper). Sampling stations have been classified into three groups using Cluster Analysis (CA) in a convincing way. Results revealed that among all the sampling stations, three significantly different groups: (1) stations having highest pollution sources (HW and MW), (2) Moderate polluted area (SW and IW) & (3) control site (VW) of the study stretch. The PCA generated 2 significant factors having eigenvalues >1 which explain 82.78% of the variance in the dataset. Each soil quality parameter with strong correlation coefficient value was considered to be significant (p<0.01). Therefore, the present study revealed that mining activities, dumping of municipal sewage, disposal of biomedical waste and industrial effluent including solid waste (e.g fly ash) have greater influence on the soil quality. Results also showed that the concentrations of heavy metals (Cu, Mn and Zn) in highly polluted soil are beyond the permissible limit. The pollutant levels were significantly different in the three groups of soil pollution character samples, which were confirmed by ANOVA analysis.

Nuclear Magnetic Resonance-Based Metabolomics Approach Revealed the Intervention Effect of Using Complementary and Alternative Medicine (CAM) by CKD Patients.

Chronic kidney disease (CKD) is the end point of a number of systemic chronic diseases. The prevalence of CKD is increasing worldwide and recent epidemiological studies are showing the high prevalence of renal failure in CKD patients using complementary and alternative medicines (CAMs). Clinicians believe that biochemical profiles of CKD patients using CAM (referred here to as CAM-CKD) may be different compared to those on standard clinical treatment and should be managed differently. The present study aims to explore the potential of the NMR-based metabolomics approach to reveal the serum metabolic disparity between CKD and CAM-CKD patients with respect to normal control (NC) subjects and if the differential metabolic patterns can provide rationale for the efficacy and safety of standard and/or alternative therapies. Serum samples were obtained from 30 CKD patients, 43 CAM-CKD patients, and 47 NC subjects. The quantitative serum metabolic profiles were measured using 1D 1H CPMG NMR experiments performed at 800 MHz NMR spectrometer. The serum metabolic profiles were compared using various multivariate statistical analysis tools available on MetaboAnalyst (freely available web-based software) such as partial least-squares discriminant analysis (PLS-DA) and random forest (a machine learning) classification method. The discriminatory metabolites were identified based on variable importance in projection (VIP) statistics and further evaluated for statistical significance (i.e., p < 0.05) using either Student t-test or ANOVA statistics. PLS-DA models were capable of clustering CKD and CAM-CKD with considerably high values of Q 2 and R 2. Compared to CAM-CKD patients, the sera of CKD patients were characterized by (a) elevated levels of urea, creatinine, citrate, glucose, glycerol, and phenylalanine and phenylalanine-to-tyrosine ratio (PTR) and (b) decreased levels of various amino acids (such leucine, isoleucine, valine, and alanine), high-density lipoproteins, lactate, and acetate. These changes suggested that CKD patients manifest severe oxidative stress, hyperglycemia (with dampened glycolysis), increased protein energy wasting, and reduced lipid/membrane metabolism. Statistically significant and strong positive correlation of PTR with serum creatinine levels suggested the role of oxidative stress in the progression of kidney disease. Significant differences in metabolic patterns between CKD and CAM-CKD patients were observed. With respect to NC subjects, the serum metabolic changes were more aberrant in CKD patients compared to CAM-CKD patients. The aberrant metabolic changes in CKD patients with manifestations of higher oxidative stress compared to CAM-CKD patients could explain clinical discrepancies between CKD and CAM-CKD patients and further advocate the use of different treatment strategies for CKD and CAM-CKD patients.

Chemical Imaging – Is an Image Always Worth a Thousand Spectra?

