An outbreak of a novel coronavirus disease (i.e., COVID-19) has been recorded in Wuhan, China since late December 2019, which subsequently became pandemic around the world. Although COVID-19 is an acutely treated disease, it can also be fatal with a risk of fatality of 4.03% in China and the highest of 13.04% in Algeria and 12.67% Italy (as of 8th April 2020). The onset of serious illness may result in death as a consequence of substantial alveolar damage and progressive respiratory failure. Although laboratory testing, e.g., using reverse transcription polymerase chain reaction (RT-PCR), is the golden standard for clinical diagnosis, the tests may produce false negatives. Moreover, under the pandemic situation, shortage of RT-PCR testing resources may also delay the following clinical decision and treatment. Under such circumstances, chest CT imaging has become a valuable tool for both diagnosis and prognosis of COVID-19 patients. In this study, we propose a weakly supervised deep learning strategy for detecting and classifying COVID-19 infection from CT images. The proposed method can minimise the requirements of manual labelling of CT images but still be able to obtain accurate infection detection and distinguish COVID-19 from non-COVID-19 cases. Based on the promising results obtained qualitatively and quantitatively, we can envisage a wide deployment of our developed technique in large-scale clinical studies.

A reduced removal of dysfunctional mitochondria is common to aging and age-related neurodegenerative pathologies such as Alzheimer’s disease (AD). Strategies for treating such impaired mitophagy would benefit from the identification of mitophagy modulators. Here we report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds. From a library of naturally occurring compounds, the workflow allowed us to identify 18 small molecules, and among them two potent mitophagy inducers (Kaempferol and Rhapontigenin). In nematode and rodent models of AD, we show that both mitophagy inducers increased the survival and functionality of glutamatergic and cholinergic neurons, abrogated amyloid-β and tau pathologies, and improved the animals’ memory. Our findings suggest the existence of a conserved mechanism of memory loss across the AD models, this mechanism being mediated by defective mitophagy. The computational–experimental screening and validation workflow might help uncover potent mitophagy modulators that stimulate neuronal health and brain homeostasis.

No abstract

The concentrations, sources, and risks of heavy metals (Fe, Al, Mn, Cr, Co, Ni, Cu, Zn, As, Cd, W, Pb, and Tl) in sediments in five river-lake ecosystems in the Poyang Lake region were studied. The concentrations of the heavy metals varied spatially, with most of the highest concentrations in the Raohe river-lake ecosystem (RH). All heavy metals except As, Cd, W, and Tl were enriched in sediments possessing high total organic carbon contents or in finer sediments. Based on enrichment factors and statistical methods, it was found that Cd in sediments in the Xiushui (XS), Ganjiang (GJ), Xinjiang (XJ) river-lake ecosystems, and RH; Mn in the XS, GJ, and RH; and W in the XS and GJ were greatly affected by anthropogenic inputs. Moreover, the origins of Cu, Zn, and As require more attention due to the high concentrations found. The high enrichment factor of Cd in the sediments indicated that this metal might cause significant pollution in the environment. The results of the modified potential ecological risk index revealed that the XS, GJ, RH, and XJ were at considerable ecological risk, while the sediments in the Fuhe river-lake ecosystem (FH) were at moderate ecological risk, with Cd contributing the highest proportion of risk. The hazard score fundamentally validated the modified potential ecological risk analysis and revealed a mean toxicity of 57.80% to the benthic organisms in the RH.

Using land use types in multiple spatial scales (entire basin, buffer zones, and slopes) as well as statistical and spatial analysis, relationships between land use types and concentrations of dissolved trace metals were determined in the Le'an River Basin, China. The result showed that farmland and urban land were determined as the source of the pollutants, while forestland and grassland were identified as the sink of the pollutants. The temporal differences of relationships between land use types and concentrations of dissolved trace metals mainly due to the discrepancy of rainfall characteristics. Land use type close to river was a better indicator for the effectiveness of concentrations of trace metals, especially at scale of 0-200 m. Forestland and grassland on lower slopes greatly affected the water quality, and the former had no significant or weak influences on higher slopes. Urban land had the greater positive correlations with concentrations of dissolved trace metals on higher slopes, which are mainly due to frequent mining activity. Further analysis suggested that the buffer zones with low slope needed to be seriously taken into consideration for effective land use management in similar basin.

Surface water samples were collected from 20 sampling sites throughout the Ganjiang River during pre-monsoon, monsoon, and post-monsoon seasons, and the concentrations of dissolved trace elements were determined by inductively coupled plasma-mass spectrometry (ICP-MS) for the spatial and seasonal variations, risk assessment, source identification, and categorization for risk area. The result demonstrated that concentrations of the elements exhibited significant seasonality. The high total element concentrations were detected at sites close to the intensive mining and urban activities. The concentrations of the elements were under the permissible limits as prescribed by related standards with a few exceptions. The most of heavy metal pollution index (HPI) values were lower than the critical index limit, indicating the basically clean water used as habitat for aquatic life. As was identified as the priority pollutant of non-carcinogenic and carcinogenic concerns, and the inhabitants ingesting the surface water at particular site might be subjected to the integrated health risks for exposure to the mixed trace elements. Multivariate statistical analyses confirmed that Zn, As, Cd, and Tl were derived from mining and urban activities; V, Cd, and Pb exhibited mixed origin; and Co, Ni, and Cu mainly resulted from natural processes. Three categorized risk areas corresponded to high, moderate, and low risks, respectively. As a whole, the upstream of the Ganjiang River was identified as the high-risk area relatively.

No abstract

No abstract