The value of artificial intelligence and imaging diagnosis in the fight against COVID-19.

The outbreak of the new type of coronavirus pneumonia (COVID-19) has caused a huge impact on the world. In this case, only by adhering to the prevention and control methods of early diagnosis, early isolation, and early treatment, can the spread of the virus be prevented to the greatest extent. This article uses artificial intelligence-assisted medical imaging diagnosis as the research object, combines artificial intelligence and CT medical imaging diagnosis, introduces an intelligent COVID-19 detection system, and uses it to achieve COVID-19 disease screening and lesion evaluation. CT examination has the advantages of fast speed and high accuracy, which can provide a favorable basis for clinical diagnosis. This article collected 32 lung CT scan images of patients with confirmed COVID-19. Two professional radiologists analyzed the CT images using traditional imaging diagnostic methods and artificial intelligence-assisted imaging diagnostic methods, and the comparison showed the gap between the two methods. According to experiments, CT imaging diagnosis assisted by artificial intelligence only takes 0.744 min on average, which can save a lot of time and cost compared with the average time of 3.623 min for conventional diagnosis. In terms of comprehensive test accuracy, it can be concluded that the combination of artificial intelligence and imaging diagnosis has extremely high application value in COVID-19 diagnosis.

A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy

Recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide. While COVID-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system. Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disorder. Studies have shown that HCM patients with COVID-19 have a higher mortality rate;however, the reason for this phenomenon is not yet elucidated. Herein, we conducted transcriptomic analyses to identify shared biomarkers between HCM and COVID-19 to bridge this knowledge gap. Differentially expressed genes (DEGs) were obtained using the Gene Expression Omnibus ribonucleic acid (RNA) sequencing datasets, GSE147507 and GSE89714, to identify shared pathways and potential drug candidates. We discovered 30 DEGs that were common between these two datasets. Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules. We discovered that HCM is linked to COVID-19 progression based on a functional analysis under ontology terms. Based on the DEGs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and microRNAs was also discovered. Lastly, our research suggests that the potential drugs we identified might be helpful for COVID-19 therapy.

Carbon dots for effective photodynamic inactivation of virus.

The antiviral function of carbon dots (CDots) with visible light exposure was evaluated, for which the model bacteriophages MS2 as a surrogate of small RNA viruses were used. The results show clearly that the visible light-activated CDots are highly effective in diminishing the infectivity of MS2 in both low and high titer samples to the host E. coli cells, and the antiviral effects are dot concentration- and treatment time-dependent. The action of CDots apparently causes no significant damage to the structural integrity and morphology of the MS2 phage or the breakdown of the capsid proteins, but does result in the protein carbonylation (a commonly used indicator for protein oxidation) and the degradation of viral genomic RNA. Mechanistically the results may be understood in the framework of photodynamic effects that are associated with the unique excited state properties and processes of CDots. Opportunities for potentially broad applications of CDots coupled with visible/natural light in the prevention and control of viral transmission and spread are highlighted and discussed.

Interventions to reduce post-traumatic stress disorder symptoms in health care professionals from 2011 to 2021: a scoping review.

OBJECTIVES: This scoping review aimed to describe available interventions for decreasing (post-traumatic stress disorder) PTSD symptoms among healthcare professionals in hospital care. METHOD: A scoping review was conducted following Arksey and O'Malley's framework. PubMed, EMBASE, Cochrane Library, CINAHL, PsycINFO, Web of Science, Scopus and ProQuest were searched for original research published in English from 2011 to 2021, on 8 July 2021. We included studies that described interventions that focused on reducing the PTSD symptoms of healthcare professionals. A narrative synthesis was adopted to synthesise the data. RESULTS: A total of eight studies out of 2558 articles were identified. Six used a quantitative study design and two adopted qualitative methods. cognitive behavioural therapy and mindfulness-based interventions were the most commonly adopted. Most studies used a combination of different intervention strategies. Trauma-related knowledge, emotion regulation and relaxation skill training, and psychological support from peers and psychologists were three core intervention components. The duration ranged from 2 weeks to 6 months. Healthcare professionals who participated in training programmes reported both positive experiences and suggestions for the improvement of PTSD-reducing interventions in their qualitative feedback. CONCLUSIONS: The scoping review provides a practical summary of the intervention characteristics for reducing the PTSD symptoms of healthcare professionals. Hospitals and managers could use the overview of interventions to assist healthcare professionals with PTSD symptoms. More research investigating the effects of PTSD symptom-reducing interventions for healthcare professionals with appropriate follow-up assessments is needed in the future.

Emerging antibody-based therapeutics against SARS-CoV-2 during the global pandemic.

SARS-CoV-2 antibody therapeutics are being evaluated in clinical and preclinical stages. As of 11 October 2020, 13 human monoclonal antibodies targeting the SARS-CoV-2 spike protein have entered clinical trials with three (REGN-COV2, LY3819253/LY-CoV555, and VIR-7831/VIR-7832) in phase 3. On 9 November 2020, the US Food and Drug Administration issued an emergency use authorization for bamlanivimab (LY3819253/LY-CoV555) for the treatment of mild-to-moderate COVID-19. This review outlines the development of neutralizing antibodies against SARS-CoV-2, with a focus on discussing various antibody discovery strategies (animal immunization, phage display and B cell cloning), describing binding epitopes and comparing neutralizing activities. Broad-neutralizing antibodies targeting the spike proteins of SARS-CoV-2 and SARS-CoV might be helpful for treating COVID-19 and future infections. VIR-7831/7832 based on S309 is the only antibody in late clinical development, which can neutralize both SARS-CoV-2 and SARS-CoV although it does not directly block virus receptor binding. Thus far, the only cross-neutralizing antibody that is also a receptor binding blocker is nanobody VHH-72. The feasibility of developing nanobodies as inhaled drugs for treating COVID-19 and other respiratory diseases is an attractive idea that is worth exploring and testing. A cocktail strategy such as REGN-COV2, or engineered multivalent and multispecific molecules, combining two or more antibodies might improve the efficacy and protect against resistance due to virus escape mutants. Besides the receptor-binding domain, other viral antigens such as the S2 subunit of the spike protein and the viral attachment sites such as heparan sulfate proteoglycans that are on the host cells are worth investigating.