Multivariate Data Analysis Approach Research Articles

HVO and biodiesel impact on diesel fuel stability: a multivariate data analysis approach

HVO and biodiesel impact on diesel fuel stability: a multivariate data analysis approach

Global biochemical profiling of fast-growing Antarctic bacteria isolated from meltwater ponds by high-throughput FTIR spectroscopy.

Fourier transform infrared (FTIR) spectroscopy is a biophysical technique used for non-destructive biochemical profiling of biological samples. It can provide comprehensive information about the total cellular biochemical profile of microbial cells. In this study, FTIR spectroscopy was used to perform biochemical characterization of twenty-nine bacterial strains isolated from the Antarctic meltwater ponds. The bacteria were grown on two forms of brain heart infusion (BHI) medium: agar at six different temperatures (4, 10, 18, 25, 30, and 37°C) and on broth at 18°C. Multivariate data analysis approaches such as principal component analysis (PCA) and correlation analysis were used to study the difference in biochemical profiles induced by the cultivation conditions. The observed results indicated a strong correlation between FTIR spectra and the phylogenetic relationships among the studied bacteria. The most accurate taxonomy-aligned clustering was achieved with bacteria cultivated on agar. Cultivation on two forms of BHI medium provided biochemically different bacterial biomass. The impact of temperature on the total cellular biochemical profile of the studied bacteria was species-specific, however, similarly for all bacteria, lipid spectral region was the least affected while polysaccharide region was the most affected by different temperatures. The biggest temperature-triggered changes of the cell chemistry were detected for bacteria with a wide temperature tolerance such Pseudomonas lundensis strains and Acinetobacter lwoffii BIM B-1558.

Modeling rainfall extremes along the coastal and Northern parts of Ghana

The main objective of the study was to determine the appropriate distribution for extreme rainfall along the coastal and northern sectors of Ghana. For stakeholders and policymakers to make appropriate risk-mitigating measures to lessen the damage caused by flood and drought, it is necessary to make proper inferences about extreme rainfall. In this study, we used both the multivariate and univariate extreme value data analysis approaches. The Generalized Extreme Value (GEV) with the Block Maxima approach and Generalized Pareto Distribution (GPD) with the Peak over the threshold (that is all excesses and decluster peaks approaches) were used in this study. Historical gridded monthly maximum rainfall data from 1970 to 2020 were obtained from the Climatic Research Unit and were grouped as the coastal and northern stations. The Maximum Likelihood Estimation method was used to estimate the model parameters, and both the unit root test and the Mann-Kendall tests were used to test for trend in the data. With the multivariate extreme modelling approach, the logistic bivariate GEV model was chosen as the “best” model. However, the dependence value was 0.965, so the extreme rainfall should be modelled independently using the univariate extreme value approaches. Hence, based on the information criteria and analysis of deviance approaches, the GEV distribution was considered the “best” fit for the extreme rainfall dataset for the northern part of Ghana. In contrast, the GPD distribution was the “best” fit for the coastal station. Comparatively, for the volume of rainfall in the year 2020, the extreme rainfall is expected to be higher in the coastal station of Ghana in the next two years. Also, extreme rainfall in 2 years would not exceed the maximum occurrence of rainfall (279.267), which happened in September 2020 at the northern station of Ghana.

LIBS for prospecting and Raman spectroscopy for monitoring: two feasibility studies for supporting in-situ resource utilization

Laser-induced breakdown spectroscopy (LIBS) and Raman spectroscopy are still rather new techniques for in-situ exploration of extraterrestrial planetary surfaces but have shown their suitability and great potential in several successful robotic missions already. Next to serving primary scientific applications, both methods can also be used in the context of in-situ resource utilization (ISRU) such as scouting for wanted substances and the surveillance of extraction processes. Here, we present two laboratory studies conducted in the context of ISRU with a focus on the chain from prospecting to extracting oxygen from lunar regolith. For LIBS, with optimized data processing and combined with state-of-the-art multivariate data analysis approaches, we show the potential of the technique for identifying samples with increased ilmenite content and for elemental quantification. The measurements were done using lunar regolith simulant and low pressures simulating vacuum on atmosphereless bodies such as the Moon. With Raman spectroscopy, we analyzed lunar regolith simulant samples that underwent electrochemical alteration for oxygen extraction and production of metal alloys demonstrating the potential of Raman spectroscopy for ISRU process monitoring. We also discuss the results in a broader context, evaluating the potential of both methods for other aspects of ISRU support.

