Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Ochratoxin A (OTA) is one of the most prevalent and toxic mycotoxins. Ultrasensitive and convenient detection of OTA is urgent demanded for public health. In this work, a dual-readout immunoassay was established for the detection of OTA based on Ce 4+ oxidizing 3,3′,5,5′-tetramethylbenzidine (TMB) and Ce 3+ inducing aggregation induced emission (AIE) of Au nanoclusters (AuNCs). Under alkaline phosphatase (ALP), the ascorbic acid 2-phosphate (AA2P) can form ascorbic acid (AA) by dephosphorylation. The AA can reduce Ce 4+ to generate Ce 3+ , which induced the AIE of AuNCs to enhance the fluorescence intensity of AuNCs. Meanwhile, unreacted Ce 4+ oxidized TMB to form blue oxTMB. Thus, a dual-readout immunoassay was developed based on AIE of AuNCs and TMB as substrate. The limits of detection (LODs) were as low as 0.62 ng/mL for fluorescent assay and 0.81 ng/mL for colorimetric assay. The recoveries of OTA from corn were 94.4%-107.7% for the fluorescent mode and 93.7%-106.9% for the colorimetric mode. The results verified that the cerium ions triggered dual-readout immunoassay was reliable to sensitive detect OTA in corn samples.
Drug-drug interactions (DDIs) trigger unexpected pharmacological effects in vivo, often with unknown causal mechanisms. Deep learning methods have been developed to better understand DDI. However, learning domain-invariant representations for DDI remains a challenge. Generalizable DDI predictions are closer to reality than source domain predictions. For existing methods, it is difficult to achieve out-of-distribution (OOD) predictions. In this article, focusing on substructure interaction, we propose DSIL-DDI, a pluggable substructure interaction module that can learn domain-invariant representations of DDIs from source domain. We evaluate DSIL-DDI on three scenarios: the transductive setting (all drugs in test set appear in training set), the inductive setting (test set contains new drugs that were not present in training set), and OOD generalization setting (training set and test set belong to two different datasets). The results demonstrate that DSIL-DDI improve the generalization and interpretability of DDI prediction modeling and provides valuable insights for OOD DDI predictions. DSIL-DDI can help doctors ensuring the safety of drug administration and reducing the harm caused by drug abuse.
In the present work, a simple and sensitive turn-on fluorescence method for DNA detection was developed. It was explored based on the N-methyl mesoporphyrin IX (NMM)/G-quadruplex DNA system as a reporter and exonuclease III (Exo III)-aided target recycling amplification to ensure sensitivity. Our method showed an ultra-wide detection range from 10 fM to 100 nM with a low linear detection limit of 0.76 nM. It also had excellent selectivity in a selectivity experiment.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)