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Investigating the possible protective effect of caffeic acid phenethyl ester on aquaporin-2 changes in renal ischemia-reperfusion injury in rats
No full textObjective: We aimed to investigate the changes in renal aquaporins (AQP) of rats in renal ischemia-reperfusion (I/R) injury and the protective effects of caffeic acid phenethyl ester (CAPE) against these changes. Methods: Forty-five adult rats were divided into six groups: control, sham (right nephrectomy), I/R (right nephrectomy + left kidney I/R), I/R+CAPE (I/R procedure after i.p. CAPE), sham + CAPE, and sham + dimethyl sulfoxide. Blood urea nitrogen, Cr, and K+ levels were measured in the sera. Tissues were stained with hematoxylin and eosin for histopathological examination. For immunohistochemical analysis, AQP2 was applied using the streptavidin/biotin/peroxidase system. AQP2 gene expression in kidney tissues was examined by polymerase chain reaction (PCR). Results: In the I/R, congestion, inflammation, and necrosis were found to increase compared to the control. In the I/R+CAPE, improvement was observed in necrosis compared to the I/R. There was a decrease in AQP2 expression in the I/R. In PCR, no significant difference was observed in AQP2 gene expression between the I/R and control and between the I/R+CAPE and I/R. Conclusion: Renal I/R inhibits the production of AQP2 in the kidney and causes histological and biochemical damage. CAPE administration before I/R has a protective effect on the kidney
Barriers and facilitators impacting the implementation of digital interventions targeted at mental health and musculoskeletal disorders in the workplace: a scoping review protocol
No full textBackground: The digital transition in the workplace has increased trends such as permanent connectivity, an increased sedentary lifestyle, and reduced social interaction, leading to additional psychosocial and ergonomic risks for workers. Musculoskeletal disorders (MSDs) and mental health problems are particularly prevalent, posing a significant burden. To address these challenges, organisations can implement digital or blended interventions targeting MSDs and mental health problems. However, there is still limited evidence on combined workplace interventions targeting both MSDs and mental health problems and respective facilitators and barriers for their successful implementation and sustainability. The objective of this scoping review is to identify barriers and facilitators to the implementation of blended and digital interventions targeted at combined mental health and MSDs in the workplace. Methods: Bibliographic databases will be searched for studies published since 2014 and reported on the implementation of interventions with a digital component targeted at mental health and MSDs in the workplace. Studies will be included if the intervention was delivered within, or access provided through, the workplace. The title and abstract screen and the full-text screening will be completed independently by two reviewers, with a third reviewer resolving any arising conflicts in the process. Results: Descriptive characteristics of the study design, workplace sector, mode of working, intervention details, mode of intervention delivery, outcomes, and barriers and facilitators will be extracted. Results will be reported in accordance with the PRISMA for Scoping Reviews checklist and a narrative synthesis used to summarise characteristics of included studies, as well as barriers and facilitators to the implementation of interventions. Discussion: The findings from this review will provide practical recommendations relevant to researchers and practitioners developing or implementing digital interventions in the workplace targeting mental health conditions and MSDs. Systematic review registration: Research Registry, reviewregistry1847, https://www.researchregistry.com/browse-the-registry#registryofsystematicreviewsmeta-analyses/registryofsystematicreviewsmeta-analysesdetails/66671b683a0f410028a230bd/. © 2025 Elsevier B.V., All rights reserved.European Commission, EU, (101137256); European Commission, E
Can Asclepius in Mythology Be a Role Model for Today's Physicians?