A nose for trouble: ecotoxicological implications for climate change and disease in Saiga antelope (S. t. tatarica)

In recent decades, Saiga antelope (Saiga t. tatarica) mass die-offs have become more common. The mass die-off of 2015 in central Kazakhstan, recorded 140,000 individual deaths across multiple herds. Previously, research has shown atmospheric humidity, the bacterium Pasteurella multocida serotype B, and resultant haemorrhagic septicaemia, were the primary cause. However, other synergistic factors may have impacted this process. Here we use a multivariate compositional data analysis (CoDA) approach to assess what other factors may have been involved. We show a pollutant linkage mechanism where relative humidity and dewpoint temperature combine with environmental pollutants, potentially toxic elements (e.g., Hg, As), complex carbon compounds (e.g., Acetone, Toluene), and inorganic compounds (e.g., CHx, SO2) which affected the Saiga during the calving season (start and peak) and at the onset of the mass die-off. We suggest a mechanism for this process. Upon arrival at their carving grounds, the Saiga experienced a sudden precipitation event, a spike in temperatures, and resultant high humidity occurs. The infectious bacterium P. multocida serotype B then spreads. Further, environmental pollutants contained within steppe soils are released to the air, forming localised smog events, these synergistically combine, and mass die-off occurs.

Non-Destructive Assessment of Microstructural Changes in Kabuli Chickpeas during Storage

The potential of hyperspectral imaging (HSI) and synchrotron phase-contrast micro computed tomography (SR-µCT) was evaluated to determine changes in chickpea quality during storage. Chickpea samples were stored for 16 wk at different combinations of moisture contents (MC of 9%, 11%, 13%, and 15% wet basis) and temperatures (10 °C, 20 °C, and 30 °C). Hyperspectral imaging was utilized to investigate the overall quality deterioration, and SR-µCT was used to study the microstructural changes during storage. Principal component analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used as multivariate data analysis approaches for HSI data. Principal component analysis successfully grouped the samples based on relative humidity (RH) and storage temperatures, and the PLS-DA classification also resulted in reliable accuracy (between 80 and 99%) for RH-based and temperature-based classification. The SR-µCT results revealed that microstructural changes in kernels (9% and 15% MC) were dominant at higher temperatures (above 20 °C) as compared to lower temperatures (10 °C) during storage due to accelerated spoilage at higher temperatures (above 20 °C). Chickpeas which had internal irregularities like cracked endosperm and air spaces before storage were spoiled at lower moisture from 8 wk of storage.

Identification of Potential Biomarkers and Metabolic Changes in the Serum of Breast Lump Patients Among Kelantanese Based on 1H NMR Metabolomics

This case-control study, conducted at the Hospital USM BestARi unit, aimed to identify the serum metabolic fingerprint among individuals with breast lumps and healthy controls, and to discover potential biomarkers. Serum samples from healthy controls, benign breast lump patients, and malignant breast lump patients were analyzed using proton nuclear magnetic resonance spectroscopy (1H NMR). A multivariate data analysis approach was employed, with the OPLS-DA and clustered heat map techniques effectively differentiating between the three groups. The study revealed significant metabolite variations across the groups and proposed D-glucose, glycerol, and glycine as potential biomarkers for breast cancer diagnosis. Metabolic pathways such as alanine, aspartate, glutamate metabolism, and glycine, serine, and threonine metabolism were implicated. The metabolomics approach coupled with multivariate analysis successfully identified key metabolites leading to group separation and suggested altered metabolic pathways. However, further research and integration with other ‘omics’ technologies are necessary for clinical translation.

