A reliable and robust tool for supporting
the panel test in virgin
olive oil classification is still required. We propose four chemometric
approaches based on t test, principal component analysis
(PCA) and linear discriminant analysis (LDA), applied for combining
sensorial data, and chemical measurements. The former was from the
panel test, and the latter was from headspace solid-phase microextraction–gas
chromatography–mass spectrometry quantitation of 73 volatile
organic compounds (VOCs) of 1223 typical commercial virgin olive oils,
with most of them recognized as difficult to classify with accuracy
by the panel test. The approaches were developed and validated, and
the best results, with 83.5% correct classification, were using the
PCA–LDA approach. Among the other methods, developed for proposing
simplified procedures based on a smaller number of VOCs, the best
method gave 80.1% correct classification only using 10 VOCs. All of
the approaches suggested that octane, heptanal, pent-1-en-3-ol, Z-3-hexenal, nonanal, and 4-ethylphenol should be considered
as a basis of volatiles for classification of olive oil samples.
A new series of pyrazolo[5,1-c][1,2,4]benzotriazine 5-oxide 8-alkyloxy-/aryloxy-/arylalkyloxy and 8-aryl-/arylalkylderivatives variously substituted at the 3-position were synthesized and binding studies at the benzodiazepine site on GABA(A) receptor were carried out. The pharmacological profile was identified for compounds 10, 11, 16(+), 16(-), and 17 by considering six potential benzodiazepine actions: motor coordination, anticonvulsant action, spontaneous motility and explorative activity, potential anxiolytic-like effects, mouse learning and memory modulation, and finally, ethanol-potentiating action. Compound 17 stands out as the compound that improves mouse memory processes selectively, safely, and in a statistically significant manner. From a ligand-based pharmacophoric model, we identified a hydrogen bond interaction area HBp-3 near the lipophilic area. This new pharmacophoric model allowed us to identify four structural compound typologies and thus to rationalize the affinity data of all compounds.
During the last few years increasing interest has been focused on antioxidants as potentially useful agents in the prevention of the onset and progression of cognitive dysfunction. In this randomized, double-blind, controlled, parallel arm study, the effects of daily consumption of an antioxidant mix on cognitive function in healthy older adults were evaluated. After a 1 week run-in period, 80 subjects aged 60 years or more, and with no evidence of cognitive dysfunction, were randomly allocated to a mix of four bioactive compounds (bacopa, lycopene, astaxanthin, and vitamin B12) or matched placebo, taken orally once a day for 8 weeks. The primary objective of the study was to evaluate the changes in trial making test (TMT) scores from baseline to 8 weeks of treatment, analyzed in the following hierarchical order: TMT-B, TMT-A, and TMT-B minus TMT-A. TMT-B increased in the control group (+3.46 s) and decreased in the active group (−17.63 s). The treatment difference was −21.01 s in favor of the active group (95% C.I. −26.80 to −15.2, p < 0.0001). The decrease in TMT-A was significantly higher in the active group (−6.86 s) than in the control group (−0.37 s). TMT-B minus TMT-A increased in the control group (+3.84 s) and decreased in the active group (−10.46 s). The increase in letter fluency in the verbal fluency test (VFT) was also significantly higher in the active group and statistically significant (+5.28 vs. +1.07 words; p < 0.001). Our findings provide encouraging evidence that regular dietary supplementation with bacopa, lycopene, astaxanthin, and vitamin B12 may be an effective dietary approach for counteracting cognitive changes associated with brain aging.
Alignment of molecules is a crucial and time-consuming step in any 3D-QSAR study. For this reason, the field interaction and geometrical overlap (FIGO) procedure presented in this paper is particularly relevant because it can provide an objective and automatic superposition of ligands through the computation of an appropriate alignment index (AI). Ligand overlay takes place via a simplex optimization of the AI function. Experimental design strategies (full factorial design, D-optimal design) are used to define the starting positions of the superposing molecules. Overlay experiments are carried out to test the performance of the method. Comparison between the results obtained with FIGO and known ligand-receptor X-ray crystallographic data (Protein Data Bank) suggests that FIGO is an effective and reliable computational procedure.
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