Silencing TRPM2 enhanced erastin- and RSL3-induced ferroptosis in gastric cancer cells through destabilizing HIF-1α and Nrf2 proteins.

Ferroptosis is a regulated form of cell death driven by small molecules or conditions that induce lipid-based reactive oxygen species (ROS) accumulation. Cation channel transient receptor potential melastatin-2 (TRPM2) is crucial for cancer cell survival. Our bioinformatic analysis revealed that TRPM2 is associated with cellular responses to chemical stimulus and oxidative stress, implying the potential role of TRPM2 in ferroptosis. Gastric cancer cells were treated with the ferroptosis-inducer, Erastin and RSL3. siRNA transfection was used to silence TRPM2. The levels of GSH, Fe2+, ROS and lipid peroxidation, and the activity of GPx activity were evaluated by flow cytometry and spectrophotometer. The effect of TRPM2 on ubiquitination of HIF-1α and Nrf2 were evaluated by co-immunoprecipitation. Erastin and RSL3 induced the up-regulation of TRPM2 in gastric cancer cell lines, especially in SGC7901 and MGC803. These two cells also showed stronger resistance to Erastin and RSL3 than the other cell lines. TRPM2 knockdown reduced the concentration of GSH and GPx activity, but enhanced the concentration of Fe2+, ROS and lipid peroxidation, which are significant indicators of ferroptosis. Importantly, silencing TRPM2 enhanced the inhibitory effects of Erastin and RSL3 on gastric cancer cell viability, migration, and invasion. TRPM2 stabilized and finally elevated the abundance of HIF-1α and Nrf2 in SGC7901 and MGC803 cells upon Erastin and RSL3. Activation of HIF-1α impaired Erastin- and RSL3-induced ferroptosis after TRPM2 knockdown. Collectively, silencing TRPM2 enhanced Erastin- and RSL3-induced ferroptosis in gastric cancer cells through destabilizing HIF-1α and Nrf2 proteins. Supplementary Information: The online version contains supplementary material available at 10.1007/s10616-022-00545-z.

Shikonin induces cell autophagy via modulating the microRNA -545-3p/guanine nucleotide binding protein beta polypeptide 1 axis, thereby disrupting cellular carcinogenesis in colon cancer.

Shikonin (SHK), a major component of shiverweed, was provided with anti-tumor effects via multiple targets and signal pathways. Nevertheless, the specific mechanism of its function in colorectal cancer (CRC) still needed to be further explored. The study was designed to examine the role of SHK in CRC and its specific mechanism on the cell tumor behavior of CRC. Collection of clinical samples was performed, and test of microRNA (miR)-545-3p and guanine nucleotide-binding protein beta polypeptide 1 (GNB1) in the samples was conducted; Selection of CRC cell line was exerted, and examination of miR-545-3p and GNB1 was performed; After treatment of shikonin (SHK), correlated plasmids were transfected, test of cell advancement was performed. Test of the protein of autophagy-correlated proteins light chain 3-II/light chain 3I and p63 was performed. The interaction of miR-545-3p with GNB1 was explored, and the action of SHK in vivo was tested. SHK repressed the advancement of SW480 cells with elevated apoptosis and autophagy and the cells quantities in G0/G1 phase. MiR-545-3p was elevated in CRC. SHK boosted miR-545-3p, repression of miR-545-3p or augmentation of GNB1 was able to turn around the function of SHK on CRC, and GNB1 was the target gene of miR-545-3p.All in all, SHK stimulates apoptosis and autophagy in CRC via miR-545-3p/GNB1 signaling axis, firstly demonstrating the regulatory mechanism of SHK in CRC via miR-545-3p/GNB1 axis.

Fuzzy Sampled-Data Control for Synchronization of T-S Fuzzy Reaction-Diffusion Neural Networks With Additive Time-Varying Delays.