NIR-hyperspectral imaging and machine learning for non-invasive chemotype classification in Cannabis sativa L

The current public acceptance rate towards medical cannabis feasibility has led to a worldwide increase in this plant species production. Nevertheless, the currently transforming legal framework does not prevent the originally unlawful knowledge around cannabis breeding, which lacks quality control regulations or standards for correct manufacturing processes, a fact that could subsequently lead to uncontrolled and even harmful crop products. In this line, the objective of this work was to develop a non-invasive methodology for cannabis chemotype classification in different cultivars during the plant cultivation process, in order to keep undoubtful production control over cannabis crops. Hence, hyperspectral imaging (HSI), coupled with various multivariate data analysis approaches, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), enabled the non-invasive in-situ analysis of the plants. Hence, two PLS-DA classification models were trained with the plant spectral data for three chemotypes, based on the cannabinoid content of the plant inflorescences, with the difference between both approaches being the regard of the stem part of the plant as a bias. Thus, obtained sensitivity and specificity values in the inflorescences were 0.845/0.845 for Chemotype I, 0.954/0.920 for Chemotype II, and 0.888/0.925 for Chemotype III. At last, a hierarchical PLS-DA, which considered the stem as a bias, presented an overall 94.7 % trueness in the external validation of 57 different plant individuals, divided as 92.3 % trueness for chemotype I, 100.0 % trueness for chemotype II and 88.9 % trueness for chemotype III. Based on these results, the proof of concept for comprehensive agricultural control of cannabis crops through a non-invasive analytical technique was demonstrated, a previously unproven fact. Therefore, this work could further pave the way for non-invasive technology development for horticultural quality control in medical cannabis productions, as this emerging industry will require strict control over the cannabis chemotypes, with the strong advantage of avoiding destructive and time-consuming analytical techniques such as chromatography.

In situ process analytical technology for real time viable cell density and cell viability during live-virus vaccine production

Viable cell density (VCD) and cell viability (CV) are key performance indicators of cell culture processes in biopharmaceutical production of biologics and vaccines. Traditional methods for monitoring VCD and CV involve offline cell counting assays that are both labor intensive and prone to high variability, resulting in sparse sampling and uncertainty in the obtained data. Process analytical technology (PAT) approaches offer a means to address these challenges. Specifically, in situ probe-based measurements of dielectric spectroscopy (also commonly known as capacitance) can characterize VCD and CV continuously in real time throughout an entire process, enabling robust process characterization. In this work, we propose in situ dielectric spectroscopy as a PAT tool for real time analysis of live-virus vaccine (LVV) production. Dielectric spectroscopy was collected across 25 discreet frequencies, offering a thorough evaluation of the proposed technology. Correlation of this PAT methodology to traditional offline cell counting assays was performed, in which VCD and CV were both successfully predicted using dielectric spectroscopy. Both univariate and multivariate data analysis approaches were evaluated for their potential to establish correlation between the in situ dielectric spectroscopy and offline measurements. Univariate analysis strategies are presented for optimal single frequency selection. Multivariate analysis, in the form of partial least squares (PLS) regression, produced significantly higher correlations between dielectric spectroscopy and offline VCD and CV data, as compared to univariate analysis. Specifically, by leveraging multivariate analysis of dielectric information from all 25 spectroscopic frequencies measured, PLS models performed significantly better than univariate models. This is particularly evident during cell death, where tracking VCD and CV have historically presented the greatest challenge. The results of this work demonstrate the potential of both single and multiple frequency dielectric spectroscopy measurements for enabling robust LVV process characterization, suggesting that broader application of in situ dielectric spectroscopy as a PAT tool in LVV processes can provide significantly improved process understanding. To the best of our knowledge, this is the first report of in situ dielectric spectroscopy with multivariate analysis to successfully predict VCD and CV in real time during live virus-based vaccine production.

