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
Taxonomic description and phylogenetic placement of a new xanthogalum species (Apiaceae) from Turkey
(Taylor & Francis, 2025) Tuncay, Hüseyin Onur; Ekici, Miraç; Uzun, Fatma Selin; Lyskov, Dmitry; Akalın, Emine
The genus Xanthogalum, represented by the species X. purpurascens and X. turcicum in Turkey, forms a small but taxonomically significant group characterized by decurrent leaves and large fruits with broad, undulate wings. During extensive field surveys (2020–2025) in northeastern Turkey, a distinctive population was discovered exhibiting a combination of morphological characters not found in any previously described Turkish species. Xanthogalum ozlemiae Tuncay & Akalın sp. nov. is described herein based on comprehensive morphological, anatomical, and molecular evidence. The new species is distinguished from its Turkish congeners by white petals with brownish lines (vs. yellow to yellow – green), densely hairy rays and pedicels, shorter pedicels (0.5–2.5 mm), amphisto matic leaves with sparse hairs restricted to upper surface veins, and distinctly unequal mericarps with markedly different wing widths (1.9–3.5 mm vs. 0.5–1 mm). Phylogenetic analyses based on ITS sequences support its placement within Xanthogalum and indicate close affinity with a white – petaled lineage. A detailed morphological description, anatomical characterization, comparisons with related species, an identification key, and a preliminary conservation assessment are provided. Given its extremely restricted distribution and ongoing habitat threats from tourism development, the species is assessed as Endangered (EN) under IUCN criteria.
SFNN: A secure and diverse recommender system through graph neural network and regularized variational autoencoder
(Elsevier, 2025) Bahi, Abderaouf; Gasmi, Ibtissem; Bentrad, Sassi; Azizi, Mohamed Walid; Khantouchi, Ramzi; Uzun-Per, Meryem
Recommender systems are frequently improved to filter information and provide users with the most relevant items. However, they face limitations in balancing appropriate and diverse recommendations while ensuring the security and integrity of user data. A new recommender system based on secure fusion neural network is pre sented in this paper. It guarantees data integrity and confidentiality while balancing accuracy and diversity. It integrates a graph neural network that models user-item interactions to improve accuracy, with a regularized variational autoencoder whose evidence lower bound loss function is enhanced by a diversity-promoting regu larization term that favors latent-space dispersion, thereby improving recommendation diversity. To optimize the combination of the two neural networks scores, an adaptive fusion mechanism is introduced to generate final predictions that consider diverse user preferences while maintaining relevance. Furthermore, our approach uses blockchain technology to encrypt and secure data storage, ensuring the integrity and confidentiality of users’ data. The experiments conducted on three datasets show that the proposed model can achieve an accuracy of 78.13 % with an intra-list diversity of 46.82 % for Retail Rocket dataset, an accuracy of 82.44 % with an intra-list diversity of 37.78 % for clothing dataset, and an accuracy of 86.16 % with an intra-list diversity of 47.65 % for MovieLens-1 M dataset.
Impact of lightness differences in digitally simulated composite resin restorations on perceived smile attractiveness
(Wiley, 2025) Ntovas, Panagiotis; Ünal, Tuna; Korkut, Bora; Ferraris, Federico; Fehmer, Vincent; Sailer, Irena
Objectives: To investigate the effect of lightness differences between digitally simulated composite restorations and anterior maxillary teeth, in combination with restoration type, and clinical experience on perceived smile attractiveness. Materials and Methods: An imaging software program (Adobe Photoshop CC 2023) was used to digitally manipulate a frontal full-face portrait of a smiling female model, to create five types of moderate-sized composite resin restorations of moderate size. For each restoration 14 lightness differences were simulated. The image was digitally modified to simulate five different types of composite resin restorations (Class III, Class IV, Class V, diastema closure (bilateral and unilateral approach)). Each restoration was adjusted through 7 incremental increases and 7 incremental decreases of 1 unit in lightness (L* value), yielding a total of 70 images. The smile attractiveness of each picture was rated by 80 dentists and 80 laypersons, ranged from 21 to 77years using a visual analog scale. The Wilcoxon signed-rank test was employed to assess whether the mean of a sample significantly differed from the control (p<0.05). Results: Among the different restoration types, crown fracture repairs (Class IV) had the greatest negative impact, followed by proximal restorations (Class III), diastema closures (Bilateral approach), diastema closures (Unilateral approach), and, lastly, cervical restorations (Class V), which had the least impact on perceived smile attractiveness (p≤0.05). The influence of lightness differences, whether toward a darker or lighter restoration, was dependent on both the type of restoration and the observer's experience. Conclusions: The effect of lightness difference on perceived smile attractiveness was significantly influenced by both the type of composite resin restoration and the observer's experience. Dental professionals perceived lightness discrepancies as less attrac tive compared to laypersons, suggesting that experience plays a key role in the perception of esthetic outcomes. Clinical Significance: The repositioning of an esthetic direct dental restoration is highly influenced by the dentist's chromatic perception which is more sensitive than that of a layperson who evaluates its matching with the natural tooth. The findings of the present study can support evidence-based clinical decision-making.
