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Verbascum Thapsus Özütü İçeren Hemostatik Kitosan Jelatin Kriyojel Üretimi, Karakterizasyonu ve Hemostatik Etkisinin İncelenmesi
No full textKanama, insan yaşamı için en büyük tehditlerden biridir ve travma ölümlerinin yaklaşık %40'ına sebep olur. Farklı polimerlerin kombinasyonundan oluşan yeni ve biyoaktif hemostatik biyomalzemeler son yıllarda büyük ilgi görmeye başlamıştır. Bu çalışmada, Verbascum thapsus özütü içeren hemostatik kitosan/jelatin kriyojel üretilerek, morfolojik, kimyasal ve biyolojik olarak karakterize edilmiştir ve in vitro araştırılmıştır. Kriyojeller özgün gözenekli yapısı, hızlı sıvı absorpsiyonu ve hücre infiltrasyon özellikleri nedeniyle hemostatik uygulamalar için oldukça uygundur. Kitosan ve jelatin fonksiyonel grupları aracılığıyla trombosit yapışmasını ve agregasyonunu artırırken, VT özütünün kullanımı, içeriğindeki bileşenlerle biyoaktivitesi artarak pıhtı oluşumunu hızlandırarak kanama süresini kısaltır. SEM ile 225 ile 478 µm arasındaki ortalama gözenek çaplarına sahip, birbirine bağlı, makro gözenekli yapı gözlemlendi. Kriyojeller %3500'lük yüksek oranlarda şişme gösterdi. V. thapsus içeren kriyojeller, E. coli'ye karşı %89'a ve S. aureus'a karşı %78'e kadar bakteriyel inhibisyon gösterdi. Kriyojellerin hücre canlılığı bir insan fibroblast hücre hattı ile test edildi. Kriyojellerin kan uyumluluğu %1'lik hemoliz oranı ile kanıtlandı. V. thapsus özütünün hemostatik aktivitesi in vitro tam kan pıhtılaşma testi ile araştırıldı. Kitosan/jelatin kriyojellerin kan pıhtılaşma indeksi (BCI), V. thapsus özütü eklenerek 11,9'dan 6,5'e düşürüldü ve pıhtılaşma süresi azaltıldı. V. thapsus özütü içeren kitosan/jelatin kriyojeller, kontrolsüz kanama uygulamaları için büyük hemostatik potansiyel gösterdi.Hemorrhage is one of the biggest threats to human life and it causes approximately 40% of trauma deaths. To control bleeding, hemostatic dressings combining different polymers have gained interest in recent years. In this study, chitosan/gelatin hemostatic cryogels containing Verbascum Thapsus was fabricated, characterized morphologically, chemically, and biologically, and investigated hemostatic activity in vitro. Cryogels having unique porosity, rapid absorption, and cell infiltration characteristics are suitable for hemostatic applications. The combination of chitosan and gelatin creates a matrix enhancing platelet adhesion and aggregation via functional groups, whereas the use of V. thapsus extract increases bioactivity to accelerate clot formation and shorten coagulation time. The interconnected macroporous structure and average pore diameters of 225-478 µm were observed by SEM. The cryogels showed a high swelling ratio of 3350%. The V. Thapsus containing cryogels demonstrated bacterial inhibitions up to 89% against E. coli and 78% against S. aureus. The cell viability of the cryogels was investigated by a human fibroblast cell line. The blood compatibility of cryogels was proved with the 1% hemolysis ratio. The blood hemostatic activity of V. thapsus extract was investigated by using an in vitro whole blood clotting assay. The blood clotting index (BCI) of the chitosan/gelatin cryogels was improved from 11.9 to 6.5 with adding V. thapsus extract and the clotting time was decreased. The V. thapsus extract containing chitosan/gelatin cryogels demonstrated great hemostatic potential for hemorrhage applications
ALK'nin Tirozin Kinaz Bölgesini Hedefleyen Yeni İlaç Adaylarının Kapsamlı İn Silico Yaklaşımlarla Tanımlanması
No full textAnaplastik büyük hücreli lenfoma hücre hatlarında füzyon protein ortağı olarak keşif edilen Anaplastik Lenfoma Kinaz'ın (ALK), keşifinden bu yana ALK çeşitli füzyon ortakları ile çok sayıda kanserde rol oynadığı ortaya çıkmıştır. Rol oynadıkları kanser şu şekilde sıralanabilir: küçük hücreli olmayan akciğer kanseri (NSCLC); anaplastik büyük hücreli lenfoma (ALCL); nöroblastom; rabdomiyosarkom; vb. Son yılda, ABD Gıda ve İlaç Dairesi (FDA) ALK'yi hedefleyen birçok bileşik onay almıştır. Bu gelişmelere rağmen, yakın zamanda yapılan bir çalışma ALK pozitif NSCLC hastalarının yaklaşık yarısının hastalık ilerlemesi yaşanacağını vurgulanmıştır, başlangıç tedavisi olan Alectinib, ikinci nesil ALK inhibitörü ve üçüncü nesil ALK inhibitörü Lorlatinib rağmen. Bu noktaları göze alarak, bu çalışma ALK'nin tirozin kinaz alanını