Segmental Lung Recruitment in Patients with Bilateral COVID-19 Pneumonia Complicated by Acute Respiratory Distress Syndrome: A Case Report.

Bilateral COVID-19 pneumonia is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and usually leads to life-threatening acute respiratory distress syndrome (ARDS). Treatment of patients with ARDS is difficult and usually involves protective mechanical ventilation and various types of recruitment maneuvers. A segmental lung recruitment maneuver by independent lung ventilation has been described as a successful recruitment maneuver in patients with lobar pneumonia, and may, therefore, be useful for the treatment of patients with bilateral COVID-19 pneumonia complicated by ARDS in the critical phase of the disease when all other therapeutic options have been exhausted. The aim of this case series was to present a case report of four mechanically ventilated patients with severe bilateral COVID-19 pneumonia complicated by ARDS using the segmental lung recruitment maneuver. The effect of the segmental lung recruitment maneuver was assessed by the increase in PaO2/FiO2 ratio and the lung ultrasound (LUS) scoring system (0 points-presence of sliding lungs with A-lines or one or two isolated B-lines; 1 point-moderate loss of lung ventilation with three to five B lines; 2 points-severe loss of lung ventilation with more than five B lines (B pattern); and 3 points-lung consolidation) determined 12, 24, and 48 h after segmental lung recruitment. In three of four patients with bilateral COVID-19 pneumonia complicated by ARDS, an increase in the PaO2/FiO2 ratio and an improvement in the LUS scoring system were observed 48 h after segmental lung recruitment. In conclusion, the segmental lung recruitment maneuver in patients with bilateral COVID-19 complicated by ARDS is an effective method of lung recruitment and may be a useful treatment method.

SARS-CoV-2 Spike and Nucleocapsid Antibody Response in Vaccinated Croatian Healthcare Workers and Infected Hospitalized Patients: A Single Center Cohort Study.

Studies assessing the dynamics and duration of antibody responses following SARS-CoV-2 infection or vaccination are an invaluable tool for vaccination schedule planning, assessment of risk groups and management of pandemics. In this study, we developed and employed ELISA assays to analyze the humoral responses to Nucleocapsid and Spike proteins in vaccinated health-care workers (HCW) and critically ill COVID-19 patients. Sera of more than 1000 HCWs and critically ill patients from the Clinical Hospital Center Rijeka were tested across a one-year period, encompassing the spread of major SARS-CoV-2 variants of concern (VOCs). We observed 97% of seroconversion in HCW cohort as well as sustained anti-Spike antibody response in vaccinees for more than 6 months. In contrast, the infection-induced anti-Nucleocapsid response was waning significantly in a six-month period. Furthermore, a substantial decrease in vaccinees' anti-Spike antibodies binding to Spike protein of Omicron VOC was also observed. Critically ill COVID-19 patients had higher levels of anti-Spike and anti-Nucleocapsid antibodies compared to HCWs. No significant differences in anti-Spike and anti-Nucleocapsid antibody levels between the critically ill COVID-19 patients that were on non-invasive oxygen supplementation and those on invasive ventilation support were observed. However, stronger anti-Spike, but not anti-Nucleocapsid, antibody response correlated with a better disease outcome in the cohort of patients on invasive ventilation support. Altogether, our results contribute to the growing pool of data on humoral responses to SARS-CoV-2 infection and vaccination.

SARS-CoV-2 Viral Load in the Pulmonary Compartment of Critically Ill COVID-19 Patients Correlates with Viral Serum Load and Fatal Outcomes.

While SARS-CoV-2 detection in sputum and swabs from the upper respiratory tract has been used as a diagnostic tool, virus quantification showed poor correlation to disease outcome and thus, poor prognostic value. Although the pulmonary compartment represents a relevant site for viral load analysis, limited data exploring the lower respiratory tract is available, and its association to clinical outcomes is relatively unknown. Using bronchoalveolar lavage (BAL) and serum samples, we quantified SARS-CoV-2 copy numbers in the pulmonary and systemic compartments of critically ill patients admitted to the intensive care unit of a COVID-19 referral hospital in Croatia during the second and third pandemic waves. Clinical data, including 30-day survival after ICU admission, were included. We found that elevated SARS-CoV-2 copy numbers in both BAL and serum samples were associated with fatal outcomes. Remarkably, the highest and earliest viral loads after initiation of mechanical ventilation support were increased in the non-survival group. Our results imply that viral loads in the lungs contribute to COVID-19 disease severity, while blood titers correlate with lung virus titers, albeit at a lower level. Moreover, they suggest that BAL SARS-CoV-2 copy number quantification at ICU admission may provide a predictive parameter of clinical COVID-19 outcomes.

Human serum from SARS-CoV-2-vaccinated and COVID-19 patients shows reduced binding to the RBD of SARS-CoV-2 Omicron variant.

BACKGROUND: The COVID-19 pandemic is caused by the betacoronavirus SARS-CoV-2. In November 2021, the Omicron variant was discovered and immediately classified as a variant of concern (VOC), since it shows substantially more mutations in the spike protein than any previous variant, especially in the receptor-binding domain (RBD). We analyzed the binding of the Omicron RBD to the human angiotensin-converting enzyme-2 receptor (ACE2) and the ability of human sera from COVID-19 patients or vaccinees in comparison to Wuhan, Beta, or Delta RBD variants. METHODS: All RBDs were produced in insect cells. RBD binding to ACE2 was analyzed by ELISA and microscale thermophoresis (MST). Similarly, sera from 27 COVID-19 patients, 81 vaccinated individuals, and 34 booster recipients were titrated by ELISA on RBDs from the original Wuhan strain, Beta, Delta, and Omicron VOCs. In addition, the neutralization efficacy of authentic SARS-CoV-2 wild type (D614G), Delta, and Omicron by sera from 2× or 3× BNT162b2-vaccinated persons was analyzed. RESULTS: Surprisingly, the Omicron RBD showed a somewhat weaker binding to ACE2 compared to Beta and Delta, arguing that improved ACE2 binding is not a likely driver of Omicron evolution. Serum antibody titers were significantly lower against Omicron RBD compared to the original Wuhan strain. A 2.6× reduction in Omicron RBD binding was observed for serum of 2× BNT162b2-vaccinated persons. Neutralization of Omicron SARS-CoV-2 was completely diminished in our setup. CONCLUSION: These results indicate an immune escape focused on neutralizing antibodies. Nevertheless, a boost vaccination increased the level of anti-RBD antibodies against Omicron, and neutralization of authentic Omicron SARS-CoV-2 was at least partially restored. This study adds evidence that current vaccination protocols may be less efficient against the Omicron variant.

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm.

Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020-3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406-0.9992, 0.9404-0.9998 and 0.9797-0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks.

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.