RESUMEN
Serotonin (5-hydroxytryptamine, 5-HT) is a pivotal monoamine neurotransmitter, which is widely distributed in human brain for biological, physical and psychopathological processes. The content of 5-HT can support diagnose of various diseases. To selectively detect 5-HT is very important in clinical medicine. Here, a novel microbiosensor for 5-HT is studied on acupuncture needle. Molecularly imprinted film enwrapped 5-HT was electropolymerized onto bimetallic gold/platinum (Au/Pt) nanoparticles on acupuncture needle microelectrode (ANME). Au/Pt nanostructure exhibited active sites to catalyze the oxidation of 5-HT and bind the generated polymer. 5-HT can be enwrapped by the functional monomer of pyrrole (Py) in the process of electropolymerization with suitably electroactive conformation. Comparing with interfaces of single metal or molecularly imprinted layer, synergistic microbiosensor exhibit better performance for 5-HT. 5-HT can be adsorbed and catalytically oxidized by the imprinted cavities. Under optimized conditions, the peak current linearly increases with the concentration of 5-HT from 0.03 to 500 µM, and a detection limit of 0.0106 µM is obtained. The performance of this microbiosensor is competitive with previous studies. Furthermore, the prepared microbiosensor showed effective application to analyze 5-HT in human serum and urine. Interestingly, the microbiosensor expressed the real-time monitoring ability to 5-HT from stimulated PC12 cells by K+. The microbiosensor also exhibited high selectivity, stability and reproducibility, which is promising in view of the low price, fast response and simple operation.
RESUMEN
This article proposes a new, to the best of our knowledge, separate absorption and multiplication (SAM) APD based on GaN/ß-Ga2O3 heterojunction with high gains. The proposed APD achieved a high gain of 1.93 × 104. We further optimized the electric field distribution by simulating different doping concentrations and thicknesses of the transition region, resulting in the higher avalanche gain of the device. Furthermore, we designed a GaN/ß-Ga2O3 heterojunction instead of the single Ga2O3 homogeneous layer as the multiplication region. Owing to the higher hole ionization coefficient, the device offers up to a 120% improvement in avalanche gain reach to 4.24 × 104. We subsequently clearly elaborated on the working principle and gain mechanism of GaN/ß-Ga2O3 SAM APD. The proposed structure is anticipated to provide significant guidance for ultraweak ultraviolet light detection.
RESUMEN
The field of Chinese medical natural language processing faces a significant challenge in training accurate entity recognition models due to the limited availability of high-quality labeled data. In response, we propose a joint training model, MCBERT-GCN-CRF, which achieves high performance in identifying medical-related entities in Chinese electronic medical records. Additionally, we introduce CM-NER, a 5-step framework that effectively mitigates the effects of noise in weakly labeled data and establishes a principled connection between supervised and weakly supervised named entity recognition. We demonstrate significant improvements in recall rate and accuracy. Our approach outperforms traditional fully supervised pre-training models and other state-of-the-art methods by suppressing noise in weakly labeled data. Our proposed framework achieves an F1 score of 86.29% on the CCKS-2019 dataset, significantly higher than pre-trained model baselines ranging from 74.17 to 83.06%, and higher than the top-performing named entity recognition supervised learning models in the CCKS-2019 competition. Our results demonstrate the effectiveness of our proposed framework and highlight the potential of leveraging unlabeled data to train accurate models for named entity recognition in Chinese medical natural language processing. This research has significant implications for advancing natural language processing techniques in the medical domain and improving patient care.
