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EVALUATION OF LOW-TEMPERATURE WASTE HEAT WITH ORC SYSTEMS: PERFORMANCE COMPARISON OF DIFFERENT WORKING FLUIDS
No full textAsimetrik Maliyet Davranışının BIST Gayrimenkul Yatırım Ortaklığı (GYO) Sektöründe İncelenmesi: Bir Panel Veri Analizi
No full textTransskleral Sütürlü Göz İçi Lens İmplantasyonu İle Yamane Tekniğinin Karşılaştırmalı Analizi
No full textIntegration of Artificial Intelligence in Oral Mucositis Management in Cancer Patients: Current Approaches and Clinical Implications
No full textOral mukozit, kemoterapi ve radyoterapi gibi kanser tedavilerinin yaygın ve ağrılı bir yan etkisi olup, özellikle lösemi veya baş-boyun kanseri tedavisi gören hastalarda yüksek oranda görülmektedir (ArbabiSarjou ve ark., 2022; Reuss ve ark., 2023; Kusiak ve ark., 2020). Bu durum, hasta konforunu ve tedaviye uyumu olumsuz etkilediğinden, yenilikçi yönetim stratejilerine ihtiyaç duyulmaktadır. Yapay zeka (YZ) teknolojilerinin entegrasyonu, oral mukozit yönetiminde izlem, risk tahmini ve kişiselleştirilmiş bakım gibi birçok alanda önemli avantajlar sunmaktadır. Bu derlemenin amacı, kanser hastalarında sık görülen ve yaşam kalitesini olumsuz etkileyen oral mukozitin tanı, izlem ve yönetiminde YZ uygulamalarının güncel rolünü ve potansiyelini özetlemektir.YZ tabanlı sistemler, semptom takibi ve hasta izleminde etkinlik sağlayarak, hasta verilerinin farklı kaynaklardan toplanıp analiz edilmesine olanak tanır. Bu sayede risk faktörleri daha iyi belirlenebilir ve bireyselleştirilmiş tedavi stratejileri geliştirilebilir (García-Saisó ve ark., 2024; Vakili ve ark., 2024). Bir çalışmada, baş boyun kanseri hastalarında oral mukoziti tahmin etmek için derin öğrenme modeliyle birlikte termal görüntülemenin kullanımını gösterilmiştir. Model, mukozitin erken tanımlanması potansiyelini göstererek %82 test doğruluğu elde etmiştir (Thukral et al., 2023). Başka bir çalışmada Evrişimli Sinir Ağları (CNN) mimarisinin, mukozit taramasında etkili olduğu vurgulanmıştır, ancak aşırı uyum gibi sorunlar olduğu bildirilmiş ve model eğitiminde daha fazla iyileştirmeye ihtiyaç olduğu ifade edilmiştir (Kapoor & Mahajan, 2023).YZ, mevcut tedavi yöntemlerinin etkinliğini arttırmada kullanılabilir. Örneğin, fotobiyomodülasyon tedavisi oral mukozitlerin önlenmesinde önerilen yöntemlerdendir. (Zadik ve ark., 2019). Fotobiyomodülasyon tedavisi sırasında doz ve zamanlamanın YZ ile optimize edilmesi durumunda, komplikasyon riski azaltılarak tedavi başarısı arttırılabilir. Ayrıca, YZ’nin analitik kapasitesi, kanser tedavisine bağlı oral mikrobiyota değişiklikleri ile mukozit gelişimi arasındaki ilişkiyi ortaya koyarak, önleyici yaklaşımlar geliştirilmesine katkı sağlayabilir (Reuss ve ark., 2023; Triarico ve ark., 2022). ). Klinik karar destek sistemleri gibi platformlar, gerçek zamanlı veri entegrasyonu ile tedavi önerileri sunarak, onkologlara yardımcı olmaktadır (Oehring ve ark., 2023). Bu sistemler aynı şekilde oral mukozit gibi yan etkilerin yönetiminde kullanılabilir.Etik boyutlar ve hasta bakış açısı da önemlidir. Araştırmalar, hastaların YZ’yi destekleyici bir araç olarak tercih ettiğini, insan kararının yerini almamasını istediklerini göstermektedir (Hilbers ve ark., 2025; Hantel ve ark., 2024). Bu nedenle, YZ uygulamalarının geliştirilmesinde hasta beklentileri ve etik standartlar gözetilmelidir.Sonuç olarak, YZ’nin klinik iş akışlarına entegrasyonu, oral mukozit yönetiminde izlem, kişiselleştirilmiş müdahaleler ve optimal tedavi stratejileriyle bakım kalitesini artırma potansiyeline sahiptir. Bu gelişmeler, kanser tedavisi sürecinde hasta sonuçlarını ve yaşam kalitesini iyileştirebilir.Anahtar Kelimeler: Mukozit, kanser, yapay zekaOral mucositis, a prevalent and painful side effect of cancer treatments like chemotherapy and radiotherapy, significantly impacts patient comfort and treatment adherence, particularly in those undergoing treatment for leukemia or head and neck cancers (ArbabiSarjou et al., 2022; Reuss et al., 2023; Kusiak et al., 2020). Innovative management strategies are thus essential. Artificial intelligence (AI) technologies offer advantages in monitoring, risk prediction, and personalized care for oral mucositis. This review summarizes the current role and potential of AI applications in the diagnosis, monitoring, and management of this condition, which frequently affects cancer patients and negatively impacts their quality of life.AI-based systems enhance symptom tracking and patient monitoring by enabling the collection and analysis of patient data from diverse sources, facilitating better identification of risk factors and individualized treatment strategies (García-Saisó et al., 2024; Vakili et al., 2024). A