Knowledge, Attitudes, and Practices of COVID-19 Vaccination among Adults in Singapore: A Cross-Sectional Study.

Public health measures promoting compliance of COVID-19 vaccination requires understanding of knowledge, attitudes, and practices (KAP). This study explored the KAP and risk factors influencing COVID-19 vaccination, including changes in preventive practices before and after vaccination in a high-income country, Singapore. An online cross-sectional study among Singaporeans and permanent residents aged 21 years and older was conducted from July to August 2021. Univariate and multivariable logistic regressions using RStudio version 1.4.1106 was performed to assess associations between demographic factors, KAP, and vaccination status. P values < 0.05 were considered statistically significant. A total of 869 respondents completed the survey. Individuals with higher knowledge (adjusted odds ratio [aOR] = 2.00, P = 0.024), perceived efficacy (aOR = 1.19, P = 0.004), perceived safety (aOR = 1.20, P = 0.005), and willingness to uptake (aOR = 1.55, P < 0.001) scores were more likely to be vaccinated. There was a significant increase in the use of proper handwashing techniques among the vaccinated group before and after vaccinations. The governmental risk communication approaches have been useful in instilling high levels of vaccine knowledge. High levels of good attitudes about and knowledge of COVID-19 vaccination were associated with a high level of vaccination practices. Good perceived vaccine efficacy and confidence in government were also associated with positive vaccine uptake. This study paves the way for more targeted government measures to be implemented to improve vaccination rates of COVID-19 booster vaccines in a high-income country like Singapore.

On-demand nanozyme signal enhancement at the push of a button for the improved detection of SARS-CoV-2 nucleocapsid protein in serum.

We developed an innovative 3D printed casing that incorporates a lateral-flow immunoassay, dehydrated signal enhancement reagents, and a sealed buffer chamber. With only the push of a button for signal enhancement, our device detected the SARS-CoV-2 N-protein in 40 min at concentrations as low as 0.1 ng mL-1 in undiluted serum.

Automation of Lung Ultrasound Interpretation via Deep Learning for the Classification of Normal versus Abnormal Lung Parenchyma: A Multicenter Study.

Lung ultrasound (LUS) is an accurate thoracic imaging technique distinguished by its handheld size, low-cost, and lack of radiation. User dependence and poor access to training have limited the impact and dissemination of LUS outside of acute care hospital environments. Automated interpretation of LUS using deep learning can overcome these barriers by increasing accuracy while allowing point-of-care use by non-experts. In this multicenter study, we seek to automate the clinically vital distinction between A line (normal parenchyma) and B line (abnormal parenchyma) on LUS by training a customized neural network using 272,891 labelled LUS images. After external validation on 23,393 frames, pragmatic clinical application at the clip level was performed on 1162 videos. The trained classifier demonstrated an area under the receiver operating curve (AUC) of 0.96 (±0.02) through 10-fold cross-validation on local frames and an AUC of 0.93 on the external validation dataset. Clip-level inference yielded sensitivities and specificities of 90% and 92% (local) and 83% and 82% (external), respectively, for detecting the B line pattern. This study demonstrates accurate deep-learning-enabled LUS interpretation between normal and abnormal lung parenchyma on ultrasound frames while rendering diagnostically important sensitivity and specificity at the video clip level.

Microbial signatures in the lower airways of mechanically ventilated COVID-19 patients associated with poor clinical outcome.

Respiratory failure is associated with increased mortality in COVID-19 patients. There are no validated lower airway biomarkers to predict clinical outcome. We investigated whether bacterial respiratory infections were associated with poor clinical outcome of COVID-19 in a prospective, observational cohort of 589 critically ill adults, all of whom required mechanical ventilation. For a subset of 142 patients who underwent bronchoscopy, we quantified SARS-CoV-2 viral load, analysed the lower respiratory tract microbiome using metagenomics and metatranscriptomics and profiled the host immune response. Acquisition of a hospital-acquired respiratory pathogen was not associated with fatal outcome. Poor clinical outcome was associated with lower airway enrichment with an oral commensal (Mycoplasma salivarium). Increased SARS-CoV-2 abundance, low anti-SARS-CoV-2 antibody response and a distinct host transcriptome profile of the lower airways were most predictive of mortality. Our data provide evidence that secondary respiratory infections do not drive mortality in COVID-19 and clinical management strategies should prioritize reducing viral replication and maximizing host responses to SARS-CoV-2.

d-dimer and Death in Critically Ill Patients With Coronavirus Disease 2019.

OBJECTIVES: Hypercoagulability may be a key mechanism for acute organ injury and death in patients with severe coronavirus disease 2019, but the relationship between elevated plasma levels of d-dimer, a biomarker of coagulation activation, and mortality has not been rigorously studied. We examined the independent association between d-dimer and death in critically ill patients with coronavirus disease 2019. DESIGN: Multicenter cohort study. SETTING: ICUs at 68 hospitals across the United States. PATIENTS: Critically ill adults with coronavirus disease 2019 admitted to ICUs between March 4, 2020, and May 25, 2020, with a measured d-dimer concentration on ICU day 1 or 2. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary exposure was the highest normalized d-dimer level (assessed in four categories: < 2×, 2-3.9×, 4-7.9×, and ≥ 8× the upper limit of normal) on ICU day 1 or 2. The primary endpoint was 28-day mortality. Multivariable logistic regression was used to adjust for confounders. Among 3,418 patients (63.1% male; median age 62 yr [interquartile range, 52-71 yr]), 3,352 (93.6%) had a d-dimer concentration above the upper limit of normal. A total of 1,180 patients (34.5%) died within 28 days. Patients in the highest compared with lowest d-dimer category had a 3.11-fold higher odds of death (95% CI, 2.56-3.77) in univariate analyses, decreasing to a 1.81-fold increased odds of death (95% CI, 1.43-2.28) after multivariable adjustment for demographics, comorbidities, and illness severity. Further adjustment for therapeutic anticoagulation did not meaningfully attenuate this relationship (odds ratio, 1.73; 95% CI, 1.36-2.19). CONCLUSIONS: In a large multicenter cohort study of critically ill patients with coronavirus disease 2019, higher d-dimer levels were independently associated with a greater risk of death.