Improving the early identification of COVID-19 pneumonia: a narrative review

A highly specific aptamer for the SARS-CoV-2 spike protein from the authentic strain

DNA aptamers are oligonucleotides that specifically bind to target molecules, similar to how antibodies bind to antigens. We identified an aptamer named MEZ that is highly specific to the receptor-binding domain, RBD, of the SARS-CoV-2 spike protein from the Wuhan-Hu-1 strain. The SELEX procedure was utilized to enrich the initial 31-mer oligonucleotide library with the target aptamer. The aptamer identification was performed using the novel protocol based on nanopore sequencing developed in this study. The MEZ aptamer was chemically synthesized and tested for binding with the SARS-CoV-2 RBD of the spike protein from different strains. The Kd is 6.5 nM for the complex with the RBD from the Wuhan-Hu-1 strain, which is comparable with known aptamers; the advantage is that the MEZ aptamer is smaller than known analogs. The proposed aptamer is highly selective for the RBD protein from the Wuhan-Hu-1 strain and does not form complexes with the RBD from Beta, Delta and Omicron strains. Experimental and theoretical studies together revealed the molecular mechanism of aptamer binding. The aptamer occupies the same binding site as ACE2 when bound to the RBD. The 3'-end of the MEZ aptamer is important for complex formation and is responsible for the discrimination of the RBD protein from a specific strain. The 5'-end is responsible for the formation of a loop in the 3D structure of the aptamer, which is important for proper binding.

Performance of the ROX index in predicting high flow nasal cannula failure in COVID-19 patients: a systematic review and meta-analysis

COVID-19 patients with acute hypoxemic respiratory failure (AHRF) benefit from high flow nasal cannula (HFNC) oxygen therapy. However, delays in initiating invasive ventilation after HFNC failure are associated with poorer outcomes. The respiratory oxygenation (ROX) index, combining SpO2/FiO2 and respiratory rate, can predict HFNC failure. This meta-analysis evaluated the optimal ROX index cut-offs in predicting HFNC failure among COVID-19 patients at different measurement timings and clinical settings. Three databases were searched for eligible papers. From each study, we reconstructed the confusion matrices at different cut-offs, fitted linear mixed models to estimate the ROX index distribution function, and derived the area under the summary receiver operator characteristic curve (sAUC) and optimal cut-offs to predict HFNC failure. 24 studies containing 4790 patients were included. Overall sAUC was 0.771 (95% CI: 0.666-0.847) (optimal cut-off: 5.23, sensitivity: 0.732, specificity: 0.690). The cut-off values to achieve 80%, 90% sensitivity, 80%, 90% specificity were 5.70, 6.69, 4.45, 3.37, respectively. We stratified the analysis by ROX measurement time and estimated optimal cut-offs and cut-offs to achieve 80% sensitivity and specificity. For 2-6 h and 6-12 h post-HFNC initiation, we propose the use of 80% specific cut-offs to rule in HFNC failure of < 5.33 and < 3.69, respectively. For 12-24 h post-HFNC initiation, we propose the use of the 80% sensitive cut-off of > 6.07 to rule out HFNC failure. Our analysis confirms the overall utility of the ROX index in risk stratification of COVID-19 patients with AHRF receiving HFNC and provides potentially useful cut-offs for different times from HFNC initiation.

Factors Associated with Pneumonia in Patients Hospitalized with COVID-19 and the Role of Vaccination

Patients with COVID-19 can develop different forms of the illness with more or less severe symptoms. A 2-year retrospective cohort study was conducted to evaluate the factors associated with the development of pneumonia in patients hospitalized with COVID-19 from March 2020 to February 2022. A total of 385 patients (59.0% males) with a mean age of 69.0 ± 16.0 years were included. At hospital admission, 318 patients (82.6%) reported one or more comorbidities, namely 201 (52.2%) subjects were affected by hypertension, 98 (25.5%) type 2 diabetes, 84 (21.8%) obesity, 36 (9.4%) cancer, and 14 (3.6%) suffered from kidney disease and were being treated with dialysis, and 76 (19.7%) resulted in being vaccinated with a higher prevalence of BNT162b2 vaccine (15.0%). Pneumonia was diagnosed in 276 (71.7%) patients. Multivariate regression analysis showed that pneumonia in COVID-19 patients was positively associated with type 2 diabetes (OR 1.81; 95% CI 1.00-3.27), obesity (OR 2.52; 95% CI 1.27-4.98), and negatively with hypertension (OR 0.58; 95% CI 0.35-0.96). Vaccination against SARS-CoV-2 resulted in a strongly protective factor against the development of pneumonia in COVID-19 patients (OR 0.49; 95% CI 0.28-0.85).

Differences in acoustic features of cough by pneumonia severity in patients with COVID-19: a cross-sectional study

Background

Acute respiratory syndrome due to coronavirus 2 (SARS-CoV-2) is characterised by heterogeneous levels of disease severity. It is not necessarily apparent whether a patient will develop a severe disease or not. This cross-sectional study explores whether acoustic properties of the cough sound of patients with coronavirus disease (COVID-19), the illness caused by SARS-CoV-2, correlate with their disease and pneumonia severity, with the aim of identifying patients with a severe disease.

Methods

Voluntary cough sounds were recorded using a smartphone in 70 COVID-19 patients within the first 24 h of their hospital arrival, between April 2020 and May 2021. Based on gas exchange abnormalities, patients were classified as mild, moderate, or severe. Time- and frequency-based variables were obtained from each cough effort and analysed using a linear mixed-effects modelling approach.

Results

Records from 62 patients (37% female) were eligible for inclusion in the analysis, with mild, moderate, and severe groups consisting of 31, 14 and 17 patients respectively. 5 of the parameters examined were found to be significantly different in the cough of patients at different disease levels of severity, with a further 2 parameters found to be affected differently by the disease severity in men and women.

Conclusions

We suggest that all these differences reflect the progressive pathophysiological alterations occurring in the respiratory system of COVID-19 patients, and potentially would provide an easy and cost-effective way to initially stratify patients, identifying those with more severe disease, and thereby most effectively allocate healthcare resources.

Associations of symptom combinations with in-hospital mortality of coronavirus disease-2019 patients using South Korean National data

BACKGROUND

There are various risk factors for death in coronavirus disease-2019 (COVID-19) patients. The effects of symptoms on death have been investigated, but symptoms were considered individually, rather than in combination, as predictors. We examined the effects of symptom combinations on in-hospital mortality.

METHODS

Data from the Korea Disease Control and Prevention Agency were analyzed. A cohort of 5,153 patients confirmed with COVID-19 in South Korea was followed from hospitalization to death or discharge. An exploratory factor analysis was performed to identify symptom combinations, and the hazard ratios (HRs) of death were estimated using the Cox proportional hazard model.

RESULTS

Three sets of symptom factors were isolated for symptom combination. Factor 1 symptoms were cold-like symptoms, factor 2 were neurological and gastrointestinal symptoms, and factor 3 were more severe symptoms such as dyspnea and altered state of consciousness. Factor 1 (HR 1.14, 95% confidence interval [95% CI] 1.01-1.30) and factor 3 (HR 1.25, 95% CI 1.19-1.31) were associated with a higher risk for death, and factor 2 with a lower risk (HR 0.71, 95% CI 0.71-0.96).

CONCLUSIONS

The effect on in-hospital mortality differed according to symptom combination. The results are evidence of the effects of symptoms on COVID-19 mortality and may contribute to lowering the COVID-19 mortality rate. Further study is needed to identify the biological mechanisms underlying the effects of symptom combinations on mortality.