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  A Novel MRI-Based Deep Learning–Radiomics Framework for Evaluating Cerebrospinal Fluid Signal in Central Nervous System Infection

  (Frontiers Media SA, 2025) Cüce, Ferhat; Tulum, Gökalp; Isik, Muhammed Ikbal; Jalili, Marziye; Girgin, Güven; Karadaş, Ömer; Baş, Niray; Özcan, Berza; Savaşci, Ümit; Şakir, Sena; Karadaş, Akçay Övünç; Teomete, Eda; Osman, Onur; Rasheed, Jawad; Rasheed, Jawad;

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  Introduction: Accurate and timely diagnosis of central nervous system infections (CNSIs) is critical, yet current gold-standard techniques like lumbar puncture (LP) remain invasive and prone to delay. This study proposes a novel noninvasive framework integrating handcrafted radiomic features and deep learning (DL) to identify cerebrospinal fluid (CSF) alterations on magnetic resonance imaging (MRI) in patients with acute CNSI.Methods: Fifty-two patients diagnosed with acute CNSI who underwent LP and brain MRI within 48 h of hospital admission were retrospectively analyzed alongside 52 control subjects with normal neurological findings. CSF-related signals were segmented from the ventricular system and sub-lentiform nucleus parenchyma, including perivascular spaces (PVSs), using semi-automated methods on axial T2-weighted images. Two hybrid models (DenseASPP-RadFusion and MobileASPP-RadFusion), fusing radiomics and DL features, were developed and benchmarked against base DL architectures (DenseNet-201 and MobileNet-V3Large) via 5-fold nested cross-validation. Radiomics features were extracted from both original and Laplacian of Gaussian–filtered MRI data.Results: In the sub-lentiform nucleus parenchyma, the hybrid DenseASPP-RadFusion model achieved superior classification performance (accuracy: 78.57 ± 4.76%, precision: 84.09 ± 3.31%, F1-score: 76.12 ± 6.86%), outperforming its corresponding base models. Performance was notably lower in ventricular system analyses across all models. Radiomics features derived from fine-scale filtered images exhibited the highest discriminatory power. A strict, clinically motivated patient-wise classification strategy confirmed the sub-lentiform nucleus region as the most reliable anatomical target for distinguishing infected from non-infected CSF.Discussion: This study introduces a robust and interpretable MRI-based deep learning–radiomics pipeline for CNSI classification, with promising diagnostic potential. The proposed framework may offer a noninvasive alternative to LP in selected cases, particularly by leveraging CSF signal alterations in PVS-adjacent parenchymal regions. These findings establish a foundation for future multicenter validation and integration into clinical workflows.

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  Unveiling the Multifaceted Nature of Spirituality: Assessing the Dimensions of the Spirituality Inventory in the Turkish Context—A Preliminary Adaptation and Validity Reliability Study

  (Springer, 2025) Ekşi, Halil; Günel, Dilek; Akgündüz, Ece Yağcı; Gürkan, Emre; Çavuş, Fadile Zeynep; Altıntaş, Yasemin; Akgündüz, Ece Yağcı

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  The measurement of spirituality, a concept with significant implications for mental health, requires instruments that can assess its complexity and diversity without cultural limitations. This study adapts the Dimensions of Spirituality Inventory (DSI) into Turkish, examining its validity and reliability. A linguistic equivalence study with 43 participants showed coefficients between .79 and .91. The test–retest correlation coefficients of the subscales ranged from .35 to .78. The 53-item scale was then administered to 671 adults, and confirmatory factor analysis indicated an acceptable fit. The internal consistency reliability coefficient was .90. Positive correlations with related scales confirmed acceptable criterion-related validity. The study concluded that the Turkish version of the DSI is a valid and reliable tool for use in scientific research in Türkiye.

