İstanbul Sağlık ve Teknoloji Üniversitesi Kurumsal Akademik Arşivi
DSpace@İSTÜN, Üniversite mensupları tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Investigation of the effects of melatonin on granulosa cell proliferation and DNA methylation
(Proceedings of the Bulgarian Academy of Sciences, 2025) Türkmen, Ervanur; Gündoğan, Gül İpek; Arslan, Halil İbrahim; Gök Yurttaş, Asiye; Elgün, Tuğba
Melatonin, a pineal hormone with antioxidant and regulatory functions, has emerged as a key modulator of ovarian physiology. Its presence in fol licular fluid suggests important roles in granulosa cell function, follicle devel opment, and reproductive outcomes. However, its effects on granulosa cell tumour (GCT) biology and epigenetic regulation remain insufficiently defined. This study aimed to investigate the effects of melatonin on proliferation and global DNA methylation in human granulosa tumour cells (COV434) com pared with healthy endothelial controls (HUVECs). COV434 and HUVEC cells were treated with melatonin at 1, 10, 100, and 1000 µM. Cell viabil ity and proliferation were assessed using the MTT [3-(4.5-dimethylthiazol-2- yl)-diphenyl tetrazolium bromide] (Cambridge, UK) assay and xCELLigence RTCA system (Roche), while DNA methylation was quantified with a 5-mC ELISA kit (Epigentek Group Inc, USA). Experimental groups included nega tive, sham, melatonin-treated, and positive controls. Melatonin showed a cell type-dependent effect. In COV434 cells, proliferation was significantly inhib ited, with an IC50 of 10.55 µM, whereas HUVECs displayed increased prolifer ation at higher doses. DNA methylation levels decreased in both cell types in a dose-dependent manner, reaching the highest significance in COV434 cells at 1000 µM (p < 0.001). In conclusion, melatonin demonstrated a dose-dependent inhibitory effect on COV434 cell proliferation while simultaneously reducing global DNA methylation levels.
Origin-order classification of axillary third-part branching: Donor-based dissection–CTA correlation for surgical planning
(Springer Nature Link, 2025) Temizsoy Korkmaz, Fulya; Coşkun, Osman; Gürses, İlke Ali; Gayretli, Özcan; Özdemir, Sevim; Öztürk, Adnan; Kale, Ayşin
Purpose Variations in the branching of the subscapular artery (SSA), anterior circumflex humeral artery (ACHA), and posterior circumflex humeral artery (PCHA) are directly relevant to reconstructive planning with subscapular-system flaps and to humeral-head perfusion in shoulder surgery. Evidence organized around an origin-order–based framework remains limited. We aimed to address this gap by comparing body-donor dissections and computed tomography angiography (CTA) to provide a clinically useful classification and morphometric reference. Methods We performed a two-arm cross-sectional morphometric study: body-donor dissection (28 donors; bilateral, 56 sides) and CTA (25 patients; bilateral, 50 sides). In total, 104 sides were evaluated; 96 were classifiable. Classification used the sequential SSA–ACHA–PCHA origin order and common-trunk presence. Ostial diameters and SSA → CSA distances were measured; the radial nerve (NR)–SSA relationship was assessed in donors. origin_order__five_type_classif… Results In donors, Type-1, -2, -3, and -4 accounted for 36.5%, 32.7%, 23.1%, and 7.7%, respectively; Type-5 was absent. In CTA, Type-1, -4, and -5 comprised 93.2%, 2.3%, and 4.5% (one bilateral case); Types-2/-3 were not observed. Inter-modality comparison showed a longer SSA → CSA distance and smaller TDA/CSA diameters in CTA (all p < 0.001), while the SSA ostial diameter was similar. A posterior NR course relative to the SSA was associated with a longer SSA → CSA distance (p = 0.026). Conclusion An origin-order–based classification, corroborated across dissection and CTA, yields a practical map for (i) single-pedicle harvesting within the subscapular system and chimeric flap design, and (ii) avoiding iatrogenic compromise of humeral-head vascularity during shoulder procedures. Incorporating presurgical CTA mapping of the SSA and its branches may enhance safety where variants (e.g., short/combined trunks, rare Type-5) are suspected.
