Publications by authors named "B Rasulev"

Magnetic nanoparticles (NPs) are gaining significant interest in the field of biomedical functional nanomaterials because of their distinctive chemical and physical characteristics, particularly in drug delivery and magnetic hyperthermia applications. In this paper, we experimentally synthesized and characterized new FeO-based NPs, functionalizing its surface with a 5-TAMRA cadaverine modified copolymer consisting of PMAO and PEG. Despite these advancements, many combinations of NP cores and coatings remain unexplored.

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  • Boron-dipyrromethene (BODIPY) compounds have valuable optical properties and are used in various fields like imaging and electronics; to enhance their design, understanding the link between their structures and optical properties is essential.
  • A machine learning-based model was developed, showing high predictive accuracy for the BODIPY compounds' maximum absorption wavelength, proving its effectiveness through strong correlation coefficients.
  • The study used computational chemistry methods to optimize BODIPY structures and employed machine learning techniques to analyze 131 compounds, highlighting the significance of molecular characteristics like branching and specific functional groups in determining their properties.
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Many non-natural amino acids can be incorporated by biological systems into coded functional peptides and proteins. For such incorporations to be effective, they must not only be compatible with the desired function but also evade various biochemical error-checking mechanisms. The underlying molecular mechanisms are complex, and this problem has been approached previously largely by expert perception of isomer compatibility, followed by empirical study.

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  • Advances in colorectal cancer (CRC) treatment face challenges due to fast tumor spread and screening issues, especially for early-onset CRC, highlighting the need for better therapies.
  • The Rictor-mTORC2-AKT pathway is critical in promoting metastasis, and selective inhibitors targeting mTORC2, like Veratridine (VTD), show promise in reducing tumor growth and metastasis.
  • VTD acts by destabilizing Rictor, leading to decreased cancer stem cell populations and aggressive tumor behaviors in CRC, offering a new potential approach to treatment.
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This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material's capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure-property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR).

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