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Comparison of Oxidative Stress Effects Between Drug-Eluting Stents and Drug-Coated Balloons: Insights Into Vascular Response and Clinical Implications
No full textBackground: Percutaneous coronary intervention (PCI) has evolved with the development of drug-eluting stents (DES) and drug-coated balloons (DCB), both of which play a crucial role in reducing restenosis and major adverse cardiovascular events (MACE). Despite their benefits, the oxidative stress response triggered by these interventions and its implications for vascular healing remain unclear. Aims: This study aims to compare the oxidative stress burden associated with DES and DCB by evaluating changes in plasma total peroxidase (TP) levels over time. Methods: This observational, comparative study included 110 patients (60 DES and 50 DCB) who underwent PCI for stable coronary artery disease. TP levels were measured pre-procedure, 24 h postprocedure, and 1 month after PCI. Statistical comparisons were used to determine significant differences in oxidative stress between the two groups over time. Results: Preoperative TP levels were comparable between the DES and DCB groups (431.00 ± 4.56 vs. 436.50 ± 4.14 µmol/L, p = 0.110). At 24 h post-PCI, TP levels increased significantly in both groups (DES: 449.50 ± 6.51 µmol/L, DCB: 442.50 ± 4.14 µmol/L), with a greater rise observed in the DES group, though not statistically significant (p = 0.42). At the 1-month follow-up, TP levels decreased significantly below baseline in both groups, with a more pronounced reduction in the DCB group (398.50 ± 4.14 vs. 406.50 ± 4.51 µmol/L, p = 0.11). Conclusion: This study reveals that both DES and DCB procedures trigger an initial rise in oxidative stress markers, emphasizing their acute vascular impact. Intriguingly, although not statistically significant, the DCB group demonstrated a trend toward a more rapid decline in oxidative stress at 1 month post-PCI. These preliminary findings highlight the potential of DCBs to foster quicker vascular recovery, warranting further investigation into their long-term clinical advantages
Validity and reliability study of the Turkish version of the Addiction Severity Index
Get PDFObjective: The increasing prevalence of alcohol and substance use disorders (ASUD) worldwide has raised the demand for more efficient treatment and monitoring. Comprehensive assessment tools are crucial for evaluating substance use, as well as medical, legal, and psychosocial aspects to provide holistic care. The Addiction Severity Index (ASI) is a commonly used tool to assess these dimensions. The purpose of this research is to validate the ASI-Treatnet version in a sample from Turkiye. Method: The research was carried out at AMATEM clinics in Istanbul and Antalya, involving 141 patients who had been diagnosed with ASUD based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria. The ASI was translated into Turkish, and its reliability and validity were assessed through a methodology that included testing for test-retest and interrater reliability using Spearman-Brown correlation coefficients. Results: The results indicated high reliability for all subscales. Internal consistency was assessed through Cronbach’s alpha, resulting in an acceptable value of 0.82. Concurrent validity was established by examining the correlations with the Michigan Alcoholism Screening Test, the Drug Abuse Screening Test, and the Beck Depression Inventory, all of which demonstrated significant correlations with the ASI subscales. Conclusion: The study revealed that the Turkish adaptation of the ASI is a reliable and valid instrument for evaluating ASUD. Its strong correlations with established screening tools confirm its concurrent validity. The ASI’s multidimensional approach allows for a comprehensive assessment, facilitating individualized treatment planning and monitoring. Future research could explore expanded and online-adapted versions of the ASI to enhance its practicality
Genomic-transcriptomic differences in peripheral blood of healthy aging: insights for neurodegenerative disease
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Commentary on "The relationship between risky sexual behaviors and sexual health literacy and self-esteem in young women"
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Metabolomics-based potential plasma metabolite biomarkers for metabolic syndrome and its five components: a casecontrol study
No full textMeeting Abstract : LB-R-39-02...
