Publications by authors named "M A Pirtskhalava"

Background: Over the past decade, numerous studies have been conducted to determine the role of homocysteine and methylenetetrahydrofolate reductase () gene polymorphisms in the pathogenesis of polycystic ovary syndrome (PCOS), yet the results are inconsistent. The aim of the current study was to determine the association between homocysteine levels (Hcy), ) and A1298C polymorphisms, and pregnancy outcomes in Georgian women with PCOS.

Materials And Methods: This case-control study included 177 female participants, of which 96 women were diagnosed with PCOS, and 81 age-matched women were without PCOS.

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Antimicrobial peptides (AMPs) are promising tools for combating microbial resistance. However, their therapeutic potential is hindered by two intrinsic drawbacks-low target affinity and poor in vivo stability. Macrocyclization, a process that improves the pharmacological properties and bioactivity of peptides, can address these limitations.

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Antimicrobial peptides (AMPs) have emerged as promising candidates in combating antimicrobial resistance - a growing issue in healthcare. However, to develop AMPs into effective therapeutics, a thorough analysis and extensive investigations are essential. In this study, we employed an approach to design cationic AMPs , followed by their experimental testing.

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The study of antioxidants is of pivotal importance in biomedicine as these molecules could be involved in biological pathways associated with disease. The identification of new antioxidants together with the acquisition of a deeper knowledge on their biology, could lead to the use of these compounds as drugs for innovative treatments. Plants are an important reservoir of phytodrugs that in many cases can be isolated with good extraction yields directly from the vegetal source and are often endowed with a low toxicity profile.

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Antiviral peptides (AVPs) are bioactive peptides that exhibit the inhibitory activity against viruses through a range of mechanisms. Virus entry inhibitory peptides (VEIPs) make up a specific class of AVPs that can prevent envelope viruses from entering cells. With the growing number of experimentally verified VEIPs, there is an opportunity to use machine learning to predict peptides that inhibit the virus entry.

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