This article focuses on the exponential synchronization problem of T-S fuzzy reaction-diffusion neural networks (RDNNs) with additive time-varying delays (ATVDs). Two control strategies, namely, fuzzy time sampled-data control and fuzzy time-space sampled-data control are newly proposed. Compared with some existing control schemes, the two fuzzy sampled-data control schemes cannot only tolerate some uncertainties but also save the limited communication resources for the considered systems. A new fuzzy-dependent adjustable matrix inequality technique is proposed. According to different fuzzy plant and controller rules, different adjustable matrices are introduced. In comparison with some traditional estimation techniques with a determined constant matrix, the fuzzy-dependent adjustable matrix approach is more flexible. Then, by constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the fuzzy-dependent adjustable matrix approach, new exponential synchronization criteria are derived for T-S fuzzy RDNNs with ATVDs. Meanwhile, the desired fuzzy time and time-space sampled-data control gains are obtained by solving a set of linear matrix inequalities (LMIs). In the end, some simulations are presented to verify the effectiveness and superiority of the obtained theoretical results.

Adaptive Event-Triggered Synchronization of Reaction-Diffusion Neural Networks.

This article focuses on the design of an adaptive event-triggered sampled-data control (ETSDC) mechanism for synchronization of reaction-diffusion neural networks (RDNNs) with random time-varying delays. Different from the existing ETSDC schemes with predetermined constant thresholds, an adaptive ETSDC mechanism is proposed for RDNNs. The adaptive ETSDC mechanism can be promptly adaptively adjusted since the threshold function is based on the current sampled and latest transmitted signals. Thus, the adaptive ETSDC mechanism can effectively save communication resources for RDNNs. By taking the influence of uncertain factors, the random time-varying delays are considered, which belongs to two intervals in a probabilistic way. Then, by constructing an appropriate Lyapunov-Krasovskii functional (LKF), new synchronization criteria are derived for RDNNs. By solving a set of linear matrix inequalities (LMIs), the desired adaptive ETSDC gain is obtained. Finally, the merits of the adaptive ETSDC mechanism and the effectiveness of the proposed results are verified by one numerical example.

Pinning Synchronization of Directed Coupled Reaction-Diffusion Neural Networks With Sampled-Data Communications.

This paper focuses on the design of a pinning sampled-data control mechanism for the exponential synchronization of directed coupled reaction-diffusion neural networks (CRDNNs) with sampled-data communications (SDCs). A new Lyapunov-Krasovskii functional (LKF) with some sampled-instant-dependent terms is presented, which can fully utilize the actual sampling information. Then, an inequality is first proposed, which effectively relaxes the restrictions of the positive definiteness of the constructed LKF. Based on the LKF and the inequality, sufficient conditions are derived to exponentially synchronize the directed CRDNNs with SDCs. The desired pinning sampled-data control gain is precisely obtained by solving some linear matrix inequalities (LMIs). Moreover, a less conservative exponential synchronization criterion is also established for directed coupled neural networks with SDCs. Finally, simulation results are provided to verify the effectiveness and merits of the theoretical results.

Long non-coding RNA GATA6-AS inhibits gastric cancer cell proliferation by downregulating microRNA-25-3p.

The abnormal growth of endothelial cells is involved in several types of diseases, including gastric cancer. The long non-coding RNA GATA6-AS is a key regulator of endothelial cell growth and may therefore also play an important role in gastric cancer. In the present study it was found that GATA6-AS was downregulated in tumor tissues compared with adjacent normal tissues. Moreover, plasma levels of GATA6-AS were linearly associated with GATA6-AS expression levels in tumor tissues and not in normal tissues. MicroRNA (miR)-25-3p was upregulated in tumor tissues compared with adjacent normal tissues and was inversely associated with GATA6-AS in tumor tissues only. The overexpression of miR-25-3p in gastric cancer cells resulted in no significant changes in the expression levels of GATA6-AS, whereas overexpression of GATA6-AS led to significantly downregulated miR-25-3p levels. Furthermore, overexpression of GATA6-AS inhibited cancer cell proliferation, with no effect on migration and invasion. The overexpression of miR-25-3p resulted in increased proliferation of cancer cells and attenuated the effects of GATA6-AS overexpression. Thus, it is postulated that GATA6-AS inhibits proliferation of gastric cancer cells by downregulating miR-25-3p.

New variable-order fractional chaotic systems for fast image encryption.

New variable-order fractional chaotic systems are proposed in this paper. A concept of short memory is introduced where the initial point in the Caputo derivative is varied. The fractional order is defined by the use of a piecewise constant function which leads to rich chaotic dynamics. The predictor-corrector method is adopted, and numerical solutions of fractional delay equations are obtained. Then, this concept is extended to fractional difference equations, and generalized chaotic behaviors are discussed numerically. Finally, the new fractional chaotic models are applied to block image encryption and each block has a different fractional order. The new chaotic system improves security of the image encryption and saves the encryption time greatly.