A multivariate data analysis approach for the investigation of in vitro derived metabolites of ACP-105 in comparison with human in vivo metabolites

Selective androgen receptor modulators (SARMs) such as ACP-105 are prohibited in sports due to their anabolic properties. ACP-105 has in previous equine studies shown to undergo extensive metabolism, which makes its metabolite profile important to investigate in humans, since the metabolism is unknown in this species. The aims of the study were to systematically optimize in vitro microsome incubations for improved metabolite yield and to utilize a multivariate data analysis (MVDA) approach to aid the metabolite discovery. Microsomes together with S9 fractions were used at optimal conditions, both with and without phase II additives. Furthermore, the relevance of the in vitro derived metabolites was evaluated as analytical targets in doping control by comparison with results from a human post-administration urine sample collected after a single dose of 100 µg ACP-105. All samples were analyzed with liquid chromatography - Orbitrap mass spectrometry.The use of the systematical optimization and MVDA greatly simplified the search and a total of 18 in vitro metabolites were tentatively identified. The yield of the two main monohydroxylated isomers increased by 24 and 10 times, respectively. In the human urine sample, a total of seven metabolites of ACP-105, formed by a combination of hydroxylations and glucuronic acid conjugations, were tentatively identified. The main metabolites were two monohydroxylated forms that are suggested as analytical targets for human doping control after hydrolysis. All the in vivo metabolites could be detected with the MVDA approach on the in vitro models, demonstrating its usefulness for prediction of the in vivo metabolite profile.

SERS mapping combined with chemometrics, for accurate quantification of methotrexate from patient samples

Despite the technological development in Raman instrumentation that has democratized access to 2D sample scanning capabilities, most quantitative surface-enhanced Raman scattering (SERS) analyses are still performed by only acquiring a single or a few spectra per sample and performing univariate data analysis on those. This strategy can however reach its limit when analytes need to be detected and quantified in complex matrices. In that case, surface fouling and competition between the target analyte and interfering compounds can impair univariate SERS data analysis, underlining the need for a more in-depth data analysis strategy based on exploiting of full-spectrum information.In this paper, a multivariate data analysis strategy was developed, for analyzing SERS maps of methotrexate (MTX) from patient samples, including all steps from baseline correction, selection of wavelength, and the relevant pixels in the map (image threshold segmentation), as well as quantitative model construction based on partial-least squares regression.Among the different baseline correction methods evaluated, standard normal variable transformation and Savitzky-Golay smoothing proved to be more suitable, while the genetic algorithm wavelength screening method was able to screen out MTX-related SERS spectral regions more efficiently. Importantly, with the here-developed process, it was sufficient to use MTX-spiked commercial serum when building quantitative models, removing the need to work with MTX-spiked patient samples, and consequently enabling time- and resource-saving quantitative analyses. Besides, the developed multivariate data analysis approach showed superior performances compared with univariate analysis, with 30 % improved sensitivity (detection limit of 5.7 µM), 25 % higher reproducibility (average relative standard variation of 15.6 %), and 110 % better accuracy (average prediction error of −10.5 %).

In Situ Monitoring of Drug Precipitation from Digesting Lipid Formulations Using Low-Frequency Raman Scattering Spectroscopy.

Low-frequency Raman spectroscopy (LFRS) is a valuable tool to detect the solid state of amorphous and crystalline drugs in solid dosage forms and the transformation of drugs between different polymorphic forms. It has also been applied to track the solubilisation of solid drugs as suspensions in milk and infant formula during in vitro digestion. This study reports the use of LFRS as an approach to probe drug precipitation from a lipid-based drug delivery system (medium-chain self-nanoemulsifying drug delivery system, MC-SNEDDS) during in vitro digestion. Upon lipolysis of the digestible components in MC-SNEDDS containing fenofibrate as a model drug, sharp phonon peaks appeared at the low-frequency Raman spectral region (<200 cm-1), indicating the precipitation of fenofibrate in a crystalline form from the formulation. Two multivariate data analysis approaches (principal component analysis and partial least squares discriminant analysis) and one univariate analysis approach (band ratios) were explored to track these spectral changes over time. The low-frequency Raman data produces results in good agreement with in situ small angle X-ray scattering (SAXS) measurements with all data analysis approaches used, whereas the mid-frequency Raman requires the use of PLS-DA to gain similar results. This suggests that LFRS can be used as a complementary, and potentially more accessible, technique to SAXS to determine the kinetics of drug precipitation from lipid-based formulations.

