Background: Coronavirus disease 2019 (COVID-19) can cause serious complications such as multiorgan failure and death which are difficult to predict. We conducted this retrospective case-control observational study with the hypothesis that low serum albumin at presentation can predict serious outcomes in COVID-19 infection. Methods:We included severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reverse transcriptase-polymerase chain reaction (RT-PCR) confirmed, hospitalized patients from March to July 2020 in a tertiary care hospital in the USA. Patients were followed for 21 days for the development of the primary endpoint defined as the composite outcome which included acute encephalopathy, acute kidney injury, the requirement of new renal replacement therapy, acute hypercoagulability, acute circulatory failure, new-onset heart failure, acute cardiac injury, acute arrhythmia, acute respiratory distress syndrome (ARDS), high flow oxygen support, intensive care unit (ICU) stay, mechanical ventilation or death; and the secondary endpoint of death only. Univariate and multivariate logistic regression analyses were performed to study the effect of albumin level and outcomes. Results:The mean age was 56.76 years vs. 55.67 years (P = 0.68) in the normal albumin vs. the low albumin group. We noticed an in-verse relationship between serum albumin at presentation and serious outcomes. The low albumin group had a higher composite outcome (93.88% vs. 6.12%, P < 0.05) and higher mortality (13.87% vs. 2.38%, P < 0.05) in comparison to the normal albumin group. The multivariate logistic regression analysis revealed higher odds of having composite outcomes with lower albumin group (odds ratio (OR) 10.88, 95% confidence interval (CI) 4.74 -24.97, P < 0.05). In the subgroup analysis, the multivariate logistic regression analysis revealed higher odds of having composite outcomes with the very low albumin group (OR 7.94, 95% CI 1.70 -37.14, P < 0.05).Conclusions: Low serum albumin on presentation in COVID-19 infection is associated with serious outcomes not limited to mortality. The therapeutic option of albumin infusion should be investigated.

Introduction The primary aim was to develop convolutional neural network (CNN)‐based artificial intelligence (AI) models for pneumothorax classification and segmentation for automated chest X‐ray (CXR) triaging. A secondary aim was to perform interpretability analysis on the best‐performing candidate model to determine whether the model's predictions were susceptible to bias or confounding. Method A CANDID‐PTX dataset, that included 19,237 anonymized and manually labelled CXRs, was used for training and testing candidate models for pneumothorax classification and segmentation. Evaluation metrics for classification performance included Area under the receiver operating characteristic curve (AUC‐ROC), sensitivity and specificity, whilst segmentation performance was measured using mean Dice and true‐positive (TP)‐Dice coefficients. Interpretability analysis was performed using Grad‐CAM heatmaps. Finally, the best‐performing model was implemented for a triage simulation. Results The best‐performing model demonstrated a sensitivity of 0.93, specificity of 0.95 and AUC‐ROC of 0.94 in identifying the presence of pneumothorax. A TP‐Dice coefficient of 0.69 is given for segmentation performance. In triage simulation, mean reporting delay for pneumothorax‐containing CXRs is reduced from 9.8 ± 2 days to 1.0 ± 0.5 days (P‐value < 0.001 at 5% significance level), with sensitivity 0.95 and specificity of 0.95 given for the classification performance. Finally, interpretability analysis demonstrated models employed logic understandable to radiologists, with negligible bias or confounding in predictions. Conclusion AI models can automate pneumothorax detection with clinically acceptable accuracy, and potentially reduce reporting delays for urgent findings when implemented as triaging tools.

Influenza vaccines could be improved by platforms inducing cross-reactive immunity. Immunodominance of the influenza hemagglutinin (HA) head in currently licensed vaccines impedes induction of cross-reactive neutralizing stem-directed antibodies. A vaccine without the variable HA head domain has the potential to focus the immune response on the conserved HA stem. This first-in-human dose-escalation open-label phase 1 clinical trial (NCT03814720) tested an HA stabilized stem ferritin nanoparticle vaccine (H1ssF) based on the H1 HA stem of A/New Caledonia/20/1999. Fifty-two healthy adults aged 18 to 70 years old enrolled to receive either 20 μg of H1ssF once ( n  = 5) or 60 μg of H1ssF twice ( n  = 47) with a prime-boost interval of 16 weeks. Thirty-five (74%) 60-μg dose participants received the boost, whereas 11 (23%) boost vaccinations were missed because of public health restrictions in the early stages of the COVID-19 pandemic. The primary objective of this trial was to evaluate the safety and tolerability of H1ssF, and the secondary objective was to evaluate antibody responses after vaccination. H1ssF was safe and well tolerated, with mild solicited local and systemic reactogenicity. The most common symptoms included pain or tenderness at the injection site ( n  = 10, 19%), headache ( n  = 10, 19%), and malaise ( n  = 6, 12%). We found that H1ssF elicited cross-reactive neutralizing antibodies against the conserved HA stem of group 1 influenza viruses, despite previous H1 subtype head-specific immunity. These responses were durable, with neutralizing antibodies observed more than 1 year after vaccination. Our results support this platform as a step forward in the development of a universal influenza vaccine.

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Background The use of ≥30 mL/Kg fluid bolus in congestive heart failure (CHF) patients presenting with severe sepsis or septic shock remained controversial due to the paucity of data. Methods The retrospective case-control study included 671 adult patients who presented to the emergency department of a tertiary care hospital from January 01, 2017 to December 31, 2019 with severe sepsis or septic shock. Patients were categorized into the CHF group and the non-CHF group. The primary outcome was to evaluate the compliance with ≥30 mL/Kg fluid bolus within 6 hours of presentation. The comparison of baseline characteristics and secondary outcomes were done between the groups who received ≥30 mL/Kg fluid bolus. For the subgroup analysis of the CHF group, it was divided based on if they received ≥30 mL/Kg fluid bolus or not, and comparison was done for baseline characteristics and secondary outcomes. Univariate and multivariable analyses were performed to explore the differences between the groups for in-hospital mortality and mechanical ventilation. Results The use of ≥30 mL/Kg fluid bolus was low in both the CHF and non-CHF groups [39% vs. 66% (p<0.05)]. Mortality was higher in the CHF group [33% vs 18% (p<0.05)]. Multivariable analysis revealed that the use of ≥30 mL/Kg fluid bolus decreased the chances of mortality by 12% [OR 0.88, 95% CI 0.82–0.95 (p<0.05)]. The use of ≥30 mL/Kg fluid bolus did not increase the odds of mechanical ventilation [OR 0.99, 95% CI 0.93–1.05 (p = 0.78)]. In subgroup analysis, the use of ≥30 mL/Kg fluid bolus decreased the chances of mortality by 5% [OR 0.95, 95% CI 0.90–0.99, (p<0.05)] and did not increase the odds of mechanical ventilation. The presence of the low ejection fraction did not influence the chance of getting fluid bolus. Conclusion The use of ≥30 mL/Kg fluid bolus seems to confer protection against in-hospital mortality and is not associated with increased chances of mechanical ventilation in heart failure patients presenting with severe sepsis or septic shock.