KLF15 protects against isoproterenol-induced cardiac hypertrophy via regulation of cell death and inhibition of Akt/mTOR signaling.

Increasing evidence indicate that the Krüppel-like factor KLF15, a member of Cys2/His2 zinc-finger DNA-binding proteins, attenuates cardiac hypertrophy. However, the role of KLF15 in cardiovascular system is largely unknown and the exact molecular mechanism of its protective function is not fully elucidated. In the present study, we established a mouse model of cardiac hypertrophy and found that KLF15 expression was down-regulated in hypertrophic hearts. To evaluate the roles of KLF15 in cardiac hypertrophy, we generated transgenic mice overexpressing KLF15 of KLF15 knockdown mice and subsequently induced cardiac hypertrophy. The results indicated that KLF15 overexpression protects mice from ISO-induced cardiac hypertrophy, with reduced ratios of heart weight (HW)/body weight (BW) and cross-sectional area. We also observed that KLF15 overexpression attenuated cardiac fibrosis, inhibited apoptosis and induced autophagy in cardiomyocytes compared with KLF15 knockdown mice. More importantly, we found that the KLF15 overexpression inhibited the Akt/mTOR signaling pathway. Taken together, our findings imply that KLF15 possesses potential anti-hypertrophic and anti-fibrotic functions, possibly via regulation of cell death pathways and the inhibition of Akt/mTOR axis. KLF15 may constitute an efficient candidate drug for the treatment of heart failure and other cardiovascular diseases.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

A collaborative emergency decision making approach based on BWM and TODIM under interval 2-tuple linguistic environment.

Emergencies require various emergency departments to collaborate to achieve timely and effective emergency responses. Thus, the overall performance of emergency response is influenced not only by the efficiency of each department alternative but also by the coordination effect among different department alternatives. This paper proposes a collaborative emergency decision making (CEDM) approach considering the synergy among different department alternatives based on the best-worst method (BWM) and TODIM (an acronym in Portuguese of interactive and multiple attribute decision making) method within an interval 2-tuple linguistic environment. First, the evaluation information provided by decision makers (DMs) is represented by interval 2-tuple linguistic variables to reflect and model the underlying diversity and uncertainty. On the basis of the DMs' evaluations, the individual and collaborative performance evaluations of multi-alternative combinations composed of different department alternatives are constructed. Then, the BWM is extended into interval 2-tuple linguistic environment to obtain the weights of evaluation criteria, where the group decision making is taken into account in an interval fuzzy mathematical programming model. Furthermore, to derive more practical and accurate decision results, an interval 2-tuple linguistic TODIM (ITL-TODIM) method is proposed by considering the DMs' psychological behaviours. In the developed ITL-TODIM method, both the gain and loss degrees of one alternative relative to another are simultaneously computed. Finally, a numerical example is presented to illustrate the applicability of the proposed method. Sensitivity and comparative analyses are also provided to demonstrate the effectiveness and advantages of the proposed approach.