Combined ambient ionization mass spectrometric and chemometric approach for the differentiation of hemp and marijuana varieties of Cannabis sativa

BackgroundHemp and marijuana are the two major varieties of Cannabis sativa. While both contain Δ9-tetrahydrocannabinol (THC), the primary psychoactive component of C. sativa, they differ in the amount of THC that they contain. Presently, U.S. federal laws stipulate that C. sativa containing greater than 0.3% THC is classified as marijuana, while plant material that contains less than or equal to 0.3% THC is hemp. Current methods to determine THC content are chromatography-based, which requires extensive sample preparation to render the materials into extracts suitable for sample injection, for complete separation and differentiation of THC from all other analytes present. This can create problems for forensic laboratories due to the increased workload associated with the need to analyze and quantify THC in all C. sativa materials.MethodThe work presented herein combines direct analysis in real time—high-resolution mass spectrometry (DART-HRMS) and advanced chemometrics to differentiate hemp and marijuana plant materials. Samples were obtained from several sources (e.g., commercial vendors, DEA-registered suppliers, and the recreational Cannabis market). DART-HRMS enabled the interrogation of plant materials with no sample pretreatment. Advanced multivariate data analysis approaches, including random forest and principal component analysis (PCA), were used to optimally differentiate these two varieties with a high level of accuracy.ResultsWhen PCA was applied to the hemp and marijuana data, distinct clustering that enabled their differentiation was observed. Furthermore, within the marijuana class, subclusters between recreational and DEA-supplied marijuana samples were observed. A separate investigation using the silhouette width index to determine the optimal number of clusters for the marijuana and hemp data revealed this number to be two. Internal validation of the model using random forest demonstrated an accuracy of 98%, while external validation samples were classified with 100% accuracy.DiscussionThe results show that the developed approach would significantly aid in the analysis and differentiation of C. sativa plant materials prior to launching painstaking confirmatory testing using chromatography. However, to maintain and/or enhance the accuracy of the prediction model and keep it from becoming outdated, it will be necessary to continue to expand it to include mass spectral data representative of emerging hemp and marijuana strains/cultivars.

NMR-Based Metabolomics: A New Paradigm to Unravel Defense-Related Metabolites in Insect-Resistant Cotton Variety through Different Multivariate Data Analysis Approaches.

Cotton (Gossypium hirsutum) is an economically important crop and is widely cultivated around the globe. However, the major problem of cotton is its high vulnerability to biotic and abiotic stresses. It has been around three decades since the cotton plant was genetically engineered with genes encoding insecticidal proteins (mainly Cry proteins) with an aim to protect it against insect attack. Several studies have been reported on the impact of these genes on cotton production and fiber quality. However, the metabolites responsible for conferring resistance in genetically modified cotton need to be explored. The current work aims to unveil the key metabolites responsible for insect resistance in Bt cotton and also compare the conventional multivariate analysis methods with deep learning approaches to perform clustering analysis. We aim to unveil the marker compounds which are responsible for inducing insect resistance in cotton plants. For this purpose, we employed 1H-NMR spectroscopy to perform metabolite profiling of Bt and non-Bt cotton varieties, and a total of 42 different metabolites were identified in cotton plants. In cluster analysis, deep learning approaches (linear discriminant analysis (LDA) and neural networks) showed better separation among cotton varieties compared to conventional methods (principal component analysis (PCA) and orthogonal partial least square discriminant analysis (OPLSDA)). The key metabolites responsible for inter-class separation were terpinolene, α-ketoglutaric acid, aspartic acid, stigmasterol, fructose, maltose, arabinose, xylulose, cinnamic acid, malic acid, valine, nonanoic acid, citrulline, and shikimic acid. The metabolites which regulated differently with the level of significance p < 0.001 amongst different cotton varieties belonged to the tricarboxylic acid cycle (TCA), Shikimic acid, and phenylpropanoid pathways. Our analyses underscore a biosignature of metabolites that might involve in inducing insect resistance in Bt cotton. Moreover, novel evidence from our study could be used in the metabolic engineering of these biological pathways to improve the resilience of Bt cotton against insect/pest attacks. Lastly, our findings are also in complete support of employing deep machine learning algorithms as a useful tool in metabolomics studies.