hedefleyebilecek yeni bileşikler keşfetmek ve geliştirmek için iki farklı yola odaklanmıştır. İlk yaklaşım olarak 200'den fazla α-carboline türevi tasarladık. Devamında moleküler yanaştırma (Docking), moleküler dinamik (MD) simülasyonlarını MM/PBSA ile serbest bağlanma enerjisi hesaplamalarından oluşan in silico protokolleri kullanarak tasarımlarımızın hedefimize karşı bağlanma özelliklerini araştırdık. İkinci yaklaşım olarak büyük bir doğal ürün veritabanını aynı amaca yönelik yeni bir ilaç adayı keşfetme adına sanal olarak taradık. Devamında bağlanma özelliklerini ilk yaklaşımda kullanılan yöntemlerle inceledik. Elde edilen bütün sonuçları göz önünde bulundurarak, sonuçlar takip eden şekilde özetlenebilir. Üç umut verici ilaç adayı aralarından yükselmiştir test edilen bileşikler arasında, bileşik 208, 209 ve CNP0106316.1. Serbest bağlanma enerjileri ise sırasıyla -9.08, -9.80 ve -11.6 kcal/mol olarak bulunmuştur. Ek olarak, ismi geçen bileşikler ilgili MD simülasyonlarında stabil bağlanma profilleri göstermişlerdir.After the first description of Anaplastic Lymphoma Kinase (ALK) in an anaplastic large cell lymphoma cell line as a nucleophosmin (NPM) fusion partner, ALK and its various fusion partners have been implicated in numerous cancers such as non-small cell lung cancer (NSCLC), anaplastic large cell lymphoma (ALCL), neuroblastoma, rhabdomyosarcoma. In the last decade, several compounds targeting ALK have been developed and approved by the Food and Drug Administration (FDA). Despite the advances of generations of ALK inhibitors, a recent study highlighted that around half of the ALK-positive NSCLC patients will go through disease progression in response to first-line Alectinib and Lorlatinib, which is a second-generation and third generation ALK inhibitors, respectively. Given these points, this study focused on two distinct paths to discover and develop novel compounds that could target tyrosine kinase domain of the ALK. Firstly, we designed more than 200 α-carboline derivatives and investigated their binding properties against ALK tyrosine kinase by using in silico protocols consisting of molecular docking studies, molecular dynamics simulations, and MM/PBSA binding free energy calculation. As a second approach, we virtually screened a large natural product database to a novel drug candidate for possibly inhibiting ALK TK and investigated their binding properties by similar ways utilised in the first approach. Considering the obtained results, we developed three promising candidates, compounds 208, 209 and CNP106316.1 with -9.08, -9.80, and -11.6 kcal/mol and binding energies respectively, which demonstrated improved binding profiles over their respective MD simulations, 300ns for carbolines, 500ns for the NP
Analysis of Geometric Features in Anatolian Seljuk Kümbets
No full textThe funerary structures known as k & uuml;mbets, developed during the Anatolian Seljuk period (1077-1307), represent a distinctive architectural typology. This study first demonstrates that each Anatolian Seljuk k & uuml;mbet is unique in its sectional geometry. It then employs statistical methods to analyze these structures, with the aim to understand their architectural styles and formation principles through measurable geometric features. The scope includes the analysis of 56 section drawings of 67 freestanding k & uuml;mbets. The methodology involves data collection and preparation, feature selection, and dataset refinement using a box plot technique, followed by correlation analysis. Among the 28 correlations analyzed, 18 are statistically significant. One of the strongest correlations indicates a strong inverse relationship between the cap's inner angle and cap height (r = - 0.93), while the weakest is a positive relationship between cap height and interior wall height (r = 0.27).Scientific Research Projects Coordination Unit (BAP) of Istanbul Technical University [MAB-2023-44405]This research is supported by the Scientific Research Projects Coordination Unit (BAP) of Istanbul Technical University (Project Code: MAB-2023-44405). The authors would like to thank Arzum Dincel and Goekce Suba & scedil;& imath;, academic interns, for updating the data in Table 1 and preparing Fig. 4
Enhancing Sentiment Analysis in Stock Market Tweets Through Bert-Based Knowledge Transfer