No full textTo be a good physician, role models are needed who have the characteristics of a good physician and have professional, ethical and professional values. The purpose of this study is to seek an answer to the question of whether history and medical symbols can be used to determine what characteristics a role model has and who should be taken as a role model. In mythology, Asclepius is known as the god of health and healing. When the characteristics of Asclepius with his snake staff, that he brought from history are investigated, we come across the portrait of a physician, who is well-educated, reliable, respected, careful, quick to make decisions, open to innovation, a lifelong learner, and interested in health. In fact, these characteristics are the basic characteristics desired in a good physician and are valid for all physicians today, including neuroscientists such as neurologists, neurosurgeons, and neuroradiologists
Surgical Fear and Related Factors Before Total Knee Arthroplasty
No full textObjective: The aim of this descriptive study was to determine the level of surgical fear and related factors in patients undergoing total knee arthroplasty. Methods: The study sample consisted of 181 patients undergoing total knee arthroplasty in a training and research hospital in Izmir. The data were collected between April 1 and September 1, 2023 via the “Patient Identification Form” and the “Surgical Fear Scale”. Descriptive statistics, Mann Whitney U test, Kruskal Wallis test and Spearman correlation analysis were used to evaluate the data. Results: The mean score on the Surgical Fear Scale was found to be 28.35 ± 16.89 (out of 80 points), indicating that the level of fear experienced by patients scheduled for total knee arthroplasty was relatively low. The mean scores for the short?term and long?term fear subscale of the Surgical Fear Scale (16.10 ± 9.91 and 12.24 ± 9.44 out of 40, respectively) also indicated that the short?term and long?term fear levels were low. The factors associated with the level of surgical fear were found to be previous knee surgery history and type of anesthesia (p:.001). Conclusion: The study findings showed that patients undergoing total knee arthroplasty experienced low levels of fear and that previous knee surgery experience and type of anesthesia were associated with this fear. surgical fear levels. © 2025 Elsevier B.V., All rights reserved
AI-Enhanced Test Automation Tool for Desktop Applications
No full textTest automation is an essential part of the software testing process. This study aims to develop an AI-enhanced test automation tool for testing the user interfaces of desktop applications. The detection of each object in the graphical user interfaces of the software will be carried out using the object detection capabilities of YOLOv9 and Faster R-CNN models. The study emphasizes the importance of preprocessing steps for achieving successful outcomes in object detection processes. These preprocessing steps include image resizing, data augmentation techniques, and balancing the dataset. Additionally, the correct selection and optimization of hyperparameters (e.g., learning rate, number of epochs, network depth, and anchor box dimensions) in object detection models play a critical role in improving model performance. In this study, data analysis techniques using Python were utilized for hyperparameter optimization. Hyperparameters were evaluated and optimized based on metrics such as model accuracy, loss curves, and training time. As a result, high performance was achieved for both the test automation tool and the object detection process. This approach demonstrates the power of artificial intelligence and data analytics in test automation processes, serving as a significant example for both educational and practical applications.Havelsa
Diagnosis of Mild Traumatic Brain Injury with Machine Learning-Based Decision Support Systems|Makine grenmesi Tabanli Karar Destek Sistemleri ile Hafif Travmatik Beyin Hasarinin Tanisi
