A new perspective on the potential application of RIPK1 in the treatment of sepsis.

RIPK1 is a global cellular sensor that can determine the survival of cells. Generally, RIPK1 can induce cell apoptosis and necroptosis through TNF, Fas and lipopolysaccharide stimulation, while its scaffold function can sense the fluctuation of cellular energy and promote cell survival. Sepsis is a nonspecific disease that seriously threatens human health. There is some dispute in the literature about the role of RIPK1 in sepsis. In this review, the authors attempt to comprehensively discuss the differential results for RIPK1 in sepsis by summarizing the underlying molecular mechanism and putting forward a tentative idea as to whether RIPK1 can serve as a biomarker for the monitoring of treatment and progression in sepsis.

Sepsis is a syndrome that poses a serious threat to human life and health and is classified as a medical emergency by the WHO. RIPK1 can regulate the onset of apoptosis and necrosis in several ways and is known as a sensor of cell survival status. A series of clinical trials of RIPK1 drugs has been conducted this year and have demonstrated promising efficacy in inflammatory diseases, in particular. In this paper, the authors summarize recent studies on the function and mechanism of RIPK1 in sepsis and combine them with the progress in RIPK1 drug development to provide information for the study of RIPK1 in sepsis.

The Relationship Between Personality Traits and Clinical Decision-Making, Anxiety and Stress Among Intern Nursing Students During COVID-19: A Cross-Sectional Study.

Purpose: The aim of this study was to investigate whether the personality traits of intern nursing students could serve as valid predictors of their psychological status and clinical decision making. Additionally, we aimed to understand the psychological state of intern nursing students during the regular epidemic prevention and control stage of COVID-19. Participants and Methods: This study was designed as a cross-sectional survey. A total of 181 intern nursing students involved in clinical placements participated in this study. Participants provided relevant data by completing the Big Five Inventory-44, the Self-Rating Anxiety Scale, the Perceived Stress Scale 14, and the Clinical Decision-Making in Nursing Scale. Results: The results showed that neuroticism (ß = -0.282, p < 0.01) and openness (ß = 0.302, p < 0.001) played significant roles in predicting clinical decision-making skills among intern nursing students. Regression analysis also showed extraversion (ß = -0.249, p < 0.01), openness (ß = 0.2, p < 0.01), and neuroticism (ß = 0.391, p < 0.001) could significantly predict stress in intern nursing students. The agreeableness (ß = -0.354, p < 0.001) and neuroticism (ß = 0.237, p < 0.01) could also predict the anxiety of intern nursing students. Additionally, some intern nursing students still suffered from anxiety and stress in the context of the ongoing pandemic. Conclusion: Personality traits are good predictors of clinical decision-making, anxiety and stress among intern nursing students. In conclusion, the openness in personality traits of intern nursing students should be valued and cultivated in clinical work, which will benefit the development of nursing talents.

Uncertainty Estimation With Neural Processes for Meta-Continual Learning.

The ability to evaluate uncertainties in evolving data streams has become equally, if not more, crucial than building a static predictor. For instance, during the pandemic, a model should consider possible uncertainties such as governmental policies, meteorological features, and vaccination schedules. Neural process families (NPFs) have recently shone a light on predicting such uncertainties by bridging Gaussian processes (GPs) and neural networks (NNs). Their abilities to output average predictions and the acceptable variances, i.e., uncertainties, made them suitable for predictions with insufficient data, such as meta-learning or few-shot learning. However, existing models have not addressed continual learning which imposes a stricter constraint on the data access. Regarding this, we introduce a member meta-continual learning with neural process (MCLNP) for uncertainty estimation. We enable two levels of uncertainty estimations: the local uncertainty on certain points and the global uncertainty p(z) that represents the function evolution in dynamic environments. To facilitate continual learning, we hypothesize that the previous knowledge can be applied to the current task, hence adopt a coreset as a memory buffer to alleviate catastrophic forgetting. The relationships between the degree of global uncertainties with the intratask diversity and model complexity are discussed. We have estimated prediction uncertainties with multiple evolving types including abrupt/gradual/recurrent shifts. The applications encompass meta-continual learning in the 1-D, 2-D datasets, and a novel spatial-temporal COVID dataset. The results show that our method outperforms the baselines on the likelihood and can rebound quickly even for heavily evolved data streams.

