Okan University GCRIS Standard Database
Not a member yet
7992 research outputs found
Sort by
Engineering Quantum Dot-Based Nanozymes for Advanced Biomedical Applications
No full textQuantum dot-based nanozymes (QDNs) represent a cutting-edge convergence of quantum dot technology and enzyme-mimicking nanomaterials, offering outstanding prospects in biomedical fields including bioimaging, biosensing, targeted drug delivery, and photodynamic therapy. Recent advances have leveraged precise surface modification techniques, heteroatom doping, and innovative core–shell architectures to amplify catalytic efficiency, biocompatibility, and specificity, thereby enhancing therapeutic and diagnostic precision. These multifunctional QDNs exhibit superior catalytic performance, stability across physiological conditions, and tunable optical properties, positioning them at the forefront of nanozyme research for combating complex biomedical challenges such as antimicrobial resistance and tumor targeting. Nonetheless, pivotal challenges persist, including inherent toxicity concerns, bioaccumulation, immunogenicity, and gaps in standardization that hinder clinical translation. Addressing these issues involves developing biodegradable and non-toxic quantum dots, optimizing surface passivation, and establishing robust preclinical evaluation protocols. Future directions emphasize integrating multifunctional therapeutic modalities within single QDN platforms, advancing scalable and reproducible manufacturing methodologies, and fostering regulatory frameworks to accelerate clinical adoption. This review underscores both the transformative potential and the critical barriers facing QDNs, guiding their evolution into clinically impactful biomedical tools. © 2026 Elsevier B.V
Pelvic Floor Dysfunction Following Gynecologic Cancer Surgery and Adjuvant Therapy: Epidemiology, Mechanisms, and Management-A Systematic Review
No full textIntroduction and HypothesisThis systematic review synthesizes current evidence on the prevalence, risk factors, pathophysiology, clinical manifestations, and management of pelvic floor disorders (PFD) following gynecologic cancer surgery and adjuvant therapy, with an emphasis on rehabilitation, multidisciplinary care, and quality of life.MethodsFollowing PRISMA 2020 guidelines, PubMed, Embase, Scopus, and Web of Science were searched for English-language studies published between January 2000 and May 2025. Eligible studies included adult women with gynecologic malignancies reporting outcomes related to urinary or fecal incontinence, pelvic organ prolapse, chronic pelvic pain, or sexual dysfunction. Data were extracted using a standardized form, qualitatively synthesized, and the risk of bias was assessed using the Newcastle-Ottawa Scale.ResultsThirty studies met the inclusion criteria. PFD were highly prevalent, particularly after radical surgery and adjuvant therapies. Stress urinary incontinence and prolapse were more frequent after radical surgery, while urgency incontinence, vaginal stenosis, and chronic pelvic pain were linked to radiotherapy. Chemotherapy was associated with neurogenic bladder and bowel dysfunction. Independent risk factors included advanced age, obesity, and postmenopausal status. PFD significantly impaired physical, psychological, sexual, and social quality of life. Nerve-sparing and minimally invasive techniques showed promise in reducing dysfunction. Conservative measures-pelvic floor muscle training, biofeedback, vaginal dilators, and lifestyle modification-were effective for many patients, while surgical procedures such as slings and sacrocolpopexy were reserved for severe cases. Emerging options include local estrogen or DHEA after radiotherapy and onabotulinumtoxinA for refractory urge incontinence.ConclusionsPFD are underrecognized yet common and disabling complications in women treated for gynecologic cancers. Multidisciplinary management integrating pelvic floor rehabilitation and individualized survivorship care is essential to improve outcomes and quality of life. Further research should establish standardized screening, preventive strategies, and evidence-based rehabilitation protocols for this high-risk population
Recent Advances in Employing Nanoemulsions in Food Science: An Update
No full textRecent advancements in nanoemulsion technology have profoundly influenced the food industry, providing innovative approaches to enhance food product quality, stability, and functionality. Nanoemulsions (20 − 200 nm) exhibit distinct physicochemical characteristics, such as a large surface area, controlled release of bioactive compounds, high loading capacity, improved bioavailability, and biphasic behavior. This review explores the latest developments in nanoemulsion types, their components, and their applications across various food sectors, focusing on the properties they confer to food products and packaging systems. Particular attention is given to their ability to improve the bioavailability of poorly soluble nutrients, stabilize food formulations, and integrate natural preservatives to extend shelf life. The article also examines emerging trends, including the use of eco-friendly surfactants, encapsulation of plant-based bioactives, antioxidants, vitamins, and fatty acids, and their potential to enhance functional and fortified food products. Our review demonstrates that nanoemulsions significantly improve nutrient delivery, extending the shelf life of food products, and enhancing sensory characteristics. Furthermore, they offer promising avenues for sustainable packaging solutions. However, challenges related to long-term stability, regulatory approval, and consumer perception remain critical areas for future research. Furthermore, challenges like scalability, regulatory hurdles, and consumer acceptance are addressed. Based on this review, a comprehensive update on the current state of nanoemulsions in food science is essential, with a focus on establishing and refining specific standards to align with industry needs for healthier, safer, and more sustainable food systems. © 2026 The Author(s). Published by the OICC Press
