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1.
Nat Commun ; 13(1): 1444, 2022 03 17.
Article in English | MEDLINE | ID: covidwho-1751716

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection continues to have devastating consequences worldwide. Recently, great efforts have been made to identify SARS-CoV-2 host factors, but the regulatory mechanisms of these host molecules, as well as the virus per se, remain elusive. Here we report a role of RNA G-quadruplex (RG4) in SARS-CoV-2 infection. Combining bioinformatics, biochemical and biophysical assays, we demonstrate the presence of RG4s in both SARS-CoV-2 genome and host factors. The biological and pathological importance of these RG4s is then exemplified by a canonical 3-quartet RG4 within Tmprss2, which can inhibit Tmprss2 translation and prevent SARS-CoV-2 entry. Intriguingly, G-quadruplex (G4)-specific stabilizers attenuate SARS-CoV-2 infection in pseudovirus cell systems and mouse models. Consistently, the protein level of TMPRSS2 is increased in lungs of COVID-19 patients. Our findings reveal a previously unknown mechanism underlying SARS-CoV-2 infection and suggest RG4 as a potential target for COVID-19 prevention and treatment.


Subject(s)
COVID-19 , Virus Internalization , Animals , Humans , Mice , RNA , SARS-CoV-2 , Serine Endopeptidases/genetics
3.
Med Image Anal ; 69: 101975, 2021 04.
Article in English | MEDLINE | ID: covidwho-1039485

ABSTRACT

The outbreak of COVID-19 around the world has caused great pressure to the health care system, and many efforts have been devoted to artificial intelligence (AI)-based analysis of CT and chest X-ray images to help alleviate the shortage of radiologists and improve the diagnosis efficiency. However, only a few works focus on AI-based lung ultrasound (LUS) analysis in spite of its significant role in COVID-19. In this work, we aim to propose a novel method for severity assessment of COVID-19 patients from LUS and clinical information. Great challenges exist regarding the heterogeneous data, multi-modality information, and highly nonlinear mapping. To overcome these challenges, we first propose a dual-level supervised multiple instance learning module (DSA-MIL) to effectively combine the zone-level representations into patient-level representations. Then a novel modality alignment contrastive learning module (MA-CLR) is presented to combine representations of the two modalities, LUS and clinical information, by matching the two spaces while keeping the discriminative features. To train the nonlinear mapping, a staged representation transfer (SRT) strategy is introduced to maximumly leverage the semantic and discriminative information from the training data. We trained the model with LUS data of 233 patients, and validated it with 80 patients. Our method can effectively combine the two modalities and achieve accuracy of 75.0% for 4-level patient severity assessment, and 87.5% for the binary severe/non-severe identification. Besides, our method also provides interpretation of the severity assessment by grading each of the lung zone (with accuracy of 85.28%) and identifying the pathological patterns of each lung zone. Our method has a great potential in real clinical practice for COVID-19 patients, especially for pregnant women and children, in aspects of progress monitoring, prognosis stratification, and patient management.


Subject(s)
COVID-19/diagnostic imaging , Lung/diagnostic imaging , Adolescent , Adult , Aged , Aged, 80 and over , Female , Humans , Machine Learning , Male , Middle Aged , SARS-CoV-2 , Severity of Illness Index , Tomography, X-Ray Computed , Ultrasonography , Young Adult
4.
Crit Care ; 24(1): 700, 2020 12 22.
Article in English | MEDLINE | ID: covidwho-992530

ABSTRACT

BACKGROUND: Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19. METHOD: The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated. RESULTS: The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3-10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI] 1.02 ~ 1.08; P < 0.001; Akaike information criterion [AIC] = 272; C-index = 0.903) or as a categorical variable (HR 10.76, 95% CI 2.75 ~ 42.05; P = 0.001; AIC = 272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC = 286; C-index = 0.866). An LUS score cut-off > 12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively. CONCLUSIONS: The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.


Subject(s)
COVID-19/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Point-of-Care Systems , Respiratory Distress Syndrome/diagnostic imaging , Ultrasonography/methods , Adult , Aged , COVID-19/mortality , Female , Hospital Mortality , Hospitalization , Humans , Male , Middle Aged , Pneumonia, Viral/mortality , Pneumonia, Viral/virology , Prognosis , Prospective Studies , Respiratory Distress Syndrome/mortality , Respiratory Distress Syndrome/virology , SARS-CoV-2 , Time-to-Treatment , Tomography, X-Ray Computed
6.
J Clin Nurs ; 29(21-22): 4270-4280, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-719388

ABSTRACT

AIM AND OBJECTIVE: To explore nurses' experiences regarding shift patterns while providing front-line care for COVID-19 patients in isolation wards of hospitals in Shanghai and Wuhan during the novel coronavirus pandemic. Our findings will help to optimise shift work scheduling, use the existing nursing workforce more efficiently and improve nursing quality. BACKGROUND: Nurses are one of the main professionals fighting against COVID-19. Providing care for COVID-19 patients is challenging. In isolation wards, the workload has increased, and the workflow and shift patterns are completely different from the usual. More importantly, there is a shortage of nurses. Therefore, it is essential and urgent to arrange nurses' shifts correctly and use the existing workforce resources efficiently. DESIGN: A qualitative descriptive study of 14 nurses in Chinese hospitals was conducted. METHODS: Semi-structured interviews were used based on the phenomenological research method; data were analysed using Colaizzi's method of data analysis. This study aligns with the COREQ checklist. RESULTS: Four themes were extracted: assess the competency of nurses to assign nursing work scientifically and reasonably, reorganise nursing workflow to optimise shift patterns, communicate between managers and front-line nurses to humanise shift patterns, and nurses' various feelings and views on shift patterns. CONCLUSION: It is necessary to arrange shift patterns scientifically and allocate workforce rationally to optimise nursing workforce allocation, reduce nurses' workload, improve nursing quality and promote physical and mental health among nurses during the COVID-19 pandemic. RELEVANCE TO CLINICAL PRACTICE: This study emphasised nurses' experiences on shift patterns in isolation wards, providing useful information to manage shift patterns. Nursing managers should arrange shifts scientifically, allocate nursing workforce rationally, formulate emergency plans and establish emergency response rosters during the COVID-19 pandemic.


Subject(s)
COVID-19/nursing , Nursing Staff, Hospital/organization & administration , Personnel Staffing and Scheduling/organization & administration , Workflow , Adult , China , Female , Humans , Middle Aged , Pandemics , Qualitative Research , SARS-CoV-2 , Workload/psychology
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