Regulations currently in force enable to claim that the lead content in perovskite solar cells is low enough to be safe, or no more dangerous, than other electronics also containing lead. However, the actual environmental impact of lead from perovskite is unknown. Here we show that the lead from perovskite leaking into the ground can enter plants, and consequently the food cycle, ten times more effectively than other lead contaminants already present as the result of the human activities. We further demonstrate that replacing lead with tin represents an environmentally-safer option. Our data suggest that we need to treat the lead from perovskite with exceptional care. In particular, we point out that the safety level for lead content in perovskite-based needs to be lower than other lead-containing electronics. We encourage replacing lead completely with more inert metals to deliver safe perovskite technologies.

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Cardiovascular disease (CVD), cancer and diabetes are serious threat to human health and more and more aroused people's attention. It is important to find the safe and effective prevention and treatment methods for the three deadly diseases. At present, a generally attention in the possible positive effects of edible berries for the three deadly diseases has been noted. Berry phytochemical compounds regulate different signaling pathways about cell survival, growth and differentiation. They contribute to the prevention and treatment of CVD, cancer and diabetes. This article reviews previous experimental evidence, several common berry phytochemical compounds and their possible mechanisms involved in three deadly diseases were summarized.

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BackgroundPost-stroke depression (PSD) is a frequent complication that worsens rehabilitation outcomes and patient quality of life. This study developed a risk prediction model for PSD based on patient clinical and socio-psychology features for the early detection of high risk PSD patients.ResultsRisk predictors included a history of brain cerebral infarction (odds ratio [OR], 3.84; 95% confidence interval [CI], 2.22-6.70; P < 0.0001) and four socio-psychological factors including Eysenck Personality Questionnaire with Neuroticism/Stability (OR, 1.18; 95% CI, 1.12-1.20; P < 0.0001), life event scale (OR, 0.99; 95% CI, 0.98-0.99; P = 0.0007), 20 items Toronto Alexithymia Scale (OR, 1.06; 95% CI, 1.02-1.10; P = 0.002) and Social Support Rating Scale (OR, 0.91; 95% CI, 0.87-0.90; P < 0.001) in the logistic model. In addition, 11 rules were generated in the tree model. The areas under the curve of the ROC and the accuracy for the tree model were 0.85 and 0.86, respectively.MethodsThis study recruited 562 stroke patients in China who were assessed for demographic data, medical history, vascular risk factors, functional status post-stroke, and socio-psychological factors. Multivariate backward logistic regression was used to extract risk factors for depression in 1-month after stroke. We converted the logistic model to a visible tree model using the decision tree method. Receiver operating characteristic (ROC) was used to evaluate the performance of the model.ConclusionThis study provided an effective risk model for PSD and indicated that the socio-psychological factors were important risk factors of PSD.

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