Ultrasmall and highly biocompatible carbon dots derived from natural plant with amelioration against acute kidney injury.

BACKGROUND: Acute kidney injury (AKI) refers to a tricky clinical disease, known by its high morbidity and mortality, with no real specific medicine for AKI. The carbonization product from Pollen Typhae (i.e., Pu-huang in China) has been extensively employed in clinic, and it is capable of relieving the renal damage and other diseases in China since acient times. RESULTS: Inspired by the carbonization process of Traditional Chinese Medicine (TCM), a novel species of carbon dots derived from Pollen Typhae (PT-CDs) was separated and then collected using a one-pot pyrolysis method. The as-prepared PT-CDs (4.85 ± 2.06 nm) with negative charge and abundant oxygenated groups exhibited high solubility, and they were stable in water. Moreover, the rhabdomyolysis (RM)-induced AKI rat model was used, and it was first demonstrated that PT-CDs had significant activity in improving the level of BUN and CRE, urine volume and kidney index, and histopathological morphology in RM-induced AKI rats. It is noteworthy that interventions of PT-CDs significantly reduced degree of inflammatory reaction and oxidative stress, which may be correlated with the basial potential mechanism of anti-AKI activities. Furthermore, cytotoxicity assay and biosafety evaluation exhibited high biocompatibility of PT-CDs. CONCLUSION: This study offers a novel relieving strategy for AKI based on PT-CDs and suggests its potential to be a related candidate for clinical applications.

Correction to: Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

[This corrects the article DOI: 10.1007/s10389-023-01833-4.].

Effect of daily new cases of COVID-19 on public sentiment and concern: Deep learning-based sentiment classification and semantic network analysis.

Aim: This study explored the influence of daily new case videos posted by public health agencies (PHAs) on TikTok in the context of COVID-19 normalization, as well as public sentiment and concerns. Five different stages were used, based on the Crisis and Emergency Risk Communication model, amidst the 2022 Shanghai lockdown. Subject and Methods: After dividing the duration of the 2022 Shanghai lockdown into stages, we crawled all the user comments of videos posted by Healthy China on TikTok with the theme of daily new cases based on these five stages. Third, we constructed the pre-training model, ERNIE, to classify the sentiment of user comments. Finally, we performed semantic network analyses based on the sentiment classification results. Results: First, the high cost of fighting the epidemic during the 2022 Shanghai lockdown was why ordinary people were reluctant to cooperate with the anti-epidemic policy in the pre-crisis stage. Second, Shanghai unilaterally revised the definition of asymptomatic patients led to an escalation of risk levels and control conditions in other regions, ultimately affecting the lives and work of ordinary people in the area during the initial event stage. Third, the public reported specific details that affected their lives due to the long-term resistance to the epidemic in the maintenance stage. Fourth, the public became bored with videos regarding daily new cases in the resolution stage. Finally, the main reason for the negative public sentiment was that the local government did not follow the central government's anti-epidemic policy. Conclusion: Our results suggest that the methodology used in this study is feasible. Furthermore, our findings will help the Chinese government or PHAs improve the possible behaviors that displease the public in the anti-epidemic process.