Crosstalk between nano/micro particulate technologies and Chinese medicine: a bibliometric analysis

Background:Chinese medicine is widely applied in Asian and Western countries for its outstanding therapeutic effect,but there are still unsolved problems such as unclear active ingredients and pharmacological effects.The application of nano/microparticle technology in Chinese medicine may become a promising strategy to solve this problem in the near future(provided that more efforts are made to in-depth exploration).In this work,a comprehensive analysis of the field was carried out from a bibliometrics perspective,and further the publishing trends and research hotspots were figured out.Methods:The articles or reviews from 2000 to 2020 were retrieved in the database of Web of Science Core Collection.The documents were processed by Clarivate Analytic,Visualization of Similarities viewer,Statistical Analysis Toolkit for Informetrics and bibliometric online platform,and the data were visualized.Finally,a bibliometric summary,citation analysis results and research trends were described.Results:A bibliometric analysis of 773 articles of interest showed that research in this field had continued to grow in recent years,with a high degree of interdisciplinary integration.Secondly,this field attached great importance to the quantitative analysis of Chinese medicine combined with nano/micro particle technology.Chinese medicine,nanoparticles and liposomes were the most accessed keywords.China was the main contributing country,and the top-10 contributing organizations were all located in China.Co-authorship was a common phenomenon in this field.Conclusion:The published literature on the application of nano/micro particle technology in Chinese medicine was summarized.The results suggested that bibliometric analysis could predict possible directions for future research in this field.

Research on nanosciences involvement in pharmaceutical education should be reinforced

Inclusion of nanoscience in pharmaceutical education should be reinforced,in order to match the demand of current pharmaceutical talent cultivation.

Characterization of oral bacterial diversity of irradiated patients by high-throughput sequencing

The objective of this study was to investigate the compositional profiles and microbial shifts of oral microbiota during head-and-neck radiotherapy.Bioinformatic analysis based on 16S rRNA gene pyrosequencing was performed to assess the diversity and variation of oral microbiota of irradiated patients.Eight patients with head and neck cancers were involved in this study.For each patient, supragingival plaque samples were collected at seven time points before and during radiotherapy.A total of 147 232 qualified sequences were obtained through pyrosequencing and bioinformatic analysis,representing 3 460 species level operational taxonomic units(OTUs) and 140 genus level taxa.Temporal variations were observed across different time points and supported by cluster analysis based on weighted UniFrac metrics.Moreover,the low evenness of oral microbial communities in relative abundance was revealed by Lorenz curves.This study contributed to a better understanding of the detailed characterization of oral bacterial diversity of irradiated patients.

Modeling of Diffusion Transport through Oral Biofilms with the Inverse Problem Method

Aim The purpose of this study was to develop a mathe-matical model to quantitatively describe the passive trans-port of macromolecules within dental biofilms. Methodology Fluorescently labeled dextrans with different molecular mass （3 kD,10 kD,40 kD,70 kD,2 000 kD） were used as a series of diffusion probes. Streptococcus mutans,Streptococcus sanguinis,Actinomyces naeslundii and Fusobacterium nucleatum were used as inocula for biofilm formation. The diffusion processes of different probes through the in vitro biofilm were recorded with a confocal laser microscope. Results Mathematical function of biofilm penetration was constructed on the basis of the inverse problem method. Based on this function,not only the relationship between average concentration of steady-state and molecule weights can be analyzed,but also that between penetrative time and molecule weights. Conclusion This can be used to predict the effective concentration and the penetrative time of anti-biofilm medicines that can diffuse through oral biofilm. Further-more,an improved model for large molecule is proposed by considering the exchange time at the upper boundary of the dental biofilm.

Speaker-independent speech emotion recognition by fusion of functional and accompanying paralanguage features

Functional paralanguage includes considerable emotion information, and it is insensitive to speaker changes. To improve the emotion recognition accuracy under the condition of speaker-independence, a fusion method combining the functional paralanguage features with the accompanying paralanguage features is proposed for the speaker-independent speech emotion recognition. Using this method, the functional paralanguages, such as laughter, cry, and sigh, are used to assist speech emotion recognition. The contributions of our work are threefold. First, one emotional speech database including six kinds of functional paralanguage and six typical emotions were recorded by our research group. Second, the functional paralanguage is put forward to recognize the speech emotions combined with the accompanying paralanguage features. Third, a fusion algorithm based on confidences and probabilities is proposed to combine the functional paralanguage features with the accompanying paralanguage features for speech emotion recognition. We evaluate the usefulness of the functional paralanguage features and the fusion algorithm in terms of precision, recall, and F1-measurement on the emotional speech database recorded by our research group. The overall recognition accuracy achieved for six emotions is over 67% in the speaker-independent condition using the functional paralanguage features.

Speech emotion recognition with unsupervised feature learning

Emotion-based features are critical for achieving high performance in a speech emotion recognition(SER) system. In general, it is difficult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms(including K-means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.