Correction: Deciphering the chemical bonding of the trivalent oxygen atom in oxygen doped graphene.

[This corrects the article DOI: 10.1039/D4SC00142G.].

Deciphering the chemical bonding of the trivalent oxygen atom in oxygen doped graphene.

Recently, planar and neutral tricoordinated oxygen embedded in graphene has been imaged experimentally (Nat. Commun., 2019, 10, 4570-4577). In this work, this unusual chemical species is studied utilizing a variety of state-of-the-art methods and combining periodic calculations with a fragmental approach. Several factors influencing the stability of trivalent oxygen are identified. A σ-donation and a π-backdonation mechanism between graphite and oxygen is established. π-Local aromaticity, with a delocalized 4c-2e bond involving the oxygen atom and the three nearest carbon atoms aids in the stabilization of this system. In addition, the framework in which the oxygen is embedded is crucial too to the stabilization, helping to delocalize the "extra" electron pair in the virtual orbitals. Based on the understanding gathered in this work, a set of organic molecules containing planar and neutral trivalent oxygen is theoretically proposed for the first time.

LDS-CNN: a deep learning framework for drug-target interactions prediction based on large-scale drug screening.

Background: Drug-target interaction (DTI) is a vital drug design strategy that plays a significant role in many processes of complex diseases and cellular events. In the face of challenges such as extensive protein data and experimental costs, it is suggested to apply bioinformatics approaches to exploit potential interactions to design new targeted medications. Different data and interaction types bring difficulties to study involving incompatible and heterology formats. The analysis of drug-target interactions in a comprehensive and unified model is a significant challenge. Method: Here, we propose a general method for predicting interactions between small-molecule drugs and protein targets, Large-scale Drug target Screening Convolutional Neural Network (LDS-CNN), which used unified encoding to achieve the calculation of the different data formats in an integrated model to realize feature abstraction and potential object prediction. Result: On 898,412 interaction data involving 1683 small-molecule compounds and 14,350 human proteins from 8.8 billion records, the proposed method achieved an area under the curve (AUC) of 0.96, an area under the precision-recall curve (AUPRC) of 0.95, and an accuracy of 90.13%. The experimental results illustrated that the proposed method attained high accuracy on the test set, indicating its high predictive ability in drug-target interaction prediction. LDS-CNN is effective for the prediction of large-scale datasets and datasets composed of data with different formats. Conclusion: In this study, we propose a DTI prediction method to solve the problems of unified encoding of large-scale data in multiple formats. It provides a feasible way to efficiently abstract the features among different types of drug-related data, thus reducing experimental costs and time consumption. The proposed method can be used to identify potential drug targets and candidates for the treatment of complex diseases. This work provides a reference for DTI to process large-scale data and different formats with deep learning methods and provides certain suggestions for future research.

Ligustrazine-Loaded Borneol Liposome Alleviates Cerebral Ischemia-Reperfusion Injury in Rats.

Our team's pharmacological and clinical trials proved that ligustrazine/borneol spray had a definite effect on ischemic stroke (IS). To solve the shortcomings of ligustrazine/borneol spray, such as low bioavailability, short half-life, and poor compatibility between borneol and ligustrazine, ligustrazine-loaded borneol liposomes (LIP@TMP) were successfully prepared by a thin-film ultrasonication method. The average particle size of LIP@TMP was 282.4 ± 3.6 nm, the drug loading rate was 14.5 ± 0.6%, and the entrapment efficiency was 42.7 ± 1.0%, which had excellent stability and sustained release ability. In addition, live/dead fluorescent staining and the CCK-8 test confirmed that LIP@TMP had good biocompatibility. Moreover, middle cerebral artery occlusion (MCAO) rat model experiments further demonstrated that LIP@TMP could significantly alleviate cerebral ischemia and reperfusion injury by improving neurological scores, reducing cerebral infarct volume, promoting neurogenesis, inhibiting inflammation, and reducing tissue damage. In addition, LIP@TMP enhanced neuronal marker doublecortin (DCX) and neuronal nuclei (NEUN), inhibited inflammatory factors (TNF-α and IL-1ß), and reduced apoptosis signal molecules (TUNEL and caspase-3). The findings of this study suggested that the prepared LIP@TMP had tremendous potential for the treatment of cerebral ischemia.