No full textBakal, Mehmet/0000-0003-2897-3894;One of the widely studied text classification efforts is sentiment analysis. It is a specific examination involving natural language processing and machine learning methods to understand semantic orientation from textual data. Working social media posts, such as tweets, for sentiment analysis, is quite common among researchers due to the speed of information dissemination. In this regard, forecasting stock market tweets is a widely studied research topic. Some studies have revealed a strong connection between sentiment and stock market performance, while others have not found any notable associations. The proposed work shows two distinct approaches to sentiment analysis over the stock market tweets. The first approach employs traditional machine learning algorithms, including logistic regression, random forest, and XGBoost. The second approach constructs deep learning (as a subfield of machine learning) models using LSTM and CNN algorithms to classify the test instances into positive, negative, or neutral classes through ten randomly shuffled data splits. In this study, the labeled data size is gradually increased utilizing a pre-trained model, FinBERT. It is exclusively employed to label unlabeled data instances to integrate them into the experiments. The goal is to monitor the effect of the additional newly-labeled examples on the sentiment analysis performance. The experiments showed that the average F1-score improved by 20% for the deep learning models and 17% for the machine learning models. In the end, the paper reveals a strong positive correlation between training data size and the classification performance of the experimental approaches.Abdullah Gul UniversityNot applicable
Efficacy of Combinatorial Inhibition of Hedgehog and Autophagy Pathways on the Survival of AML Cell Lines
No full textAcute myeloid leukemia (AML) is a common hematopoietic disease that results from diverse genetic abnormalities. Dysregulation of important signaling pathways, including the PI3K/AKT/mTOR, Wnt and Hedgehog pathways, plays crucial roles in the development of AML. Hedgehog pathway (Hh) is a conserved signaling pathway that is crucial throughout embryogenesis. Hh plays an important role in the regulation of autophagy, known as the cellular recycling process of organelles and unwanted proteins. Many studies have noted that the modulation of autophagy could act as a survival mechanism in AML. Considering the pivotal role of autophagy and Hh signaling in AML, understanding the relationship between these pathways is important for overcoming leukemia. Therefore, we examined the efficacy of Hh inhibition by GLI-ANTagonist 61 (GANT61) in MOLM-13 and CMK cells via 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenil-2H-tetrazolium bromide (MTT) cell viability assays. GANT61 resulted in decreased cell viability in both cell lines. Therefore, we focused on the outcome of autophagy modulation in AML cells. We observed that the autophagy inhibitors ammonium chloride (NH4CI), chloroquine (CQ), and nocodazole led to a significant reduction in the proliferation of both cell lines. Cotreatment with autophagy pathway inhibitors and GANT61 synergistically affected both AML cell lines. Moreover, dual targeting of these pathways resulted in arrest at the G0/G1 phase in MOLM-13 cells but not in CMK cells. Furthermore, the combination of nocodazole and GANT61 increased the expression level of LC3B-II in both cell lines. Compared with that in the untreated control cells, the GLI1 gene expression level in both cell lines was significantly lower after GANT61 and autophagy cotreatment. In conclusion, targeting Hh and autophagy could be a favorable option to combat AML.TUBITAK [216S319]Funding This work was supported by TUBITAK with project number 216S319. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments We acknowledge the flow cytometry facility at Abdullah Gil University, Central Research Laboratory
Comparative Study on Bending Performances of 3D-Printed Monolithic and Adhesively Bonded Sandwich Structures With Various Auxetic Cores: An Innovative Production Approach
