Current Topics in Medicinal Chemistry - Volume 25, Issue 2, 2025

The mutual activations of multiple signaling pathways are the key factors in the development and progression of myocardial cell injuries.

This research aimed to compare the different degrees of myocardial injury after coronary stenting, permanent pacemaker implantations, or cardiac radiofrequency ablation and to investigate the effects of the mutual activation of TNF-α/NF-κB, TLR2/TLR4, and ROS/MDA signaling pathways on myocardial injury in elderly patients after coronary stents or permanent pacemakers or radiofrequency ablation.

We determined reactive oxygen species (ROS), malondialdehyde (MDA), toll-like receptor 2 (TLR2), toll-like receptor 4 (TLR4), nuclear factor kappa B (NF-κB), tumor necrosis factor-α (TNF-α) and high-sensitive cardiac troponin T (hs-cTnT) as markers of myocardial injury in patients.

The levels of ROS, MDA, TLR2, TLR4, NF-κB, TNF-α, and hs-cTnT were increased in patients with permanent pacemaker implantations when compared to patients with cardiac radiofrequency ablation (P < 0.01) at 6 months and were further increased in patients with coronary stenting compared to patients with cardiac radiofrequency ablation and permanent pacemaker implantations at 6 months, respectively (P < 0.01). This research confirmed that ROS, MDA, TLR2, TLR4, NF-κB, and TNF-α predicted myocardial injury severity.

Oxidative stress (ROS/MDA signaling pathway) may be linked to immune response (TLR2/TLR4 signaling pathway) and pro-inflammatory response (TNF-α/NF-κB signaling pathway) in myocardial injury, and ROS/MDA signaling may play a dominant role.

Epilepsy remains the most common and chronic disorder demanding long-term management. The impact of epilepsy disease is a cause of great concern and has resulted in efforts to develop treatment for epilepsy. It occurs due to an increase in neuronal excitability produced by changes affecting the voltage-dependent properties of Voltage-gated Sodium Channels (VGSCs).

Weka, a popular suite for machine learning techniques, was used on a dataset comprising 1781 chemical compounds, showing inhibition activity for sodium channel protein IX alpha subunit. After the analysis of the dataset obtained from ChEMBL, molecular fingerprints were computed for the molecules by the ChemDes server. Different classifiers available in the Weka software were explored to find out the algorithm that could be more suitable for the dataset or produce the highest accuracy for the classification of molecules as active or inactive.

In this work, a comprehensive comparison of different classifiers in the Weka suite for the prediction of active, inactive, and intermediate classes of molecules showing inhibition against human NaV1.7 protein was made. The prediction accuracy of these classifiers was assessed based on performance measures, including accuracy, Root Mean Squared Error (RMSE), Receiver Operating Characteristic (ROC), precision, Mathews Correlation Coefficient (MCC), recall, and F-measure. The comparison of results for model performance demonstrated that the OneR classifier performed best over others when validated using percentage split, cross-validation, and supplied test methods. J48 and Bagging also performed equally well in the prediction of different classes with an MCC value of 1, ROC area equal to 1, and RMSE close to 0.

Machine Learning (ML) tools provide a fast, reliable, and cost-effective approach required to identify or predict inhibitory molecules for the treatment of a disease. This study shows that the ML methods, particularly OneR, J48, and Bagging have the ability to identify active and inactive classes of compounds for the human NaV1.7 protein target. Such predictive models may provide a reliable and time-saving approach that can aid in the design of potential inhibitors for the treatment of epilepsy disease.

Iron oxide nanoparticles demonstrate tremendous potential in preserving the ecological balance of the environment since they act as antimicrobial agents and efficient photocatalysts. However, environmental sustainability has challenged the synthesis protocols of nanomaterials.

This study compares the green synthesis method with the scalable chemical synthesis method. In this work, Iron oxide nanoparticles were fabricated via the green chemistry technique utilizing the leaf extract of Mentha spicata (M-IONP) and also via the chemical co-precipitation method (C-IONP). The synthesized IONPs were analyzed by different characterization methods such as XRD, FTIR, SEM analysis, ZETA potential measurements, and DLS spectroscopy analysis.

The biosynthesized and chemically synthesized IONPs were analyzed for their mechanistic action against different applications like antimicrobial, antioxidant, and degradation of harmful dyes. Interestingly, the biosynthesized IONPs (M-IONP) exhibited more effective antimicrobial efficacy towards Gram-positive and Gram-negative organisms than chemically synthesized IONPs.

The green synthesized M-IONP also showed significant antioxidant propensity similar to that of the standards taken. Additionally, green-synthesized M-IONP exhibited enhanced degradation efficacies against Methylene blue, chromium, and sulphamethoxazole in comparison to chemically synthesized IONP.