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Light-Responsive Liposome as a Smart Vehicle for the Delivery of Anticancer Herbal Medicine To Skin
No full textSunlight is composed of various wavelengths, including visible light, ultraviolet (UV) rays, and infrared radiation that serves as a double-edged sword for humans via providing the energy for sustaining life on Earth and also acting as a source of hazardous UV radiation. The skin, as the largest protective part of the body, is exposed to sunlight daily, making it critical to protect this organ from its harmful effects. Accordingly, this research aims to fabricate a new type of light-responsive liposome to deliver herbal medicine as protective compounds with antioxidant and anticancer properties. The light-responsive part of this liposome has the capability of cleavage after exposure to UV-A light (the main UV-parts of sunlight) and improves drug release pattern. In detail, a light-responsive compound was fabricated at first and then was used along with phospholipids and curcumin (a type of herbal drug)-loaded cyclodextrin for the fabrication of liposomes using the thin-film hydration method. The physicochemical analysis confirmed the fabrication of spherical liposomes approximately 145 nm in size, which released around 62% of the therapeutic cargo over 120 h when exposed to UV irradiation. Besides, it showed anticancer ability (against melanoma cancer cells) while having a protecting effect for the normal cell line. Therefore, it could be a candidate for further application in skin-protecting products like wound healing compounds or anticancer usage. © Qatar University and Springer Nature Switzerland AG 2024.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (121N152); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAKEmerging Sources Citation Inde
Hydrovoltaic Energy Harvesting: A Systematic Review and Bibliometric Analysis of Technological Innovations, Research Trends, and Future Prospects
No full textThe growing demand for sustainable energy solutions has positioned hydrovoltaic energy harvesting (HEH) as a promising renewable technology that converts water-based phenomena into electricity. Despite its potential, the field lacks a comprehensive synthesis of its research progress and technological evolution. This study addresses this gap through an integrated bibliometric and systematic review approach. Bibliometric analysis of 52 peerreviewed articles, sourced via Scopus, reveals an exponential growth in HEH research from 2020 to 2024, driven predominantly by East Asian countries, with China leading the field. Key thematic clusters were identified using tools such as Bibliometrix and VOSviewer, highlighting innovations in materials like MXenes and nanostructured carbon and their role in enhancing energy conversion efficiency. A systematic review of the selected studies categorizes advancements in fundamental mechanisms, device architecture, and scalable applications. Notable findings include the development of flexible hydrovoltaic devices for wearable electronics and hybrid systems for integrated energy generation and storage. These advancements underline HEH's potential for addressing climate challenges and energy inequities, particularly in underrepresented regions like Africa and South America. The review highlights strategic investment priorities for HEH systems, including scaling, integrating hybrid technologies, and fostering global collaborations to accelerate HEH's transition to practical, scalable renewable energy systems.Emerging Sources Citation Inde
A Nonlinear Mathematical Model to Describe the Transmission Dynamics of the Citrus Canker Epidemic
No full textIn this article, a mathematical model is proposed to define the transmission dynamics of one of the most dangerous plant diseases, citrus canker, by using integer and fractional derivatives. For the fractional-order generalisation, the well-known Caputo fractional derivative is used with the singular-type kernel. The basic features of the proposed integer- and fractional-order models are defined by using well-known mathematical concepts. The proposed model is numerically solved by using the Chebyshev spectral collocation scheme. Some graphical justifications are also given to visualise the disease transmission in the population of citrus plants over time. This research study contains the first non-linear mathematical model of citrus canker transmission, which is the main novelty of this article. © 2025 Elsevier B.V., All rights reserved.Emerging Sources Citation Inde
Effect of Channel Thickness on the Particle Diffusion and Permeability of Carbon Nanotubes a Membrane in Reverse Electrodialysis Process Using Molecular Dynamics Simulation