Antibodies from primary humoral responses modulate the recruitment of naive B cells during secondary responses.

Vaccines generate high-affinity antibodies by recruiting antigen-specific B cells to germinal centers (GCs), but the mechanisms governing the recruitment to GCs on secondary challenges remain unclear. Here, using preclinical SARS-CoV and HIV mouse models, we demonstrated that the antibodies elicited during primary humoral responses shaped the naive B cell recruitment to GCs during secondary exposures. The antibodies from primary responses could either enhance or, conversely, restrict the GC participation of naive B cells: broad-binding, low-affinity, and low-titer antibodies enhanced recruitment, whereas, by contrast, the high titers of high-affinity, mono-epitope-specific antibodies attenuated cognate naive B cell recruitment. Thus, the directionality and intensity of that effect was determined by antibody concentration, affinity, and epitope specificity. Circulating antibodies can, therefore, be important determinants of antigen immunogenicity. Future vaccines may need to overcome-or could, alternatively, leverage-the effects of circulating primary antibodies on subsequent naive B cell recruitment.

Visits to the emergency department by community-dwelling people with dementia during the first 2 waves of the COVID-19 pandemic in Ontario: a repeated cross-sectional analysis.

BACKGROUND: Community-dwelling people with dementia have been affected by COVID-19 pandemic health risks and control measures that resulted in worsened access to health care and service cancellation. One critical access point in health systems is the emergency department. We aimed to determine the change in weekly rates of visits to the emergency department of community-dwelling people with dementia in Ontario during the first 2 waves of the COVID-19 pandemic compared with historical patterns. METHODS: We conducted a population-based repeated cross-sectional study and used health administrative databases to compare rates of visits to the emergency department among community-dwelling people with dementia who were aged 40 years and older in Ontario during the first 2 waves of the COVID-19 pandemic (March 2020-February 2021) with the rates of a historical period (March 2019-February 2020). Weekly rates of visits to the emergency department were evaluated overall, by urgency and by chapter from the International Statistical Classification of Diseases and Related Health Problems, 10th Revision. We used Poisson models to compare pandemic and historical rates at the week of the lowest rate during the pandemic period and the latest week. RESULTS: We observed large immediate declines in rates of visits to the emergency department during the COVID-19 pandemic (rate ratio [RR] 0.50, 95% confidence interval [CI] 0.47-0.53), which remained below historical levels by the end of the second wave (RR 0.88, 95% CI 0.83-0.92). Rates of both nonurgent (RR 0.33, 95% CI 0.28-0.39) and urgent (RR 0.51, 95% CI 0.48-0.55) visits to the emergency department also declined and remained low (RR 0.68, 95% CI 0.59-0.79, RR 0.91, 95% CI 0.86-0.96), respectively. Visits for injuries, and circulatory, respiratory and musculoskeletal diseases declined and remained below historical levels. INTERPRETATION: Prolonged reductions in visits to the emergency department among people with dementia during the first 2 pandemic waves raise concerns about patients who delay seeking acute care services. Understanding the long-term effects of these reductions requires further research.

The risk of death or unplanned readmission after discharge from a COVID-19 hospitalization in Alberta and Ontario.

BACKGROUND: The frequency of readmissions after COVID-19 hospitalizations is uncertain, as is whether current readmission prediction equations are useful for discharge risk stratification of COVID-19 survivors or for comparing among hospitals. We sought to determine the frequency and predictors of death or unplanned readmission after a COVID-19 hospital discharge. METHODS: We conducted a retrospective cohort study of all adults (≥ 18 yr) who were discharged alive from hospital after a nonpsychiatric, nonobstetric, acute care admission for COVID-19 between Jan. 1, 2020, and Sept. 30, 2021, in Alberta and Ontario. RESULTS: Of 843 737 individuals who tested positive for SARS-CoV-2 by reverse transcription polymerase chain reaction during the study period, 46 412 (5.5%) were adults admitted to hospital within 14 days of their positive test. Of these, 8496 died in hospital and 34 846 were discharged alive (30 336 discharged after an index admission of ≤ 30 d and 4510 discharged after an admission > 30 d). One in 9 discharged patients died or were readmitted within 30 days after discharge (3173 [10.5%] of those with stay ≤ 30 d and 579 [12.8%] of those with stay > 30 d). The LACE score (length of stay, acuity, Charlson Comorbidity Index and number of emergency visits in previous 6 months) for predicting urgent readmission or death within 30 days had a c-statistic of 0.60 in Alberta and 0.61 in Ontario; inclusion of sex, discharge locale, deprivation index and teaching hospital status in the model improved the c-statistic to 0.73. INTERPRETATION: Death or readmission after discharge from a COVID-19 hospitalization is common and had a similar frequency in Alberta and Ontario. Risk stratification and interinstitutional comparisons of outcomes after hospital admission for COVID-19 should include sex, discharge locale and socioeconomic measures, in addition to the LACE variables.