Flexural Performance of Geopolymer-Based Composite Beams Under Different Curing Regimes
No full textElectrical curing is a viable alternative to traditional thermal curing for geopolymer materials due to its capability for rapid and internal geopolymerization. In this research, reinforced geopolymer-based composite beams were successfully fabricated at a macroscale using a binary system of fly ash (FA) and granulated blast furnace slag (GBFS). The mixture was activated with a solution of sodium silicate (Na2SiO3) and sodium hydroxide (NaOH) with a fixed molar ratio of 2:1 for both, and aggregate-to-binder and activator-to-binder (A/B) ratios of 2.5 and 0.7, respectively. To ensure electrical conductivity, individual fiber systems were employed, including carbon fiber (CF), steel fiber (SF), and waste wire erosion (WWE), each incorporated at a dosage of 0.5 vol.% of the total mix volume. In addition, carbon black (CB) was introduced as a conductive filler at a constant dosage of 2.0 vol.% of the binder content in selected specimens. Each beam specimen contained only one type of conductive reinforcement or filler. A total of twelve reinforced geopolymer-based composite beams with a 150 mm square section and a span of 1300 mm, with a clear span of 1200 mm, were successfully cast and reinforced based on reinforced concrete beam designs and standards, with a dominant goal of enhancing beam behavior under flexure. The beams were cured in ambient curing conditions, or using thermal curing at 80 degrees C for 24 h, and using electrical curing from the fresh states with a fixed voltage of 25 V. Notwithstanding a common beam size and reinforcement pattern, distinct curing methods significantly influenced beam structure properties. Peak loads were between 20.8 and 31.5 kN, initial stiffness between 1.75 and 6.09 kN/mm, and total energy absorption between 690 and 1550 kN/mm, with a post-peak energy component of between 0.12 and 0.55. Displacement-based ductility measures spanned from 3.2 to 8.1 units with a distinct improvement in electrical curing regimes, especially in the SF-reinforced specimens; this indicates that electrical curing in reinforced geopolymer composite materials works as a governing mechanism in performance rather than simply a method for enhancing the strength of materials
Retraction: Forecasted Nanopumping Mechanism of Carbon Nanotube-Based Architectures Under Varying Electric Field Amplitudes and Atomic Imperfections: A Thermo-Mechanical Examination
No full textAdvanced Competent Bayesian Regularization Neural Network for Mathematical Modeling of the Immune Diabetes Regulation System
No full textIn this research, the numerical investigations of the fractional order immune diabetes regulation system by using a competent Bayesian regularization neural network procedure have been provided. The fractional order derivatives are used to get better results in comparison with the integer order. The division of the mathematical system is performed in resting and activated macrophages, and the antigen, autolytic, and beta cells. The data generalization is accessible by using the traditional Adam scheme in order to decrease the mean square error, while the data is separated into testing 16%, training 70%, and substantiation 14%. The designed neural network structure is updated by using the optimization tests through Bayesian regularization, a single layer sigmoid activation function, and twenty-five neurons. As conventional modeling schemes depend on shortening traditions or linear calculations, while the stochastic BRNN can perform complicated data patterns and deliver precise calculations of system performance. The correctness of the designed optimizer is obtained through the overlapping of the outcomes and lesser absolute error for each class of the model. Moreover, few curves based on state transitions, regression, error histograms provide the competences of the proposed solver.Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) [IMSIU-DDRSP2501]This work was supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University (IMSIU) (grant number IMSIU-DDRSP2501) .Science Citation Index Expande
A Numerical Method for the Solution of the Two-Dimensional Time-Fractional Cable Equation of Distributed-Order Involving Riesz Space-Fractional Operators
No full textthis paper, we propose an efficient hybrid numerical approach to obtain approximate solutions to the two-dimensional time-fractional cable equation of distributed-order involving Riesz space-fractional operators. This combined numerical approach includes two numerical approaches in time and space directions. The weighted and shifted Gr & uuml;nwald difference numerical method is used to approximate the fractional problem in the time direction, and the fractional compact numerical method is applied in the direction of the space variable. The stability and convergence analyses are studied for the proposed numerical approach. To show the effectiveness of the presented numerical method, some numerical examples are given, and the numerical results for these examples are plotted. Also, this numerical approach is compared with other methods.University of TabrizThis research was supported by the University of Tabriz. All authors read and approved the final manuscript.Science Citation Index Expande
