NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns.

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

1H HR-MAS NMR-Based Metabolomic Fingerprinting to Distinguish Morphological Similarities and Metabolic Profiles of Maytenus ilicifolia, a Brazilian Medicinal Plant.

Maytenus ilicifolia or "Espinheira-Santa" is a renowned Brazilian medicinal plant usually used against intestinal and stomach ulcers. Other species with similar thorny leaves have raised great confusion in order to discern the authentic M. ilicifolia. Misidentifications can lead to product adulteration of authentic M. ilicifolia with other species, which can be found on the Brazilian market. The intake of misclassified herbal products potentially could be fatal, demanding faster reliable fingerprinting-based classification methods. In this study, the use of 1H HR-MAS NMR metabolomics fingerprinting and principal component analysis (PCA) allowed an evaluation of the authenticity for both collected and commercial M. ilicifolia samples, from the content of the flavanol, (-)-epicatechin (2), by observing variations in metabolic patterns. Plant specimen types from cultivated and natural habitats were analyzed by considering seasonal and topological differences. The interand intraplant topological metabolic profiles were found to be affected by seasonal and/or ecological trends such as sunlight, shade, rain, and the presence of pathogens. Moreover, several commercial samples, labeled as M. ilicifolia, were evaluated, but most of these products were of an inadequate quality.

Use of data processing for rapid detection of the prostate-specific antigen biomarker using immunomagnetic sandwich-type sensors.

Diagnosis of cancer using electroanalytical methods can be achieved at low cost and in rapid assays, but this may require the combination with data treatment for determining biomarkers in real samples. In this paper, we report an immunomagnetic nanoparticle-based microfluidic sensor (INµ-SPCE) for the amperometric detection of the prostate-specific antigen (PSA) biomarker, the data of which were treated with information visualization methods. The INµ-SPCE consists of eight working electrodes, reference and counter electrodes. On the working electrodes, magnetic nanoparticles with secondary antibodies with the enzyme horseradish peroxidase were immobilized for the indirect detection of PSA in a sandwich-type procedure. Under optimal conditions, the immunosensor could operate within a wide range from 12.5 to 1111 fg·L-1, with a low detection limit of 0.062 fg·L-1. Multidimensional projections combined with feature selection allowed for the distinction of cell lysates with different levels of PSA, in agreement with results from the traditional enzyme-linked immunosorbent assay. The approaches for immunoassays and data processing are generic, and therefore the strategies described here may provide a simple platform for clinical diagnosis of cancers and other types of diseases.

High-throughput simultaneous quantitation of multi-analytes in tobacco by flow injection coupled to high-resolution mass spectrometry.

The high-throughput screening by flow injection coupled to high-resolution mass spectrometry (HTS-FIA-HRMS) is a powerful technique that enables the identification of several types of samples in a short period of time, either with qualitative or quantitative purposes. Sensory attributes of tobacco are affected by its chemical composition, and it is very important to quantify multi-analytes in a high-throughput methodology. HTS-FIA-HRMS coupled to multivariate analysis was used to create calibration models for 27 analytes, or group of compounds, of tobacco sensory interest. The models were validated by different approaches, including permutation test to avoid overfitting, evaluation of the equipment repeatability by control samples, reproducibility comparison of results from two different equipment and analysts, and with a blind test analysis. All tests demonstrated a good response to the proposed method. No statistical difference between the errors of both equipment was observed, with less than 7% error from the control samples, and a blind test error between 5.96% and 20.10%. The partial least squares (O-PLS) regression models were applied to 815 samples, and a principal component analysis (PCA) was performed from the predicted concentration values, aiming at the non-supervised classification based on tobacco type. We expect that this proposed methodology shows not only the applicability in tobacco samples, but also demonstrates a guideline to an efficient performance of multi-analytes target analysis using the flow injection mass spectrometry with reliable and robust validation steps.

Innovative Approaches for Estimating the Levels of Tobacco-Specific Nitrosamines in Cured Tobacco Samples.

Tobacco-specific nitrosamines (TSNAs), mainly the 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), are known carcinogens. Part of the NNK found in smoke is provided from matrix-bound NNK, and its determination is extremely relevant. However, the reference extraction procedure of matrix-bound NNK is time-consuming and labor-intensive and has a limited analytical capacity. Three different methodologies were proposed to predict matrix-bound NNK: simple linear regression (LR) with soluble NNK; multiple linear regression (MLR) considering soluble NNK and characteristic parameters of the samples; and orthogonal partial least-squares (O-PLS) regression using high-throughput screening by flow injection analysis coupled to high-resolution mass spectrometry (HTS-FIA-HRMS) data. Simple linear regression showed a high influence of matrix and leaf origin. Although an existing linearity trend has been observed ( R2 = 0.62) for the global model, higher correlation values were achieved for matrix and country segregation models. Multiple linear regression predicted matrix-bound NNK with more satisfactory efficiency than simple linear regression models. The coefficients of determination were 0.87 and 0.94 for flue-cured Virginia and air-cured Burley, respectively. However, this method has a limited application, since previous information about the sample is required. The proposed method based on HTS-FIA-HRMS and O-PLS has shown the most suitable performance in the prediction of matrix-bound NNK, with errors comparable to the reference method, and a higher throughput. In addition, this approach allows to determine other soluble nitrosamines, namely N'-nitrosoanatabine, N'-nitrosoanabasine, and N-nitrosonornicotine, with relative percentage errors between 5.25 and 11.98%. Therefore, the third approach is the best method for a large number of cured tobacco for accuracy in determination of TSNAs.

In-line monitoring of cocrystallization process and quantification of carbamazepine-nicotinamide cocrystal using Raman spectroscopy and chemometric tools.

A cocrystallization process may involve several molecular species, which are generally solid under ambient conditions. Thus, accurate monitoring of different components that might appear during the reaction is necessary, as well as quantification of the final product. This work reports for the first time the synthesis of carbamazepine-nicotinamide cocrystal in aqueous media with a full conversion. The reactions were monitored by Raman spectroscopy coupled with Multivariate Curve Resolution - Alternating Least Squares, and the quantification of the final product among its coformers was performed using Raman spectroscopy and Partial Least Squares regression. The slurry reaction was made in four different conditions: room temperature, 40°C, 60°C and 80°C. The slurry reaction at 80°C enabled a full conversion of initial substrates into the cocrystal form, using water as solvent for a greener method. The employment of MCR-ALS coupled with Raman spectroscopy enabled to observe the main steps of the reactions, such as drug dissolution, nucleation and crystallization of the cocrystal. The PLS models gave mean errors of cross validation around 2.0 (% wt/wt), and errors of validation between 2.5 and 8.2 (% wt/wt) for all components. These were good results since the spectra of cocrystals and the physical mixture of the coformers present some similar peaks.