No full textAdopting innovative technology and solutions is critical for ensuring clean water. Several methods may be used to remove salts from water. They may be divided into two categories: membranes and heat. Reverse electrodialysis, which uses a membrane, is an efficient way of separating substances. Prior research investigated systemlevel factors, but the nanoscale mechanisms that drive ion and water penetration across membranes were poorly understood. This study closed a research gap by investigating the influence of carbon nanotube membrane thickness on particle mobility and fluid dynamics in reverse electrodialysis systems. The research is contributed to the enhancement of energy conversion efficiency and membrane performance in reverse electrodialysis systems by offering a comprehensive understanding of the influence of channel thickness on particle transport and selectivity through the carbon nanotube membrane. Molecular dynamics simulations using the LAMMPS software package are conducted to examine the effect of carbon nanotube thickness variation (1-layer vs 2-layer) on fluid flow, ionic current, hydrogen bonding, and fluid density. To the findings, increasing the thickness of a carbon nanotube from one layer to two layers decreases the fluid flow rate to 203.79 atoms/ns and the current from 5.31 e/ns to 5.15 e/ns. Additionally, the number of broken hydrogen bonds decreases from 116 to 105, indicating decreased permeability and increased stability of the hydrogen-bonding network. In addition to offering useful information for the construction of more effective and selective membranes in renewable energy applications, these results provided a molecular understanding of how carbon nanotube thickness affected reverse electrodialysis effectiveness.Deanship of Research & Graduate Studies at King Khalid University, KSA [RGP. 2/253/46]The authors extend their appreciation to the Deanship of Research & Graduate Studies at King Khalid University, KSA, for funding this work through a research group program under grant number RGP. 2/253/46Science Citation Index Expande
Assessment of Stiffness-Dependent Autophagosome Formation and Apoptosis in Embryonal Rhabdomyosarcoma Tumor Cells
No full textRemodeling of the extracellular matrix (ECM) eventually causes the stiffening of tumors and changes to the microenvironment. The stiffening alters the biological processes in cancer cells due to altered signaling through cell surface receptors. Autophagy, a key catabolic process in normal and cancer cells, is thought to be involved in mechano-transduction and the level of autophagy is probably stiffness-dependent. Here, we provide a methodology to study the effect of matrix stiffness on autophagy in embryonal rhabdomyosarcoma cells. To mimic stiffness, we seeded cells on GelMA hydrogel matrices with defined stiffness and evaluated autophagy-related endpoints. We also evaluated autophagy-dependent pathways, apoptosis, and cell viability. Specifically, we utilized immunocytochemistry and confocal microscopy to track autophagosome formation through LC3 lipidation. This approach suggests that the use of GelMA hydrogels with defined stiffness represents a novel method to evaluate the role of autophagy in embryonal rhabdomyosarcoma and other cancer cells. © 2024. Springer Science+Business Media, LLC
Deploying a Novel Deep Learning Framework for Segmentation of Specific Anatomical Structures on Cone-Beam CT
No full textBayrakdar, Ibrahim Sevki/0000-0001-5036-9867AimCone-beam computed tomography (CBCT) imaging plays a crucial role in dentistry, with automatic prediction of anatomical structures on CBCT images potentially enhancing diagnostic and planning procedures. This study aims to predict anatomical structures automatically on CBCT images using a deep learning algorithm.Materials and methodsCBCT images from 70 patients were analyzed. Anatomical structures were annotated using a regional segmentation tool within an annotation software by two dentomaxillofacial radiologists. Each volumetric dataset comprised 405 slices, with relevant anatomical structures marked in each slice. Seventy DICOM images were converted to Nifti format, with seven reserved for testing and the remaining sixty-three used for training. The training utilized nnUNetv2 with an initial learning rate of 0.01, decreasing by 0.00001 at each epoch, and was conducted for 1000 epochs. Statistical analysis included accuracy, Dice score, precision, and recall results.ResultsThe segmentation model achieved an accuracy of 0.99 for nasal fossa, maxillary sinus, nasopalatine canal, mandibular canal, foramen mentale, and foramen mandible, with corresponding Dice scores of 0.85, 0.98, 0.79, 0.73, 0.78, and 0.74, respectively. Precision values ranged from 0.73 to 0.98. Maxillary sinus segmentation exhibited the highest performance, while mandibular canal segmentation showed the lowest performance.ConclusionThe results demonstrate high accuracy and precision across most structures, with varying Dice scores indicating the consistency of segmentation. Overall, our segmentation model exhibits robust performance in delineating anatomical features in CBCT images, promising potential applications in dental diagnostics and treatment planning.Science Citation Index Expande
