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COVID-19 pandemic response measures have brought unprecedented challenges to the UN 2030 Agenda for the Sustainable Development Goals (SDGs) and a quantitative, multi-dimensional assessment of the impacts of these response measures on SDG progress is required. Here, we use an adaptive multi-regional input-output model to quantitatively assess the impact of pandemic responses on global and national SDG progress and show that COVID-19 pandemic responses (i.e., movement restrictions, fiscal stimuli, and trade restrictions) have reduced overall progress towards the SDGs by 7.2%, with socio-economic sustainability scores declining by 23.9% while environmental sustainability scores improved by 11.6% compared with the BAU trend. Developing countries suffered slightly greater reductions in overall sustainability (7.8%) than developed countries (6.4%). Under all post-pandemic futures, pandemic responses impeded overall progress towards the 2030 Agenda and worsened inequality in sustainable development between countries, particularly for socioeconomic targets. A post-pandemic strategy toward the UN 2030 Agenda requires sustainable pandemic responses, which not only address inequality among countries but also lessen the trade-offs between SDGs.

Additional file 3: Fig S2. Risk of bias summary: Judgment of the risk of bias for each included study with Cochrane quality assessment tool.

Additional file 1: Table S1. Main characteristic of included studies.

Additional file 2: Fig S1. Risk of bias graph: Quality assessment about each risk of bias item.

In normal adult retinas, NGF receptor TrkA is expressed in retinal ganglion cells (RGC), whereas glia express p75(NTR). During retinal injury, endogenous NGF, TrkA, and p75(NTR) are up-regulated. Paradoxically, neither endogenous NGF nor exogenous administration of wild type NGF can protect degenerating RGCs, even when administered at high frequency. Here we elucidate the relative contribution of NGF and each of its receptors to RGC degeneration in vivo. During retinal degeneration due to glaucoma or optic nerve transection, treatment with a mutant NGF that only activates TrkA, or with a biological response modifier that prevents endogenous NGF and pro-NGF from binding to p75(NTR) affords significant neuroprotection. Treatment of normal eyes with an NGF mutant-selective p75(NTR) agonist causes progressive RGC death, and in injured eyes it accelerates RGC death. The mechanism of p75(NTR) action during retinal degeneration due to glaucoma is paracrine, by increasing production of neurotoxic proteins TNF-α and α(2)-macroglobulin. Antagonists of p75(NTR) inhibit TNF-α and α(2)-macroglobulin up-regulation during disease, and afford neuroprotection. These data reveal a balance of neuroprotective and neurotoxic mechanisms in normal and diseased retinas, and validate each neurotrophin receptor as a pharmacological target for neuroprotection.

This research integrated the Revised Universal Soil Loss Equation (RUSLE) and the soil statistical analysis with RS and GIS techniques to quantify soil and nutrient loss risk. The Letianxi watershed, located in the southwest part of Hubei Province of China was taken as a case. A system was established for rating soil erodibility, slope length/gradient, rainfall erosivity and conservation practices. The rating values serve as inputs into a Revised Universal Soil Loss Equation (RUSLE) to calculate the risk for soil degradation processes, namely soil water erosion. Two Landsat TM senses in 1997 and ETM 2002, respectively, were used to produce land use/cover maps of the study area based on the maximum likelihood classification method. These maps were then used to generate the conservation practice factor in the RUSLE. In order to assess the effect of land cover and landscape position on soil nutrient consisting of Soil Organic Mater (SOM), Total N (TN), Total P (TP) and Total K (TK), soil samples were collected from the top to foot of hill slope in the study area. The three categories consisted of a typical land cover structure for each polygon in the study area: Soil under forest cover (T1), soil under agriculture crops cover (T2) and soil under no vegetation cover (T3). Annual loss of total N (8.24 kg ha?1), total P (5.88 kg ha?1) and total K (6.98 kg ha?1), per unit loss of soil (t ha?1), was maximum from the soil under no vegetation cover (T3). The loss of total N ranged between 5.30 and 32.27 kg ha?1, total P ranged between 2.14 and 12.42 kg ha?1, total K ranged from 2.12 to 10.31 kg ha-1 whereas organic matter loss varied between 10.65 and 236.16 kg ha-1, from three different land covers. ERmapper and Arc/Info software were used to manage and manipulate thematic data, to process satellite images and tabular data source. Results showed that 110.72 km2 (27.09%) was exposed to very slight soil loss and 227.01 km2 (55.55%) was exposed to slight soil loss. This study demonstrates the effectiveness of Geo-information technology in generating a soil and nutrient loss map.