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There is currently no cure or disease-modifying treatment for post-traumatic osteoarthritis (PTOA). This study aims to assess the efficacy of dimethyl fumarate (DMF), a US-FDA approved drug for multiple sclerosis, as a treatment for PTOA. PTOA was induced in male Lewis rats by medial meniscal transection (MMT) surgery, and DMF was intra-articularly administered once, one week following surgery. PTOA progression was evaluated using histological, molecular, and radiographic analyses, while secondary allodynia was measured longitudinally, and pain-related markers expression were analyzed in dorsal root ganglion (DRG). 3D radiographic imaging by µCT analysis revealed that DMF treatment attenuated cartilage degradation by decreasing cartilage lesions, surface roughness, and osteophyte formation in the proximal tibiae. Histological analysis showed that DMF markedly inhibited cartilage erosion and cartilage surface fibrillation. Gene expression and Luminex analysis indicated that DMF suppressed joint inflammation by inhibiting inflammatory cytokines. DMF mitigated allodynic pain behavior at 6 weeks and repressed pain mediator expression (Calca, Tac1, Trpv1) in lumbar DRGs. Additionally, DMF treatment inhibited inflammatory gene expression and cytokine secretion induced by IL1β stimulation of human articular chondrocytes in vitro. Mechanistically, DMF treatment reduced colony-stimulating factor 2 (CSF2 or GM-CSF) level in the synovial fluid in vivo and inhibited its expression in human OA chondrocytes. Furthermore, siRNA targeting of CSF2 reduced inflammatory gene expression in human chondrocytes. The findings suggest that DMF reduced inflammatory gene expression, inhibited cartilage degeneration, and mitigated PTOA development in rats. It also alleviated pain behavior indicating its potential as a disease-modifying therapy for PTOA., Competing Interests: 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., (Copyright © 2025. Published by Elsevier Masson SAS.)

Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.Intrusion detection systems (IDSs) are one of the significant aspect of cybersecurity that involve the monitoring and analysis, with the intention of identifying and reporting of dangerous activities that would help to prevent the attack.Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Decision Tree (DT), Long Short-Term Memory (LSTM), and Artificial Neural Network (ANN) are the vector figures incorporated into the study through the results. These models are subjected to various test to established the best results on the identification and prevention of network violation. Based on the obtained results, it can be stated that all the tested models are capable of organizing data originating from network traffic. thus, recognizing the difference between normal and intrusive behaviors, models such as SVM, KNN, RF, and DT showed effective results. Deep learning models LSTM and ANN rapidly find long-term and complex pattern in network data. It is extremely effective when dealing with complex intrusions since it is characterised by high precision, accuracy and recall.Based on our study, SVM and Random Forest are considered promising solutions for real-world IDS applications because of their versatility and explainability. For the companies seeking IDS solutions which are reliable and at the same time more interpretable, these models can be promising. Additionally, LSTM and ANN, with their ability to catch successive conditions, are suitable for situations involving nuanced, advancing dangers., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)