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A Small Indole Derivative Isolated From Caper (Capparis Ovata) as an Inducer of P53-Mediated Apoptosis in Prostate Cancer: Comprehensive In Vitro and In Silico Studies
(Wiley, 2025-12-31) Acar, Ozden Ozgun; Gazioglu, Isil; Oruc, Hatice; Kale, Elif; Senol, Halil; Topcu, Gulacti; Sen, Alaattin
Natural products with stunning chemical diversity have been extensively researched for their anticancer potential for more than fifty years. This study aimed to determine the effect of indole derivative 1H-indole-2-hydroxy-3-carboxylic acid (IHCA), isolated as a novel alkaloid from Capparis ovata, on selected tumor suppressor, apoptotic, and cell cycle regulatory genes, which are known to be important in cancer pathophysiology, on Caco-2 and LNCaP cells in comparison with Taxol. The molecular mechanism of IHCA's anticancer activity is essentially undefined. Different concentrations of IHCA increased the expression levels of apoptosis-related genes, including BCL-2 and TNF-alpha. In addition, the tumor suppressor genes PTEN, P53, and RB were increased in LNCaP and Caco-2 cells. KRAS, an oncogenic gene, was significantly downregulated by IHCA in LNCaP cells. Western blot results showed that the protein expression levels of P53 and PTEN in LNCaP cells were increased when treated with IHCA, whereas CDK4 and TNF-alpha were decreased. Finally, IHCA and doxorubicin significantly increased P53-driven luciferase activity compared to the control. The results strongly suggest that the novel natural compound IHCA has an anticancer effect involving the regulation of the P53 gene and its networks in vitro. The molecular docking and MD simulation analyses reveal that IHCA exhibits superior binding potential to the MDM2 protein compared to Nutlin-3a. MD simulations further confirm that IHCA maintains a more stable and consistent interaction with MDM2, as indicated by lower RMSD values and reduced ligand fluctuation. These results highlight IHCA's potential as a more effective MDM2 inhibitor, suggesting its promise as a lead compound for anticancer drug development.Clinical Trial Registration: Not applicable.
Looking for Stability in Chaos: A Scoping Review of Relational Turbulence Theory from a Dyadic Perspective
(Wiley, 2025-11-21) Lagap, Adar Cem; Gungor, Duygu
The current scoping review overviews articles that apply the relational turbulence model/theory to guide the implementation of actor-partner interdependence modeling within a structural equation modeling framework. Sixteen studies are examined in the final synthesis of the review. Research themes center on communication strategies and social connection, dispositional and situational factors, and, lastly, mental and physical health. Current work illustrates that scholars are primarily interested in sources of relational uncertainty and its intrapersonal and interpersonal consequences. Sources of partner influence and their implications for relational dynamics are also examined across the synthesized studies. Overall, more actor effects than partner effects were statistically significant. Commercial statistical programs appear preferred for analyzing dyadic data, and assessments of fit indices are reported to evaluate proposed analytic models in this body of research. Methodological and theoretical limitations are highlighted, and implications for future research are discussed.
Citation - WoS: 1
A Comprehensive Analysis of Acoustic Emission Signals To Distinguish the Different Damage Types for Fiber-Reinforced Polymers: A Review
(Wiley, 2025-12-03) Yilmaz, Cagatay
Fiber-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.

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