Trends in Health Service Use for Canadian Adults With Dementia and Parkinson Disease During the First Wave of the COVID-19 Pandemic.

Importance: Persons with dementia and Parkinson disease (PD) are vulnerable to disruptions in health care and services. Objective: To examine changes in health service use among community-dwelling persons with dementia, persons with PD, and older adults without neurodegenerative disease during the first wave of the COVID-19 pandemic. Design Setting and Participants: Repeated cross-sectional analysis using population-based administrative data among community-dwelling persons with dementia, persons with PD, and adults 65 years and older at the start of each week from March 1 through the week of September 20, 2020 (pandemic period), and March 3 through the week of September 22, 2019 (historical period), in Ontario, Canada. Exposures: COVID-19 pandemic as of March 1, 2020. Main Outcomes and Measures: Main outcomes were weekly rates of emergency department visits, hospitalizations, nursing home admissions, home care, virtual and in-person physician visits, and all-cause mortality. Poisson regression models were used to calculate weekly rate ratios (RRs) with 95% CIs comparing pandemic weeks with historical levels. Results: Among those living in the community as of March 1, 2020, persons with dementia (n = 131 466; mean [SD] age, 80.1 [10.1] years) were older than persons with PD (n = 30 606; 73.7 [10.2] years) and older adults (n = 2 363 742; 74.0 [7.1] years). While all services experienced declines, the largest drops occurred in nursing home admissions (RR for dementia: 0.10; 95% CI, 0.07-0.15; RR for PD: 0.03; 95% CI, 0.00-0.21; RR for older adults: 0.11; 95% CI, 0.06-0.18) and emergency department visits (RR for dementia: 0.45; 95% CI, 0.41-0.48; RR for PD: 0.40; 95% CI, 0.34-0.48; RR for older adults: 0.45; 95% CI, 0.44-0.47). After the first wave, most services returned to historical levels except physician visits, which remained elevated (RR for dementia: 1.07; 95% CI, 1.05-1.09; RR for PD: 1.10, 95% CI, 1.06-1.13) and shifted toward virtual visits. Older adults continued to experience lower hospitalizations. All-cause mortality was elevated across cohorts. Conclusions and Relevance: In this population-based repeated cross-sectional study in Ontario, Canada, those with dementia, those with PD, and older adults sought hospital care far less than usual, were not admitted to nursing homes, and experienced excess mortality during the first wave of the pandemic. Most services returned to historical levels, but virtual physician visits remained a feature of care. While issues of equity and quality of care are still emerging among persons with neurodegenerative diseases, policies to support virtual care are necessary.

Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning.