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  NRBO-AGP: A Novel Feature Selection Approach for Accurate Protein Solubility Prediction

  (Elsevier Ltd., 2025) Elmi, Zahra; Elmi, Soheila; Danishvar, Sebelan;

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  Protein solubility determines how well a protein dissolves in an aqueous solution, and this property is a criticalfactor in the functional analysis of proteins and biotechnological applications. Accurately estimating solubilitycan provide significant advantages in areas such as protein engineering and drug discovery. This study proposes anew feature selection method, Newton-Raphson-based Optimization and Adaptive Gradient Perturbation (NRBOAGP) for predicting protein solubility. The research combines the accuracy and speed of the Newton-Raphsonmethod with the capacity of population-based optimization techniques to balance exploration and exploitation. Using 3144 protein sequences from the eSOL database, descriptor features were obtained for each protein,resulting in a dataset with 3104 features. The performance of NRBO-AGP was compared with eight differentmetaheuristic algorithms and evaluated using five regression models: MLP, AdaBoost, Gradient Boosting Trees,Random Forest, and Support Vector Regressor (SVR). The best results were obtained with the Gradient Boostingand Random Forest. Mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination(𝑅2) metrics were used for performance evaluation. The results show that NRBO-AGP outperforms other metaheuristic algorithms in all regression models. The best results were achieved with Gradient Boosting and RandomForest, reaching MAE:0.0001 ± 0.0000, RMSE: 0.0008 ± 0.0000, and 𝑅2: 0.9908 ± 0.0005, and MAE: 0.0002 ± 0.0000,RMSE: 0.0025 ± 0.0000, and 𝑅2: 0.9908 ± 0.0005. These findings show that NRBO-AGP is an effective feature selection tool for predicting protein solubility. Multiple statistical analyses based on Friedman and Nemenyi testsshow that the NBRO-AGP method exhibits statistically significant superior performance (𝑝 < .05) compared toother metaheuristic algorithms in MAE and RMSE metrics and also achieves the highest performance in the 𝑅2score.

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  From Plate to Planet: Nutritional and Environmental Sustainability of Turkish Cuisine Across the Regions of Türkiye

  (Taylor & Francis, 2025) Çelik, Zehra Margot; Barcın Güzeldere, Hatice Kübra; Ede Çintesun, Elif; Bayram, Hatice Merve; Çintesun, Elif Ede

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  This study aims to analyze the nutrient profile, carbon and water foot-prints of traditional menus across geographical regions of Türkiye. The recipes were selected from cookbooks and were analyzed through Google Trends. The menus showed significantly higher levels of energy, protein, carbohydrates and several vitamins and minerals (p < 0.05). Carbon footprint analysis revealed Southeast Anatolia had the highest footprint (5.54 ± 0.55 kg CO2 eq/d), while Central Anatolia had the lowest (2.01 ± 0.23 kg CO2 eq/d) (p < 0.05). Water footprint data indicated Marmara had the highest values (4165.03 ± 386.95 L/kg/d), with Central Anatolia having lowest (1132.14 ± 101.18 L/kg/d) (p < 0.05). However, no statistically significant differences were observed between traditional menus and the EAT-Lancet Planetary Diet. These findings highlight the importance of developing sustainability strategies that preserve cultural heritage while promoting plant-based ingredients, seasonal and local foods, and eco-friendly cooking methods to mitigate environmental impact.

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  Global Logistics Efficiency After COVID-19: A Cross-Country Analysis Using the Logistics Performance Index

  (Taylor & Francis, 2025) Bayraktar, Erkan; Zaim, Selim; Güdelek, Mehmet; Gölgeci, İsmail

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  This study analyses the World Bank’s Logistics Performance Index (LPI) to quantify changes in logistics performance before and after COVID-19, using a slack-based measurement (SBM) model of data envelopment analysis (DEA) and the Malmquist Index (MI), which indicates efficiency changes over time. The LPI analysis shows performance gains, but challenges in timeliness and international shipments. The SBM-DEA model indicates efficiency improvements mainly through technological advancements. After the pandemic, the decline in efficiency in customs operations and international shipments signals a need for streamlined procedures and cost reductions. The analysis also shows that the post-pandemic recovery varied by region. This study contributes to the theory by showing how dynamic capabilities and institutional resilience influence logistics adaptability and widen global performance gaps. Informed by the resource-based view and dynamic capabilities theory, it advises policymakers to enhance logistics infrastructure, modernise customs, and reduce costs to tackle postpandemic challenges in underperforming regions.

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