New Results on Stability Analysis for Delayed Markovian Generalized Neural Networks With Partly Unknown Transition Rates.

The stability of delayed Markovian generalized neural networks is studied where the transition rates of the modes are partly unknown. The partly unknown transition rates generalize the traditional works that are with all known transition rates. Then, a Lyapunov-Krasovskii functional (LKF) with a delay-product-type (DPT) term is constructed. The DPT term is not only simple but also fully utilizes the information of time delay. Based on the new DPT LKF, stability criteria are presented, which are with lower computational complexity and less conservative. In the end, the validity and superiorities of the analytical results are verified by several examples.

A New Approach to Stabilization of Chaotic Systems With Nonfragile Fuzzy Proportional Retarded Sampled-Data Control.

This paper is concerned with the problem of stabilization of chaotic systems via nonfragile fuzzy proportional retarded sampled-data control. Compared with existing sampled-data control schemes, a more practical nonfragile fuzzy proportional retarded sampled-data controller is designed, which involves not only a signal transmission delay but also uncertainties. Based on the Wirtinger inequality, a new discontinuous Lyapunov-Krasovskii functional (LKF), namely, Wirtinger-inequality-based time-dependent discontinuous (WIBTDD) LKF, is the first time to be proposed for sampled-data systems. With the WIBTDD LKF approach and employing the developed estimation technique, a less conservative stabilization criterion is established. The desired fuzzy proportional retarded sampled-data controller can be obtained by solving a set of linear matrix inequalities. Finally, numerical examples are given to demonstrate the effectiveness and advantages of the proposed results.

A New Approach to Stochastic Stability of Markovian Neural Networks With Generalized Transition Rates.

This paper investigates the stability problem of Markovian neural networks (MNNs) with time delay. First, to reflect more realistic behaviors, more generalized transition rates are considered for MNNs, where all transition rates of some jumping modes are completely unknown. Second, a new approach, namely time-delay-dependent-matrix (TDDM) approach, is proposed for the first time. The TDDM approach is associated with both time delay and its time derivative. Thus, the TDDM approach can fully capture the information of time delay and would play a key role in deriving less conservative results. Third, based on the TDDM approach and applying Wirtinger's inequality and improved reciprocally convex inequality, stability criteria are derived. In comparison with some existing results, our results are not only less conservative but also involve lower calculation complexity. Finally, numerical examples are provided to show the effectiveness and advantages of the proposed results.

Quantized Sampled-Data Control for Synchronization of Inertial Neural Networks With Heterogeneous Time-Varying Delays.

This paper is concerned with the problem of synchronization for inertial neural networks (INNs) with heterogeneous time-varying delays (HTVDs) through quantized sampled-data control. The control scheme, which takes the communication limitations of quantization and variable sampling into account, is first employed for tackling the synchronization of INNs. A novel Lyapunov-Krasovskii functional (LKF) is constructed for synchronizing an error system. Compared with existing LKFs by the largest upper bound of all HTVDs, the proposed LKF is superior, since it can make full use of the information on the lower and upper bounds of each HTVD. Based on the LKF and a new integral inequality technique, less conservative synchronization criteria are derived. The desired quantized sampled-data controller is designed by solving a set of linear matrix inequalities. Finally, a numerical example is given to illustrate the effectiveness and conservatism reduction of the proposed results.

Improved results on state feedback stabilization for a networked control system with additive time-varying delay components' controller.

This paper investigates the problems of stability and stabilization for a networked control system (NCS) with additive time-varying delay components' controller. Firstly, stability of a NCS with additive time-varying delays is investigated. A novel approach with free parameters is proposed. By constructing a new Lyapunov-Krasovskii functional (LKF) with two free parameters, stability criteria are obtained. The obtained stability criteria depend not only on upper bounds of delays but also free parameters. In addition, input-output method is extended to study the stability problem for the NCS. Compared with other approaches such as input-output method, the free-parameter approach is more flexible and effective in reducing the conservatism. Then, based on the stability results, a state feedback controller is designed to guarantee the asymptotically stable of the closed-loop systems. Finally, numerical examples are provided to show the effectiveness and less conservatism of the proposed results.