Complementary and integrative medicine for the treatment of tourette’s syndrome
(Wiley, 2025) Pringsheim, Tamara; Deans, Catherine; Anis, Saar; Bhatia, Poonam; Black, Kevin; Değirmenci, Yıldız; Gilbert, Donald; Hartmann, Andreas; Hull, Mariam; Malaty, Irene
Background: There is widespread interest in complementary and integrative medicine (CIM) among people with Tourette's syndrome (TS). Objective: To perform a systematic review of evidence on the use of CIM to reduce tics and improve tic-related quality of life. Methods: We included clinical studies of CIM in children, adolescents and adults with TS and chronic tic disorders, and assessed the change in tic severity and/or tic-related quality of life using validated scales. Risk of bias of randomized controlled trials was assessed using the risk of bias tool of the American Academy of Neurology, which classifies studies into Class I, II, III or IV based on quality criteria. Results: 49 clinical studies and three systematic reviews were included. Most studies were rated Class IV and therefore at high risk of bias. Class I studies demonstrated efficacy of functional MRI neurofeedback, 5-Ling granule, Jingxin Zhidong formula, and Ningdong granule in reducing tic severity. Class II studies suggest efficacy of mindfulness-based intervention for tics, acupuncture combined with atlantoaxial joint bone setting therapy, and art therapy. Systematic reviews summarizing the Chinese literature on acupuncture, acupuncture with herbal medicine and massage therapy suggest greater reduction in tics compared to conventional treatments but there is low confidence in the evidence due to poor methodological quality of included studies. Conclusions: Evidence to support the use of complementary and integrative medicine for TS is limited in methodological quality and widespread applicability. These limitations prohibit evidence-based recommendations for general use among individuals with TS.
Decoding surgical proficiency and complexity: A machine learning framework for robotic herniorrhaphy
(Springer Nature Link, 2025) Shin, Thomas H.; Fanta, Abeselom; Gökçal, Fahri; Shields, Mallory; Benlice, Çiğdem; Kudsi, Omar Yusef
Objective To evaluate the predictive value of objective performance indicators (OPIs) for case complexity assessment and explore their role in quantifying skill acquisition during robotic ventral herniorrhaphy. Summary background data Despite advances in herniorrhaphy techniques, unclear metrics of case complexity have signifi cant implications for operative planning, resource allocation, and patient outcomes. While existing complexity definitions rely primarily on clinical factors external to operator behavior, the expanding adoption of robotic platforms in ventral her nia repair provides unprecedented access to quantifiable surgical performance metrics. However, the relationship between these objective performance indicators and both case complexity and skill development remains incompletely understood, representing a gap that machine learning approaches may help address. Methods OPI and clinical data from 561 consecutive robotic ventral hernia repairs over eight years were analyzed using iterative ensemble machine learning models to predict case complexity. Dimensional reduction analyses using t-distributed stochastic neighbor embedding tracked skill evolution, with Euclidean distances calculated between successive cases to quantify skill acquisition over time. Results Gradient boosting models integrating clinical and OPI variables achieved F1 score of 0.87, while OPIs alone scored 0.58. Longitudinal analysis revealed high OPI variability during early cases, stabilizing within 10 months despite increas ing case complexity, indicating skill acquisition may compensate for procedural difficulty. Dimensional reduction analyses captured this evolution through weighted Euclidean distances. Conclusions Objective performance indicators poorly predict case complexity independently, yet their temporal evolution reveals surgical skill acquisition. The concurrent stabilization of OPI stochasticity and progression to more complex cases demonstrates that surgical proficiency and complexity assessment are interdependent phenomena, establishing digital metrics as tools for understanding the dynamic relationship between surgeon learning and case difficulty.