study using thermal imaging combined with a deep learning model to predict oral mucositis in head and neck cancer patients achieved 82% test accuracy, indicating potential for early identification (Thukral et al., 2023). Another study highlighted the effectiveness of Convolutional Neural Networks (CNNs) in mucositis screening, while noting issues like overfitting, suggesting the need for further model training improvements (Kapoor & Mahajan, 2023).AI can enhance existing treatment methods. For example, photobiomodulation therapy, a recommended method for preventing oral mucositis (Zadik et al., 2019), could benefit from AI-optimized dosage and timing, potentially reducing complications and increasing treatment success. AI's analytical capabilities can also contribute to preventive approaches by revealing the relationship between oral microbiota changes due to cancer treatments and mucositis development (Reuss et al., 2023; Triarico et al., 2022). Clinical decision support systems offer treatment recommendations through real-time data integration, assisting oncologists (Oehring et al., 2023) in managing side effects like oral mucositis.Ethical dimensions and patient perspectives are important. Research indicates that patients prefer AI as a supportive tool rather than a replacement for human decision-making (Hilbers et al., 2025; Hantel et al., 2024). Therefore, patient expectations and ethical standards should be considered in AI application development.In conclusion, integrating AI into clinical workflows has the potential to enhance the quality of care in oral mucositis management through monitoring, personalized interventions, and optimal treatment strategies, ultimately improving patient outcomes and quality of life during cancer treatment.Keywords: Mucositis, cancer, artificial intelligence</p
Investigation of the binding affinity of a newly synthesized copper(II) complex to DNA and enzymes (catalase/trypsin/urease) using spectrofluorimetry and in silico approaches
No full textIn recent years, there has been a growing interest in the synthesis of novel compounds with medicinal potential, particularly those exhibiting antioxidant properties, due to their ability to delay, prevent, or eliminate oxidative damage in target cells. Understanding the interactions between these compounds and major biological targets—including DNA, catalase, trypsin, and urease—is essential for improving their bioactivity and therapeutic potential. The synthesis and comprehensive characterization of a novel copper(II) complex, [Cu(3,5ClSal-Phe)(CH₃OH)]—featuring a Schiff base ligand derived from 3,5-chlorosalicylaldehyde and L-phenylalanine—were carried out using electronic absorption spectroscopy, FTIR, ESI-MS, ESR and X-ray diffraction. Electronic absorption and fluorescence spectroscopy were employed to investigate the interactions between the complex and key biomolecules such as CT-DNA, catalase, trypsin, and urease. The complex was found to bind CT-DNA through minor groove interaction, while its fluorescence quenching with catalase, trypsin, and urease proceeds via a static mechanism. To better understand the molecular basis of these biological effects, docking simulations were employed using DNA and three key enzymes, trypsin, urease, and catalase, as molecular targets. Among all targets, the strongest binding affinity was observed with catalase (−9.31 kcal/mol), where the complex formed hydrogen bonds with Arg111, His361, Phe333, and Arg71, as well as a halogen interaction with Tyr357. Interactions with trypsin and urease were also energetically favorable, predominantly involving polar and hydrophobic residues. Docking protocols were validated through redocking (RMSD <2.0 Å), ensuring reliability of the predicted binding modes. In vitro assessment of the complex's antioxidant activity, conducted using the DPPH radical scavenging assay, revealed a moderate scavenging efficiency
Psychosocial Functionality and Predictors in Bariatric Surgery Candidates
No full textBackground: Obesity is a critical global health issue with increasing prevalence. Although bariatric surgery is effective, relapses are common. Pre-bariatric functioning may significantly influence these relapses.Objective: To evaluate psychosocial functioning in individuals undergoing bariatric surgery, examining depressive symptoms, self-esteem, body satisfaction, disordered eating symptoms, and sociodemographic factors. This cross-sectional study identifies predictors of psychosocial functioning to guide interventions for sustained postoperative well-being.Methods: The study included 175 individuals (81.7% female) attending routine preoperative evaluations at Kocaeli University Faculty of