Photobiomodulation reduces the cytokine storm syndrome associated with Covid-19 in the zebrafish model

Although the exact mechanism of the pathogenesis of COVID-19 is not fully understood, oxidative stress and the release of pro-inflammatory cytokines have been highlighted as playing a vital role in the pathogenesis of the disease. In this sense, alternative treatments are needed to reduce the inflammation caused by COVID-19. Therefore, this study aimed to investigate the potential effect of red PBM as an attractive therapy to downregulate the cytokine storm caused by COVID-19 from a zebrafish model. RT-PCR analyses and protein-protein interaction prediction among SARS-CoV-2 and Danio rerio proteins showed that rSpike was responsible for generating systemic inflammatory processes with significantly increased pro-inflammatory (il1b, il6, tnfa, and nfkbiab), oxidative stress (romo1) and energy metabolism (slc2a1a, coa1) mRNA markers, with a pattern like those observed in COVID-19 cases in humans. On the other hand, PBM treatment decreased the mRNA levels of these pro-inflammatory and oxidative stress markers compared with rSpike in various tissues, promoting an anti-inflammatory response. Conversely, PBM promotes cellular and tissue repair of injured tissues and significantly increases the survival rate of rSpike-inoculated individuals. Additionally, metabolomics analysis showed that the most impacted metabolic pathways between PBM and the rSpike-treated groups were related to steroid metabolism, immune system, and lipids metabolism. Together, our findings suggest that the inflammatory process is an incisive feature of COVID-19, and red PBM can be used as a novel therapeutic agent for COVID-19 by regulating the inflammatory response. Nevertheless, the need for more clinical trials remains, and there is a significant gap to overcome before clinical trials.publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

SARS-CoV-2 productively infects primary human immune system cells in vitro and in COVID-19 patients

Abstract: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with a hyperinflammatory state and lymphocytopenia, a hallmark that appears as both signature and prognosis of disease severity outcome. Although cytokine storm and a sustained inflammatory state are commonly associated with immune cell depletion, it is still unclear whether direct SARS-CoV-2 infection of immune cells could also play a role in this scenario by harboring viral replication. We found that monocytes, as well as both B and T lymphocytes, were susceptible to SARS-CoV-2 infection in vitro, accumulating double-stranded RNA consistent with viral RNA replication and ultimately leading to expressive T cell apoptosis. In addition, flow cytometry and immunofluorescence analysis revealed that SARS-CoV-2 was frequently detected in monocytes and B lymphocytes from coronavirus disease 2019 (COVID-19) patients. The rates of SARS-CoV-2-infected monocytes in peripheral blood mononuclear cells from COVID-19 patients increased over time from symptom onset, with SARS-CoV-2-positive monocytes, B cells, and CD4+ T lymphocytes also detected in postmortem lung tissue. These results indicated that SARS-CoV-2 infection of blood-circulating leukocytes in COVID-19 patients might have important implications for disease pathogenesis and progression, immune dysfunction, and virus spread within the host