Braindetective: An advanced deep learning application for early detection, segmentation and classification of brain tumours using MRI images
(Springer Nature Link, 2025) Tokatlı, Nazlı; Bayram, Mücahit; Ogur, Hatice; Kılıç, Yusuf; Han, Vesile; Batur, Kutay Can; Altun, Halis
This study aims to create deep learning models for the early identification and classification of brain tumours. Models like U-Net, DAU-Net, DAU-Net 3D, and SGANet have been used to evaluate brain MRI images accurately. Magnetic resonance imaging (MRI) is the most commonly used method in brain tumour diag nosis, but it is a complicated procedure due to the brain’s complex structure. This study looked into the ability of deep learning architectures to increase the accuracy of brain tumour diagnosis. We used the BraTS 2020 dataset to segment and classify brain tumours. The U-Net model designed for the project achieved an accuracy rate of 97% with a loss of 47%, DAU-Net reached 90% accuracy with a loss of 33%, DAU-Net 3D achieved 99% accuracy with a loss of 35%, and SGANet achieved 99% accuracy with a loss of 20%, all demonstrating effective outcomes. These find ings aim to improve patient care quality by speeding up medical diagnosis processes using computer-aided technology. Doctors can detect 3D tumours from MRI pictures using software developed as part of the research. The work packages correctly han dled project management throughout the study’s data collection, model creation, and evaluation stages. Regarding brain tumour segmentation, 3D U-Net architecture with multi-head attention mechanisms provides doctors with the best tools for planning surgery and giving each patient the best treatment options. The user-friendly Turkish interface enables simple MRI picture uploads and quick, understandable findings.
Dental shade assessment via various digital photograph parameters: A pilot study
(EPA - TPID, 2025) Yılmaz, Seval Fatma; Ayvalıoğlu Şamiloğlu, Demet Çağıl
OBJECTIVES: Accurate shade matching is essential for successful restorative and prosthodontic dental treatments. Various methods, including visual, digital, and spectrophotometric techniques, have been utilized for shade selection. However, there is limited data regarding the optimal photographic parameters that yield the most accurate shade matching in digital photography. This pilot study aims to evaluate color differences arising from variations in photographic parameters -specifically camera aperture and ISO-while maintaining a constant shutter speed of 1/125. MATERIALS-METHODS: Spectrophotometric shade analysis (VITA Easyshade) was performed ten times on the maxillary right central incisor of a subject and the A1 tab from the VITA Classical shade guide before photography. A total of 12 digital images were captured using a DSLR camera (Canon EOS 850D) equipped with a 100 mm macro lens and ring flash (without polarized filter). The photographic parameters included a constant shutter speed (1/125 s), varying aperture values (f/11, f/13,f/22, f/32), and ISO values (100,160,200). Colorimetric evaluations were conducted using Adobe Photoshop, analyzing CIE Lab* coordinates and calculating ΔE values. RESULTS: The lowest ∆E value was recorded with ISO 200, f/32 aperture, and 1/125 shutter speed in the tooth group (5.41 ± 1.92), while the highest ∆E value was obtained with ISO 100, f/11 aperture, and 1/250 shutter speed in the shade guide group (62.14 ± 3.90). The digital photographic ∆E values mostly remained within the clinically unacceptable threshold (ΔE > 2.7 and ≤ 5.4). CONCLUSION: Within the limitations of this pilot study, digital photography alone for shade selection remains inconclusive.Further studies are warranted to comprehensively compare shade-matching accuracy using digital photography.
Artificial intelligence in planetary science and astronomy: Applications and research potential
(Euro Planet, 2025) Kacholia, Devanshi; Verma, Nimisha; D’Amore, Mario; Angrisani, Marianna; Frigeri, Alessandro; Schmidt, Frédéric; Carruba, Valerio; Hatipoğlu, Y. Güray; Roos-Serote, Maarten; Smirnov, Evgeny; Vergara Sassarini, Natalia Amanda; Solmaz, Arif; Oszkiewicz, Dagmara; Ivanovski, Stavro
Artificial Intelligence (AI) is one of the most influential fields of the 21st century (Zhang et al., 2021). Rich, E (2019) candidly described it as “the study of how to make computers do things which, at the moment, people do better”, today AI often surpasses human ability in tasks like large scale data mining and pattern recognition - its true strength. AI’s subfields - Machine Learning (ML) and deep learning (DL), play a critical role in expanding the usage to a vast variety of fields like planetary science, astronomy, earth observations, and remote sensing, just to name a few. There is an expected inclination towards incorporating AI more frequently in the studies of planetary science given the vast and complex nature of planetary data. In fact, AI has already been instrumental in extracting meaningful insights and advancing research in both interplanetary and astronomical studies. In planetary sciences, several AI techniques have been employed in order to bridge gaps in our understanding of the varied patterns and occurrences for studying the natural features observable from the data returned by scientific payloads. For example, PCA and cluster analysis can help in detecting patterns of compositional variation from multi and hyper-spectral imagery (Moussaoui et al., 2008; D’Amore & Padovan, 2022). Furthermore, to study specific features and patterns in their occurrences, correlations with neighbouring features; unsupervised algorithms and more complex -supervised techniques can be helpful depending on the scale of the task. From simple methods of unsupervised learning like clustering used to study the spectral signatures of Jezero crater on Mars (Pletl et al., 2023) to applying large language models to track asteroids affected by gravitational effects which alter the asteroid’s orbit (Carruba et al., 2025), such applications highlight the prospects of AI in the field of planetary science. Henceforth, to develop a deeper understanding of the potential and applications of ML, below is a typical AI workflow.