No full textDemirbas, Munise Didem/0000-0001-8043-6813; Atahan, Mithat Gokhan/0000-0002-8180-5876; Apalak, Mustafa Kemal/0000-0002-3263-5735The cores of sandwich structures are typically produced monolithically using lightweight materials and specific geometries. In recent years, the advancements in additive manufacturing have enabled the design and production of novel sandwich core configurations with auxetic behavior and high energy absorption capability. In this study, an innovative production approach, namely adhesively bonded sandwich structures with auxetic cores, was proposed to ensure significant manufacturing advantages for industrial applications. Each part of the sandwich core structures with auxetic core configurations was printed separately and then bonded using an epoxy-based adhesive. To evaluate the mechanical performance of the proposed bonded sandwich structures, three-point and four-point bending tests with DIC (Digital Image Correlation) analyses were conducted. The bending test results of adhesively bonded sandwich structures were compared with those of monolithic sandwich structures and the effectiveness of the proposed innovative production method was evaluated. Re-entrant, star-shaped, and V-shaped auxetic core configurations were compared in terms of the bending performances of the adhesively bonded and monolithic sandwich structures. Monolithic and adhesively bonded sandwich structures showed similar bending behavior as far as load-carrying capacity, deformation stages, and crashworthiness performance are concerned based on three and four-point bending tests. Hence, the proposed innovative production approach can be applied to sandwich structures to enhance their repairability and support sustainable manufacturing.Scientific Research Projects Coordination Unit of Nigde Omer Halisdemir University [MMT 2024/9-BAGEP-Y]The author(s) disclosed receipt of the followingfinancial support for the research, authorship,and/or publication of this article: This study wasfinancially supported by the Scientific Research Projects Coordination Unit of Nigde Omer Halisdemir University under the contract:MMT 2024/9-BAGEP-Y
Prediction of the Diffusible Hydrogen Concentration After Electrochemical Charging Utilizing Artificial Intelligence
No full textThe concentration of diffusible hydrogen in a material is of high importance as it helps to predict the hydrogen embrittlement effect in the material, and the amount of mechanical properties' degradation after reaching a critical concentration. Despite that, a simple experimental setup is not available to measure hydrogen concentration at service. In this paper, a multi-layer perceptron (MLP) model is developed using weight initialization, which can estimate the diffusible hydrogen concentration of Face-Centred-Cubic (FCC) metals after electrochemical charging. The input properties of the model include the electrochemical charging parameters of current density, temperature, and charging time as well as the grain size of the specimen. The MLP model with and without the weight initialization was validated and tested with unseen test dataset. The model in both cases showed an excellent predictive performance with a higher accuracy and faster convergence when using weight initialization. A linear correlation of 89% between the experimental and predicted hydrogen concentration was observed. This demonstrates that for the family of FCC metals under electrochemical charging, the estimation of diffusible hydrogen concentration is a feasible path for material safety design analysis.TUBITAK; Scientific and Technological Research Council of Turkey (TUBITAK) [124M097]; National Natural Science Foundation of China [12302279]; Shanghai Gaofeng Project for University Academic Program DevelopmentThis study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 124M097. The authors thank TUBITAK for their supports. Y.L. would like to acknowledge the support of the National Natural Science Foundation of China under Grant No. 12302279, and Shanghai Gaofeng Project for University Academic Program Development
A Comprehensive Analysis of Acoustic Emission Signals To Distinguish the Different Damage Types for Fiber-Reinforced Polymers: A Review
