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Researchers from across the world are seeking to develop effective treatments for the ongoing coronavirus disease 2019 (COVID-19) outbreak, which arose as a major public health issue in 2019, and was declared a pandemic in early 2020. The pro-inflammatory cytokine storm, acute respiratory distress syndrome (ARDS), multiple-organ failure, neurological problems, and thrombosis have all been linked to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) fatalities. The purpose of this review is to explore the rationale for using photobiomodulation therapy (PBMT) of the particular wavelength 1068 nm as a therapy for COVID-19, investigating the cellular and molecular mechanisms involved. Our findings illustrate the efficacy of PBMT 1068 nm for cytoprotection, nitric oxide (NO) release, inflammation changes, improved blood flow, and the regulation of heat shock proteins (Hsp70). We propose, therefore, that PBMT 1068 is a potentially effective and innovative approach for avoiding severe and critical illness in COVID-19 patients, although further clinical evidence is required.

At the 13th meeting of the European Society for Traumatic Stress Studies in 2013, a symposium was held that brought together international researchers and clinicians who were involved in psychosocial responses to disaster. A total of six disasters that occurred in five countries were presented and discussed. Lessons learned from these disasters included the need to: (1) tailor the psychosocial response to the specific disaster, (2) provide multi-dimensional psychosocial care, (3) target at-risk population groups, (4) proactively address barriers in access to care, (5) recognise the social dimensions and sources of resilience, (6) extend the roles for mental health professionals, (7) efficiently coordinate and integrate disaster response services, and (8) integrate research and evaluation into disaster response planning.

Background Information on above-ground biomass (AGB) is important for managing forest resource use at local levels, land management planning at regional levels, and carbon emissions reporting at national and international levels. In many tropical developing countries, this information may be unreliable or at a scale too coarse for use at local levels. There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements. Model-based methods provide an efficient framework to estimate AGB. Methods Using National Forest Inventory (NFI) data for a ~1,000,000 ha study area in the miombo ecoregion, Zambia, we estimated AGB using predicted canopy cover, environmental data, disturbance data, and Landsat 8 OLI satellite imagery. We assessed different combinations of these datasets using three models, a semiparametric generalized additive model (GAM) and two nonlinear models (sigmoidal and exponential), employing a genetic algorithm for variable selection that minimized root mean square prediction error (RMSPE), calculated through cross-validation. We compared model fit statistics to a null model as a baseline estimation method. Using bootstrap resampling methods, we calculated 95 % confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data. Results Canopy cover, soil moisture, and vegetation indices were consistently selected as predictor variables. The sigmoidal model and the GAM performed similarly; for both models the RMSPE was ~36.8 tonnes per hectare (i.e., 57 % of the mean). However, the sigmoidal model was approximately 30 % more efficient than the GAM, assessed using bootstrapped variance estimates relative to a null model. After selecting the sigmoidal model, we estimated total AGB for the study area at 64,526,209 tonnes (+/− 477,730), with a confidence interval 20 times more precise than a simple design-based estimate. Conclusions Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty, while also providing spatially explicit AGB maps useful for management, planning, and reporting purposes.