No full text33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025 -- -- Istanbul; Isik University Sile Campus -- 211450Traumatic Brain Injury (TBI) is a significant health concern, particularly common in children, requiring a careful diagnostic process in emergency departments. Traditional diagnostic methods may lead to unnecessary radiation exposure in pediatric patients, making the development of alternative approaches highly important. This study examined the effectiveness of machine learning (ML) models in diagnosing mild TBI. The PECARN-ciTBI dataset was analyzed, and the class imbalance was addressed using the SMOTE method. Various ML models were optimized and compared, including decision trees, ensemble models, artificial neural networks, support vector machines, and k-nearest neighbor classifiers. Among these models, which were enhanced through hyperparameter optimization, decision trees, and ensemble models achieved high accuracy, sensitivity, and specificity values. Specifically, decision trees provided 99.94% accuracy, 97.19% sensitivity, and 99.9% specificity, while ensemble models yielded the best results with 99.93% accuracy, 97.19% sensitivity, and 100% specificity. The results indicate that machine learning models offer a reliable and effective alternative for pediatric TBI diagnosis. © 2025 Elsevier B.V., All rights reserved.Isik Universit
Long-term outcomes of ossiculoplasty techniques
No full textBackground The ideal ossiculoplasty technique should effectively restore sound transmission, be surgically feasible, biocompatible, and stable. Currently, no single material fully meets these criteria in a cost-effective manner. Objectives To evaluate and compare the long-term audiological outcomes of various ossiculoplasty techniques. Materials and Methods This retrospective study included 116 patients (aged 11-72) who underwent ossiculoplasty using cortical bone, bone cement, or titanium prostheses between 2013 and 2019. Preoperative and 2-year postoperative air and bone conduction thresholds (500-4000 Hz), air-bone gap (ABG), and hearing gains were analyzed. Results Significant postoperative ABG improvement was observed in the malleus-incus, incus-stapes bone cement, TORP, and PORP groups (p 10 dB was achieved in 76.5% of incus-stapes bone cement and 53% of TORP procedures. Conclusions and Significance Bone cement offers an effective and economical option for ossiculoplasty, particularly in incudostapedial repairs. Despite no statistically significant difference (p = 0.206), the favorable outcomes of TORP suggest it may be superior to cortical bone in patients with non-functional ossicular chains
Comment on: Postoperative evaluation of interleukin-8 and C1q/TNF-related protein-12 in patients undergoing coronary artery bypass grafting surgery
No full text[Abstract Not Available
Comparison of convolution and transformer-based deep learning models in diagnosis of diabetic retinopathy disease
No full textDiyabet hastalığı, pankreasın yeterince insülin hormonu üretememesi veya bu hormonunun görevini yapamaması nedeniyle organizmanın karbonhidrat, yağ ve proteinlerden yeterince yararlanamadığı, kan şekeri seviyesinin yükselmesi (hiperglisemi) sonucu ortaya çıkan kronik bir hastalık olarak tanımlanmaktadır. Dünya Sağlık Örgütü'nün yayınlamış olduğu kronik hastalıklar raporuna göre diyabet hastalığı yoğunluk bakımından ilk sırada yer almaktadır. Tıp1 diyabet hastalığının yan etkilerinden birisi de beş sınıfı bulunan diabetik retinopati(DR) hastalığına neden olmasıdır. DR, diyabet yan etkisine bağlı olarak gözün arka kısmında yer alan ışığa duyarlı dokuda (retina) bulunan kan damarlarının zarar görmesinden kaynaklanan ve uzun süreli hiperglisemiye bağlı olarak körlüğe sebep olan bir göz rahatsızlığı olarak tanımlanmaktadır. Uluslararası diyabet federasyonu (2021) diyabet atlası 10. Baskı verilerine göre körlüğe neden olan ilk üç hastalık arasında diyabet yer almaktadır. Bütün dünyadaki diyabet hastalarının yaklaşık %25'inde herhangi bir seviyede DR görülmektedir. Ülkemizde ortalama 2 milyon civarında diyabet hastası olup bu hastaların %25'inde DR mevcuttur. Bu çalışmada APTOS2019 veri seti kullanılarak evrişim ve dönüştürücü temelli derin öğrenme modelleri ile doktorların erken tanı koymasına yardımcı olacak bilgisayar destekli teşhis sistemi oluşturulmaktadır. Literatürde medikal görüntülerin sınıflandırılmasında sıklıkla tercih edilen, evrişim tabanlı VGG16, InceptionResNetV2, ResNet152V2, EfficientNetB0, MobileNetV2 ve dönüştürücü tabanlı Vision Transformer(Vit),VitMSN,Swin2 Transformer ve VitHybrid derin öğrenme modelleri kullanılarak veri arttırımsız iki ve veri arttırımlı ile veri arttırımsız beş sınıflı sınıflandırma yapılmıştır. Veri arttırmasız iki sınıflı sınıflandırmada kullanılan evrişim tabanlı VGG16 modeli doğruluk metrik değeri 0.97 çıktığından, veri arttırımsız beş sınıflı sınıflandırmada MobileNetV2 modeli doğruluk metrik değeri 0.80 elde edildiğinden ve veri arttırımlı beş sınıflı sınıflandırmada VGG16 modeli doğruluk 0.79 metrik değeri bulunduğundan dolayı en iyi modeller olmuştur. Kullanılan dönüştürücü temelli modellerden veri arttırmasız iki sınıflı sınıflandırmada Swin2 Transformer ve