Serum stratifin and presepsin as candidate biomarkers for early detection of COVID-19 disease progression

Presepsin in Critical Illness: Current Knowledge and Future Perspectives

The accurate identification of infections is critical for effective treatment in intensive care units (ICUs), yet current diagnostic methods face limitations in sensitivity and specificity, alongside cost and accessibility issues. Consequently, there is a pressing need for a marker that is economically feasible, rapid, and reliable. Presepsin (PSP), also known as soluble CD14 subtype (sCD14-ST), has emerged as a promising biomarker for early sepsis diagnosis. PSP, derived from soluble CD14, reflects the activation of monocytes/macrophages in response to bacterial infections. It has shown potential as a marker of cellular immune response activation against pathogens, with plasma concentrations increasing during bacterial infections and decreasing post-antibiotic treatment. Unlike traditional markers such as procalcitonin (PCT) and C-reactive protein (CRP), PSP specifically indicates monocyte/macrophage activation. Limited studies in critical illness have explored PSP's role in sepsis, and its diagnostic accuracy varies with threshold values, impacting sensitivity and specificity. Recent meta-analyses suggest PSP's diagnostic potential for sepsis, yet its standalone effectiveness in ICU infection management remains uncertain. This review provides a comprehensive overview of PSP's utility in ICU settings, including its diagnostic accuracy, prognostic value, therapeutic implications, challenges, and future directions.

A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning

BACKGROUND

COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.

METHODS

A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.

RESULTS

Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.

CONCLUSIONS

The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

Variation in presepsin and thrombomodulin levels for predicting COVID-19 mortality

Coronavirus disease (COVID-19) has caused extensive mortality globally; therefore, biomarkers predicting the severity and prognosis of COVID-19 are essential. This study aimed to evaluate the application of presepsin (P-SEP) and thrombomodulin (TM), which are biomarkers of sepsis and endothelial dysfunction, respectively, in the prognosis of COVID-19. Serum P-SEP and TM levels from COVID-19 patients (n = 183) were measured. Disease severity was classified as mild, moderate I, moderate II, or severe based on hemoglobin oxygen saturation and the history of intensive care unit transfer or use of ventilation at admission. Patients in the severe group were further divided into survivors and non-survivors. P-SEP and TM levels were significantly higher in the severe group than those in the mild group, even after adjusting for creatinine values. In addition, TM levels were significantly higher in non-survivors than in survivors. Changes in the P-SEP levels at two time points with an interval of 4.1 ± 2.2 days were significantly different between the survivors and non-survivors. In conclusion, TM and continuous P-SEP measurements may be useful for predicting mortality in patients with COVID-19. Moreover, our data indicate that P-SEP and TM values after creatinine adjustment could be independent predictive markers, apart from renal function.

Endotheliopathy in Acute COVID-19 and Long COVID

The pulmonary endothelium is a highly regulated organ that performs a wide range of functions under physiological and pathological conditions. Since endothelial dysfunction has been demonstrated to play a direct role in sepsis and acute respiratory distress syndrome, its role in COVID-19 has also been extensively investigated. Indeed, apart from the COVID-19-associated coagulopathy biomarkers, new biomarkers were recognised early during the pandemic, including markers of endothelial cell activation or injury. We systematically searched the literature up to 10 March 2023 for studies examining the association between acute and long COVID-19 severity and outcomes and endothelial biomarkers.

Potential predictors of severe course and outcome of community-acquired pneumonia

Severe pneumonia is a condition with a high risk of death and mandatory hospitalization in the intensive care unit. The incidence of severe pneumonia has increased dramatically during the pandemic of new coronavirus infection. Timely diagnosis and early initiation of adequate treatment of severe pneumonia are crucial for improving survival of critically ill patients.

The aim of this review was to analyze published scientific research on molecular markers that allow to objectively assess the severity of pneumonia and to determine treatment tactics based on the predicted outcome upon admission to the hospital. A systematic search was conducted in the electronic databases PubMed, Medline, Web of Science for the period 2019 - 2022.

Conclusion. The review focuses on the prognostic role of a number of markers of immune response, vascular transformation, as well as angiotensin II and angiotensin converting enzyme-2. Further prospective studies of potential predictors of severe pneumonia will enable using marker molecules in a comprehensive clinical and laboratory diagnosis for early prediction of the hospitalized patient’s condition and expected outcome.

Novel inflammatory biomarkers in the prognosis of COVID-19

BACKGROUND

The central role of inflammatory progression in the development of Coronavirus disease 2019 (COVID-19), especially in severe cases, is indisputable. However, the role of some novel inflammatory biomarkers in the prognosis of COVID-19 remains controversial.

OBJECTIVE

To assess the effect of some novel inflammatory biomarkers in the occurrence and prognosis of COVID-19.

METHODS

We systematically retrieved the studies related to COVID-19 and the inflammatory biomarkers of interest. The data of each biomarker in different groups were extracted, then were categorized and pooled. The standardized mean difference was chosen as an effect size measure to compare the difference between groups.

RESULTS

A total of 90 studies with 12,059 participants were included in this study. We found higher levels of endocan, PTX3, suPAR, sRAGE, galectin-3, and monocyte distribution width (MDW) in the COVID-19 positive groups compared to the COVID-19 negative groups. No significant differences for suPAR and galectin-3 were detected between the severe group and mild/moderate group of COVID-19. In addition, the deaths usually had higher levels of PTX3, sCD14-ST, suPAR, and MDW at admission compared to the survivors. Furthermore, patients with higher levels of endocan, galectin-3, sCD14-ST, suPAR, and MDW usually developed poorer comprehensive clinical prognoses.

CONCLUSIONS

In summary, this meta-analysis provides the most up-to-date and comprehensive evidence for the role of the mentioned novel inflammatory biomarkers in the prognosis of COVID-19, especially in evaluating death and other poor prognoses, with most biomarkers showing a better discriminatory ability.

Review of Medical Studies on COVID-19 During the Pandemic Period

Due to the COVID-19 pandemic, both the university hospital and the city hospital have faced a significant patient load in our city. During this period, academic articles were written that contributed significantly to the literature on both hospitals struggling with patient density. In our study, we aimed to compile medical articles about COVID-19 in our city using the Web of Science and PubMed database.