A comprehensive review of the neurological disorders reported during the current COVID-19 pandemic demonstrates that infection with SARS-CoV-2 affects the central nervous system (CNS), the peripheral nervous system (PNS) and the muscle. CNS manifestations include: headache and decreased responsiveness considered initial indicators of potential neurological involvement; anosmia, hyposmia, hypogeusia, and dysgeusia are frequent early symptoms of coronavirus infection. Respiratory failure, the lethal manifestation of COVID-19, responsible for 264,679 deaths worldwide, is probably neurogenic in origin and may result from the viral invasion of cranial nerve I, progressing into rhinencephalon and brainstem respiratory centers. Cerebrovascular disease, in particular large-vessel ischemic strokes, and less frequently cerebral venous thrombosis, intracerebral hemorrhage and subarachnoid hemorrhage, usually occur as part of a thrombotic state induced by viral attachment to ACE2 receptors in endothelium causing widespread endotheliitis, coagulopathy, arterial and venous thromboses. Acute hemorrhagic necrotizing encephalopathy is associated to the cytokine storm. A frontal hypoperfusion syndrome has been identified. There are isolated reports of seizures, encephalopathy, meningitis, encephalitis, and myelitis. The neurological diseases affecting the PNS and muscle in COVID-19 are less frequent and include Guillain-Barré syndrome; Miller Fisher syndrome; polyneuritis cranialis; and rare instances of viral myopathy with rhabdomyolysis. The main conclusion of this review is the pressing need to define the neurology of COVID-19, its frequency, manifestations, neuropathology and pathogenesis. On behalf of the World Federation of Neurology we invite national and regional neurological associations to create local databases to report cases with neurological manifestations observed during the ongoing pandemic. International neuroepidemiological collaboration may help define the natural history of this worldwide problem.
Levodopa is effective for the motor symptoms of Parkinson's disease (PD), but is associated with motor fluctuations and dyskinesia. Many patients require add-on therapy to improve motor fluctuations without exacerbating dyskinesia. The objective of this Phase III, multicenter, double-blind, placebo-controlled, parallel-group study was to evaluate the efficacy and safety of safinamide, an α-aminoamide with dopaminergic and nondopaminergic mechanisms, as add-on to l-dopa in the treatment of patients with PD and motor fluctuations. Patients were randomized to oral safinamide 100 mg/day (n = 224), 50 mg/day (n = 223), or placebo (n = 222) for 24 weeks. The primary endpoint was total on time with no or nontroublesome dyskinesia (assessed using the Hauser patient diaries). Secondary endpoints included off time, Unified Parkinson's Disease Rating Scale (UPDRS) Part III (motor) scores, and Clinical Global Impression-Change (CGI-C). At week 24, mean ± SD increases in total on time with no or nontroublesome dyskinesia were 1.36 ± 2.625 hours for safinamide 100 mg/day, 1.37 ± 2.745 hours for safinamide 50 mg/day, and 0.97 ± 2.375 hours for placebo. Least squares means differences in both safinamide groups were significantly higher versus placebo. Improvements in off time, UPDRS Part III, and CGI-C were significantly greater in both safinamide groups versus placebo. There were no significant between-group differences for incidences of treatment-emergent adverse events (TEAEs) or TEAEs leading to discontinuation. The addition of safinamide 50 mg/day or 100 mg/day to l-dopa in patients with PD and motor fluctuations significantly increased total on time with no or nontroublesome dyskinesia, decreased off time, and improved parkinsonism, indicating that safinamide improves motor symptoms and parkinsonism without worsening dyskinesia.
The study of long-term precipitation record is critically important for a country, whose food security and economy rely on the timely availability of water. In this study, the historical 102-year (1901-2002) rainfall data of the Sindh River basin (SRB), India, were analyzed for seasonal and annual trends. The Mann-Kendall test and Sen's slope model were used to identify the trend and the magnitude of the change, respectively. Spatial interpolation technique such as Kriging was used for interpolating the spatial pattern over SRB in GIS environment. The analysis revealed the significantly increasing precipitation trend in both seasonal and annual rainfall in the span of 102 years.
Wireless sensor network (WSN) is a network system that involves spatially distributed devices such as wireless sensor nodes. As the data collected by the sensor nodes and transmitted through WSNs are mostly sensitive, confidential, or personal data, secure information transmission is a critical challenge, and one of the most significant security requirements is authentication. The digital signature plays a key role in ensuring data integrity, authentication and non-repudiation. In this article, we shall present an efficient, high security level online/offline subtree-based short signature scheme (OOS-SSS) using Chebyshev chaotic maps for WSN fuzzy user data sharing over a Galois field. The proposed scheme is secure in an environment of random oracle unforgeability under chosen message attack (UF-SBSS-CMA). Notably, our new design has made multiple-time usage of offline storage possible, enabling the signer to reuse offline pre-info in polynomial time instead of having only one single attempt as in the currently available online/offline signing schemes. In addition, based on our OOS-SSS design, we can build up an aggregation scheme for wireless sensor network settings. Also, the proposed scheme can be extended with some applications attached to it to allow users to register messages and implement them on WSN. Lastly, our performance comparison reveals that the proposed scheme has the lowest computational cost among six competing schemes. INDEX TERMS Chebyshev chaotic maps, wireless sensor networks systems, subtree, short signature scheme, Random oracle.
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