Chemical images can be described as distribution maps that correlate the chemical information of an element or molecule, such as mass-to-charge ratio (m/z) or wavelength, with its intensity and/or concentration in a given sample. These images are usually obtained by mass spectrometry (MS) or optical spectroscopy techniques, where hundreds or thousands of spectra are initially acquired and dedicated image processing software is employed to construct and edit the final pictures, as well as selecting and annotating regions of interest in a sample, performing calibration procedures, etc. Mass spectrometry imaging (preferably abbreviated as MSI, to distinguish it from ion mobility spectrometry – IMS) is currently the most employed chemical imaging strategy, as can be noticed in the most recently published papers. Depending on the selected ionization technique, molecular or elemental images can be acquired. For molecular MSI, the classical matrix-assisted laser desorption/ionization (MALDI) is generally applied for imaging lipids, peptides and proteins, and the ambient ionization technique desorption electrospray ionization (DESI) is commonly applied for visualizing lipid distribution. In terms of elemental MSI, laser ablation inductively coupled plasma (LA-ICP) is undoubtedly the technique of choice, although nano-secondary ion mass spectrometry (nanoSIMS) can also be applied. Considering optical spectroscopy, the main techniques used nowadays are Raman and near-infrared radiation – NIR – spectroscopy for molecular imaging, and Synchrotron radiation X-ray fluorescence – SRXRF – and laser-induced breakdown spectroscopy – LIBS – for elemental imaging. Amongst these techniques, the best spatial resolutions are generally achieved by SRXRF (elemental imaging) and Raman spectroscopy (molecular imaging). Analytical chemistry advances in chemical imaging allow the acquisition of images with high spatial resolution, which is particularly interesting when studying specific regions or cell structures in a biological sample. For instance, in a Parkinson’s disease model, LA-ICP-MS images with good spatial resolution make the distinction of specific mouse brain regions possible and, consequently, the association of metal ion concentrations to each region,1 which is a relevant result considering micro-local metal speciation in neurodegenerative diseases. Nevertheless, there are some drawbacks in chemical imaging that demand further analytical development, such as the long analysis time and the lack of certified reference materials for quantitative analysis and method validation, as well as open-source software with advanced multivariate statistical analysis tools. Another obstacle to overcome concerns the integration of elemental and molecular imaging results. Since 2009, when one of the first review articles regarding the combination of these imaging approaches in a synergistic way was proposed by Becker and Jakubowski,2 until more recently described in reviews from 20203 and 2021,4 it has been possible to realize that there is still much work to be done in this field. This is mostly due to the fact that each imaging technique provides different spatial resolutions, making image superposition difficult, and also the absence of software and algorithms that allow the integration of different data sets in order to obtain trustworthy results and produce relevant study hypotheses. Besides that, the instrumentation for chemical imaging is rather costly and usually research groups are specialized in either molecular or elemental imaging. With these considerations, it is important to emphasize that the community involved in chemical imaging research should focus not only on the quality of the generated images in terms of resolution but also, if they are indeed worth a thousand spectra, on interpretation of the initial questions in a deep and holistic manner. After all, the main objective of chemical imaging is that the images represent how the process in question (disease, treatment, contamination, genetic modification, etc.) locally affects the system (biological, environmental, pharmaceutical sample) under study and, then, provide solutions for solving problems in different areas, such as forensic, environmental and life sciences.

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach.

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89–1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

Comparative Metabolite Profiling of Wheat Cultivars (Triticum aestivum) Reveals Signatory Markers for Resistance and Susceptibility to Stripe Rust and Aluminium (Al3+) Toxicity.

Plants continuously produce essential metabolites that regulate their growth and development. The enrichment of specific metabolites determines plant interactions with the immediate environment, and some metabolites become critical in defence responses against biotic and abiotic stresses. Here, an untargeted UHPLC-qTOF-MS approach was employed to profile metabolites of wheat cultivars resistant or susceptible to the pathogen Puccinia striiformis f. sp. tritici (Pst) and Aluminium (Al3+) toxicity. Multivariate statistical analysis (MVDA) tools, viz. principal component analysis (PCA) and hierarchical cluster analysis (HiCA) were used to qualify the correlation between the identified metabolites and the designated traits. A total of 100 metabolites were identified from primary and secondary metabolisms, including phenolic compounds, such as flavonoid glycosides and hydroxycinnamic acid (HCA) derivatives, fatty acids, amino acids, and organic acids. All metabolites were significantly variable among the five wheat cultivars. The Pst susceptible cultivars demonstrated elevated concentrations of HCAs compared to their resistant counterparts. In contrast, ‘Koonap’ displayed higher levels of flavonoid glycosides, which could point to its resistant phenotype to Pst and Al3+ toxicity. The data provides an insight into the metabolomic profiles and thus the genetic background of Pst- and Al3+-resistant and susceptible wheat varieties. This study demonstrates the prospects of applied metabolomics for chemotaxonomic classification, phenotyping, and potential use in plant breeding and crop improvement.

