Molecular Biology and Genetics / Moleküler Biyoloji ve Genetik

Permanent URI for this collectionhttps://hdl.handle.net/11147/9

Browse

Filters

2016 - 20193

Settings

Publication Index: search.filters.publicationIndex.PubMedPublisher: search.filters.publisher.Hindawi Publishing Corporation

Search Results

Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 22
    Citation - Scopus: 26
    In Vitro Antimicrobial Activity Screening of Ethanol Extract of Lavandula Stoechas and Investigation of Its Biochemical Composition
    (Hindawi Publishing Corporation, 2019) Canlı, Kerem; Yetgin, Ali; Benek, Atakan; Bozyel, Mustafa Eray; Altuner, Ergin Murat
    The aim of this study was to test antimicrobial activity of ethanol extract of Lavandula stoechas against 22 bacteria and 1 yeast. Also, biochemical composition of the extract was investigated. A wide range of Gram-positive, Gram-negative microorganisms, and multidrug resistant bacteria were selected to test the antimicrobial activity. As a result, the extract is observed to contain fenchone (bicyclo[2.2.1]heptan-2-one, 1,3,3-trimethyl-, (1R)-) and camphor (+)-2-bornanone) as major components and showed antimicrobial activity against all studied microorganisms except Escherichia coli ATCC 25922 and Klebsiella pneumoniae. The results of the study present that L. stoechas is active against MDR strains too.
  • Article
    Citation - WoS: 11
    Citation - Scopus: 11
    Mice With Catalytically Inactive Cathepsin a Display Neurobehavioral Alterations
    (Hindawi Publishing Corporation, 2017) Çalhan, Osman Yipkin; Seyrantepe, Volkan
    The lysosomal carboxypeptidase A, Cathepsin A (CathA), is a serine protease with two distinct functions. CathA protects β-galactosidase and sialidase Neu1 against proteolytic degradation by forming a multienzyme complex and activates sialidase Neu1. CathA deficiency causes the lysosomal storage disease, galactosialidosis. These patients present with a broad range of clinical phenotypes, including growth retardation, and neurological deterioration along with the accumulation of the vasoactive peptide, endothelin-1, in the brain. Previous in vitro studies have shown that CathA has specific activity against vasoactive peptides and neuropeptides, including endothelin-1 and oxytocin. A mutant mouse with catalytically inactive CathA enzyme (CathAS190A) shows increased levels of endothelin-1. In the present study, we elucidated the involvement of CathA in learning and long-term memory in 3-, 6-, and 12-month-old mice. Hippocampal endothelin-1 and oxytocin accumulated in CathAS190A mice, which showed learning impairments as well as long-term and spatial memory deficits compared with wild-type littermates, suggesting that CathA plays a significant role in learning and in memory consolidation through its regulatory role in vasoactive peptide processing.
  • Article
    Citation - Scopus: 19
    Feature Selection Has a Large Impact on One-Class Classification Accuracy for Micrornas in Plants
    (Hindawi Publishing Corporation, 2016) Yousef, Malik; Demirci, Müşerref Duygu Saçar; Khalifa, Waleed; Allmer, Jens
    MicroRNAs (miRNAs) are short RNA sequences involved in posttranscriptional gene regulation. Their experimental analysis is complicated and, therefore, needs to be supplemented with computational miRNA detection. Currently computational miRNA detection is mainly performed using machine learning and in particular two-class classification. For machine learning, the miRNAs need to be parametrized and more than 700 features have been described. Positive training examples for machine learning are readily available, but negative data is hard to come by. Therefore, it seems prerogative to use one-class classification instead of two-class classification. Previously, we were able to almost reach two-class classification accuracy using one-class classifiers. In this work, we employ feature selection procedures in conjunction with one-class classification and show that there is up to 36% difference in accuracy among these feature selection methods. The best feature set allowed the training of a one-class classifier which achieved an average accuracy of 95.6% thereby outperforming previous two-class-based plant miRNA detection approaches by about 0.5%. We believe that this can be improved upon in the future by rigorous filtering of the positive training examples and by improving current feature clustering algorithms to better target pre-miRNA feature selection.