In situ synthesis of Co-doped MoS2 nanosheet for enhanced mimicking peroxidase activity

To enhance the catalytic activity of two-dimensional layered materials as versatile materials, the modification of transition metal dichalcogenide nanosheets such as MoS₂ by doping with heteroatoms has drawn great interests. However, few reports are available on the study of the enzyme-like activity of doped MoS₂. In this study, a facile in situ hydrothermal method for the preparation of various ultrathin transition metals (Fe, Cu, Co, Mn, and Ni) doped MoS₂ nanosheets has been reported. Through the density functional theory (DFT) and steady-state kinetic analysis, the Co-doped MoS₂ nanosheets exhibited the highest peroxidase-like catalytic activity among them. Furthermore, a typical colorimetric assay for H₂O₂ was presented based on the catalytic oxidation of colorless 3,3',5,5'-tetramethylbenzidine (TMB) to a blue product (oxTMB) by Co-MoS₂. The proposed colorimetric method showed excellent tolerance under extreme conditions and a broad linear range from 0.0005 to 25 mM for H₂O₂ determination. Concerning the practical application, in situ detection of H₂O₂ generated from SiHa cells was also fulfilled, fully confirming the great practicability of the proposed method in biosensing fields.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10853-022-07201-z.

Improving deep learning method for biomedical named entity recognition by using entity definition information

BACKGROUND

Biomedical named entity recognition (NER) is a fundamental task of biomedical text mining that finds the boundaries of entity mentions in biomedical text and determines their entity type. To accelerate the development of biomedical NER techniques in Spanish, the PharmaCoNER organizers launched a competition to recognize pharmacological substances, compounds, and proteins. Biomedical NER is usually recognized as a sequence labeling task, and almost all state-of-the-art sequence labeling methods ignore the meaning of different entity types. In this paper, we investigate some methods to introduce the meaning of entity types in deep learning methods for biomedical NER and apply them to the PharmaCoNER 2019 challenge. The meaning of each entity type is represented by its definition information.

MATERIAL AND METHOD

We investigate how to use entity definition information in the following two methods: (1) SQuad-style machine reading comprehension (MRC) methods that treat entity definition information as query and biomedical text as context and predict answer spans as entities. (2) Span-level one-pass (SOne) methods that predict entity spans of one type by one type and introduce entity type meaning, which is represented by entity definition information. All models are trained and tested on the PharmaCoNER 2019 corpus, and their performance is evaluated by strict micro-average precision, recall, and F1-score.

RESULTS

Entity definition information brings improvements to both SQuad-style MRC and SOne methods by about 0.003 in micro-averaged F1-score. The SQuad-style MRC model using entity definition information as query achieves the best performance with a micro-averaged precision of 0.9225, a recall of 0.9050, and an F1-score of 0.9137, respectively. It outperforms the best model of the PharmaCoNER 2019 challenge by 0.0032 in F1-score. Compared with the state-of-the-art model without using manually-crafted features, our model obtains a 1% improvement in F1-score, which is significant. These results indicate that entity definition information is useful for deep learning methods on biomedical NER.

CONCLUSION

Our entity definition information enhanced models achieve the state-of-the-art micro-average F1 score of 0.9137, which implies that entity definition information has a positive impact on biomedical NER detection. In the future, we will explore more entity definition information from knowledge graph.