Doxorubicin (DOX), one of the most effective anticancer drugs, is known to generate progressive cardiac damage, which is due, in part, to DOX-induced reactive oxygen species (ROS). The elevated ROS ...often induce oxidative protein modifications that result in alteration of protein functions. This study demonstrates that the level of proteins adducted by 4-hydroxy-2-nonenal (HNE), a lipid peroxidation product, is significantly increased in mouse heart mitochondria after DOX treatment. A redox proteomics method involving two-dimensional electrophoresis followed by mass spectrometry and investigation of protein databases identified several HNE-modified mitochondrial proteins, which were verified by HNE-specific immunoprecipitation in cardiac mitochondria from the DOX-treated mice. The majority of the identified proteins are related to mitochondrial energy metabolism. These include proteins in the citric acid cycle and electron transport chain. The enzymatic activities of the HNE-adducted proteins were significantly reduced in DOX-treated mice. Consistent with the decline in the function of the HNE-adducted proteins, the respiratory function of cardiac mitochondria as determined by oxygen consumption rate was also significantly reduced after DOX treatment. Treatment with Mn(III) meso-tetrakis(N-n-butoxyethylpyridinium-2-yl)porphyrin, an SOD mimic, averted the doxorubicin-induced mitochondrial dysfunctions as well as the HNE–protein adductions. Together, the results demonstrate that free radical-mediated alteration of energy metabolism is an important mechanism mediating DOX-induced cardiac injury, suggesting that metabolic intervention may represent a novel approach to preventing cardiac injury after chemotherapy.
•Doxorubicin increased total HNE-adducted proteins in cardiac mitochondria.•Redox proteomics identified several HNE-targeted cardiac mitochondria proteins important for energy metabolism.•Doxorubicin reduced the activities of complex I, SDHA, ATP synthase, and DLD in ETC.•MnP averted doxorubicin-induced mitochondrial dysfunction and HNE adductions.
A T-stub Square Ring Resonator (SRR) based Ultra-Wide Band (UWB) Band Pass Filter (BPF) is studied and investigated in this paper. The proposed filter is based on coupled feed line connected to the ...T-stub SRR. Ultra-wideband characteristics can be realized by adjusting the T-stub lengths and coupling the gaps between both sides of waveguides and SRR. The characteristics of the T-stub SRR show that the miniaturized UWB BPF can be operated at THz frequencies. The proposed UWB filter is simulated and analyzed using the Finite Differential Time Domain (FDTD) solver-based Computer Simulation Technology (CST) studio suite. The resonance conditions are explained and the transmission performance of the filter agrees with the simulated and theoretical calculations. The proposed filter is best suitable for Electronic-Plasmonic Integrated Circuits (EPICs).
Manganese superoxide dismutase (MnSOD) is a mitochondrially localized primary antioxidant enzyme, known to be essential for the survival of aerobic life and to have important roles in tumorigenesis. ...Here, we show that MnSOD deficiency in skin tissues of MnSOD-heterozygous knockout (Sod2(+/-)) mice leads to increased expresson of uncoupling proteins (UCPs). When MnSOD is deficient, superoxide radical and its resulting reactive oxygen species (ROS) activate ligand binding to peroxisome proliferator-activated receptor alpha (PPARα), suggesting that the activation of PPARα signaling is a major mechanism underlying MnSOD-dependent UCPs expression that consequently triggers the PI3K/Akt/mTOR pathway, leading to increased aerobic glycolysis. Knockdown of UCPs and mTOR suppresses lactate production and increases ATP levels, suggesting that UCPs contribute to increased glycolysis. These results highlight the existence of a free radical-mediated mechanism that activates mitochondria uncoupling to reduce ROS production, which precedes the glycolytic adaptation described as the Warburg Effect.
Abstract Doxorubicin (Dox) is a potent, broad-spectrum chemotherapeutic drug used around the world. Despite its effectiveness, it has a wide range of toxic side effects, many of which most likely ...result from its inherent pro-oxidant activity. It has been reported that Dox has toxic effects on normal tissues, including brain tissue. The present study tested the protective effect of a xanthone derivative of Garcinia Mangostana against Dox-induced neuronal toxicity. Xanthone can prevent Dox from causing mononuclear cells to increase the level of tumor necrosis factor-alpha (TNFα). We show that xanthone given to mice before Dox administration suppresses protein carbonyl, nitrotyrosine and 4-hydroxy-2′-nonenal (4HNE)-adducted proteins in brain tissue. The levels of the pro-apoptotic proteins p53 and Bax and the anti-apoptotic protein Bcl-xL were significantly increased in Dox-treated mice compared with the control group. Consistent with the increase of apoptotic markers, the levels of caspase-3 activity and TUNEL-positive cells were also increased in Dox-treated mice. Pretreatment with xanthone suppressed Dox-induced increases in all indicators of injury tested. Together, the results suggest that xanthone prevents Dox-induced central nervous system toxicity, at least in part, by suppression of Dox-mediated increases in circulating TNFα. Thus, xanthone is a good candidate for prevention of systemic effects resulting from reactive oxygen generating anticancer therapeutics.
