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Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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Department: search.filters.department.Abdullah Gül University

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  • Article
    Predicting Respiratory Infection and Symptoms Development Using Gene Set Enrichment Scores and Machine Learning
    (Elsevier Sci Ltd, 2026) Aydin, Zafer; Isik, Yunus Emre
    Recent advancements in precision medicine enable personalized predictions grounded in individual-level genetic data. However, relying solely on a single type of data can decrease prediction accuracy and limit the biological interpretability of the resulting models. Incorporating predefined genetic knowledge, such as derived gene sets, can improve performance and provide deeper biological insights for complex diseases, including respiratory infections. This study aimed to evaluate the usability of enrichment scores (ES), calculated using gene sets from the Molecular Signatures Database (MSigDB), as a feature representation for machine learning models to predict respiratory viral infections and symptom development. In addition, the proposed feature representation approach was extensively compared with the de facto gene-level expression representation. A total of 36,834 predefined gene sets were compiled from the MSigDB, and their ES values were calculated. Experiments used the GSE73072 dataset from Gene Expression Omnibus, containing gene expression profiles before and after virus exposure. Various machine learning and feature selection algorithms were applied to ES-based and probe-level feature sets. The results showed that both feature representation approaches achieved an area under the precision-recall curve (AUPRC) value greater than 0.90 for all tasks. Compared with the Respiratory Viral DREAM Challenge leaderboard phase, our models showed a 14.8% improvement in pre-exposure predictions (T0) and a 17.4% improvement in symptom classification. Using enrichment scores as a feature representation generally resulted in better performance than probe-level representation when predicting respiratory infections and symptom development. Identifying key gene sets through feature selection and comparing them with essential genes for respiratory viruses enabled a more comprehensive analysis, providing deeper insights into the pathways that contribute to these predictions.
  • Article
    Electrospun PCL/PEG Nanofibers Incorporating Plantago Lanceolata Extract and Clove Oil for Dual-Function Wound Dressings
    (IOP Publishing Ltd, 2026) Teke, Selin Nur; Yuruk, Adile; Isoglu, Ismail Alper
    In this study, we developed electrospun polycaprolactone/polyethylene glycol (PCL/PEG) nanofibers loaded with Plantago lanceolata (P. lanceolata) extract and clove oil to evaluate their combined potential for wound healing and antibacterial activity. PCL/PEG nanofibers were electrospun and post-loaded with P. lanceolata extract at 5%, 10%, and 15% (w/v), together with 1% (v/v) clove oil. Scanning electron microscope analysis showed a uniform, bead-free nanofibrous structure, with fiber diameters ranging from 768 +/- 140 nm to 892 +/- 206 nm and pore sizes from 3.93 +/- 0.97 & micro;m to 6.20 +/- 1.16 & micro;m. The nanofibers exhibited swelling ratios between 103.23 +/- 16.42% and 133.93 +/- 40.45% within 1 h and showed gradual degradation ranging from 25.92 +/- 2.84% to 57.76 +/- 0.92% over 21 d, with cumulative extract release approaching a plateau by day 28 under the experimental conditions. The incorporation of plant extract and essential oil initially increased the water contact angle from 15.97 +/- 1.07 degrees to 48.61 +/- 7.85 degrees, indicating that the nanofiber surface remained hydrophilic; at higher extract contents, the nanofibers transitioned to a superhydrophilic state. Antibacterial activity was primarily governed by clove oil, yielding up to 85.16 +/- 0.36% efficacy against E. coli (E. coli) and 79.90 +/- 0.29% against S. aureus (S. aureus). While P. lanceolata extract alone showed limited antibacterial activity, its presence within clove oil-loaded nanofibers consistently enhanced antibacterial performance at the composite level. In vitro scratch assays demonstrated pronounced wound closure at later time points (72-96 h), particularly for nanofibers containing higher extract concentrations, confirming the dominant contribution of P. lanceolata to the healing response. Overall, the electrospun PCL/PEG nanofibers represent a dual-function wound dressing, with clove oil contributing antibacterial protection and P. lanceolata extract supporting wound healing.
  • Article
    Effects of Group Aerobic Exercise on Health and Social Adaptation in Earthquake Survivors: A Qualitative Study
    (Lippincott Williams and Wilkins, 2026) Yardimci, Fatma Betul; Kaya, M. Siyabend; Anaforoglu, Bahar; Altay Ili, Hafize; Apak, Hidir
  • Article
    Dissecting the Strain and Sex Specific Connectome Signatures of Unanesthetized C57BL/6J and DBA/2J Mice Using Magnetic Resonance Imaging
    (Wiley, 2026) Neuberger, Thomas; Kamens, Helen M.; Zhang, Nanyin; Ünsal, Hayreddin Said; Arefin, Tanzil M.
