The objective of this research was to investigate the genotoxic potential of the oil of L. (sunflower) seeds via the Ames test as well as its oxidative properties and lipid composition. The pre-incubation method, system metabolic activation (S9 fraction) and five strains (TA97, TA98, TA100, TA1535 and TA102) were employed for the Ames test. The oxidative stability and fatty acid composition were analyzed by standard methods and gas chromatography. A revertant analysis showed no significant differences between the treatment doses (10-200 μl/plate) and the negative controls, regardless of S9 and S9, and included all of the strains. Chromatographic analysis showed high levels of polyunsaturated fatty acids, followed by monounsaturated, saturated and total trans-isomers. Among the polyunsaturated, monounsaturated and saturated fatty acids, linoleic, oleic and palmitic acids predominated. The results suggest that the sunflower oil is not genotoxic as indicated by frameshift mutations and base pair substitutions regardless of the treatment dose, but shows dose-dependent toxicity. The oxidative properties of the sunflower oil were consistent with the requirements of national and international standards. However, its composition could also indicate phytotherapeutic properties.
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http://dx.doi.org/10.1016/j.toxrep.2016.09.006 | DOI Listing |
Int J Pharm
January 2025
Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ, 08854, USA; Center for Structured Organic Particulate Systems (C-SOPS), Cranbury, NJ, 08512, USA.
This study used Raman and near-infrared (NIR) spectroscopy to monitor small real-time changes in powder blends and tablets in low-dose pharmaceutical formulations. The research aims to enhance process analytical technology (PAT) in pharmaceutical manufacturing, ensuring high-quality and uniform products with applications to produce drugs with narrow therapeutic indices (NTI). The study utilizes Raman and NIR spatially resolved spectroscopy (SRS) techniques to monitor a moderate cohesive material's active pharmaceutical ingredient (API) concentrations during manufacturing.
View Article and Find Full Text PDFSci Rep
January 2025
NASA Ames Research Center, Planetary Systems Branch, Moffett Field, CA, USA.
As we assess the habitability of other worlds, we are limited by being able to only study terrestrial life adapted to terrestrial conditions. The environments found on Earth, though tremendously diverse, do not approach the multitude of potentially habitable environments beyond Earth, and so limited terrestrial adaptive capabilities tell us little about the fundamental biochemical boundaries of life. One approach to this problem is to use experimental laboratory evolution to adapt microbes to these novel environmental conditions.
View Article and Find Full Text PDFToxicol Mech Methods
January 2025
Drug Safety Research and Evaluation, Research, Takeda Pharmaceutical Company Limited, Fujisawa, Japan.
The rat S9 microsome fraction is commonly used to assess compound metabolite formation during genotoxicity assessments. However, methods using S9 have not been standardized for genotoxicity studies, and different experimental methods are used at various facilities. Therefore, this study investigated whether the differences between the two experimental conditions (1) S9 inducers, phenobarbital + beta-naphthoflavones vs.
View Article and Find Full Text PDFChem Biol Interact
December 2024
Laboratory of Biophysics, University of Gdańsk, Gdańsk, Poland. Electronic address:
Breast cancer was the most frequent cause of cancer death in females in 2022. Despite the development of personalized therapies, chemotherapy frequently remains the only available treatment method. However, the administration of classic antineoplastic drugs, like cisplatin (CDDP), often causes severe side effects and may lead to drug resistance making the therapy inefficient.
View Article and Find Full Text PDFFront Plant Sci
December 2024
Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, United States.
In plant breeding and genetics, predictive models traditionally rely on compact representations of high-dimensional data, often using methods like Principal Component Analysis (PCA) and, more recently, Autoencoders (AE). However, these methods do not separate genotype-specific and environment-specific features, limiting their ability to accurately predict traits influenced by both genetic and environmental factors. We hypothesize that disentangling these representations into genotype-specific and environment-specific components can enhance predictive models.
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