Enhancing Indoor Positioning Performance Through Wi-Fi/RSSI-Based Machine Learning Classifiers
No full textKapalı alan konumlandırma (IP), gerçek zamanlı kapalı alan lokalizasyonunun (IL) önemli bir bileşenidir ve kullanıcı veya cihaz konumlarının kapalı alanlarda belirlenmesine katkıda bulunur. Küresel Konumlandırma Sistemi (GPS), açık alanlarda etkili bir şekilde çalışsa da, çoklu yol yayılımı, görüş hattı olmaması ve sinyal bozulması gibi zorluklar nedeniyle kapalı alanlarda etkinliği azalır. Bu sorunu çözmek için, Wi-Fi konumlandırma sistemi, beş erişim noktası sensörü, etiket olarak kullanıcı ve alınan sinyal gücü (RSS) ile makine öğrenimi sınıflandırıcılarına dayalı bir kapalı alan konumlandırma tekniği geliştirilmiştir. Makine öğrenimi, kapalı alan konumlandırma sistemlerinin çeşitli koşullara uyum sağlamasını, belirsizlikleri yönetmesini ve veri odaklı öğrenme yoluyla doğruluğu sürekli artırmasını mümkün kılar. Parmak izi tabanlı lokalizasyon, yüksek boyutlu verilerle ilgili zorluklarla karşılaşır ve bu zorluklar temel bileşen analizi (PCA) gibi boyut azaltma yöntemleriyle ele alınır. Karar Ağaçları (DT), Yerel Ayırt Edici Analiz (LDA), Destek Vektör Makinesi (SVM), K-en yakın komşu (KNN), Lojistik Regresyon (LR), Yapay Sinir Ağları (ANN) ve Aşırı Öğrenme Makinesi (OS-ELM) gibi sınıflandırma algoritmaları, konumlandırma için temel özellikleri çıkarmak ve IP tespiti uygulamak için kullanılır. Ortaya çıkan sonuçlar, doğruluk, tahmin hızı ve eğitim süresi açısından analiz edilir.Araştırma, kapalı alan konumlandırmada çeşitli sınıflandırıcıların güçlü ve zayıf yönlerini anlamak için bir temel oluşturur. Karşılaştırmalı analiz, OS-ELM'nin olağanüstü doğruluk, hızlı tahmin hızı ve minimal eğitim süresi ile öne çıktığını ortaya koyarak, onu gerçek zamanlı uygulamalar için umut verici bir seçenek haline getirmektedir. Çalışma, OS-ELM'nin ve dinamik kapalı alan ortamlarında doğruluk ve uyarlanabilirliği artırmak için hibrit yaklaşımların iyileştirilmesini vurgulayarak gelecekteki araştırma yönlerini ortaya koymaktadır.Indoor positioning (IP) is a pivotal component in real-time indoor localization (IL), contributing to the identification of user or device locations within confined spaces. Global positioning system (GPS) excels in outdoor positioning, but its efficacy diminishes indoors due to challenges like multipath propagation, non-line of sight, and signal distortion. To address this, an indoor positioning technique has developed, including Wi-Fi positioning system, five access points sensors, user as a tag, and based on the received signal strength power (RSS) with machine learning classifiers. Machine learning empowers indoor positioning systems to adapt to diverse conditions, manage uncertainties, and enhance accuracy continually through data- driven learning. Fingerprinting localization faces challenges with high-dimensional data, addressed by dimensionality reduction methods like principal component analysis (PCA). Classification algorithms, such as Decision Trees (DT), Local Discriminator Analysis (LDA), Support Vector Machine (SVM), K-nearest neighbor (KNN), Logistic Regression (LR), Artificial Neural Networks (ANN), and Extreme Learning Machine (OS-ELM) are employed to extract key characteristics for localization and hence implement IP detection. The ensuing results are analyzed for accuracy, prediction speed, and training time. The research sets a foundation for understanding the strengths and limitations of various classifiers in indoor positioning. The comparative analysis reveals that OS ELM exhibits exceptional accuracy, rapid prediction speed, and minimal training time, positioning it as a promising choice for real-time applications. The study concludes by outlining future research directions, emphasizing the refinement of OS-ELM and hybrid approaches to enhance accuracy and adaptability in dynamic indoor environments
Artificial Intelligence Anxiety Levels of Faculty of Health Sciences Students and Affecting Factors