BACKGROUND: SARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection. METHODS: We linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada. Using ensemble-based algorithms, we examined 484 factors, including individual-level demographics, healthcare use, comorbidities, functional status, and laboratory results; and community-level characteristics to identify factors predictive of infection. Analyses were performed separately for January to April (early wave 1) and May to August (late wave 1). FINDINGS: Among 80,784 LTC residents, 64,757 (80.2%) were tested for SARS-Cov-2 (median age 86 (78-91) years, 30.6% male), of whom 10.2% of 33,519 and 5.2% of 31,238 tested positive in early and late wave 1, respectively. In the late phase (when restriction of visitors, closure of communal spaces, and universal masking in LTC were routine), regional-level characteristics comprised 33 of the top 50 factors associated with testing positive, while laboratory values and comorbidities were also predictive. The c-index of the final model was 0.934, and sensitivity was 0.887. In the highest versus lowest risk quartiles, the odds ratio for infection was 114.3 (95% CI 38.6-557.3). LTC-related geographic variations existed in the distribution of observed infection rates and the proportion of residents at highest risk. INTERPRETATION: Machine learning informed evaluation of predicted and observed risks of SARS-CoV-2 infection at the resident and LTC levels, and may inform initiatives to improve care quality in this setting. FUNDING: Funded by a Canadian Institutes of Health Research, COVID-19 Rapid Research Funding Opportunity grant (# VR4 172736) and a Peter Munk Cardiac Centre Innovation Grant. Dr. D. Lee is the Ted Rogers Chair in Heart Function Outcomes, University Health Network, University of Toronto. Dr. Austin is supported by a Mid-Career investigator award from the Heart and Stroke Foundation. Dr. McAlister is supported by an Alberta Health Services Chair in Cardiovascular Outcomes Research. Dr. Kaul is the CIHR Sex and Gender Science Chair and the Heart & Stroke Chair in Cardiovascular Research. Dr. Rochon holds the RTO/ERO Chair in Geriatric Medicine from the University of Toronto. Dr. B. Wang holds a CIFAR AI chair at the Vector Institute.

The impact of the first wave of the COVID-19 pandemic on health service use by persons with dementia in Ontario, Canada: A population-based time series analysis

Background Little has been quantified, at a population-level, about the magnitude of heath service disruption to persons living with dementia in community settings during the COVID-19 pandemic. Sustained access to health care services is particularly important for persons with dementia and other neurodegenerative diseases as they are vulnerable to decline. Method Health administrative data from Ontario, Canada were used to examine patterns of health service use among all persons with Alzheimer disease and related dementias (dementia) who were alive and living in the community. This cohort was compared to persons with Parkinson?s disease (PD) as well as all older adults (age 65+ years) without neurodegenerative diseases. Rates of all-cause hospital admissions, emergency department visits, primary care and specialist physician visits and home care visits were analyzed for all individuals alive and eligible for provincial health insurance at the start of each weekly period from March 1, 2020 to September 20, 2020 (pandemic period) and from March 3, 2019 to September 22, 2019 (pre-pandemic period). Rates of health service use during specific weeks in the pandemic period (i.e., lowest week, last available week) were compared to corresponding weeks in the pre-pandemic period within each cohort using percent changes. Results On March 1, 2020, 128,696 persons with dementia, 30,099 with PD and 2,460,358 older adults were eligible for provincial health services. Across cohorts and services, dramatic declines in use of health services were observed at the lowest week: hospitalization (-38.7% dementia, -72.3% PD, -44.2% older adults);emergency department (-54.9% dementia, -57.7% PD, -53.6% older adults);home care (-14.8% dementia, -19.4% PD, -7.4% older adults). Health services varied in how quickly they rebounded to pre-pandemic levels within cohorts;notably, by the end of the study period, emergency department visits had increased to a level higher than corresponding 2019 weekly rates (24.2% dementia, 15.2% PD, 7.4% older adults). Conclusions The first wave of the COVID-19 pandemic meaningfully and immediately disrupted use of health care services for persons living with dementia and PD and may have resulted in long-term consequences that should be monitored.

Analysis of Psychological Trends and Policy Recommendations of Medical Staff in Northern China in the Latter Stages of the COVID-19 Pandemic.