Medicine. Most participants (94.3%) were morbidly obese (body mass index (BMI) >= 40). Psychosocial functioning was assessed using the Obesity-Related Problems Scale (OP-S), with 51.4% scoring in the severe range (>= 60). Depressive symptoms (Beck Depression Inventory (BDI)), Rosenberg Self-Esteem Scale (RSES), body satisfaction Scale (BSS), and Eating Disorder Examination Questionnaire (EDE-Q) were also evaluated. Correlation and regression analyses identified predictors of psychosocial functioning.Results: The mean OP-S score was 55.81 +/- 24.77. OP-S scores were significantly correlated with depressive symptoms (r = 0.462, p = 0.001), disordered eating symptoms (r = 0.410, p = 0.002), self-esteem (r = -0.322, p = 0.004), and body satisfaction (r = -0.240, p = 0.018). Regression analysis identified depressive symptoms (beta = 0.24, p = 0.02) and disordered eating symptoms (beta = 0.20, p = 0.03) as significant predictors.Conclusion: Depressive symptoms and disordered eating symptoms are predictors of psychosocial functioning among individuals undergoing bariatric surgery. Addressing these factors through psychiatric evaluations can enhance psychosocial functioning, reduce relapse risk, and improve quality of life. Multidisciplinary care is essential in bariatric treatment
Holistic analysis of liquefaction risks and land subsidence for building areas located in active fault zones
No full textIn active fault zones, land subsidence and surface liquefaction are potential hazards that can seriously threaten urban infrastructure and, consequently, human life. Following the devastating 1999 Marmara Earthquake, newly established organized industrial zones led to a rapid increase in population, construction, and building density in the eastern Marmara region, particularly centered around the İzmit district of Kocaeli Province, Türkiye. The rapid urbanization observed along the North Anatolian Fault Zone and in alluvial areas with low soil stability has exposed urban areas to compounded geohazard risks such as earthquakes, liquefaction, and land subsidence. In this study, spatio-temporal vertical surface deformations along the eastern Marmara segment of the North Anatolian Fault were detected using the advanced Small Baseline Subset (SBAS) InSAR technique, based on both ascending and descending mode Sentinel-1A SAR data between 2014 and 2020. In liquefaction studies, geophysical measurements were analyzed to assess liquefaction potential. Within the scope of the study, surface deformation and liquefaction risks were evaluated holistically using geospatial information technologies. As a final product, a multi-risk index was generated for the buildings in the region. This index is highly significant in terms of predicting potential future threats and supporting proactive mitigation measures
INVESTIGATION OF THE EFFECT OF WEB 2.0 APPLICATIONS PREPARED ON THE SUBJECT OF "GEOMETRIC SHAPES AND OBJECTS" ON THE ACADEMIC SUCCESS OF PRIMARY SCHOOL 3RD GRADE STUDENTS
No full textThe this research was conducted to examine the effects of Web 2.0 applications, prepared in accordancewith the objectives of the subject of "Geometric Shapes and Objects" in the 3rd grade mathematicscourse on the academic success of primary school students. The sample of the study consists of a totalof 68 students, 35 in the experimental group and 33 in the control group, studying in a state primaryschool in Kocaeli province. In this study, where a quasi-experimental design with a pre-test-post-testcontrol group was used, which is one of the quantitative research methods, the "Geometric Shapes andObjects Achievement (GŞCB)" Test, which was developed by Köşker and Gökbulut (2024) and consistsof 23 multiple-choice questions, was applied to both groups as a pre-test and post-test.In theexperimental group of the study, lessons were conducted for 2 weeks (10 lesson hours) with Web 2.0applications prepared by the researcher, and in the control group, the same unit was completed in equaltime with the traditional teaching method. Graphical examination, skewness and kurtosis criteria wereexamined to determine whether the data obtained before and after the application conformed to a normaldistribution. The scores of the students from the Geometric Shapes and Objects Achievement Test wereanalyzed using the independent samples t-test and the dependent samples t-test. According to theanalysis results, it was seen that there was a significant difference between the GŞCB Achievement Testpost-test scores of the experimental and control group students in favor of the experimental group andthat the use of Web 2.0 tools increased student success on the "Geometric Shapes and Objects Topic" inthe Geometry unit. </p