New approaches for ecological adaptation study: from population genetics to landscape genomics

Ecological adaptation or local adaptation is one of the most important responses of species to the changing environment. Understanding the molecular mechanism of local adaptation provides a theoretical foundation for the protection and utilization of the species. Landscape genomics studies the interaction between the genetic variation of species and landscape characteristics on the genomic level which quantify the response of genetic variation caused by environmental changes at the genomic level, and can provide new insights for local adaptation research as compared with the classic population genetics methods. In this review, we first summarized series methods for local adaptation research, with particular emphasis on the introduction of generalized linear mixed models, multivariate statistical analysis, non-linear models, and calculation tools involved in genotype-environment associations (GEAs), and we also compared the advantages and disadvantages of these methods. Next, we introduced the newly developed method “risk of non-adaptedness (RONA)”, which evaluates the current and predicts future adaptability of species by combining the allele frequency and the changes in environmental factors. Finally, using case studies of tree local adaptation, we provided suggestions for the future study directions on tree local adaptation.

Analysis of I-DESI dimensions of the digital economy development of the Russian Federation and EU-28 using multivariate statistics

This paper continues the authors’ earlier analysis, in which we used five principle dimensions of the International Digital Economy and Social Index (I-DESI) for the 28 countries of the EU and the Russian Federation to examine how Russia’s development relates to that of other EU countries. The aim of this paper is not to establish a ranking, but to determine the relationship between each dimension and the groups into which these 29 countries can be divided by multivariate statistical analysis tools and to analyze the group to which the Russian Federation belongs. We use Principal Component Analysis (PCA) to map our data to a lower-dimensional space (revealing two latent dimensions), and to analyze causal relations between the principal dimensions using partial correlation coefficients, concluding that two of the five main dimensions can be explained by three independent dimensions. Thereafter, we use cluster analysis to group our objects (i. e. the 29 countries) into clusters, and multidimensional scaling (MDS) to visualize the location of these groups and countries on the plane of the two components (from the PCA), focusing on the Russian Federation. According to our results, the Russian Federation can be classified as a moderately developed country in terms of its I-DESI score, but its location on the plane of principle components differs from the group of moderately developed EU-countries, forming a separate “group” on its own, largely owing to the unique characteristics of the country’s digital development.

Distinguishing NASH Histological Severity Using a Multiplatform Metabolomics Approach.

Nonalcoholic fatty liver disease (NAFLD) is categorized based on histological severity into nonalcoholic fatty liver (NAFL) or nonalcoholic steatohepatitis (NASH). We used a multiplatform metabolomics approach to identify metabolite markers and metabolic pathways that distinguish NAFL from early NASH and advanced NASH. We analyzed fasting serum samples from 57 prospectively-recruited patients with histologically-proven NAFLD, including 12 with NAFL, 31 with early NASH and 14 with advanced NASH. Metabolite profiling was performed using a combination of liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy analyzed with multivariate statistical and pathway analysis tools. We targeted 237 metabolites of which 158 were quantified. Multivariate analysis uncovered metabolite profile clusters for patients with NAFL, early NASH, and advanced NASH. Also, multiple individual metabolites were associated with histological severity, most notably spermidine which was more than 2-fold lower in advanced fibrosis vs. early fibrosis, in advanced NASH vs. NAFL and in advanced NASH vs. early NASH, suggesting that spermidine exercises a protective effect against development of fibrosing NASH. Furthermore, the results also showed metabolic pathway perturbations between early-NASH and advanced-NASH. In conclusion, using a combination of two reliable analytical platforms (LC-MS and NMR spectroscopy) we identified individual metabolites, metabolite clusters and metabolic pathways that were significantly different between NAFL, early-NASH, and advanced-NASH. These differences provide mechanistic insights as well as potentially important metabolic biomarker candidates that may noninvasively distinguish patients with NAFL, early-NASH, and advanced-NASH. The associations of spermidine levels with less advanced histology merit further assessment of the potential protective effects of spermidine in NAFLD.

