Comparative transcriptomic analyzes of human lung epithelial cells infected with wild-type SARS-CoV-2 and its variant with a 12-bp missing in the E gene

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus that caused a global outbreak of coronavirus disease 2019 (COVID-19) pandemic. To elucidate the mechanism of SARS-CoV-2 replication and immunogenicity, we performed a comparative transcriptome profile of mRNA and long non-coding RNAs (lncRNAs) in human lung epithelial cells infected with the SARS-CoV-2 wild-type strain (8X) and the variant with a 12-bp deletion in the E gene (F8). In total, 3,966 differentially expressed genes (DEGs) and 110 differentially expressed lncRNA (DE-lncRNA) candidates were identified. Of these, 94 DEGs and 32 DE-lncRNAs were found between samples infected with F8 and 8X. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyzes revealed that pathways such as the TNF signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved. Furthermore, we constructed a lncRNA-protein-coding gene co-expression interaction network. The KEGG analysis of the co-expressed genes showed that these differentially expressed lncRNAs were enriched in pathways related to the immune response, which might explain the different replication and immunogenicity properties of the 8X and F8 strains. These results provide a useful resource for studying the pathogenesis of SARS-CoV-2 variants.

Research on Detection and Early Warning Mechanism of Emergency Public Health Medical Education System Based on Internet of Things Technology.

Sudden public health and medical education events have tested the stability of society to a great extent. The government need to strengthen capacity building, make use of system dynamic supervision, warn public health events in advance, and minimize the impact scope and related harmfulness of events. This not only facilitates the rapid mobilization of resources by the later government but also facilitates the comprehensive and detailed deployment and arrangement of decision-makers. As we all know, the Internet of Things is used by all walks of life because of its outstanding advantages of low power consumption, low cost, and wide range. Therefore, this article takes the Internet of Things as the technical basis of the system. According to the actual demand and resource design, it includes two system functions: detection and early warning. The results show that: (1) considering the practical principle, the evaluation system interface found that the scores of font size and color style are all below 80%, which need to be optimized and adjusted; the overall interface basically meets the needs of users. (2) The throughput of the three methods is different. The CoAP-E has superior throughput. (3) With the increase in packet loss rate, the request success rate of the CoAP method decreases in a "drop" manner. The CoAP-E method in this article has the best performance. (4) When the packet loss rate is 25%, the network adaptability of this method is the strongest, and the retransmission rate is less than 18%; the CoAP method is as high as 35%. (5) When the number of concurrent requests is less than 2500, there is no obvious difference between the three methods; the optimal performance of the dynamic load balancing method is 10.1 s. (6) The system comprehensively considers various factors of five site selections. The highest comprehensive score of Final Site, 5 is 8.7, which can be used as the resettlement place of emergency rescue facilities. This article starts from the characteristics and needs of public emergencies, and the final set of the system runs well. It can quickly reflect public health emergencies and medical education events. Use the most effective system functions for risk control, and maximize the analysis, organization, and coordination of events. The follow-up optimization of system details needs to be studied.

Predicting the Hate: A GSTM Model based on COVID-19 Hate Speech Datasets

COVID-19 crisis has been accompanied by copious hate speeches widespread on social media. It reinforces the fragmentation of the world, resulting in more significant racial discrimination and distrust between people, leading to crimes, and injuring individuals spiritually or physically. Hate speech is hard to crack for a global recovery in the post-epidemic era. Conducting with Twitter datasets, this paper aims to find the key indicators that influence the trend of hate speech, then builds a Gaussian Spatio-Temporal Mixture (GSTM) model for trends prediction based on the pre-analysis. Findings show that in the early period, the participation of influential users is closely related to the emergence of sentiment peaks, and the interval time is around one week. After hate speech waves up, the indicator of total exposure becomes more critical, suggesting that grass-root release influences at this stage. Compared with three classical time-series predicting models, the GSTM model shows better peak prediction ability and lower residual mean. This work enriches the approaches of predicting unknown but foreseeable hate speeches accompanied by future pandemics.

Recurrence of Lymphedema and Related Characteristics in Discharged Breast Cancer Patients During The COVID-19 Pandemic: A Multicenter, Cross-Sectional Survey (preprint)

Background: To evaluate recurrence of lymphedema and its influencing factors in discharged breast cancer patients with treated lymphedema during the COVID-19 pandemic and to propose feasible improvements. Methods: A multicenter, cross-sectional, hospital-based survey of discharged breast cancer patients was conducted during the COVID-19 pandemic in eight first-class hospitals in Wuhan city, China. Norman Questionnaire was used for assessing lymphedema, and multivariable binary logistic regression was performed to risk factors of moderate or severe lymphedema. Difference in living characteristics, anxiety and depression between no or mild group and moderate or severe group were compared. Preference in management of lymphedema were collected. Results: 202 valid patients were included. 191 participants reported recurred lymphedema (prevalence: 94.6%, 95%CI 90.5% to 97.3%). 134 of them was mild and 57 were moderate/severe. In the 191 patients, the main symptom was swelling (140, 69.3%) and pain (56, 27.7%). Multivariable regression showed that age (odds ratio, 1.06, 95%CI: 1.02-1.10), radical surgery (OR=4.35, 95%CI: 1.54-12.50) and fully complete radiotherapy (OR=2.62, 95%CI: 1.17-5.87, P=0.019) was associated with the risk of moderate or severe lymphedema. No significant difference in lifestyles was observed but moderate or severe group experienced higher rate of anxiety and depression. patients preferred treatment in hospital and self-care at home equally. Conclusion: The high recurrence rate of lymphedema in breast cancer patients with during COVID-19 should draw our great attention, continuous efforts should be made to identify patient at risk of lymphedema and distribute feasible guidance and education for self-management for these patients.

