Zhuang, C. | Search bioRxiv

bioRxiv2020

Peramivir, an anti-influenza virus drug, exhibits potential anti-cytokine storm effects

Coronavirus Disease 2019 (COVID-19) infected by Severe Acute Respiratory Syndrome Coronavirus -2 (SARS-CoV-2) has been declared a public health emergency of international concerns. Cytokine storm syndrome (CSS) is a critical clinical symptom of severe COVID-19 patients, and the macrophage is recognized as the direct host cell of SARS-CoV-2 and potential drivers of CSS. In the present study, peramivir was identified to reduce TNF- by partly intervention of NF-{kappa}B activity in LPS-induced macrophage model. In vivo, peramivir reduced the multi-cytokines in serum and bronchoalveolar lavage fluid (BALF), alleviated the acute lung injury and prolonged the survival time in mice. In human peripheral blood mononuclear cells (hPBMCs), peramivir could also inhibit the release of TNF-. Collectively, we proposed that peramivir might be a candidate for the treatment of COVID-19 and other infections related CSS. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/201806v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@667e34org.highwire.dtl.DTLVardef@e7bf85org.highwire.dtl.DTLVardef@ced400org.highwire.dtl.DTLVardef@c055a5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2020

Unsupervised Neural Network Models of the Ventral Visual Stream

Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural prediction accuracy in multiple ventral visual cortical areas that equals or exceeds that of models derived using todays best supervised methods, and that the mapping of these neural network models hidden layers is neuroanatomically consistent across the ventral stream. Moreover, we find that these methods produce brain-like representations even when trained on noisy and limited data measured from real childrens developmental experience. We also find that semi-supervised deep contrastive embeddings can leverage small numbers of labelled examples to produce representations with substantially improved error-pattern consistency to human behavior. Taken together, these results suggest that deep contrastive embedding objectives may be a biologically-plausible computational theory of primate visual development.

neuroscience↗