Big Data Classification Efficiency Based on Linear Discriminant Analysis

The proliferation of online platforms recently has led to unprecedented increase in data generation; this has given rise to the concept of big data which characterizes data in terms of volume, velocity, variety, and veracity. One of the common multivariate statistical data analysis tools is linear discriminant analysis (LDA) which relies on the concept of obtaining the separation among groups through LDA. The prediction of the class of a given class of data points can be achieved through classification, a supervised learning technique but prior to a classification process, a classification model must first be built using classification algorithms. Several classification algorithms are available for prediction tasks. LDA is commonly used for the reduction of the dimensionality of datasets. In this article, the use of LDA to improve the classification performance of different classification model was presented.

Physicochemical Aspects and Sensory Profiles as Various Potential Factors for Comprehensive Quality Assessment of Nü-Er-Cha Produced from Rhamnus heterophylla Oliv.

Nü-Er-Cha, produced from the leaves of Rhamnus heterophylla Oliv., is known as an herbal tea and used in the treatment of bleeding, irregular menstruation and dysentery. A method was developed for the quality assessment of herbal tea, Nü-Er-Cha, adopting physical parameters, chemical constituents and sensory profiles as various potential factors. Their inner relationship was mined by multivariate statistical analysis tools, and the three factors were integrated by a technique for order preference by a similarity to ideal solution (TOPSIS) approach to comprehensively analyze the characters of Nü-Er-Cha. Viscosity was also introduced to the physical parameter determination besides conductivity, pH and color. Seven common peaks of eight batches of Nü-Er-Cha were marked by a high performance liquid chromatography (HPLC) fingerprint. They were further identified by HPLC mass spectrometry/mass spectrometry (HPLC-MS/MS) as hydroxybenzoic acids and flavanol glycosides. Fifty trained members participated in the sensory evaluation. Significant correlations between total sensory scores and conductivity, viscosity as well as pH were observed, a relatively innovative result for the quality assessment of herbal teas. The common peaks, belonging to hydroxybenzoic acids and flavanol glycosides, were mainly related to the color of infusions and leaves. The result of the TOPSIS analysis showed that S3 and S4 ranked as the top two in the comprehensive quality assessment. This may be related to rhamnetin triglycoside with a galactose/glucose and two rhamnoses, which had a higher peak response in S3 and S4 than that in the other samples. The present study may contribute to a better understanding of the relationship regarding physical properties, chemical composition and sensory profiles, and it may supply ideas for the comprehensive quality assessment of the herbal tea Nü-Er-Cha.

Spatial-multivariate statistical analyses to assess water quality for irrigation of the central part of Kuwait

Multivariate statistical analysis and ArcGIS tools have been used to assess groundwater quality in the central part of Kuwait. The chemical data for groundwater samples collected from 71 bore wells are assessed to determine their suitability for drinking and irrigation purposes using the irrigation parameter indices. The WATEQ4F program is used to investigate the degree of saturation of groundwater with respect to the minerals calcite, dolomite, aragonite, halite, gypsum, magnesite, and brucite. In multivariate statistical analysis, principal component analysis (PCA) and R-mode and Q-mode cluster analysis are used to classify the groundwater into different clusters. In the PCA, the cumulative variance percentages of the first three components are 89.5%, expressed for four main chemical compounds, of which sodium chloride and sodium sulphate are the first principal component (PC1)compounds, with a variance of 65%, followed by the carbonates and silicates in PC2 and PC3, which are 15% and 9%, respectively. The Q-mode cluster mean value analysis identified four distinct groundwater chemical types, in which cluster I is mainly NaCl and SiO2, followed by Na2SO4 in cluster II, and the concentration of HCO3− revealed that clusters III and IV were mainly carbonate groups. In addition, the TDS and the TH clusters' mean values decrease from cluster I to cluster IV, indicating that the quality of the groundwater improves sequentially from cluster I to cluster IV. The results reveal that the groundwater in the study area is not suitable for drinking, but it is suitable for irrigation purposes.