The Intention to Use Digital Banking Services among Gen Z in Indonesia Based on Technology Acceptance Model (TAM)

&#x0D; As information technology advances, societal demands rise, and smartphone features and sophistication rise along with them, banks must adapt to meet the current demand. For Indonesians, who are accustomed to use cash, digital banking will become new digital transformation. There is no information available on the variables that might affect Indonesians’ behavior intentions about using digital banking services. In particular, among generation Z in Indonesia following the COVID-19 outbreak, this research seeks to investigate the connection between perceived usefulness, perceived ease of use, and self-efficacy in consumers' intention for using digital banking services based on the Technology Acceptance Model (TAM). For the survey data gathered from 150 generation Z, multivariate data analysis approaches (reliability and validity test, multiple linear regression, collinearity analysis, and path estimations) are utilized. The results of this study's analysis demonstrate that perceived usefulness indicates a great influence on the attitude and intention to use digital banking services, compared to the perceived ease of use and self-efficacy. Therefore, for customers to completely comprehend the advantages associated with using digital banking services, it is critical to boost their perception of the usefulness of the services in their daily life.&#x0D;

Untargeted metabolomics and phenotype data indicate the therapeutic and prophylactic potential of Lysimachia candida Lindl. towards high-fat high-fructose-induced metabolic syndrome in rats.

The present study evaluated the therapeutic potential of the medicinal plant Lysimachia candida Lindl. against metabolic syndrome in male SD rats fed with a high-fat high-fructose (HFHF) diet. Methanolic extract of Lysimachia candida Lindl. (250 mg kg-1 body weight p.o.) was administrated to the HFHF-fed rats daily for 20 weeks. Blood samples were collected, and blood glucose levels and relevant biochemical parameters were analysed and used for the assessment of metabolic disease phenotypes. In this study, Lysimachia candida decreased HFHF diet-induced phenotypes of metabolic syndrome, i.e., obesity, blood glucose level, hepatic triglycerides, free fatty acids, and insulin resistance. Liquid chromatography-mass spectrometry-based metabolomics was done to study the dynamics of metabolic changes in the serum during disease progression in the presence and absence of the treatment. Furthermore, multivariate data analysis approaches have been employed to identify metabolites responsible for disease progression. Lysimachia candida Lindl. plant extract restored the metabolites that are involved in the biosynthesis and degradation of amino acids, fatty acid metabolism and vitamin metabolism. Interestingly, the results depicted that the treatment with the plant extract restored the levels of acetylated amino acids and their derivatives, which are involved in the regulation of beta cell function, glucose homeostasis, insulin secretion, and metabolic syndrome phenotypes. Furthermore, we observed restoration in the levels of indole derivatives and N-acetylgalactosamine with the treatment, which indicates a cross-talk between the gut microbiome and the metabolic syndrome. Therefore, the present study revealed the potential mechanism of Lysimachia candida Lindl. extract to prevent metabolic syndrome in rats.

Insights into tablet sticking: a quantitative case study with an ibuprofen and methocarbamol-based formulation.

