Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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  • Article
    Citation - WoS: 13
    Citation - Scopus: 17
    Machine Learning-Assisted Prediction of the Toxicity of Silver Nanoparticles: a Meta-Analysis
    (Springer, 2023) Bilgi, Eyüp; Öksel Karakuş, Ceyda
    Silver nanoparticles are likely to be more dangerous than other forms of silver due to the intracellular release of silver ions upon dissolution and the formation of mixed ion-containing complexes. Such concerns have resulted in an ever-growing pile of scientific evaluations addressing the safety aspects of nanosilver with widely varying methodological approaches. The substantial differences in the conduct/design of nanotoxicity screening have led to the generation of conflicting findings that may be accurate in their narrative but fail to provide a complete picture. One strategy to maximize the use of individual risk assessments with potentially biased estimates of toxicological effects is to homogenize results across several studies and to increase the generalizability and human relevance of their findings. Here, we collected a large pool of data (n=162 independent studies) on the cytotoxicity of nanosilver and unrevealed potential triggers of toxicity. Two different machine learning approaches, decision tree (DT) and artificial neural network (ANN), were primarily employed to develop models that can predict the cytotoxic potential of nanosilver based on material- and assay-related parameters. Other machine learning algorithms (logistic regression, Gaussian Naive Bayes, k-nearest neighbor, and random forest classifiers) were also applied. Among several attributes compared, exposure concentration, duration, zeta potential, particle size, and coating were found to have the most substantial impact on nanotoxicity, with biomolecule- and microorganism-assisted surface modifications having the most beneficial and detrimental effects on cell survival, respectively. Such machine learning-assisted efforts are critical to developing commercially viable and safe nanosilver-containing products in the ever-expanding nanobiomaterial market.
  • Article
    Citation - WoS: 1
    Citation - Scopus: 1
    Exploring the Heterogeneity of Ige-Mediated Food Allergy Through Latent Class Analysis
    (S. Karger AG, 2022) Akarsu, Ayşegül; Öksel Karakuş, Ceyda; Ocak, Melike; Oral, Nihan; Bilgi, Eyüp; Şahiner, Ümit Murat; Soyer, Özge; Şekerel, Bülent Enis
    Introduction: Food allergy (FA) is a heterogeneous disease with multiple morbidities and a huge burden for patients and healthcare systems. Variable manifestations, comorbidities (atopic dermatitis [AD], asthma, and/or allergic rhinitis [AR]), severity (anaphylaxis), and outcomes suggest the existence of different endotypes that cluster analyses may reveal. In this study, we aimed to investigate distinct subgroups among patients with FAs using data from 524 children/adolescents. Methods: 524 patients with IgE-mediated FA (353 male [67%]; median age 4.4 years [IQR:3.0-6.8]), 354 (68%) had multiple FA. The history of AD, asthma, AR, and anaphylaxis was recorded in 59.4%, 35.5%, 24.2%, and 51.2% of the patients, respectively. Latent class analysis was carried out to distinguish clinical FA phenotypes using five potential markers of allergy severity (single/multiple FA, never/inactive/current asthma and AD, AR, and anaphylaxis). Results: Three distinct phenotypes were identified: (1) multiple FA with eczema and respiratory multimorbidity (42%), (2) multiple FA with persistent eczema (34%), and (3) single FA with respiratory multimorbidity without eczema (24%). Compared with the single FA cluster, the prevalence of AD was significantly higher in multiple FA groups. Cluster 1 had the highest frequency of AR and allergic asthma, and the lowest rate of total tolerance of FA. Discussion: We put forward the hypothesis of underlying pathogenesis according to the clinical phenotypes. While skin barrier defect may play a dominant role in the pathogenesis in Cluster 2, immune dysregulation may be dominant in Cluster 3. In Cluster 1, the most severe group, a combination of both skin barrier defects and immune dysregulation may be responsible for the clinical features.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 7
    Five New Cardenolides Transformed From Oleandrin and Nerigoside by Alternaria Eureka 1e1bl1 and Phaeosphaeriasp. 1e4cs-1 and Their Cytotoxic Activities
    (Elsevier Ltd., 2021) Karakoyun, Çiğdem; Küçüksolak, Melis; Bilgi, Eyüp; Doğan, Gamze; Çömlekçi, Yiğit Ege; Bedir, Erdal
    Biotransformation of oleandrin (1) and nerigoside (2) by endophytic fungi; Alternaria eureka 1E1BL1 and Phaeospheria sp. 1E4CS-1, has led to the isolation of five new metabolites (3, 5, 6, 7 and 8) together with a known compound (4). The structures of the biotransformation products were elucidated by 1D-, 2D NMR and HR-MS. Phaeospheria sp. mainly provided monooxygenation reactions on the A and B rings, whereas A. eureka afforded both monooxygenated and desacetylated derivatives of the substrates. Cytotoxic activity of the compounds was tested against a non-cancerous (HEK-293) and four cancer (PANC-1, MIA PaCa-2, DU 145 and A549) cell lines by MTT cell viability assay. All compounds were less cytotoxic than oleandrin, which had IC50 values ranging between 2.7 and 41.9 nM. Two of the monohydroxylated metabolites, viz. 7(?)-hydroxy oleandrin (3) and 1(?)-hydroxy oleandrin (7), were also potent with IC50 values from 18.45 to 39.0 nM, while desacetylated + monohydroxylated, or dihydroxylated products had much lower cytotoxicity. Additionally, the lesser activity of 2 and its metabolite (6) possessing diginose as sugar residue inferred that oleandrose moiety is important for the toxicity of oleandrin as well as hydrophobicity of the steroid core. © 2020 Phytochemical Society of Europe