Albumin Binds COVID-19 Spike 1 Subunit and Predicts In-Hospital Survival of Infected Patients—Possible Alteration by Glucose

U-shaped relationship found between fibrinogen-to-albumin ratio and systemic inflammation response index in osteoporotic fracture patients

The relationship between the Systemic Inflammatory Response Index (SIRI) and the Fibrinogen-to-albumin ratio (FAR) has not been extensively investigated. The objective of this study was to determine the independent relationship between FAR and SIRI in people with osteoporotic fractures (OPF). A cross-sectional study was conducted using retrospective data from 3431 hospitalized OPF patients. The exposure variable in this study was the baseline FAR, while the outcome variable was the SIRI. Covariates, including age, gender, BMI, and other clinical and laboratory factors, were adjusted. Cross-correlation analysis and linear regression models were applied. The generalized additive model (GAM) investigated non-linear relationships. Adjusted analysis revealed an independent negative association between FAR and SIRI in OPF patients (β = - 0.114, p = 0.00064, 95% CI - 0.180, - 0.049). A substantial U-shaped association between FAR and SIRI was shown using GAM analysis (p < 0.001). FAR and SIRI indicated a negative association for FAR below 6.344% and a positive correlation for FAR over 6.344%. The results of our study revealed a U-shaped relationship between SIRI and FAR. The lowest conceivable FAR for a bone-loose inflammatory disease might be 6.344%, suggesting that this has particular significance for the medical diagnosis and therapy of persons with OPF. Consequently, the term "inflammatory trough" is proposed. These results offer fresh perspectives on controlling inflammation in individuals with OPF and preventing inflammatory osteoporosis.

An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model

Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R2 = 0.9765) and (iii) the prediction of the disease severity and the need of patient transfer to the intensive care unit. We report a significant decrease in serotonin levels in patients who needed positive airway pressure oxygen and/or were intubated. Furthermore, 5-hydroxy tryptophan, allantoin, and glucuronic acid metabolites were increased in COVID-19 patients and collectively they can serve as biomarkers to predict disease progression. The ability to quickly identify which patients will develop life-threatening illness would allow the efficient allocation of medical resources and implementation of the most effective medical interventions. We would advocate that the same approach could be utilized in future viral outbreaks to help hospitals triage patients more effectively and improve patient outcomes while optimizing healthcare resources.

CHARACTERISTICS OF OLOKIZUMAB PHARMACOKINETICS IN PATIENTS WITH NOVEL CORONAVIRUS INFECTION COVID-19

The aim of the article is to study pharmacokinetic characteristics of intravenous olokizumab in patients with moderate COVID-19 to relieve a hyperinflammation syndrome.

Materials and methods. The pharmacokinetic study was conducted as a part of a phase III clinical study (RESET, NCT05187793) on the efficacy and safety of a new olokizumab regimen (intravenous, at the doses of 128 mg or 256 mg) in COVID-19 patients. Plasma concentrations of olokizumab were determined by the enzyme immunoassay. The population analysis was performed using a previously developed pharmacokinetic model based on a linear two compartment.

Results. The pharmacokinetic analysis included the data from 8 moderate COVID-19 patients who had been administrated with olokizumab intravenously at the dose of 128 mg. According to the analysis results in this population, there was an increase in the drug clearance, compared with the data obtained in healthy volunteers and the patients with rheumatoid arthritis: 0.435, 0.178 and 0.147 l/day, respectively. The parameters analysis within the framework of a population pharmacokinetic model showed that the main factors for the increased olokizumab clearance are a high body mass index. In addition, the presence of COVID-19 itself is an independent factor in increasing the drug clearance.

Conclusion. After the intravenous olokizumab administration, an increase in the drug clearance is observed in moderate COVID-19 patients against the background of the disease course. The main contribution to the increased clearance is made by the characteristics of the population of COVID-19 patients associated with the risk of a severe disease and inflammation. When administered intravenously at the dose of 128 mg, a therapeutically significant olokizumab level was maintained throughout the acute disease phase for 28 days.

Mortality Predictors in Severe SARS-CoV-2 Infection

Background and Objectives: The severe forms of SARS-CoV-2 pneumonia are associated with acute hypoxic respiratory failure and high mortality rates, raising significant challenges for the medical community. The objective of this paper is to present the importance of early quantitative evaluation of radiological changes in SARS-CoV-2 pneumonia, including an alternative way to evaluate lung involvement using normal density clusters. Based on these elements we have developed a more accurate new predictive score which includes quantitative radiological parameters. The current evolution models used in the evaluation of severe cases of COVID-19 only include qualitative or semi-quantitative evaluations of pulmonary lesions which lead to a less accurate prognosis and assessment of pulmonary involvement. Materials and Methods: We performed a retrospective observational cohort study that included 100 adult patients admitted with confirmed severe COVID-19. The patients were divided into two groups: group A (76 survivors) and group B (24 non-survivors). All patients were evaluated by CT scan upon admission in to the hospital. Results: We found a low percentage of normal lung densities, PaO2/FiO2 ratio, lymphocytes, platelets, hemoglobin and serum albumin associated with higher mortality; a high percentage of interstitial lesions, oxygen flow, FiO2, Neutrophils/lymphocytes ratio, lactate dehydrogenase, creatine kinase MB, myoglobin, and serum creatinine were also associated with higher mortality. The most accurate regression model included the predictors of age, lymphocytes, PaO2/FiO2 ratio, percent of lung involvement, lactate dehydrogenase, serum albumin, D-dimers, oxygen flow, and myoglobin. Based on these parameters we developed a new score (COV-Score). Conclusions: Quantitative assessment of lung lesions improves the prediction algorithms compared to the semi-quantitative parameters. The cluster evaluation algorithm increases the non-survivor and overall prediction accuracy.COV-Score represents a viable alternative to current prediction scores, demonstrating improved sensitivity and specificity in predicting mortality at the time of admission.