Aim: Since the 2019 coronavirus disease (COVID-19) outbreak, medical staff have faced greater psychological stress and are prone to psychological problems such as anxiety and depression, as confirmed by several studies. This study further clarifies the psychological status of Chinese medical staff during the stable phase of the pandemic through a cross-sectional investigation in a large population sample in northern China. Methods: Subjects: Clinical frontline medical staff from seven hospitals in Liaoning Province were recruited from November 2020 to February 2021. Research Tools: The research tools used were the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), Simplified Coping Style Questionnaire (SCSQ), and General Status Questionnaire. Statistical Analysis: SPSS 22.0, ANOVA variance analysis, and multiple logistics regression were used for statistical analysis. P-values of <0.05 indicated significant statistical differences. Results: A total of 3,144 medical staff completed the survey (599 men [19.1%] and 2,545 women [80.9%]; 1,020 doctors [32.4%] and 2,124 nurses [67.6%]). Among all subjects, the rates of anxiety and depression were 21.1% (663/3, 144) and 43.9% (1,381/3,144), respectively. Multiple logistic comparative analysis revealed that age (OR = 1.272, 95% CI = 1.036-1.561, P = 0.022), the need for psychological counseling (OR = 1.566, 95% CI = 1.339-1.830, P < 0.001), and the coexistence of depression (OR = 0.050, 95% CI = 0.038-0.066, P < 0.001) were significantly associated with anxiety. Coexisting anxiety was also associated with the occurrence of depression (OR = 0.050, 95% CI = 0.038-0.065, P < 0.001). Conclusions: In the later stages of the pandemic in China, the occurrence rates of anxiety and depression among medical staff remain high. In addition to age, there is little correlation between anxiety or depression and general factors such as gender and profession. As a special group, medical staff show different psychological changes at various times during a stressful event. Concerning for the psychological needs of medical staff and different psychologically oriented policy implementation are needed.

Statins and SARS-CoV-2 Infection: Results of a Population-Based Prospective Cohort Study of 469 749 Adults From 2 Canadian Provinces.

Background Small observational studies have suggested that statin users have a lower risk of dying with COVID-19. We tested this hypothesis in a large, population-based cohort of adults in 2 of Canada's most populous provinces: Ontario and Alberta. Methods and Results We examined reverse transcriptase-polymerase chain reaction swab positivity rates for SARS-CoV-2 in adults using statins compared with nonusers. In patients with SARS-CoV-2 infection, we compared 30-day risk of all-cause emergency department visit, hospitalization, intensive care unit admission, or death in statin users versus nonusers, adjusting for baseline differences in demographics, clinical comorbidities, and prior health care use, as well as propensity for statin use. Between January and June 2020, 2.4% of 226 142 tested individuals aged 18 to 65 years, 2.7% of 88 387 people aged 66 to 75 years, and 4.1% of 154 950 people older than 75 years had a positive reverse transcriptase-polymerase chain reaction swab for SARS-CoV-2. Compared with 353 878 nonusers, the 115 871 statin users were more likely to test positive for SARS-CoV-2 (3.6% versus 2.8%, P<0.001), but this difference was not significant after adjustment for baseline differences and propensity for statin use in each age stratum (adjusted odds ratio 1.00 [95% CI, 0.88-1.14], 1.00 [0.91-1.09], and 1.06 [0.82-1.38], respectively). In individuals younger than 75 years with SARS-CoV-2 infection, statin users were more likely to visit an emergency department, be hospitalized, be admitted to the intensive care unit, or to die of any cause within 30 days of their positive swab result than nonusers, but none of these associations were significant after multivariable adjustment. In individuals older than 75 years with SARS-CoV-2, statin users were more likely to visit an emergency department (28.2% versus 17.9%, adjusted odds ratio 1.41 [1.23-1.61]) or be hospitalized (32.7% versus 21.9%, adjusted odds ratio 1.19 [1.05-1.36]), but were less likely to die (26.9% versus 31.3%, adjusted odds ratio 0.76 [0.67-0.86]) of any cause within 30 days of their positive swab result than nonusers. Conclusions Compared with statin nonusers, patients taking statins exhibit the same risk of testing positive for SARS-CoV-2 and those younger than 75 years exhibit similar outcomes within 30 days of a positive test. Patients older than 75 years with a positive SARS-CoV-2 test and who were taking statins had more emergency department visits and hospitalizations, but exhibited lower 30-day all-cause mortality risk.

Impact of the COVID-19 Pandemic on Home Care Services Among Community-Dwelling Adults With Dementia.