VitHybrid doğruluk metrik değeri 0.98 elde edildiğinden, veri arttırmasız beş sınıflı sınıflandırmada Swin2 Transformer doğruluk 0.85 metrik değeri çıktığından ve veri arttırımlı beş sınıflı sınıflandırmada VitHybrid modeli doğruluk 0.82 metrik değeri bulunduğundan dolayı en iyi modeller olmuştur.Diabetes is defined as a chronic disease that occurs as a result of elevated blood sugar levels (hyperglycaemia), in which the organism cannot make sufficient use of carbohydrates, fats and proteins due to the inability of the pancreas to produce enough insulin hormone or the inability of this hormone to function. According to the chronic diseases report published by the World Health Organisation, diabetes ranks first in terms of intensity. One of the side effects of diabetes is that it causes diabetic retinopathy (DR), which has five classes. DR is defined as an eye condition caused by damage to the blood vessels in the light-sensitive tissue (retina) at the back of the eye due to the side effect of diabetes and causes blindness due to long-term hyperglycaemia. According to the data of the International Diabetes Federation (2021) Diabetes Atlas 10th Edition, diabetes is among the top three diseases that cause blindness. Approximately 25% of diabetic patients all over the world have DR at any level. In our country, there are approximately 2 million diabetic patients and 25% of these patients have DR. In this study, using the APTOS2019 dataset, a computer-aided diagnosis system is created to help doctors make early diagnosis with convolution and transformer-based deep learning models. Two-class classification without data augmentation and five-class classification with and without data augmentation were performed using convolution-based VGG16, InceptionResNetV2, ResNet152V2, EfficientNetB0, MobileNetV2 and transformer-based Vision Transformer (Vit), VitMSN, Swin2 Transformer and VitHybrid deep learning models, which are frequently preferred in the classification of medical images in the literature. The convolution-based VGG16 model used in the two-class classification without data augmentation was the best models since the accuracy metric value was 0.97, the MobileNetV2 model was the best model since the accuracy metric value was 0.80 in the five-class classification without data augmentation and the VGG16 model was the best model since the accuracy metric value was 0.79 in the five-class classification with data augmentation. Among the transformer-based models used, Swin2 Transformer and VitHybrid were the best models since the accuracy metric value of 0.98 was obtained in two-class classification without data augmentation, Swin2 Transformer accuracy metric value of 0.85 was obtained in five-class classification without data augmentation and VitHybrid model accuracy metric value of 0.82 was found in five-class classification with data augmentation
Investigation of the Relationship between Somatotypes and Hand Types in Healthy Young Individuals
No full textBackground: This study aimed to examine the relationship between hand types and somatotypes of healthy young individuals. Materials: A total of 312 volunteering individuals (152 F, 160 M) from Karab & uuml;k University (Karab & uuml;k, T & uuml;rkiye), between the ages of 17 and 35 years were included in this prospective study. The somatotypes of the individuals were measured using a previously formed Excel template based on the Heath-Carter method. Factor analysis and clustering analysis were conducted with the 17 parameters measured. Results: The mean body mass index of female participants was 21.23 +/- 3.30 kg/m2, while that of males was 23.48 +/- 3.52 kg/m2. When the somatotypes of individuals were examined, 5 different groups were found to be balanced: ectomorph, endomorphic mesomorph, mesomorph endomorph, mesomorphic endomorph, and central. As a result of these factors, it was concluded that there were 4 hand types: short palm short finger, long palm long finger, wide hand long finger, narrow hand short finger. The distribution of hand types between somatotype groups, the result that endomorphic mesomorph group had long palm long finger and wide hand long finger, while balanced ectomorph group had narrow hand short finger was found to be statistically Conclusion: The difference between somatotypes was not only in body types, but also in hand anthropometry. We believe that the fact that these results can be used as anatomical data in product design, ergonomics, and preliminary design of interfaces for young individuals in the Turkish population will contribute to experts interested in this field