    Mouse models are an essential tool for understanding behavior and disease states in neuroscience research. While genetic and sex-specific effects have been reported in many neurodegenerative and psychiatric illnesses, these factors may also alter baseline neuroanatomical features of mice. This raises the question of whether the observed changes are related to the disease being studied (i.e., pathological differences) or if there are baseline strain or sex differences that may predispose animals to different responses. Over the past decade, tremendous effort has been made to map neural architecture at various scales; however, the complex relationships, including identifying genetic and sex-specific differences in brain structure and function, remain understudied. To bridge this gap, we used C57BL/6J and DBA/2J mice, two of the most widely used inbred mouse strains in neuroscience research, to investigate strain and sex-specific features of the brain connectome in awake animals using magnetic resonance imaging (MRI). By combining resting-state functional MRI and diffusion MRI, we found that the motor, sensory, limbic, and salience networks exhibit significant differences in both functional and structural domains between C57BL/6J and DBA/2J mice. Further, functional and structural properties of the brain were significantly correlated in both strains. Our results underscore the importance of considering these baseline differences when interpreting brain-behavior interactions in mouse models of human disorders.
  • Article
    Time Distributed Classification of Alzheimer’s Disease on MRI Scans
    (John Wiley and Sons Ltd, 2026) Dundar, Mehmet Sait; Yilmaz, Bulent
    The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.
  • Article
    Three-Dimensional Culture Enhances the Antimicrobial Activity of Mesenchymal Stem Cells against Shiga Toxin-Producing Escherichia Coli O157:H7 in Vitro
    (Oxford Univ Press, 2026) Fidan, Özkan; Türkyılmaz, Süheyla; Bicer, Mesude; Öztürk, Esengül; Sener, Fatma
    Aims This study examines the in vitro antibacterial activity of palatal adipose tissue-derived mesenchymal stem cells (PAT-MSCs) and the expression of antimicrobial peptide LL-37, with a particular focus on the effect of three-dimensional (3D) nanofibrillar cellulose-based hydrogel against Shiga toxin-producing Escherichia coli (STEC) harboring stx1 and/or stx2 genes isolated from mastitis milk in Turkey.Methods and results The antibacterial activity of conventionally cultured PAT-MSCs and 3D-cultured PAT-MSCs (PAT-MSCs-3D) was evaluated against STEC isolates and Escherichia coli ATCC 35150 using quantitative colony-forming unit (CFU) assay. The expression levels of antimicrobial peptide (AMP)-encoding genes were evaluated by quantitative real-time PCR, and AMP production was further validated by immunocytochemical staining. The results indicated that PAT-MSCs-3D exhibited significantly enhanced antibacterial efficacy, resulting in marked bacterial inhibition of all tested STEC strains, with bacterial reductions reaching up to 6-7 log under specific experimental conditions. Molecular and immunocytochemical analyses demonstrated increased expression of the antimicrobial peptide LL-37 in PAT-MSCs-3D compared to 2D cultures.Conclusions Our results show that culturing PAT-MSCs in 3D conditions leads to a significant enhancement in their antimicrobial properties, which could be linked to the upregulation of LL-37.
  • Article
    Radar Resolution Enhancement Based on Burg-Aided MIMO-DBS and Burg-Aided MIMO-SAR †
    (MDPI, 2026) Bekar, Muge; Bekar, Ali; Baker, Christopher John; Gashinova, Marina; Pirkani, Anum
    Autonomous systems require sensors that provide high-resolution imagery in adverse lighting and weather conditions for advanced situational awareness. In this regard, radars are a mandatory component of autonomous systems. Although Multiple-Input Multiple-Output (MIMO) radars provide high angular resolution beyond that of their actual physical dimension, much higher cross-range resolutions are required, especially in traffic congested areas, to differentiate and recognize closely positioned targets. The motion of the MIMO radar platform can be exploited to obtain higher cross-range resolution in the off-boresight direction, using Synthetic Aperture Radar (SAR) and Doppler Beam Sharpening (DBS) techniques, but improvements in the boresight direction, the most crucial direction for path planning, require the use of super-resolution techniques. This paper proposes a technique that combines the Burg algorithm with MIMO-SAR and MIMO-DBS radar data to enhance the cross-range resolution in the boresight direction and to achieve further enhanced cross-range resolution in off-boresight directions. The proposed technique is applied to both frequency domain and time domain data in back-projection (BP) and DBS image formation processing. A comprehensive comparison is made, with evaluation of corresponding performance and operational complexity. The performance of the technique is validated through simulation, lab-based and real-world experiments at a frequency of 77 GHz.
