Recent advances in optimization strategies and applications of plant bioreactors

Plant bioreactors have been the central part of molecular pharming. Vaccines, antibodies and functional foods produced by plant bioreactors with the benefits of cost-effectiveness, high scalability, rapid production, enabling post-translational modification, and no harmful pathogens contamination are increasingly accepted by the public. In February 2022, Health Canada approved the world's first plant-derived human vaccine Covifenz® for the prevention and treatment of COVID-19, marking the advent of the era of molecular pharming represented by plant bioreactors. This paper elaborates the development history of plant bioreactors, with the main host species representatives of leafy plants and seed plants, the stable and transient expression systems construction for various applications, as well as the enhancement strategies through promoter and codon optimization, "humanization” of glycosylation process, inhibition of gene silencing and protease activity, and also summarize the application of plant-derived protein products, which aim to provide a theoretical and application basis for the development of plant bioreactors. © 2023, China Biotechnology Press. All rights reserved.

Phenonizer: A fine-grained phenotypic named entity recognizer for Chinese clinical texts

Biomedical named entity recognition from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities, which could be used for profiling the clinical characteristics of patients in specific disease conditions. However, general approaches mostly rely on the coarse-grained annotations (e.g. mentions of symptom terms) of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g. 'no fever', 'no cough' and 'no hypertension') in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. Thus, we constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer (Phenonizer). The results on the test set show that Phenonizer outperform those methods based on Word2Vec with Fl-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms, and avoids the interference of negated symptoms. Finally, we tested the generalization performance of Phenonier, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonier proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance. © 2021 IEEE.

Masked Face Recognition Datasets and Validation

In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This nearly makes conventional facial recognition technology ineffective in many scenarios, such as face authentication, security check, community visit check-in, etc. Therefore, it is very urgent to boost performance of existing face recognition systems on masked faces. Most current advanced face recognition approaches are based on deep learning, which heavily depends on a large number of training samples. However, there are presently no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Synthetic Masked Face Recognition Dataset (SMFRD). As far as we know, we are the first to publicly release large-scale masked face recognition datasets that can be downloaded for free at: https://github.com/X-zhangyany/Real-World-Masked-Face-Dataset.

[Establishment of index system for population based SARS-CoV-2 nucleic acid screening].

Objective: To establish an index system of population based SARS-CoV-2 nucleic acid screening, and provide reference to determine the screening coverage appropriately. Methods: The literature review and brain storming sessions were used to develop the basic frame and index system of population based SARS-CoV-2 nucleic acid screening. Based on Delphi method and Analytic Hierarchy Process, 21 domestic experts were selected for two rounds of consultation to determine the index system of population based SARS-CoV-2 nucleic acid screening and its weight. Results: The positive indexes of experts in two rounds of consultations were both 100%. The experts' authority coefficients (Cr) were 0.88±0.08 and 0.89±0.07, respectively. And the range of coefficient of variation (CV) were (0.08, 0.24), (0.09, 0.25). The Kendall's W coordination coefficients were 0.34 and 0.22 respectively, which were statistically significant. The index system of population based SARS-CoV-2 nucleic acid screening was established, which had 4 first-level indexes, 11 second-level indexes and 58 third-level indexes. Besides, the weight of each index was determined. Conclusion: The index system of population based SARS-CoV-2 nucleic acid screening has been established, which can provide scientific reference for the health administration to determine the coverage of population based SARS-CoV-2 nucleic acid screening when local COVID-19 epidemic occurs.

COVID-19 Related Research by Data Mining in Single Cell Transcriptome Profiles

The outbreak of coronavirus disease 2019 (COVID-2019) has drawn public attention all over the world. As a newly emerging area, single cell sequencing also exerts its power in the battle over the epidemic. In this review, the up-to-date knowledge of COVID-19 and its receptor is summarized, followed by a collection of the mining of single cell transcriptome profiling data for the information in aspects of the vulnerable cell types in humans and the potential mechanisms of the disease. © 2021. All Rights Reserved.

Multi-route transmission potential of SARS-CoV-2 in healthcare facilities

Understanding the transmission mechanism of SARS-CoV-2 is a prerequisite to effective control measures. To investigate the potential modes of SARS-CoV-2 transmission, 21 COVID-19 patients from 12-47 days after symptom onset were recruited. We monitored the release of SARS-CoV-2 from the patients' exhaled breath and systematically investigated environmental contamination of air, public surfaces, personal necessities, and the drainage system. SARS-CoV-2 RNA was detected in 0 of 9 exhaled breath samples, 2 of 8 exhaled breath condensate samples, 1 of 12 bedside air samples, 4 of 132 samples from private surfaces, 0 of 70 samples from frequently touched public surfaces in isolation rooms, and 7 of 23 feces-related air/surface/water samples. The maximum viral RNA concentrations were 1857 copies/m3 in the air, 38 copies/cm2 in sampled surfaces and 3092 copies/mL in sewage/wastewater samples. Our results suggest that nosocomial transmission of SARS-CoV-2 can occur via multiple routes. However, the low detection frequency and limited quantity of viral RNA from the breath and environmental specimens may be related to the reduced viral load of the COVID-19 patients on later days after symptom onset. These findings suggest that the transmission dynamics of SARS-CoV-2 differ from those of SARS-CoV in healthcare settings.

[Progress and challenge of vaccine development against 2019-novel coronavirus (2019-nCoV)].

The outbreak of 2019-novel coronavirus (2019-nCoV) infection poses a serious threat to global public health. Vaccination is an effective way to prevent the epidemic of the virus. 2019-nCoV along with severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) belong to the same ß-genus of coronavirus family. Basing on the previous experience and the technical platform of developing SARS-CoV and MERS-CoV vaccines, scientists from all over the world are working hard and quickly on the related fields. There are substantial progress in these fields including characterizing the 2019-nCoV virus, identification of candidate antigens and epitopes, establishment of animal models, characterizing the immune responses, and the design of vaccines. The development of 2019-nCoV vaccines covers all types: inactivated virus vaccine, recombinant protein vaccine, viral vector-based vaccine, mRNA vaccine, and DNA vaccine, et al. As of March 2020, two 2019-nCoV vaccines have entered phase I clinical trials. One is named as Ad5-nCoV developed by the Chinese Institute of Biotechnology of the Academy of Military Medical Sciences and Tianjin Cansino Biotechnology Inc. Ad5-nCoV is based on the replication-defective adenovirus type 5 as the vector to express 2019-nCoV spike protein. The another vaccine is mRNA-1273 developed by the National Institute of Allergy and Infectious Diseases and Moderna, Inc.. RNA-1273 is an mRNA vaccine expressing 2019-nCoV spike protein. Although the rapid development of 2019-nCoV vaccine, it still faces many unknown challenges, including the antigenic characteristics of the 2019-nCoV, the influence of antigenic variation, the protective immune response of host, the protection of the elderly population, and the downstream manufacturing process of the new vaccine. The safety and efficacy of vaccines are the first priority for vaccine development and should be carefully evaluated.