No full textObjective: Artificial intelligence (AI) technology advancements are poised to bring significant changes to the healthcare field. As the adoption of AI systems in healthcare continues to grow, there is an increasing need to equip future healthcare professionals with the necessary knowledge and skills to work effectively with these technologies. This study explores the level of anxiety related to AI and examines the factors influencing this anxiety among university students enrolled in health sciences programs. Methods: This cross-sectional study was descriptive and correlational. The study was carried out with 450 students at the İstanbul Gedik University, Faculty of Health Sciences (Department of Nursing, Physiotherapy and Rehabilitation, Child Development, Nutrition and Diet, Occupational Health and Safety). A descriptive questionnaire and an AI anxiety scale were used to obtain the research data, which were analyzed using numerical data, descriptive statistics, analysis of variance, independent groups t-test, and post-hoc analysis. Results: The mean total score on the Artificial Intelligence Anxiety Scale (AIAS) was 109.642 ± 30.452 (min = 42; max = 147). Among the sub-dimensions of the AIAS, the mean of the Learning sub-dimension was 41.096 ± 12.083 (min = 16; max = 56), the mean of the Job Change sub-dimension was 31.118 ± 9.022 (min = 12; max = 42), the mean of the Sociotechnical Blindness sub-dimension was 21.558 ± 5.892 (min = 8; max = 28), and the mean of the AI Configuration sub-dimension was 15.871 ± 4.831 (min = 6; max = 21). Conclusion: According to this study, students from the Faculty of Health Sciences had a high level of AI anxiety. Significant differences were found between students’ AI anxiety levels according to gender, their thoughts about AI, their trust in AI-based devices, their desire to change their profession because of AI, and their use of AI in patient care. © 2025 AVES. All rights reserved.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (2209-A); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTA
Investigating the Effect of Functionalized Carbon Nanotube With Cooh Group on the Drug Delivery Process of Doxorubicin in Capillary Networks Around Cancer Tumors Using Molecular Dynamics Simulation
No full textThis study investigated the interaction between functionalized carbon nanotubes and doxorubicin, a commonly used chemotherapy drug, aiming to enhance cancer therapy. Functionalizing CNTs with carboxyl (-COOH) groups aimed to improve the precision of drug delivery system, enabling more effective targeting of cancerous tumors while minimizing side effects on healthy tissues. Molecular dynamics simulations indicated that after 10 ns, the system stabilized at a potential energy of 5.676 kcal/mol and a total energy of 6.62 kcal/mol, suggesting thermodynamic equilibrium. Increasing the atomic ratio of COOH groups from 2.5% to 10% significantly raised the maximum structural density from 0.0035 atm/& Aring;3 to 0.0042 atm/& Aring;3, thereby enhancing drug-loading capacity through stronger intermolecular interactions. Thermal stability improved as the maximum temperature decreased from 360.64 K to 346.08 K, indicating better heat dissipation and enhanced doxorubicin stability. Moreover, shear stress increased from 3.52 Pa to 3.79 Pa, indicating enhanced mechanical resistance. The mean squared displacement (MSD) decreased from 3.42 & Aring;2 to 3.24 & Aring;2, and the root mean square deviation (RMSD) decreased from 1.85 & Aring; to 1.80 & Aring; These reductions indicated decreased molecular mobility and increased structural stability. These findings demonstrate that functionalized CNTs enhanced drug encapsulation, stability, and controlled release, maximizing the therapeutic effects of doxorubicin while minimizing side effects. This study highlighted the potential of nanotechnology to revolutionize drug delivery systems and improve cancer treatment outcomes.Science Citation Index Expande
Ebeveyn Görüşleri Doğrultusunda Sıfır-Üç Yaş Arasındaki Çocukların Teknolojik Araçları Kullanım Durumlarının İncelenmesi
No full textTeknoloji günümüzde insan hayatında iletişim amaçlı kullanılmasının dışında, bilgi edinimi ve paylaşımı, fotoğraf ve video çekimi, online görüşmeler, sağlık uygulamaları gibi pek çok alanda yaygın olarak kullanılan bir araç haline gelmiştir. Ebeveynlerin hayatında yer almasının yanında çocukların da erken yaşlardan itibaren teknolojik araçlarla tanışmalarına neden olmuştur. Bu araçların yararları olabileceği gibi pek çok zararları ve etkileri de bulunmaktadır. Bu bilgilerden yola çıkılarak bu araştırmada, ebeveyn görüşleri doğrultusunda sıfır-üç yaş arası çocukların teknolojik araçları kullanım durumlarını ortaya koymak amaçlanmıştır. Bu amaç doğrultusunda, sıfır-üç yaş aralığında çocuğu olan ebeveynlerin ne sıklıkta, hangi teknolojik aracı kullandıkları, kendilerinin çocuklarına ne sıklıkta, hangi teknolojik aracı kullanmalarına izin verdikleri ve teknolojik aracı çocuklarına ne amaçla kullandırdıkları; bu ailelerin çocuklarının teknoloji kullanımının demografik değişkenlere (ebeveyn yaşı ve öğrenim durumu gibi) göre