Nitazoxanide is widely available and exerts broad-spectrum antiviral activity in vitro. However, there is no evidence of its impact on SARS-CoV-2 infection.In a multicenter, randomised, double-blind, placebo-controlled trial, adult patients presenting up to 3 days after onset of Covid-19 symptoms (dry cough, fever, and/or fatigue) were enrolled. After confirmation of SARS-CoV2 infection by RT-PCR on a nasopharyngeal swab, patients were randomised 1:1 to receive either nitazoxanide (500 mg) or placebo, TID, for 5 days. The primary outcome was complete resolution of symptoms. Secondary outcomes were viral load, laboratory tests, serum biomarkers of inflammation, and hospitalisation rate. Adverse events were also assessed.From June 8 to August 20, 2020, 1575 patients were screened. Of these, 392 (198 placebo, 194 nitazoxanide) were analysed. Median time from symptom onset to first dose of study drug was 5 (4–5) days. At the 5-day study visit, symptom resolution did not differ between the nitazoxanide and placebo arms. Swabs collected were negative for SARS-CoV-2 in 29.9% of patients in the nitazoxanide arm versus 18.2% in the placebo arm (p=0.009). Viral load was also reduced after nitazoxanide compared to placebo (p=0.006). The percent viral load reduction from onset to end of therapy was higher with nitazoxanide (55%) than placebo (45%) (p=0.013). Other secondary outcomes were not significantly different. No serious adverse events were observed.In patients with mild Covid-19, symptom resolution did not differ between nitazoxanide and placebo groups after 5 days of therapy. However, early nitazoxanide therapy was safe and reduced viral load significantly.

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Abstract-In this letter, we present a sequential closed-form semi-blind receiver for a one-way multi-hop amplify-and-forward (AF) relaying system. Assuming Khatri-Rao space-time (KRST) coding at each relay, it is shown that the system with K relays can be modeled by means of a generalized nested PARAFAC model. Decomposing this model into K +1 third-order PARAFAC models, we develop a closed-form semi-blind receiver for jointly estimating the information symbols and the individual channels, at the destination node. Each step consists of a Khatri-Rao factorization. Parameter identifiability conditions are given, and simulation results are provided to illustrate the effectiveness of the proposed semi-blind receiver.Index Terms-Multi-hop relaying, sequential closed-form semi-blind receiver, space time (ST) coding, amplify-and-forward, generalized nested PARAFAC model. I. INTRODUCTIONRelay nodes will play an important role in 5G communication systems. In such systems, the high-capacity wireless backhaul offers operators an alternative solution to conventional backhauling using multi-hop short-distanceTensor decompositions have been widely exploited for point-to-point wireless communication systems. The practical motivation for tensor modeling comes from the fact that one can simultaneously benefit from multiple (more than two) signal diversities, like space, time and frequency diversities, for instance. In the context of cooperative wireless communications, few results have been published on tensor-based receivers, and most of them are limited to a single relay scenario. In previous works [5]-[8], the problem of channel estimation for multiple input multiple output (MIMO) relaying systems was considered under a supervised approach. [13]. However, the generalization of tensor-based semi-blind receivers to the multi-hop relaying scenario has not been addressed so far to the best of authors' knowledge.

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We study the impact of scheduling algorithms on the provision of multiple services in the long term evolution (LTE) system. In order to measure how well the services are provided by the system, we use the definition of joint system capacity. In this context, we claim that scheduling strategies should consider the current satisfaction level of each service and the offered load to the system by each service. We propose a downlink-scheduling strategy according to these ideas named capacity-driven resource allocation (CRA). The CRA scheduler dynamically controls the resource sharing among flows of different services such as delay-sensitive and rate demanding ones. Moreover, CRA scheduler exploits the channel-quality knowledge to utilize the system resources efficiently. Simulation results in a multicell scenario show that the CRA scheduler is robust regarding channel quality knowledge and that it provides significant gains in joint system capacity in single and mixed service scenarios.