Thionamides, inhibitors of the thyroid peroxidase-mediated iodination, are clinically used in the treatment of hyperthyroidism. However, the use of antithyroid drugs is associated with immunomodulatory effects, and recent studies with thionamide-related heterocyclic thioderivates demonstrated direct anti-inflammatory and immunosuppressive properties. Using primary human T-lymphocytes, we show that the heterocyclic thionamides carbimazole and propylthiouracil inhibit synthesis of the proinflammatory cytokines tumor necrosis factor (TNF)␣ and interferon (IFN)␥. In addition, DNA binding of nuclear factor (NF)-B, a proinflammatory transcription factor that regulates both TNF␣ and IFN␥ synthesis, and NF-B-dependent reporter gene expression were reduced. Abrogation of NF-B activity was accompanied by reduced phosphorylation and proteolytic degradation of inhibitor of B (IB)␣, the inhibitory subunit of the NF-B complex. Carbimazole inhibited NF-B via the small GTPase Rac-1, whereas propylthiouracil inhibited the phosphorylation of IB␣ by its kinase inhibitor of B kinase ␣. Methimazole had no effect on NF-B induction, demonstrating that drug potency correlated with the chemical reactivity of the thionamide-associated sulfur group. Taken together, our data demonstrate that thioureylenes with a common, heterocyclic structure inhibit inflammation and immune function via the NF-B pathway. Our results may explain the observed remission of proinflammatory diseases upon antithyroid therapy in hyperthyroid patients. The use of related thioureylenes may provide a new therapeutic basis for the development and application of anti-inflammatory compounds.
Barbiturates are known to suppress protective immunity, and their therapeutic use is associated with nosocomial infections. Although barbiturates inhibit T cell proliferation, differentiation, and cytokine synthesis, only thiobarbiturates markedly reduce the activation of immune regulatory transcription factors such as nuclear factor-B and nuclear factor of activated T cells. In this study, we investigated barbiturate-mediated effects on the regulation of the transcription factor activator protein 1 (AP-1) in primary T lymphocytes. We show that both thiobarbiturates and their oxy-analogs inhibit AP-1-dependent gene expression and AP-1 complex formation at clinically relevant doses. Furthermore, mitogen-activated protein (MAP) kinase activity, which transcriptionally and posttranslationally regulates AP-1 complex formation, is suppressed by most barbiturates. CD3/ CD28-or phorbol 12-myristate 13-acetate (PMA)/ionomycininduced p38 and extracellular signal-regulated kinase 1/2 phosphorylation or c-jun NH 2 -terminal kinase (JNK) 1/2 kinase activity was significantly diminished by pentobarbital, thiamylal, secobarbital, or methohexital treatment. These barbiturates also inhibited the initiators of the MAP kinase cascade, the small G proteins ras and rac-1, and prevented binding to their partners raf-1 and PAK, respectively. Thiopental, unlike the other barbiturates, only reduced ras and JNK activity upon direct CD3/CD28 receptor engagement. Contrarily, upon PMA/ ionomycin stimulation, thiopental blocked AP-1-dependent gene expression independently of the small G protein ras and MAP kinases, thus suggesting an additional, unknown mechanism of AP-1 regulation. In conclusion, our results contribute to the explanation of a clinically manifested immune suppression in barbiturate-treated patients and support the idea of a MAP kinase-independent regulation of AP-1 by PKC and calcium in human T cells.
The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.
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