Systematic Tracing of Susceptible Animals to SARS-CoV-2 by a Bioinformatics Framework

Care, management, and use of ferrets in biomedical research

The ferret (Mustela putorius furo) is a small domesticated species of the family Mustelidae within the order Carnivora. The present article reviews and discusses the current state of knowledge about housing, care, breeding, and biomedical uses of ferrets. The management and breeding procedures of ferrets resemble those used for other carnivores. Understanding its behavior helps in the use of environmental enrichment and social housing, which promote behaviors typical of the species. Ferrets have been used in research since the beginning of the twentieth century. It is a suitable non-rodent model in biomedical research because of its hardy nature, social behavior, diet and other habits, small size, and thus the requirement of a relatively low amount of test compounds and early sexual maturity compared with dogs and non-human primates. Ferrets and humans have numerous similar anatomical, metabolic, and physiological characteristics, including the endocrine, respiratory, auditory, gastrointestinal, and immunological systems. It is one of the emerging animal models used in studies such as influenza and other infectious respiratory diseases, cystic fibrosis, lung cancer, cardiac research, gastrointestinal disorders, neuroscience, and toxicological studies. Ferrets are vulnerable to many human pathogenic organisms, like severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), because air transmission of this virus between them has been observed in the laboratory. Ferrets draw the attention of the medical community compared to rodents because they occupy a distinct niche in biomedical studies, although they possess a small representation in laboratory research.

The immunobiology of SARS-CoV-2 infection and vaccine responses: potential influences of cross-reactive memory responses and aging on efficacy and off-target effects

Immune responses to both SARS-CoV-2 infection and its associated vaccines have been highly variable within the general population. The increasing evidence of long-lasting symptoms after resolution of infection, called post-acute sequelae of COVID-19 (PASC) or "Long COVID," suggests that immune-mediated mechanisms are at play. Closely related endemic common human coronaviruses (hCoV) can induce pre-existing and potentially cross-reactive immunity, which can then affect primary SARS-CoV-2 infection, as well as vaccination responses. The influence of pre-existing immunity from these hCoVs, as well as responses generated from original CoV2 strains or vaccines on the development of new high-affinity responses to CoV2 antigenic viral variants, needs to be better understood given the need for continuous vaccine adaptation and application in the population. Due in part to thymic involution, normal aging is associated with reduced naïve T cell compartments and impaired primary antigen responsiveness, resulting in a reliance on the pre-existing cross-reactive memory cell pool which may be of lower affinity, restricted in diversity, or of shorter duration. These effects can also be mediated by the presence of down-regulatory anti-idiotype responses which also increase in aging. Given the tremendous heterogeneity of clinical data, utilization of preclinical models offers the greatest ability to assess immune responses under a controlled setting. These models should now involve prior antigen/viral exposure combined with incorporation of modifying factors such as age on immune responses and effects. This will also allow for mechanistic dissection and understanding of the different immune pathways involved in both SARS-CoV-2 pathogen and potential vaccine responses over time and how pre-existing memory responses, including potential anti-idiotype responses, can affect efficacy as well as potential off-target effects in different tissues as well as modeling PASC.

Ferrets: A powerful model of SARS-CoV-2

The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in recent years not only caused a global pandemic but resulted in enormous social, economic, and health burdens worldwide. Despite considerable efforts to combat coronavirus disease 2019 (COVID-19), various SARS-CoV-2 variants have emerged, and their underlying mechanisms of pathogenicity remain largely unknown. Furthermore, effective therapeutic drugs are still under development. Thus, an ideal animal model is crucial for studying the pathogenesis of COVID-19 and for the preclinical evaluation of vaccines and antivirals against SARS-CoV-2 and variant infections. Currently, several animal models, including mice, hamsters, ferrets, and non-human primates (NHPs), have been established to study COVID-19. Among them, ferrets are naturally susceptible to SARS-CoV-2 infection and are considered suitable for COVID-19 study. Here, we summarize recent developments and application of SARS-CoV-2 ferret models in studies on pathogenesis, therapeutic agents, and vaccines, and provide a perspective on the role of these models in preventing COVID-19 spread.

Analysis of CT scan images for COVID-19 pneumonia based on a deep ensemble framework with DenseNet, Swin transformer, and RegNet

COVID-19 has caused enormous challenges to global economy and public health. The identification of patients with the COVID-19 infection by CT scan images helps prevent its pandemic. Manual screening COVID-19-related CT images spends a lot of time and resources. Artificial intelligence techniques including deep learning can effectively aid doctors and medical workers to screen the COVID-19 patients. In this study, we developed an ensemble deep learning framework, DeepDSR, by combining DenseNet, Swin transformer, and RegNet for COVID-19 image identification. First, we integrate three available COVID-19-related CT image datasets to one larger dataset. Second, we pretrain weights of DenseNet, Swin Transformer, and RegNet on the ImageNet dataset based on transformer learning. Third, we continue to train DenseNet, Swin Transformer, and RegNet on the integrated larger image dataset. Finally, the classification results are obtained by integrating results from the above three models and the soft voting approach. The proposed DeepDSR model is compared to three state-of-the-art deep learning models (EfficientNetV2, ResNet, and Vision transformer) and three individual models (DenseNet, Swin transformer, and RegNet) for binary classification and three-classification problems. The results show that DeepDSR computes the best precision of 0.9833, recall of 0.9895, accuracy of 0.9894, F1-score of 0.9864, AUC of 0.9991 and AUPR of 0.9986 under binary classification problem, and significantly outperforms other methods. Furthermore, DeepDSR obtains the best precision of 0.9740, recall of 0.9653, accuracy of 0.9737, and F1-score of 0.9695 under three-classification problem, further suggesting its powerful image identification ability. We anticipate that the proposed DeepDSR framework contributes to the diagnosis of COVID-19.

The roles of primary care doctors in the COVID-19 pandemic: consistency and influencing factors of doctor's perception and actions and nominal definitions

BACKGROUND

At the end of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic broke out. As front-line health professionals, primary care doctors play a significant role in screening SARS-CoV-2 infection and transferring suspected cases. However, the performance of primary care doctors is influenced by their knowledge and role perception. A web-based cross-sectional survey was conducted to assess the consistency and influencing factors of primary care doctor's role perception and expert advice in the guidelines (regulatory definition).

METHODS

We designed the questionnaire using "Wenjuanxing" platform, distributed and collected the questionnaire through WeChat social platform, and surveyed 1758 primary care doctors from 11 community health service stations, community health service centers and primary hospitals in Zhejiang Province, China. After the questionnaire was collected, descriptive statistics were made on the characteristics of participants, and univariate analysis and multivariate analysis were used to determine the relevant factors affecting their role cognition.

RESULTS

In the reporting and referral suspected cases and patients receiving treatment, most participants' cognition of their roles were consistent with the requirements of guidelines. However, 49.54% and 61.43% of participant doctors were not in line with the government guidelines for diagnosing and classifying COVID-19 and treating suspected cases, respectively. Having a middle or senior professional title and participating in front-line COVID-19 prevention and control work is beneficial to the accurate role perception of diagnosis and classification of COVID-19, the reporting and transfer of suspected cases, and the treatment of suspected cases.

CONCLUSIONS

Primary care doctors' role perceptions in the COVID-19 pandemic are not always consistent with government guidelines in some aspects, such as transferring and diagnosing suspected cases. Therefore, it is essential to guide primary care doctors in performing their duties, especially those with lower professional titles.