Fertility Preservation in Endometriosis: Evaluating Surgical Risks and Emerging Preservation Approaches
No full textEndometriosis poses significant challenges for women of reproductive age, particularly due to its impact on ovarian reserve and fertility. In addition to endometriosis-associated infertility contributing to diminished ovarian reserve by inflammation and oxidative stress, surgical interventions, often required to manage endometriosis, can exacerbate ovarian damage, further complicating fertility preservation. This narrative review critically examines the interplay between endometriosis treatment and fertility preservation strategies, offering insights into current surgical risks and emerging approaches to mitigate their impact on reproductive potential. Furthermore, it explores traditional fertility preservation methods, including oocyte, embryo and ovarian tissue cryopreservation, alongside advances in vitrification techniques that enhance success rates. Innovative minimally invasive surgical techniques, such as carbon dioxide laser vaporization, plasma energy ablation, haemostatic sealants, and robotic-assisted laparoscopy, are evaluated for their ability to preserve ovarian tissue. Moreover, emerging trends, including the use of artificial intelligence for personalized treatment planning and bioengineering innovations, such as three-dimensional-printed ovarian scaffolds, are discussed as transformative solutions for restoring fertility. A multi-disciplinary, patient-centred approach is emphasized, integrating expertise from gynaecology, reproductive endocrinology, and bioengineering to optimize fertility preservation outcomes. By bridging technological advances and clinical practice, this review aims to provide a framework for preserving fertility while addressing the complex challenges of endometriosis.Science Citation Index Expande
Using the Group Method of Data Handling Neural Network, and the MOGWO Meta-Heuristic Algorithm to Predict the Thermophysical Properties, Heat Transfer, and Friction Factor of Magnetic Nanofluids in a Heat Sink Under a Magnetic Field
No full textThe purpose of this study is to use the group method of data handling (GMDH) neural network and the MOGWO meta-heuristic algorithm to predict the thermophysical properties, heat transfer, and friction factor of magnetic nanofluids in a heat sink under a magnetic field. The GMDH neural network and the MOGWO meta-heuristic algorithm are combined in this study. The ANN is first fed the experimental data. To better match the expected results with the experimental data and decrease the error, the meta-heuristic method tweaks the ANN's hyperparameters. By adjusting the number of iterations and associated aspects, which greatly affect the effectiveness of meta-heuristic algorithms, this situation was optimized. To find the best mode, we compare them using two metrics: R and RMSE. It was found that, as the Reynolds number increases, the fluid flow changes from a laminar state to a mixed or mixed-solid state. These changes lead to an increase in convection heat transfer, which increases the Nusselt number. Also, in laminar flows, due to the parallel and regular movement of the layers, there is less resistance to the flow, and as a result, the friction factor decreases. As the volume fraction increases, more collisions occur between solid particles and the pipe walls, which leads to an increase in the friction factor. The optimal prediction for Nu is achieved with 80 wolves and 300 iterations. Additionally, the most accurate FF prediction is attained with 50 wolves and 200 iterations. Finally, this situation may cause the flow pattern to change from a calm state to a turbulent state, which will result in a higher friction factor. On the other hand, by reducing the volume fraction, the amount of collision of solid particles with the walls will be reduced and the flow will be calmer and more stable. This suggests that the algorithms were successful in predicting the behavior of the experimental data.Deanship of Research and Graduate Studies at King Khalid University [RGP1-243-46]The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP1-243-46.Science Citation Index Expande
A Reliable Deep Neural Network Using the Radial Basis for the Spreading Virus in Computers with Kill Signals
No full textPurpose: The purpose of this work is to provide a reliable neural network process for the spreading virus in computers with kill signals. The mathematical model shows susceptible, exposed, infected individuals to form the virus inactive, and kill signals classes. Method: A structure of deep neural network (DNN) is designed by using two different hidden layers having radial basis activation functions in both layers, optimization through the Bayesian regularization, twenty and thirty numbers of neurons in primary and secondary hidden layers for the spreading virus in computers with kill signals. The stochastic DNN framework is presented to solve the spreading virus in computers with kill signals by selecting the data for training as 70 %, and 15 %, 15 % for both validation and testing. Results: The accuracy of the scheme is observed through the overlapping of the solutions along with negligible absolute error for solving the model. The consistency of the solver is observed through the process of error histogram, regression, and state transition. Novelty: The proposed DNN structure having radial basis activation function has never been applied for the spreading virus in computers with kill signals.Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [KFU252925]The work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia (Grant No. KFU252925) .Science Citation Index Expande