WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article A Machine Learning Model for Predicting Oligoclonal Band Positivity Using Routine Cerebrospinal Fluid and Serum Biochemical Markers(Oxford University Press Inc, 2025) Gözgöz, Hazar; Orhan, Oğuzhan; Akan Konuk, Başak; Akan, Pınar; 01. Izmir Institute of TechnologyOBJECTIVE: To develop and validate a machine learning model for predicting oligoclonal band (OCB) positivity using routine cerebrospinal fluid (CSF) and serum biochemical markers to improve the diagnostic accuracy and efficiency of assessing intrathecal immunoglobulin G (IgG) synthesis. METHODS: In this retrospective study (n = 1709), an ensemble model was developed using 8 refined CSF and serum parameters. Combining optimized CatBoost, XGBoost, and LightGBM classifiers, the model was trained and evaluated using a 2-phase workflow, including 5-fold cross-validation and validation on independent internal (n = 342) and external (n = 49) cohorts. RESULTS: The developed ensemble model achieved a receiver operating characteristic-area under the curve (ROC-AUC) of 0.902 on the internal test set, significantly outperforming the conventional IgG index (ROC-AUC, 0.795). At its optimal threshold, the model demonstrated an accuracy of 0.830, with a sensitivity of 0.714 and a specificity of 0.916. On the external validation cohort, it achieved 90% accuracy and 96% sensitivity. CONCLUSIONS: A novel machine learning ensemble model accurately predicts OCB positivity using routine laboratory data and demonstrates superior performance compared with the IgG index. This approach represents a significant step in applying artificial intelligence in laboratory medicine, with the potential to enhance diagnostic efficiency. Prospective, multicenter validation is essential for broader clinical implementation. © The Author(s) 2025.Article Citation - WoS: 11Citation - Scopus: 14MicroRNA-155 plays selective cell-intrinsic roles in brain-infiltrating immune cell populations during neuroinflammation(American Association of Immunologists, 2023) Thompson, J.W.; Ekiz, Hüseyin Atakan; Hu, R.; Huffaker, T.B.; Ramstead, A.G.; Ekiz, Hüseyin Atakan; Bauer, K.M.; Tang, W.W.; 04.03. Department of Molecular Biology and Genetics; 04. Faculty of Science; 01. Izmir Institute of TechnologyThe proinflammatory microRNA-155 (miR-155) is highly expressed in the serum and CNS lesions of patients with multiple sclerosis (MS). Global knockout (KO) of miR-155 in mice confers resistance to a mouse model of MS, experimental autoimmune encephalomyelitis (EAE), by reducing the encephalogenic potential of CNS-infiltrating Th17 T cells. However, cell-intrinsic roles for miR-155 during EAE have not been formally determined. In this study, we use single-cell RNA sequencing and cell-specific conditional miR-155 KOs to determine the importance of miR-155 expression in distinct immune cell populations. Time-course single-cell sequencing revealed reductions in T cells, macrophages, and dendritic cells (DCs) in global miR-155 KO mice compared with wild-type controls at day 21 after EAE induction. Deletion of miR-155 in T cells, driven by CD4 Cre, significantly reduced disease severity similar to global miR-155 KOs. CD11c Cre-mediated deletion of miR-155 in DCs also resulted in a modest yet significant reduction in the development of EAE, with both T cell- and DC-specific KOs showing a reduction in Th17 T cell infiltration into the CNS. Although miR-155 is highly expressed in infiltrating macrophages during EAE, deletion of miR-155 using LysM Cre did not impact disease severity. Taken together, these data show that although miR-155 is highly expressed in most infiltrating immune cells, miR-155 has distinct roles and requirements depending on the cell type, and we have demonstrated this using the gold standard conditional KO approach. This provides insights into which functionally relevant cell types should be targeted by the next generation of miRNA therapeutics. Copyright © 2023 by The American Association of Immunologists, Inc.