A data-driven algorithm for online detection of component and system faults in modern wind turbines at different operating zones

Advanced Fault Detection (FD) and isolation schemes are necessary to realize the required levels of reliability and availability and to minimize financial losses against failures. In particular, FD is essential in modern Wind Turbine Systems (WTSs) which are designed to generate electrical energy as efficiently and reliably as possible. This paper presents a practical FD framework using data-driven methods. The main objective is the early detection of involuntary abnormalities of various types and locations. Conventional methods are based on the exact model and/or signal patterns or hardware redundancy and they generally fail to address this issue. Alternatively, the presented algorithm is motivated by the availability of fast sensors and powerful computers yielding big data which can be explored to extract and exploit useful information. In a typical WTS, FD procedures face particular challenges attributed to high levels of measurement noise and sparse changes due to the fast dynamics as well as switching control and transients. In this scope, a minimum informative set of measured variables is proposed to describe accurately and completely the system behaviour under all operating conditions. Among data-based strategies, univariate and multivariate statistical analysis tools are recommended for this approach. Principal Component Analysis (PCA) is used in this paper for its distinguished capabilities of dimensionality reduction, features de-correlation, and noise rejection. Multi PCA models are trained as a statistical reference reflecting the data variability in local zones and used in parallel for online FD. An adaptive threshold scheme, based on a modified EWMA control chart, is also used to efficiently evaluate the resulting residuals, so the overall algorithm is robust to outliers and sensitive to small and sudden abnormalities. Static and dynamic applications are investigated for FD in modern WTSs under different operation zones. The considered abnormalities span faults having different levels of severity and range from sensors and actuators to system faults. Compared to existing methods in the literature, the proposed framework demonstrates potential applications with a broader utilization scope and promising performance.

Gastronomic Tourism: Attitudes, Motivations and Satisfaction of the Visitor in Cantons of Tungurahua, Ecuador

Gastronomic tourism is a new form of cultural tourism that seeks to satisfy the demand of a market segment related to the supply of food products. Regarding this purpose, Ecuador presents a mega diversity of flavors and natural products that reflect natural and cultural offering, particularly in gastronomy which has merged the Ecuadorian ancestral flavors with the techniques of contemporary cuisine. The purpose of this article is to characterize the segment of tourists and excursionists who are motivated to visit the cantons of Ambato, Banos and Mocha located in the province of Tungurahua due to their customs and gastronomic traditions. The methodology used is based on the philosophy of gastronomic marketing, especially, in the characterization of a set of socio-psychological variables related to the motivation and satisfaction of food and drink offerings in the selected cantons, by applying multivariate statistical and descriptive analysis tools. Among the conclusions of the study it is emphasized the need to develop a management strategy for the improvement in the visitor service delivery, the identification of new attractions, health conditions of the premises and service areas, as well as the timely marketing and promotion of the current gastronomic offering.

Influence of chemical and biochemical characteristics on the texture of Appenzeller® cheese

Texture properties are very important quality parameters of Appenzeller® cheese. The Appenzeller® Cheese Makers Association defines optimal texture quality as being characterised by a softness that lasts at least three months. During winter production, texture-related problems are sporadically encountered. In the present study, ten Appenzeller® cheese loaves with optimal texture properties aged for 90 days and ten loaves with dry and firm texture of the same age were selected and characterised through a uniaxial compression test and with physicochemical and biochemical methods. All cheeses had been produced in the same week during winter (January) in different cheese factories and ripened for three months. The aim of the study was to determine the main factors responsible for classification into the two groups based on univariate and multivariate statistical analysis tools. The three main parameters influencing the force at 33% deformation in this study were calcium, water, and free amino acids.

Stacking machine learning classifiers to identify Higgs bosons at the LHC

Machine learning (ML) algorithms have been employed in the problem of classifying signal and background events with high accuracy in particle physics. In this paper, we compare the performance of a widespread ML technique, namely, stacked generalization, against the results of two state-of-art algorithms: (1) a deep neural network (DNN) in the task of discovering a new neutral Higgs boson and (2) a scalable machine learning system for tree boosting, in the Standard Model Higgs to tau leptons channel, both at the 8 TeV LHC. In a cut-and-count analysis, stacking three algorithms performed around 16% worse than DNN but demanding far less computation efforts, however, the same stacking outperforms boosted decision trees. Using the stacked classifiers in a multivariate statistical analysis (MVA), on the other hand, significantly enhances the statistical significance compared to cut-and-count in both Higgs processes, suggesting that combining an ensemble of simpler and faster ML algorithms with MVA tools is a better approach than building a complex state-of-art algorithm for cut-and-count.

