Passively-targeted mitochondrial tungsten-based nanodots for efficient acute kidney injury treatment

Acute kidney injury(AKI)can lead to loss of kidney function and a substantial increase in mortality.The burst of reactive oxygen species(ROS)plays a key role in the pathological progression of AKI.Mitochondrial-targeted antioxidant therapy is very promising because mitochondria are the main source of ROS in AKI.Antioxidant nanodrugs with actively targeted mitochondria have achieved encouraging success in many oxidative stress-induced diseases.However,most strategies to actively target mitochondria make the size of nanodrugs too large to pass through the glomerular system to reach the renal tubules,the main damage site of AKI.Here,an ultra-small Tungsten-based nanodots(TWNDs)with strong ROS scavenging can be very effective for treatment of AKI.TWNDs can reach the tubular site after crossing the glomerular barrier,and enter the mitochondria of the renal tubule without resorting to complex active targeting strategies.To our knowledge,this is the first time that ultra-small negatively charged nanodots can be used to passively target mitochondrial therapy for AKI.Through in-depth study of the therapeutic mechanism,such passive mitochondria-targeted TWNDs are highly effective in protecting mitochondria by reducing mitochondrial ROS and increasing mitophagy.In addition,TWNDs can also reduce the infiltration of inflammatory cells.This work provides a new way to passively target mitochondria for AKI,and give inspiration for the treatment of many major diseases closely related to mitochondria,such as myocardial infarction and cerebral infarction.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.