Browsing by Author "Erturk Zararsiz, Gozde"

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Citation - WoS: 6
Citation - Scopus: 6
Diagnosis Melanoma With Artificial Intelligence Systems: A Meta-Analysis Study and Systematic Review
(Wiley, 2025) Erturk Zararsiz, Gozde; Yerlitas Tastan, Serra Ilayda; Celik Gurbulak, Elif; Erakcaoglu, Aleyna; Yilmaz Isikhan, Selen; Demirbas, Abdullah; Zararsiz, Goekmen
BackgroundOne of the most promising and rapidly advancing research areas in recent years is using dermoscopic images for automatic diagnosis with artificial intelligence and machine learning methods.ObjectiveThis study aimed to synthesize the existing studies for the clinical use of applications made with artificial intelligence methods and to summarize the predictive performance of deep learning and hybrid models-based algorithms in all these studies with a large-scale meta-analysis.MethodThe literature review was conducted between January 2006 and May 2024, and meta-analysis data were created by scanning the Web of Science (WOS), Scopus and MEDLINE databases. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist.ResultsA total of 2722 articles were evaluated. Data from 78 diagnostic tests from 39 primary studies meeting the inclusion and exclusion criteria were assessed. The pooled SROC overall model AUC was 0.96 [95% CI: 0.94-0.98], sensitivity was 0.89 [95% CI: 0.85-0.91] and specificity was 0.92 [95% CI: 0.90-0.94]. In the subgroup analyses, the pooled AUC was 0.98 [95% CI: 0.96-0.99] for HYBRID models.ConclusionRecent studies have suggested that artificial intelligence algorithms and machine learning methods should be used extensively in medicine to assist physicians, especially in diagnosing melanoma. The ability of HYBRID model algorithms to predict diseases is promising. In particular, the performance of HYBRID models was found to be high. This information can assist clinicians in interpreting the most appropriate algorithms for diagnosing melanoma.
Role of Long Non-Coding RNA X-Inactive Transcript (XIST) in Neuroinflammation and Myelination: Insights From Cerebral Organoids and Implications for Multiple Sclerosis
(MDPI, 2025) Pepe, Nihan Aktas; Acar, Busra; Zararsiz, Gozde Erturk; Guner, Serife Ayaz; Sen, Alaattin; Erturk Zararsiz, Gozde; Ayaz Guner, Serife; Aktas Pepe, Nihan
Background/Objectives: X-inactive-specific transcript (XIST) is a factor that plays a role in neuroinflammation. This study investigated the role of XIST in neuronal development, neuroinflammation, myelination, and therapeutic responses within cerebral organoids in the context of Multiple Sclerosis (MS) pathogenesis. Methods: Human cerebral organoids with oligodendrocytes were produced from XIST-silenced H9 cells, and the mature organoids were subsequently treated with either FTY720 or DMF. Gene expression related to inflammation and myelination was subsequently analyzed via qRT-PCR. Immunofluorescence staining was used to assess the expression of proteins related to inflammation, myelination, and neuronal differentiation. Alpha-synuclein protein levels were also checked via ELISA. Finally, transcriptome analysis was conducted on the organoid samples. Results: XIST-silenced organoids presented a 2-fold increase in the expression of neuronal stem cells, excitatory neurons, microglia, and mature oligodendrocyte markers. In addition, XIST silencing increased IL-10 mRNA expression by 2-fold and MBP and PLP1 expression by 2.3- and 0.6-fold, respectively. Although XIST silencing tripled IBA1 protein expression, it did not affect organoid MBP expression. FTY720, but not DMF, distinguished MBP and IBA1 expression in XIST-silenced organoids. Furthermore, XIST silencing reduced the concentration of alpha-synuclein from 300 to 100 pg/mL, confirming its anti-inflammatory role. Transcriptomic and gene enrichment analyses revealed that the differentially expressed genes are involved in neural development and immune processes, suggesting the role of XIST in neuroinflammation. The silencing of XIST modified the expression of genes associated with inflammation, myelination, and neuronal growth in cerebral organoids, indicating a potential involvement in the pathogenesis of MS. Conclusions: XIST may contribute to the MS pathogenesis as well as neuroinflammatory diseases such as and Alzheimer's and Parkinson's diseases and may be a promising therapeutic target.