ARTICLE | doi:10.20944/preprints202008.0389.v1
Subject: Medicine & Pharmacology, Pharmacology & Toxicology Keywords: ECN; neuropathic pain; oxidative stress; apoptosis; myelin sheath; spectroscopy
Online: 18 August 2020 (12:00:15 CEST)
7β-(3-ethyl-cis-crotonoyloxy)-1α-(2-methylbutyryloxy)-3,14-dehydro-Z-notonipetranone (ECN), a sesquiterpenoid obtained from a natural origin (Tussilago farfara)has proved to be effective in minimizing various side effects associated with opioids and nonsteroidal anti-inflammatory drugs. The current study focused on investigating the effects of ECN on neuropathic pain induced by partial sciatic nerve ligation (PSNL) by mainly focusing on oxidative stress, inflammatory and apoptotic proteins expression in mice. Neuropathic pain was induced in mice by PSNL surgery performed on day 1 and ECN (1 and 10 mg/kg, i.p.), was administered once daily for 11 days, starting from the third day after surgery. ECN post-treatment was found to reduce hyperalgesia and allodynia in a dose dependent manner. ECN significantly reversed the severity of neuropathic pain by improving distress symptoms and survival rate. ECN remarkably reversed the histopathological abnormalities associated with oxidative stress, apoptosis and inflammation. Furthermore, ECN prevented the suppression of antioxidants (glutathione, glutathione-S-transferase, catalase, superoxide dismutase, NF-E2-related factor-2 (Nrf2), hemeoxygenase-1 and NAD(P)H: quinone oxidoreductase) by PSNL. Moreover, pro-inflammatory cytokines (tumor necrotic factor alpha, interleukin 1 beta, interleukin 6, cyclooxygenase-2 and inducible nitric oxide synthase) expression was reduced by ECN administration. Treatment with ECN was successful in reducing caspase-3 level consistent with the observed modulation of pro-apoptotic proteins. Additionally, ECN showed protective effect on the lipid content of myelin sheath as evident from FTIR spectroscopy which showed the shift of lipid component bands to higher values. Thus, anti-neuropathic potential of ECN might be due to inhibition of oxidative stress, inflammatory mediators and pro-apoptotic proteins.
ARTICLE | doi:10.20944/preprints201909.0077.v1
Subject: Engineering, Other Keywords: active queue management (AQM); congestion control; explicit congestion notification (ECN); machine learning
Online: 6 September 2019 (16:48:42 CEST)
As more end devices are getting connected, the Internet will become more congested. A variety of congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims at mitigating the congestion on the network layer by active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.