No full textFiber-reinforced polymers (FRP) attract the attention of key industries, such as aerospace, wind energy, and automotive, as they can reduce the weight of structural components without compromising their mechanical properties. Due to FRP's anisotropic and non-homogeneous structure, their failure under different loading conditions and the corresponding failure mechanisms must be investigated. One method that progressively monitors the failure of FRP underload is Acoustic Emission (AE). AE can register the elastic stress waves in the form of digitized waveforms, released by the discontinuous events that occur in the FRP under load. These discontinuities can be clustered and identified as transverse cracking, fiber/matrix interface debonding, delamination, and fiber failure by analyzing the AE waveforms. Recently, numerous clustering approaches using machine learning algorithms, along with the varying features of AE waveforms, have been developed and are being used. These algorithms include supervised and unsupervised clustering, deep learning algorithms, and neural network methods, among others. While supervised algorithms require a training dataset to classify AE signals, unsupervised algorithms can perform clustering without training datasets. Deep learning and neural network algorithms can train themselves to cluster data, but they may require a significant amount of computer power when the dataset is large. This review paper provides comprehensive information on the clustering algorithm, along with the AE wave features, the range of features for different damage types, and the type of reinforcer
Power Factor Improvement of a Permanent-Magnet Vernier Machine with Harmonic Injected Excitation Currents
No full textPermanent-magnet vernier machines (PMVM) are recognized for their high torque density but low power factor (PF) due to high inductive reactance. This paper presents a method for improving the PF of a PMVM by injecting additional harmonics into the excitation currents. This injection is done through the motor drive, unlike many proposed methods for enhancing PF, thus eliminating any modifications needed on the machine's geometric design. In this paper, different sets of harmonic injected currents are fed to a 14-rotor pole 12-slot PMVM with short-pitched coils on Finite Element Analysis (FEA) to demonstrate the effects of individual and combined harmonic currents. Corresponding performance characteristics of each simulation case, such as PF and torque density, are investigated. Simulation results indicate that PF can be improved by the proposed method of harmonic current injection. A comparison with a similarly sized permanent-magnet synchronous machine (PMSM) is made to demonstrate that the proposed method can be an alternative to widely used PMSMs
Spec17Tre: A New Dataset in Hardware Security and Using Deep Learning for Detecting Spectre Attacks
No full textComputer performance has become a significant subject of study due to the processing of big data, the complexity of calculations and the importance of time efficiency. Many companies are improving processor operating principles to increase performance. The most common methods for this purpose are speculative execution and cache usage. While these techniques improve performance, they also introduce certain security vulnerabilities. Spectre is an attack that exploits vulnerabilities created by speculative execution, affecting all modern processor architectures. Research has shown that using machine learning to detect these attacks can be quite effective, although the features are typically gathered at the software level, which may limit detection since some performance parameters are not conveyed to the software. This study presents an analysis of Spectre attacks and their detection using machine learning and deep learning methods at the hardware level. Experiments are conducted using GEM5, a full-system hardware simulator, to ensure that only hardware-visible performance parameters are also collected. Attack detection is performed using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) methods. The LSTM method is used in conjunction with SVM and Convolutional Neural Network (CNN) techniques, and all models were tested on a new dataset, Spec17Tre, created using "519.lbm" from the SPEC CPU2017 benchmarks. The study achieved a 95% accuracy rate in attack detection using the LSTM + CNN hybrid model, which also yielded an F1 score of 0.999 for detecting applied Spectre attack scenarios.The Scientific and Technological Research Council of Trkiye (TBIdot;TAK) [123E017]; Scientific and Technological Research Council of Turkey (TUBITAK); TUBITAKThis study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant Number 123E017. The authors thank TUBITAK for their support