PWE-079 Raman Spectroscopycancer Diagnostic for Pathology of Barrett’s Oesophagus

Introduction Raman spectroscopy has been shown to accurately classify tissue pathology in a variety of conditions and organ systems. Much of this work has been performed using Raman microspectrometers on tissue sections. Despite the demonstrated potential as an accurate cancer diagnostic tool, Raman spectroscopy (RS) is yet to be adopted by the clinic for histopathology reviews. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles (e.g. tissue sample preparation, data collection, pre-processing and transferability) in its adoption for cancer diagnosis. SMART is a multicentre industry-academic collaboration with the aim of developing a pathology platform for advanced diagnosis, using developments in hardware and software. Renishaw’s Streamline™ Raman technology enables collection of Raman spectral much faster without compromising signal to noise. This study aims to assess the ability of this technique to accurately classify tissue pathology, using an oesophageal model. Methods Specimens were collected from patients with Barrett’s oesophagus (BO), dysplasia and adenocarcinoma, and snap frozen in liquid nitrogen. 8μm tissue sections were prepared onto calcium fluoride slides, with contiguous sections stained with haematoxylin and eosin (H&E) for histological comparison. Raman spectra were collected across homogeneous regions of tissue pathology, using Streamline™ acquisitions of 60 seconds/line, at 1.1μm spatial resolution. Results Advanced multivariate statistical analysis tools were used to develop pathology classification models, which were then tested using leave-one-out cross-validation. Each sample was then classified using a ‘voting classification’ for all pixels from one sample. The sensitivity and specificity of this pathology classification model using Raman Spectroscopy to discriminate dysplasia/adenocarcinoma from Barrett’s oesophagus produced sensitivity and specificities >80%. Conclusion By combining multivariate statistical analysis with Streamline™ Raman acquisition of spectral data, we have demonstrated good sensitivities and specificities. This study illustrates the potential of non-invasive rapid Raman spectral mapping measurements and development of a robust and validated oesophageal classification model that are able to classify tissue pathology. Disclosure of Interest None Declared

Raman Spectroscopy-Based Cancer Diagnostic Platform For Pathology Classification In Barrett’s Oesophagus And Its Integration Into Clinic

Introduction/ Background Raman spectroscopy (RS) has been shown to accurately classify tissue pathology in a variety of conditions and organ systems. Much of this work has been performed using Raman microspectrometers on tissue sections. Despite the demonstrated potential as an accurate cancer diagnostic tool, RS is yet to be adopted by the clinic for histopathology review. The Stratified Medicine through Advanced Raman Technologies (SMART) consortium has begun to address some of the hurdles (e.g. tissue sample preparation, data collection, pre-processing and transferability) in its adoption for cancer diagnosis. SMART is a multicentre industry-clinical-academic collaboration with the aim of developing a pathology platform for advanced diagnosis, using developments in hardware and software. Renishaw’s Streamline™ Raman imaging technology enables the collection of Raman spectra much faster without compromising signal to noise Aims This study aims to assess the ability of this technique to accurately classify tissue pathology, using an oesophageal tissue model. This demonstrates the project’s mission to deliver a robust Raman based diagnostic platform to enable clinical researchers to stage cancer, define tumour margin, build cancer diagnostic models and discover novel disease bio markers. Methods Tissue was collected from the oesophagus in patients undergoing endoscopy or resection. Specimens were collected from patients with Barrett’s oesophagus (BO), dysplasia and adenocarcinoma, and snap frozen in liquid nitrogen. 8μm tissue sections were placed onto calcium fluoride slides for spectroscopic measurement and with contiguous sections stained with haematoxylin and eosin (H&E) for histological comparison. Raman spectra were collected across homogeneous regions of tissue pathology, using Streamline™ acquisitions of 60 seconds/line, at 1.1μm spatial resolution. Classification models were constructed to discriminate pathology subtypes. Results Advanced multivariate statistical analysis tools were used to develop pathology classification models, which were then tested using leave-one-out cross-validation. Each sample was then classified using a ‘voting classification’ for all pixels from one sample. The sensitivity and specificity of this pathology classification model using RS to discriminate dysplasia/adenocarcinoma from BO produced sensitivity and specificities >80%. By combining multivariate statistical analysis with Streamline™ Raman acquisition of spectral data, we have demonstrated good sensitivities and specificities. This study illustrates the potential of non-invasive rapid Raman spectral mapping measurements and development of a robust and validated oesophageal classification model that are able to classify tissue pathology, providing a diagnostic tool for researchers and clinicians with potential application to other pathology and tissue types.

