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A simple and reliable methodology for the detection of paralytic shellfish toxins (PSTs) in bivalve tissues using potentiometric chemical sensors was developed. Five methods of PST extraction from mussel and oyster tissues were evaluated, including the AOAC-recommended method, which served as the reference. The main objective was to minimize the matrix effect of the extracts on the sensors' responses and ensure efficient toxin recovery. Extraction procedures using acetic acid with heating and water yielded the highest responses from the potentiometric chemical sensors to PSTs. The highest recovery of PSTs from bivalve tissues was achieved with extraction using acetic acid and heating. Further extract purification, which is indispensable for liquid chromatography with fluorometric detection (LC-FLD) analysis, was found to be unnecessary for analysis with chemical sensors. While water extraction can also be used as a rapid and simple PST extraction method, the lower recoveries should be considered when interpreting the results. Further research is needed to identify the compounds remaining in the extracts that cause a decrease in sensor responses and to develop procedures for their elimination. [ABSTRACT FROM AUTHOR]

The performance of an electronic tongue can be optimized by varying the number and types of sensors in the array and by employing data-processing methods. Sensor selection is typically performed empirically, with sensors picked up either by analyzing their characteristics or through trial and error, which does not guarantee an optimized sensor array composition. This study focuses on developing a method for sensor selection for an electronic tongue using simulated sensor data and Lasso regularization. Simulated sensor responses were calculated using sensor parameters such as sensitivity and selectivity, which were determined in the individual analyte solutions. Sensor selection was carried out using Lasso regularization, which removes redundant or highly correlated variables without much loss of information. The objective of the optimization of the sensor array was twofold, aiming to minimize both quantification errors and the number of sensors in the array. The quantification of toxins belonging to one of the groups of marine toxins—paralytic shellfish toxins (PSTs)—using arrays of potentiometric chemical sensors was used as a case study. Eight PSTs corresponding to the toxin profiles in bivalves due to the two common toxin-producing phytoplankton species, G. catenatum (dcSTX, GTX5, GTX6, and C1+2) and A. minitum (STX, GTX2+3), as well as total sample toxicity, were included in the study. Experimental validation with mixed solutions of two groups of toxins confirmed the suitability of the proposed method of sensor array optimization with better performance obtained for the a priori optimized sensor arrays. The results indicate that the use of simulated sensor responses and Lasso regularization is a rapid and efficient method for the selection of an optimized sensor array. [ABSTRACT FROM AUTHOR]

Aging is associated with oxidative stress that may increase susceptibility to respiratory infections (RIs). We aimed to assess the impact of exercise training on the risk of RIs in older adults and on a targeted metabolomic profile of stress oxidative lipid peroxidation-related metabolites. Methods: In an 8-month clinical trial, 38 participants over 60 years of age were allocated to an exercise group (EG), in which participants underwent 90-min training sessions three times/week(n = 20), or a control group (CG), in which participants maintained daily physical activities(n = 18). Daily respiratory symptoms and RIs number and severity were collected. Serum by-products were assessed by comprehensive two-dimensional gas chromatography coupled to mass spectrometry with time of flight analyzer. Serum metabolomic profiling comprised 76 metabolites (alcohols, aldehydes, alkanes, and ketones). Principal components analysis and ANOVA-simultaneous component analysis were used to evaluate the metabolomic profile change. Results: The odds ratio of RIs for the EG was 2.0 CI 95% [0.2;25]. The incidence of RIs was 47% [23;70] in the EG vs. 44%[12;77] in the CG. The metabolomic profiling showed that alkanes and aldehydes classes differed between the EG and the CG before and after intervention. A calibration model showed a relation between the metabolites from four main classes (ketones, alcohols, alkanes and aldehydes) and the prediction of the number of RIs. Conclusion: Moderate exercise training, in older adults, compared with no exercise in controls, did not show a difference in the risk of RIs. A pattern of lipid peroxidation was associated with the number of RIs. [ABSTRACT FROM AUTHOR]