OBJECTIVE: To examine how the COVID-19 pandemic impacted use of home care services for individuals with dementia across service types and sociodemographic strata. DESIGN: Population-based time series analysis. SETTING AND PARTICIPANTS: Community-dwelling adults with dementia in Ontario, Canada, from January 2019 to September 2020. METHODS: We used health administrative databases (Ontario Registered Persons Database and Home Care Database) to measure home care services used by participants. Poisson regression models were fit to compare weekly rates of home care services during the pandemic to historical trends with rate ratios (RRs) and 95% confidence intervals (CIs) stratified by service type (nursing, personal care, therapy), sex, rurality, and neighborhood income quintile. RESULTS: During the first wave of the pandemic, personal care fell by 16% compared to historical levels (RR 0.84, 95% CI 0.84, 0.85) and therapies fell by 50% (RR 0.50, 95% CI 0.48, 0.52), whereas nursing did not significantly decline (RR 1.02, 95% CI 1.00, 1.04). All rates had recovered by September 2020, with nursing and therapies higher than historical levels. Changes in services were largely consistent across sociodemographic strata, although the rural population experienced a larger decline in personal care and smaller rebound in nursing. CONCLUSIONS AND IMPLICATIONS: Personal care and therapies for individuals with dementia were interrupted during the early months of the pandemic, whereas nursing was only minimally impacted. Pandemic responses with the potential to disrupt home care for individuals living with dementia must balance the impacts on individuals with dementia, caregivers, and providers.

Predictors of mortality among long-term care residents with SARS-CoV-2 infection.

BACKGROUND: While individuals living in long-term care (LTC) homes have experienced adverse outcomes of SARS-CoV-2 infection, few studies have examined a broad range of predictors of 30-day mortality in this population. METHODS: We studied residents living in LTC homes in Ontario, Canada, who underwent PCR testing for SARS-CoV-2 infection from January 1 to August 31, 2020, and examined predictors of all-cause death within 30 days after a positive test for SARS-CoV-2. We examined a broad range of risk factor categories including demographics, comorbidities, functional status, laboratory tests, and characteristics of the LTC facility and surrounding community were examined. In total, 304 potential predictors were evaluated for their association with mortality using machine learning (Random Forest). RESULTS: A total of 64,733 residents of LTC, median age 86 (78, 91) years (31.8% men), underwent SARS-CoV-2 testing, of whom 5029 (7.8%) tested positive. Thirty-day mortality rates were 28.7% (1442 deaths) after a positive test. Of 59,702 residents who tested negative, 2652 (4.4%) died within 30 days of testing. Predictors of mortality after SARS-CoV-2 infection included age, functional status (e.g., activity of daily living score and pressure ulcer risk), male sex, undernutrition, dehydration risk, prior hospital contacts for respiratory illness, and duration of comorbidities (e.g., heart failure, COPD). Lower GFR, hemoglobin concentration, lymphocyte count, and serum albumin were associated with higher mortality. After combining all covariates to generate a risk index, mortality rate in the highest risk quartile was 48.3% compared with 7% in the first quartile (odds ratio 12.42, 95%CI: 6.67, 22.80, p < 0.001). Deaths continued to increase rapidly for 15 days after the positive test. CONCLUSIONS: LTC residents, particularly those with reduced functional status, comorbidities, and abnormalities on routine laboratory tests, are at high risk for mortality after SARS-CoV-2 infection. Recognizing high-risk residents in LTC may enhance institution of appropriate preventative measures.

Multi-window back-projection residual networks for reconstructing COVID-19 CT super-resolution images.

BACKGROUND AND OBJECTIVE: With the increasing problem of coronavirus disease 2019 (COVID-19) in the world, improving the image resolution of COVID-19 computed tomography (CT) becomes a very important task. At present, single-image super-resolution (SISR) models based on convolutional neural networks (CNN) generally have problems such as the loss of high-frequency information and the large size of the model due to the deep network structure. METHODS: In this work, we propose an optimization model based on multi-window back-projection residual network (MWSR), which outperforms most of the state-of-the-art methods. Firstly, we use multi-window to refine the same feature map at the same time to obtain richer high/low frequency information, and fuse and filter out the features needed by the deep network. Then, we develop a back-projection network based on the dilated convolution, using up-projection and down-projection modules to extract image features. Finally, we merge several repeated and continuous residual modules with global features, merge the information flow through the network, and input them to the reconstruction module. RESULTS: The proposed method shows the superiority over the state-of-the-art methods on the benchmark dataset, and generates clear COVID-19 CT super-resolution images. CONCLUSION: Both subjective visual effects and objective evaluation indicators are improved, and the model specifications are optimized. Therefore, the MWSR method can improve the clarity of CT images of COVID-19 and effectively assist the diagnosis and quantitative assessment of COVID-19.