FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks.

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.

Targeting drug transport mechanisms for improving platinum-based cancer chemotherapy.

INTRODUCTION: Platinum (Pt)-based antitumor agents remain important chemotherapeutic agents for treating many human malignancies. Elevated expression of the human high-affinity copper transporter 1 (hCtr1), resulting in enhanced Pt drug transport into cells, has been shown to be associated with improved treatment efficacy. Thus, targeting hCtr1 upregulation is an attractive strategy for improving the treatment efficacy of Pt-based cancer chemotherapy. AREA COVERED: Regulation of hCtr1 expression by cellular copper homeostasis is discussed. Association of elevated hCtr1 expression with intrinsic sensitivity of ovarian cancer to Pt drugs is presented. Mechanism of copper-lowering agents in enhancing hCtr1-mediated cis-diamminedichloroplatinum (II) (cisplatin, cDDP) transport is reviewed. Applications of copper chelation strategy in overcoming cDDP resistance through enhanced hCtr1 expression are evaluated. EXPERT OPINION: While both transcriptional and post-translational mechanisms of hCtr1 regulation by cellular copper bioavailability have been proposed, detailed molecular insights into hCtr1 regulation by copper homeostasis remain needed. Recent clinical study using a copper-lowering agent in enhancing hCtr1-mediated drug transport has achieved incremental improvement in overcoming Pt drug resistance. Further improvements in identifying predictive measures in the subpopulation of patients that can benefit from the treatment are needed.