Tablet sticking is a continuous accumulation of pharmaceutical powder onto tooling surfaces during compression. Its occurrence greatly impacts tablet productivity, quality attributes, and tooling age. In a previous study1, the authors proposed a multivariate data analysis approach to gain insights into tablet sticking directly on the industrial stage (Dembélé et al. 2022). The objective was to determine the combination of factors that could help distinguish between batches affected and unaffected by sticking. The present study aims to generalize this approach by extending it to quantitative predictions of punch sticking intensity. A total of 345 variables was gathered on 28 industrial batches of an ibuprofen and methocarbamol-based formulation. Using PLS regression models, it was shown that the association of granulation duration and compression force allows to significantly explain ∼60% of sticking variations of studied formulation. In addition, unlike the classification models developed in the earlier work, the validation residues in the present study were found to be normally distributed (Shapiro-Wilks p-value = 0.96) and independent from the target variable (R2=9.5%).

A multivariate data analysis approach to tablet sticking on an industrial scale: a qualitative case study of an ibuprofen-based formulation

Objectives Sticking is one of the most common and damaging issues that occur during tablet manufacturing. Sticking is the adhesion of powder onto tooling surfaces during compression. Because of the numerous factors involved in its occurrence, understanding tablet sticking requires the simultaneous investigation of these factors to clarify their possible interactions. However, conducting such a study experimentally can present a significant financial and technical burden. In this study, we aimed to leverage the large amount of data that is usually generated during industrial manufacturing to gain insights into sticking. Methods This was achieved by collecting and analyzing a total of 71 historical batches that used an ibuprofen-based formulation. We associate each batch with a hundred parameters, including a qualitative descriptor of sticking, and employ a predefined methodology based primarily on multivariate data analysis. Results and Conclusions Our results highlight the role of lubrication, water content, and the low melting point of ibuprofen in its sticking tendency. Based on these findings, we propose and discuss an industrial manufacturing data analysis approach to sticking and its associated systematic methodology, consisting of collection, exploration, and data modeling.

COVID-19 social vulnerability as a lethality conditioning factor

Brazil presented different waves of contamination, registering different cases and numbers of deaths from COVID-19 depending on the region under study due to its continental size and territorial complexity, with 5,570 municipalities, 26 states, and one federal district. The focus of the study was to verify whether socioeconomic conditions have an influence on the pandemic and, if confirmed, to describe the mechanism and degree of influence. The study used a multivariate data analysis approach with a combination of exploratory factor analysis and hierarchical clustering techniques. The factor analysis resulted in the extraction of three factors with a characteristic root greater than one, which account for 94.23% of the total variation in the data, these being: basic assistance coverage; family assistance programs; basic education; and health care. In the cluster analysis, four differentiated groups were obtained from the factors listed. Among the main results, we highlight the fact that COVID-19 affected not only the health and epidemiological spheres in Brazil but also evidenced the existing social vulnerability, as well as generated impacts in different sectors.

N-Glycomic profiling reveals dysregulated glycans related to oral cancer using MALDI-MS.

Aberrant protein N-glycosylation is one of the hallmarks of malignancy, while its role in oral cancer (OC) progression is still poorly understood making it a perfect biomarker for early-stage cancer. In this work, we describe a MALDI-MS-based N-glycomic analysis with multivariate data analysis approach for exploration and classification of the patients with OC. Various statistical analyses were used to determine differential N-glycome profiles. There were 8 glycan structures significantly increased (p < 0.05) in all OC-derived microvesicles (MVs). Importantly, the increases in these glycans were significantly greater in the early stage of OC than those in the advanced stage of OC. Additionally, N-glycomic profiles from the serum were also performed, demonstrating a significant change of 11 glycan structures (p < 0.05). Our data highlighted the effectiveness approach of MALDI-MS-based MV N-glycomic profiling on OC screening, and presented strong potential for further identifying targeted glycoproteomics signatures of the cancer.