Unignorable public health risk of avian influenza virus during COVID‐19 pandemic

alexandrossfakianakis shared this article with you from Inoreader Unignorable public health risk of avian influenza virus during COVID‐19 pandemic via Journal of Medical Virology Abstract Human infections with the newly emerging reassortant H5N6 avian influenza viruses (AIV) were reported1. AIV has been prevalent persistently worldwide, which results in a devastating impact on the poultry industry and even public health. Highly pathogenic AIV (HPAIV), including H5Ny lineages (H5N1, H5N2, H5N6, and H5N8) and H7N9 subtype, exhibit severe disease phenotype with high morbidity and mortality rates in birds, especially chickens. Although H9N2 AIV only could induce mild or no obvious clinical signs in poultry, the enlarged host species of H9N2 AIV could induce the potential threat to global health2 In late 2019, the novel coronavirus disease (COVID-19) broke out and swept the world population. Drastic mitigation measures were implemented among the international community to stop the COVID-19 crisis, including mass quarantine, physical distancing, and face mask usage. However, the elevated trend of human infections with the emer ging AIV during ongoing COVID-19 circulation still highlights the alert of the possible risk for the next human pandemic3. This article is protected by copyright. All rights reserved. View on Web

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Sparstolonin B inhibits pancreatic adenocarcinoma through the NF-κB signaling pathway

alexandrossfakianakis shared this article with you from Inoreader Sparstolonin B inhibits pancreatic adenocarcinoma through the NF-κB signaling pathway via ScienceDirect Publication: Experimental Cell Research Publication date: Available online 18 May 2022 Source: Experimental Cell Research Author(s): Yang Lyu, Bowen Duan, Ziming Liu, Fan Yang, Chen Chen, Xuejiao Jiang, Xiang Liu View on Web

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Efficacy and safety of Lenzumestrocel (Neuronata-R® inj.) in patients with amyotrophic lateral sclerosis (ALSUMMIT study): study protocol for a multicentre, randomized, double-blind, parallel-group, sham procedure-controlled, phase III trial

alexandrossfakianakis shared this article with you from Inoreader Efficacy and safety of Lenzumestrocel (Neuronata-R® inj.) in patients with amyotrophic lateral sclerosis (ALSUMMIT study): study protocol for a multicentre, randomized, double-blind, parallel-group, sham procedure-controlled, phase III trial via Trials - Latest Articles A single cycle (two repeated treatments) with intrathecal autologous bone marrow-derived mesenchymal stem cells (BM-MSCs, 26-day interval) showed safety and provided therapeutic benefit lasting 6 months in pat... View on Web

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High flow nasal cannula in the management of obstructive sleep Apnoea postoperatively. Is flow a new alternative to positive pressure?

alexandrossfakianakis shared this article with you from Inoreader High flow nasal cannula in the management of obstructive sleep Apnoea postoperatively. Is flow a new alternative to positive pressure? via ScienceDirect Publication: American Journal of Otolaryngology Publication date: Available online 17 May 2022 Source: American Journal of Otolaryngology Author(s): Abhijit S. Nair, Antonio M. Esquinas View on Web

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Machine Learning Based Forecast of Dengue Fever in Brazilian Cities using Epidemiological and Meteorological Variables

alexandrossfakianakis shared this article with you from Inoreader Machine Learning Based Forecast of Dengue Fever in Brazilian Cities using Epidemiological and Meteorological Variables via American Journal of Epidemiology - Advance Access Abstract Dengue is a serious public health concern in Brazil and globally. In the absence of a universal vaccine or specific treatments, prevention relies on vector control and disease surveillance. Accurate and early forecasts can help reduce the spread of the disease. In this study, we develop a model to predict monthly dengue cases in Brazilian cities one month ahead from 2007-2019. We compare different machine learning algorithms and feature selection methods using epidemiological and meteorological variables. We find that different models work best in different cities, and a random forests model trained on monthly dengue cases performs best overall. It produces lower errors than a seasonal naïve baseline model, gradient boosting regression, feed-forward neural network, and support vector regression. For each city, we compute the mean absolute error between predictions and true monthly dengue cases on the test set. For the median city, the error is 1 2.2 cases. This error is reduced to 11.9 when selecting the optimal combination of algorithm and input features for each city individually. Machine learning and especially decision tree ensemble models may contribute to dengue surveillance in Brazil, as they produce low out-of-sample prediction errors for a geographically diverse set of cities. View on Web

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