  • Article
    Leveraging Clinical Data for Early Heart Disease Prediction: A Machine Learning Approach With Interpretability Analysis
    (SAGE Publications Ltd, 2026) Qumsiyeh, Emma; Al-Wirdian, Qassam; Ersoz, Nur Sebnem
    Background: Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for early and accurate diagnosis to support effective prevention and treatment strategies. Methods: This study presents a machine-learning-based approach for predicting heart disease using clinical and demographic data from a publicly available dataset. Four widely used classification algorithms-Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Decision Trees-were evaluated to identify the most effective predictive model. The dataset underwent comprehensive preprocessing, including handling missing values, categorical encoding, and feature normalization, to enhance data quality and model robustness. Model performance was assessed using accuracy, precision, recall, and AUC-ROC metrics. Results: Findings show that hyperparameter-optimized models, particularly Random Forest and KNN, demonstrated strong predictive performance. Explainability techniques, specifically SHapley Additive exPlanations (SHAP), were incorporated to improve interpretability, transparency, and clinical trust. SHAP values were used to analyze feature importance and provide explanations for individual predictions. Conclusion: The results underscore the potential of interpretable machine-learning models as valuable tools for early diagnosis, risk stratification, and clinical decision support. Future research should employ larger datasets and investigate real-time predictive applications further to enhance the generalizability and clinical utility of these models.
  • Article
    Functional Characterization of Loss of RNF43 Reveals Neuronal Defects in a Caenorhabditis Elegans Model
    (MDPI, 2026) Kazan, Hasan Huseyin; Turkyilmaz, Zafer; Ekim, Burcu; Kaya, Cem; Ergun, Mehmet Ali; Sonmez, Kaan; Güzel, Sinem
    Ring finger protein 43 (RNF43) encodes a transmembrane E3 ubiquitin ligase that negatively regulates canonical Wnt signaling and is classically associated with serrated polyposis syndrome and colorectal cancer. In this study, regarding a homozygous truncating RNF43 variant (NM_001305545.1:c.1906C>T; p.Gln636Ter) in a patient segregating with a severe neurodevelopmental phenotype characterized by developmental delay, neonatal hypotonia, recurrent seizures, progressive microcephaly, and bilateral optic atrophy, the loss of polarity defective 1 (plr-1), an ortholog of RNF43, was modeled in Caenorhabditis elegans and the phenotype was primarily characterized. The results demonstrated that loss of the plr-1 disrupted gamma aminobutyric acid (GABA)ergic axon organization, reduced locomotor speed calculated from 60 s recordings, and altered developmental growth. These findings expand the phenotypic spectrum of RNF43 and support a dosage-dependent developmental role.
  • Article
    Comparison of Rapamycin and 3-Methyladenine in Cisplatin-Induced Experimental Cardiotoxicity
    (Wiley, 2026) Kaymak, Emin; Karabulut, Derya; Yalcin, Betul; Guner, Serife Ayaz; Ozturk, Emel; Findik, Fatma; Boyvat, Dudu
    In this study, we evaluated how cisplatin cardiotoxicity affects the histological and endocrine functions of the heart and autophagy while using Rapamycin(Rapa) and 3-methyladenine(3-MA) as autophagy activators and inhibitors. Control, Cisplatin (Cis), 3-methyladenine + Cisplatin (3-MA + Cis) and Rapamycin + Cisplatin (Rapa + Cis). Rapa and 3-MA were administered for 15 days, while a single dose of cisplatin was administered on the 7th day. Natriuretic peptide receptor-A(NPR-A), receptor-B(NPR-B) and biochemically atrial natriuretic peptide(ANP) and brain natriuretic peptide(BNP) levels were evaluated in heart tissue. Cis caused a statistically significant increase in NPR-A and NPR-B expression, as well as ANP and BNP levels. However, the levels of Beclin-1 and LC3B were not statistically significant. Rapa was more effective than 3-MA + Cis on NPR-A and NPR-B expressions, but did not show the same effect on ANP and NT-proBNP levels. Cis caused an increase in Beclin-1 and LC3B levels, while a decrease was observed in both 3-MA + Cis and Rapa + Cis groups. Our results revealed that Cis cardiotoxicity disrupts autophagy and endocrine function of the heart. It was concluded that by continuing the activator and inhibitor substances after Cis application, more effective results can be obtained in Beclin-1 expression than LC3B expression and that they can be effective in eliminating the toxicity of Cis.