Lysophosphatidic acid (LPA), its sphingolipid homolog sphingosine 1-phosphate (S1P) and several other related molecules constitute a family of bioactive lipid phosphoric acids that function as ...receptor-active mediators with roles in cell growth, differentiation, inflammation, immunomodulation, apoptosis and development. LPA and S1P are present in physiologically relevant concentrations in the circulation. In isolated cell culture systems or animal models, these lipids exert a range of effects that suggest that S1P and LPA could play important roles in maintaining normal vascular homeostasis and in vascular injury responses. LPA and S1P act on a series of G protein-coupled receptors, and LPA may also be an endogenous regulator of PPARgamma activity. In this review, we discuss potential roles for lysolipid signaling in the vasculature and mechanisms by which these bioactive lipids could contribute to cardiovascular disease.
One of the standard approaches for data analysis in unsupervised machine learning techniques is cluster analysis or clustering, where the data possessing similar features are grouped into a certain ...number of clusters. Among several significant ways of performing clustering, Fuzzy C-means (FCM) is a methodology, where every data point is hypothesized to be associated with all the clusters through a fuzzy membership function value. FCM is performed by minimizing an objective functional by optimally estimating the decision variables namely, the membership function values and cluster representatives, under a constrained environment. With this approach, a marginal increase in the number of data points leads to an enormous increase in the size of decision variables. This explosion, in turn, prevents the application of evolutionary optimization solvers in FCM, which thereby leads to inefficient data clustering. In this paper, a Neuro-Fuzzy C-Means Clustering algorithm (NFCM) is presented to resolve the issues mentioned above by adopting a novel Artificial Neural Network (ANN) based clustering approach. In NFCM, a functional map is constructed between the data points and membership function values, which enables a significant reduction in the number of decision variables. Additionally, NFCM implements an intelligent framework to optimally design the ANN structure, as a result of which, the optimal number of clusters is identified. Results of 9 different data sets with dimensions ranging from 2 to 30 are presented along with a comprehensive comparison with the current state-of-the-art clustering methods to demonstrate the efficacy of the proposed algorithm.
Accuracy drop in neural quantization is addressed in prior-art through Post Training Quantization (PTQ) schemes such as Percentile and Range-based calibration that remain sensitive to the data ...distribution. On the other hand, the sophisticated methods that efficiently handle the variability in data require their deployment also on the embedded devices significantly increasing the memory and latency. We solve this issue by translating PTQ as a non-linear programming problem, which is then efficiently solved block-wise in distributed manner using an evolutionary algorithm. The quantized models are also deployed on the Galaxy S23 smartphone to measure the on-device performance. MobileNetV2 and ResNet18 in Int8 precision resulted in 0.33 and 0.03 accuracy drop, respectively, which is best by the standards of PTQ. Our approach is the first-of-its-kind hardware-agnostic high-accuracy PTQ method that allows the seamless deployment of quantized networks on embedded devices.
The capability of wind power to meet the energy demand inspired researchers to develop techniques for harnessing this clean and renewable energy. As a primary step, accurate forecasting of wind ...characteristics by modelling the stochastic nature of wind is done. Although, statistical methods provide good results in forecasting, they are inferior to Deep Learning based tools while handling extreme nonlinearities in wind characteristics. The authors in this work implemented Long Short Term Memory (LSTM) networks, a deep learning based tool, for modelling wind time series data due to its efficiency in handling long term dependencies. However, several hyper-parameters like activation function and design are chosen heuristically, making the modelling process tedious and inefficient. In this study, novel multi objective optimization formulation, driven by NSGA II, is proposed to design the LSTM networks with respect to the conflicting objectives of accuracy and parsimony. The resultant optimal LSTM models are used for long term forecasting (2 years) of wind characteristics data, with an accuracy of 97%, obtained from a real wind farm in France. To demonstrate the importance of this forecasting, a study of wind power calculations on a real wind farm is conducted. For a given layout, the effect of wind frequency scenarios, generated from the time series data of wind, on the annual power calculations, is determined. The existing and forecasted values of wind speed and direction over longer periods of time resulted in realistic values of expected power from wind farm. This study demonstrates the importance of forecasting while evaluating the power which can impact research in fields such as wind farm layout optimization and control under uncertainty.
Conventional direct methods of solving Optimal Control (OC) problems lead to large scale optimization formulations, making the classical optimization solvers more preferable over evolutionary ...optimization algorithms, for solving the single and multiple objective formulations in OC. On the other hand, population based evolutionary optimization solvers have the ability to identify the global basin efficiently. Therefore, in this paper, a novel method termed as TRANSFORM Artificial Neural Network (ANN) assisted reformulation of OC, has been proposed, which transforms the large scale optimization problem into weight training exercise of auto-tuned ANNs that in turn reduces the scale of optimization by several folds. Through this reformulation, the implementation of evolutionary optimization algorithm is enabled for solving both single and multiple objective OC formulations. Three different benchmark case studies are considered from literature to test the efficiency of proposed algorithm -(a) control of a batch reactor for maximizing the yield of penicillin production, (b) optimal drug scheduling for maximizing the success rates in chemotherapy for cancer treatment, and (c) multi-objective control of plug flow reactor with energy and conversion trade-off. Results indicated an average 50-fold reduction in OC problem size due to ANN reformulation.
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