Erratum: A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

[This corrects the article DOI: 10.1016/j.patter.2020.100092.].

Rapid Resolution of severe feline infectious peritonitis using natural products (preprint)

This is the first report of a successful treatment of a severe feline infectious peritonitis (FIP) case using Lianhuaqingwen (LH) capsule, a Chinese patent medicine （natural products）and it has been used for the treatment of coronavirus disease 2019. A 1-year-old cat was diagnosed FIP and presented with severe ascites, aphagia, inflammation, breathing difficulties and weight loss. Within 22 days of LH capsule treatment, all indicators of the cat eventually returned to normal, and the ascites gradually disappeared. This case report is the first description of the clinical recovery features of a cat with a systemic FCoV infection using LH capsules.

Multimodal feature-wise co-attention method for visual question answering

VQA attracts lots of researchers in recent years. It could be potentially applied to the remote consultation of COVID-19. Attention mechanisms provide an effective way of utilizing visual and question information selectively in visual question and answering (VQA). The attention methods of existing VQA models generally focus on spatial dimension. In other words, the attention is modeled as spatial probabilities that re-weights the image region or word token features. However, feature-wise attention cannot be ignored, as image and question representations are organized in both spatial and feature-wise modes. Taking the question “What is the color of the woman’s hair” for example, identifying the hair color attribute feature is as important as focusing on the hair region. In this paper, we propose a novel neural network module named “multimodal feature-wise attention module” (MulFA) to model the feature-wise attention. Extensive experiments show that MulFA is capable of filtering representations for feature refinement and leads to improved performance. By introducing MulFA modules, we construct an effective union feature-wise and spatial co-attention network (UFSCAN) model for VQA. Our evaluation on two large-scale VQA datasets, VQA 1.0 and VQA 2.0, shows that UFSCAN achieves performance competitive with state-of-the-art models.

Morphology and Molecular Characterization of a Fungus from the Alternaria alternataSpecies Complex Causing Black Spots on Pyrus sinkiangensis(Koerle pear)

A small-spored Alternariawas found from black spots of storaged Koerle pear (Pyrus sinkiangensis), one of the economically important fruit in Xinjiang province, China. The morphology is similar to A. limoniasperaebut obviously different in secondary conidiophores and conidial septa. A phylogenetic analysis using sequence datasets of ITS, GAPDH, TEF1, RPB2, Alt a1, OPA10–2, and EndoPG genes revealed that it belonged to the Alternaria alternatacomplex group. Pathogenicity tests illustrated that the fungus was the causal pathogen of black spot on Koerle pear fruit.

A Learning-Based Model to Evaluate Hospitalization Priority in COVID-19 Pandemics.

The emergence of the novel coronavirus disease 2019 (COVID-19) is placing an increasing burden on healthcare systems. Although the majority of infected patients experience non-severe symptoms and can be managed at home, some individuals develop severe symptoms and require hospital admission. Therefore, it is critical to efficiently assess the severity of COVID-19 and identify hospitalization priority with precision. In this respect, a four-variable assessment model, including lymphocyte, lactate dehydrogenase, C-reactive protein, and neutrophil, is established and validated using the XGBoost algorithm. This model is found to be effective in identifying severe COVID-19 cases on admission, with a sensitivity of 84.6%, a specificity of 84.6%, and an accuracy of 100% to predict the disease progression toward rapid deterioration. It also suggests that a computation-derived formula of clinical measures is practically applicable for healthcare administrators to distribute hospitalization resources to the most needed in epidemics and pandemics.

Clinical findings of 35 cases with novel coronavirus pneumonia outside of Wuhan (preprint)

Objective: We sought to investigate the clinical characteristics of patients with novel coronavirus pneumonia in Xiamen, which is a city in Fujian Province, China. Methods: From January 20, 2020, to March 4, 2020, the clinical characteristics of all patients confirmed with novel coronavirus pneumonia admitted to the First Affiliated Hospital of Xiamen University, which was the only designated hospital for cases of novel coronavirus pneumonia in Xiamen, were analyzed. Results: A total of 35 cases of novel coronavirus pneumonia were confirmed. Patients were divided into the general-ward admission group and the intensive-care-unit (ICU) admission group. The most commonly observed symptoms were fever (80%), cough (51%), expectoration (28%), and fatigue (23%). Compared with patients in the general-ward group, patients in the ICU group had a higher maximum body temperature, a longer duration of fever after admission, and more symptoms of systemic pain. Considering the laboratory findings, patients in the ICU group exhibited lower peripheral blood lymphocyte ratios and lymphocyte counts but higher C-reactive protein and lactate dehydrogenase levels. All patients were discharged without death, and no patients required invasive ventilation, continuous renal replacement therapy, or extracorporeal membrane oxygenation. Conclusion: Relative to patients with novel coronavirus pneumonia in Wuhan, the cases in Xiamen were less severe.