değişip değişmediği, ebeveynlerin sosyal medya kullanma ve çocuklarının fotoğraflarını sosyal medyada paylaşma durumları incelenmiştir. Araştırmada veriler, kartopu örnekleme yöntemi kullanılarak araştırmacı tarafından hazırlanan Google form aracılığıyla, e posta adresi veya sosyal medya hesabı olan 310 ebeveyne gönderilerek, çevrimiçi olarak toplanmıştır. Elde edilen verilerin analizinde; ebeveynlerin ve çocukların sosyodemografik özelliklerini ve ebeveynlerin ve çocukların teknolojiye yönelik tutum ve davranışlarını belirlemek için betimsel analizler yapılmış olup sonrasında ki kare testi uygulanmıştır. Araştırmanın sonucunda, ebeveynlerin en sık akıllı telefon kullandıkları ortaya çıkmış, bunu TV, bilgisayar ve tablet kullanımı izlemiştir. Ebeveynlerin teknolojik araçları en çok çocuklara yemek yedirirken eğlence ve eğitim amaçlı ve ebeveyn başka bir işle meşgulken çocuklarına davranış kontrolünü sağlamak için verdiği sonucuna ulaşılmıştır. Ebeveyn görüşleri doğrultusunda, çocukların en çok akıllı telefon kullandıkları, bunu TV ve tabletin izlediği görülmektedir. Çocuklara teknolojik araç verilmediğinde ya da elinden alındığında saldırganlık ve ağlama davranışları ortaya çıktığı görülmüştür. Bunlarla birlikte çalışmaya katılan ebeveynlerin teknoloji kullanım süreleri arttıkça, çocukların da teknoloji kullanım süresinin arttığı, aynı zamanda ebeveynlerin yüzde 62,9'unun sosyal medyada çocuklarının fotoğraflarını paylaştıkları belirlenmiştir. Çocuklarının fotoğraflarını sosyal medyada paylaşan annelerin hem kendilerinin hem de çocuklarının teknoloji kullanım süresinin arttığı ortaya çıkmış, annelerin teknoloji kullanım süresi ile teknoloji kullanan çocukların televizyonla tanışma zamanı arasında da anlamlı bir ilişki bulunmuştur. Bu sonuçlar doğrultusunda ebeveynlerin çocuklarına olumlu model olmaları için teknolojik araç kullanımlarını sınırlandırmaları önerilebilir.Technology has become a widely used tool in many areas such as information acquisition and sharing, photography and video shooting, online interviews, and health applications, in addition to being used for communication purposes in human life. In addition to being a part of parents' lives, children have also been introduced to technological devices from an early age. These devices may have benefits, but they also have many harms and effects. Based on this information, this research aims to reveal the use of technological tools by children aged zero to three, in line with parents' views. For this purpose, it was examined how often and which technological tools parents of children between the ages of zero and three use, how often and which technological tools they allow their children to use, and for what purposes they allow their children to use the technological tools; whether the use of technology by children of these families varies according to demographic variables (such as parental age and educational status), and whether parents use social media and share their children's photographs on social media. Data were collected online using a Google form prepared by the researcher using the snowball sampling method, and sent to 310 parents with an e-mail address or social media account. In the analysis of the obtained data, descriptive analyses were conducted to determine the sociodemographic characteristics of parents and children and the attitudes and behaviors of parents and children towards technology, and then the chi-square test was applied. As a result of the research, it was revealed that parents use smartphones most frequently, followed by TV, computer and tablet use. It was concluded that parents give technological devices to their children mostly for entertainment and education purposes while feeding them and to control their children's behavior while the parent is busy with another task. According to parents' opinions, it is seen that children use smartphones most, followed by TV and tablet. It was observed that when technological devices are not given to children or are taken away from them, aggression and crying behaviors occur. In addition, it was determined that as the time the parents participating in the study used technology increased, the time their children used technology also increased, and at the same time, 62.9 percent of the parents shared photos of their children on social media. It has been revealed that mothers who share their children's photos on social media increase the time they and their children use technology, and a significant relationship has been found between the time mothers use technology and the time children who use technology are introduced to television. In line with these results, it can be suggested that parents limit their use of technological devices in order to be a positive role model for their children