Airway Metabolic Anomalies in Adolescents with Bronchopulmonary Dysplasia: New Insights from the Metabolomic Approach

To assess a group of adolescents with bronchopulmonary dysplasia (BPD) from a biochemical-metabolic standpoint, applying the metabolomic approach to studying their exhaled breath condensate (EBC). Twenty adolescents with BPD (mean age 14.8 years) and 15 healthy controls (mean age 15.2 years) were recruited for EBC collection, exhaled nitric oxide measurement, and spirometry. The EBC samples were analyzed using a metabolomic approach based on mass spectrometry. The obtained spectra were analyzed using multivariate statistical analysis tools. A reliable Orthogonal Projections to Latent Structures-Discriminant Analysis model showed a clear discrimination between cases of BPD and healthy controls (R(2) = 0.95 and Q(2) = 0.92). The search for putative biomarkers identified an altered complex lipid profile in the adolescents with BPD. The metabolomic analysis of EBC distinguishes cases of BPD from healthy individuals, suggesting that the lung of survivors of BPD is characterized by long-term metabolic abnormalities. The search for putative biomarkers indicated a possible role of an altered surfactant composition, which may persist far beyond infancy.

Estimating multi-annual PM2.5 air pollution levels using sVOC soil tests: Ashkelon South, Israel as a case study

Background and aimsIn most developed countries, air pollution is monitored by stationary networks of air quality monitoring stations (AQMS). Due to high installation and maintenance costs, the number of such stations (especially those measuring particulate matter (PM) is often insufficient, placing limitations on the geographic coverage of air pollution monitoring and the accuracy of exposure assessment studies based on AQMS data. Semi-volatile organic compounds (sVOCs) often spread with PM and contribute to the PM composition when emitted into the atmosphere. As a result, sVOC concentrations in soil can help, as we hypothesize, to estimate average levels of PM air pollution in geographic areas in which PM monitoring by AQMS is sparse or unavailable. MethodsSoil samples taken around the Ashkelon-South industrial area in Israel were tested for the presence of sVOCs compounds using a standard EPA 8270C protocol. PM pollution data obtained from local AQMSs were then mutually compared with sVOC soil contamination levels observed in the same locations, using spatial interpolation methods and multivariate statistical analysis tools. ResultsPM levels in the atmosphere, estimated using sVOC soil concentrations, and adjusted for several locational attributes and average levels of other commonly monitored air pollutants, revealed a reasonably strong association with PM2.5 averages actually recorded by AQMS, helping to explain 86–88% of the observed PM variation (R2-Adj. = 0.859–0.879), depending on the model type. ConclusionsThe study confirms feasibility of obtaining reasonably accurate PM air pollution estimates using sVOC soil testing. While the costs of establishment and maintenance of a dense network of AQMS may be prohibitive, the proposed approach may help to obtain reasonably accurate PM air pollution estimates for geographic areas in which PM monitoring by AQMS is sparse or unavailable.

Methods and Applications of Raman Microspectroscopy to Single-Cell Analysis

Raman spectroscopy is a powerful biochemical analysis technique that allows for the dynamic characterization and imaging of living biological cells in the absence of fluorescent stains. In this review, we summarize some of the most recent developments in the noninvasive biochemical characterization of single cells by spontaneous Raman scattering. Different instrumentation strategies utilizing confocal detection optics, multispot, and line illumination have been developed to improve the speed and sensitivity of the analysis of single cells by Raman spectroscopy. To analyze and visualize the large data sets obtained during such experiments, sophisticated multivariate statistical analysis tools are necessary to reduce the data and extract components of interest. We highlight the most recent applications of single cell analysis by Raman spectroscopy and their biomedical implications that have enabled the noninvasive characterization of specific metabolic states of eukaryotic cells, the identification and characterization of stem cells, and the rapid identification of bacterial cells. We conclude the article with a brief look into the future of this rapidly evolving research area.