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Development of Lightweight Microcellular SBS Foams for Efficient Microwave Absorption
No full textThis study presents a novel, systematic approach to developing lightweight, flexible microwave absorbing composites based on styrene–butadiene–styrene (SBS) filled with conductive and magnetic fillers. Graphite and nickel powders were combined to simultaneously introduce dielectric and magnetic loss mechanisms, allowing their combined effect to enhance microwave absorption, while a physical blowing agent created microcellular foam at two target densities (0.6 and 0.44 g cm−3) to evaluate porosity effects. Systematic variation of filler ratios and foam morphology enabled tuning of dielectric–magnetic interactions and impedance matching. Scanning electron microscopy showed nickel addition affected graphite dispersion, and higher blowing agent content produced finer, more uniform cells; dielectric and magnetic characterization revealed enhanced permittivity and magnetic permeability from interfacial polarization and magnetic dipolar effects, especially in the optimal 40 phr graphite/40 phr nickel formulation. This composite showed a minimum reflection loss (RLmin) of −18 dB and an effective absorption bandwidth of 9.71–12.50 GHz (~2.79 GHz) (at ~0.6 mm), outperforming the graphite-only composite with 40 phr filler, which exhibited only a narrow window near 8–9 GHz (~1.0 GHz). Further enhancement was achieved with the foamed version at 0.44 g cm−3, where the finer, more homogeneous cellular morphology yielded RLmin = −54 dB (10.49 GHz) and broadband coverage across the X-band (8.0–12.5 GHz) at ~0.8 mm
The Glymphatic System in Migraine: Emerging Mechanisms, Glial Biomarkers, and Diagnostic Imaging Approaches
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Determining the Knowledge and Attitudes of Nurses Working in a Psychiatric Clinic Regarding Artificial Intelligence (AI)
No full textAim/Objective: In this study, the knowledge and attitudes of nurses working in a psychiatric clinic about artificial intelligence (AI) were examined. Background: Nurses integrating AI applications into patient care can increase the quality of care and work efficiency. Learning nurses' knowledge and perspectives on AI is important to determinetheirneeds and current situations. Design: It was designed using a qualitative methodology. Methods: This study was conducted with 14 nurses working in a psychiatric clinic of a hospital in Türkiye. The data of the research were collected between November 2024 and February 2025. Data were collected through the ‘Introductory Information Form’ and the ‘Semi-structured Interview Form’. An inductive thematic analysis was performed following Braun and Clarke's six-step model. Coding was conducted manually, and intercoder reliability was ensured through independent coding and consensus meetings. Results: Six themes were identified in the study: the definition and meaning of AI, nurses' knowledge levels regarding AI application areas in psychiatric clinics, nurses' views on the risks of AI in the field of psychiatry, challenges related to the use of AI in the field of psychiatry, the benefits of AI, and making the contributions and services of clinical nurses visible through AI. Conclusions: Although nurses were aware of the benefits of AI, they stated the aspects they found risky. They also emphasised the lack of knowledge and experience regarding AI, as well as the need to introduce and promote it
Automated detection and classification of dental trauma in periapical radiographs using deep learning: a study based on the Andreasen classification
No full textObjective The aim of this study was to evaluate the performance of a deep learning (DL) model in automatically identifying dental trauma types on selected periapical radiographs, classified according to the Andreasen system. Methods and materials Selected periapical radiographs were annotated based on the Andreasen classification. Using these annotations, a YOLOv8-based DL model was developed to classify trauma types. Because of the large number of trauma subtypes and the limited dataset size, labels were later consolidated into two main categories, and a second model was trained. The performance of both models was assessed using sensitivity, precision, and F1-score. Results The initial model showed low overall performance, with a sensitivity of 0.34, precision of 0.29, and F1-score of 0.31. Among the subtypes, avulsion achieved the best performance across all metrics (F1-score: 0.83). After regrouping labels into two main categories, the model’s overall performance improved markedly (F1-score: 0.76). Performance was higher for detecting “injuries to hard dental tissues and the pulp” (F1-score: 0.82) than for “injuries to the periodontal tissues” (F1-score: 0.44). Conclusion The DL model demonstrated strong potential in identifying dental trauma on selected periapical radiographs, particularly in accurately localizing fracture lines
Baseline glucose-to-lymphocyte ratio predicts nivolumab outcomes in advanced non-small cell lung cancer: a multicenter retrospective study
No full textBackground: Biomarkers guiding immunotherapy in non-small cell lung cancer (NSCLC) are limited. The glucose-to-lymphocyte ratio (GLR), integrating metabolic and immune status, has shown prognostic value in several cancers but has not been systematically evaluated in patients receiving PD-1 blockade. Methods: We retrospectively analyzed 837 patients with advanced or metastatic NSCLC treated with nivolumab across 21 oncology centers in Turkey (2015–2025). Baseline GLR was calculated from fasting glucose and absolute lymphocyte counts. Overall survival (OS) and progression-free survival (PFS) were estimated using Kaplan–Meier and compared with log-rank tests. Multivariate Cox regression models identified independent predictors. Receiver operating characteristic (ROC) analysis determined the optimal GLR cut-off for OS, which was subsequently applied to PFS analyses for consistency. Results: The optimal GLR cut-off for mortality was ≥70.76 (AUC = 0.635, 95% CI: 0.597–0.674; p < 0.001). Patients with GLR <70.76 achieved significantly longer OS (median 24.1 vs 9.6 months; p < 0.001) and PFS (9.7 vs 5.8 months; p < 0.001) compared with those with GLR ≥70.76. In multivariate analysis, high GLR and poor ECOG performance status independently predicted worse OS. Prior thoracic radiotherapy was associated with improved outcomes. Conclusion: Baseline GLR is a practical, cost-effective biomarker that independently predicts OS in advanced NSCLC patients treated with nivolumab. Elevated GLR likely reflects metabolic dysfunction and impaired immune reserve, both unfavorable for PD-1 blockade efficacy. Prospective studies are warranted to validate GLR and define its role in clinical decision-making
Improving the fire resistance of semi-rigid polyurethane foams with cellulose-based layer coatings
No full textSome halogen-phosphorus-based flame retardants (FR), commonly used to improve the fire resistance of polyurethane (PU) foams, have been banned or restricted due to high toxicity and potential carcinogenicity. As a result, research has shifted toward sustainable, low-cost, and environmentally friendly alternatives. This study explores the application of pure cellulose sulfate (CS) and chitosan (CH) compounds onto semi-rigid polyurethane foam (SrPUF) surfaces using a layer-by-layer (LbL) coating method. Coatings were applied in three (3 BL) and six (6 BL) bilayers. Surface chemical characterization was confirmed using Fourier Transform Infrared Spectroscopy (FTIR). The flame-retardant performance of the CS/CH-coated SrPUF was evaluated. A six-bilayer coating increased the Limiting Oxygen Index (LOI) from 17 to 29%. In addition, the peak heat release rate decreased by 43.03%, CO2 emission by 79.57%, and CO emission by 67.69%. The 6 BL-coated SrPUF achieved a V-0 rating in the UL 94 vertical combustion test. During horizontal combustion tests with temperatures up to 1300 degrees C under torch flame, the coated samples showed immediate self-extinguishing behavior. These results indicate that CS/CH coatings are promising as safe, effective, and sustainable flame-retardant alternatives for SrPUF, particularly for applications in the automotive industry