Enhancing Governmental Decision-Making through Predictive Analytics with Machine Learning-Based Data-Driven Framework
Get PDFGovernment bodies around the world are going digital and slowly starting to make use of data driven technologies to make better, faster and more transparent decisions. From these technologies, machine learning (ML) has become one of the most significantly employed tools, especially via its ability to predict. Predictive analytics allows governments to identify obscure trends that previously were hidden, predict potential future scenarios with an acceptable level of certainty and better inform decision-making in important areas, such as public finance, healthcare planning, emergency management, and resource allocation. In this work we explore the use of predictive modeling (implemented as our own Linear Regression, Decision Trees, Random Forests and Artificial Neural Networks) in the context of governmental decision models. The models were tested on real-world cases such as quarterly budget planning or estimation of healthcare service demand or emergency resource allocation using publicly available data from open government data platforms. Performance was evaluated based on the well-known RMSE, MAE and R² score. Results show that Artificial Neural Network always leads the highest in predictive accuracy, especially in dense or complex data setting, and there is no significant difference between Random Forest and Neural Network (the Random Forest has more generalization between interpretability and predictive power. On the other hand, Linear Regression and Decision Trees are more interpretable but have restrictions in using non-linear or high-dimensional datasets. In addition, the paper covers practical challenges including algorithmic bias, data quality considerations, and infrastructure capabilities, and ethical implications of automated decision making. This study has implications for the growing smart governance by proposing an integrated machine learning framework suitable for evidence-based policymaking. Future work involves improving the accuracy of prediction by incorporating explainable AI methodologies and customizing the model locally to enhance transparency, accountability, and generalization across different regional offices
Reconfigurable Intelligent Surface-Assisted Antenna Design with Enhanced Beam Steering and Performance Benchmarking
Get PDFThis paper presents a high-gain wide-band planar antenna with a Reconfigurable Intelligent Surface (RIS) for modern wireless communication applications. The antenna consists of two main parts, a basic antenna part with cross-line slots and two light-dependent resistor switches, and a second part based on the RIS layer for beam steering. The RIS is constructed from 5 × 5-unit cells with two sides, forming a square geometry. The antenna substrate is a dielectric layer of FR4 epoxy glass with a thickness of 1.6 mm. The RIS inclusions are designed and tested numerically to achieve the desired electromagnetic properties at the frequency band of interest. The fabricated prototype shows a wide band covering frequencies from 0.9 GHz to 3.5 GHz with S11 below −10 dB, achieving an antenna gain varying from 10.5 dBi up to 16.8 dBi. Experimental measurements show effective aperture usage in all configurations, and beam steering from +22° to −22° is accomplished without degrading side-lobe levels. The proposed antenna performance is tested against real-world measurements to evaluate channel performance in terms of bit error rate (BER) and channel capacity (CC). The proposed LDR-controlled design achieves compact beam steering with minimal insertion loss, unlike conventional RIS-assisted antennas that rely on PIN or varactor switches.Funding agency : International Applied and Theoretical Research Center (IATRC).
Grant number : 00A119
Bridging the Gap: Ensemble Learning-Based NLP Framework for AI-Generated Text Identification in Academia
No full textBackground: The advent of Large Language Models (LLMs), including Chat Generative Pre-trained Transformer (ChatGPT) and Bard, has revolutionised text generation while raising ethical concerns regarding academic integrity. Differentiating Artificial Intelligence-Generated Texts (AIGT) from human-written content is crucial to maintaining transparency and trust in scholarly communication. Objective: This study aims to address the limitations in existing detection methods by introducing a Machine Learning (ML)-based Natural Language Processing (NLP) framework that effectively distinguishes between AI-generated and Human-Written academic texts (HWAI). Methodology: The proposed framework integrates comprehensive preprocessing, Exploratory Data Analysis (EDA), linguistic analysis, and ensemble learning techniques. Text representation was achieved using Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings. We employed two diverse datasets, Artificial Intelligence-Generated Academic (AI-GA) and HWAI, to validate the framework's efficacy, ensuring robust classification performance. Results: The ensemble model did better than individual classifiers. On the AI-GA dataset, it achieved state-of-the-art accuracy (98.67%) and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) (99.88%). The HWAI dataset achieved 96.52% accuracy and 99.37% ROC-AUC. These results highlight the framework's capability to identify unique linguistic patterns in AI-generated content. Conclusion: The framework addresses key linguistic and computational challenges and provides a scalable and reliable solution for detecting AI-generated content in academic domains. Future work will explore hybrid human-AI authorship detection and real-time deployment to enhance its practical utility across disciplines
Development of sessile oak [Quercus petraea (Matt.) Liebl.] seed coating material against rodents and evaluation of its performance on seed germination and emergence
No full textProtecting sessile oak [Quercus petraea (Matt.) Liebl.] seeds from rodents is crucial to ensure successful germination and emergence in activities such as artificial regeneration, afforestation, and seedling production. This study examined the effects of 12 natural or nature-identical substances, believed to have repellent properties, on the germination, emergence, and survival of sessile oak acorns under both laboratory and field conditions. Acorns were coated using a diatomaceous clay-based pellet system, and a Y-maze experiment was conducted to evaluate rodent behaviour. As a result of the research, among the tested substances, Ferulago confusa and Foeniculum vulgare were recommended as rodent repellents due to their success in laboratory and field trials. Diesel fuel + hair, a conventional repellent, showed poor performance and is not recommended. This study underscores the potential efficacy of natural or nature-identical coatings for protecting seeds from pests in forestry applications