Clinical implications of the rheological theory in the prevention of ventilator-induced lung injury. Is mechanical power the solution?

Elastic static power, its correlation with acute respiratory distress syndrome severity: A Bayesian post-hoc analysis of the Mechanical Power Day cross-sectional trial

OBJECTIVE

The relationship between different power equations and the severity of acute respiratory distress syndrome (ARDS) remains unclear. This study aimed to evaluate various power equations: total mechanical power, total elastic power (comprising elastic static and elastic dynamic power), and resistive power, in a cohort of mechanically ventilated patients with and without ARDS. Bayesian analysis was employed to refine estimates and quantify uncertainty by incorporating a priori distributions.

DESIGN

A Bayesian post-hoc analysis was conducted on data from the Mechanical Power Day study.

SETTING

113 intensive care units across 15 countries and 4 continents.

PATIENTS

Adults who received invasive mechanical ventilation in volume-controlled mode, with (mild and moderate/severe ARDS) and without ARDS.

INTERVENTIONS

None.

MAIN VARIABLES OF INTEREST

ARDS, Elastic static power.

RESULTS

Elastic static power was 5.8 J/min (BF: 0.3) in patients with mild ARDS and 7.4 J/min (BF: 0.9) in moderate/severe ARDS patients. Bayesian regression and modeling analysis revealed that elastic static power was independently correlated with mild (a posteriori Mean: 1.3; 95% Credible Interval [Cred. Interval]: 0.2-2.2) and moderate/severe ARDS (a posteriori Mean: 2.8; 95% Cred. Interval: 1.7-3.8) more strongly than other power equations.

CONCLUSIONS

Elastic static power was found to have the strongest correlation with ARDS severity among the power equations studied. Prospective studies are needed to further validate these findings.

The role of m6A methylation genes in predicting poor prognosis in sepsis: identifying key biomarkers and therapeutic targets

Sepsis is one of the leading causes of death among seriously ill patients worldwide, affecting more than 30 million people annually and accounting for 1-2% of hospitalizations. By analyzing gene expression omnibus (GEO) data set, our team explored the relationship between m6A methylation gene and poor prognosis of sepsis. The purpose of this present study is to examine new detection markers for patients with poor prognosis, provide theoretical basis for timely intervention and improve the survival rate of patients. First, GSE54514 transcriptome data were extracted from the GEO database 31 patients with sepsis related death and 72 sepsis survivors. Key genes were screened from differentially expressed genes (DEGs), least absolute shrinkage and selection operator (LSAAO) and random forest (RF). And then, METTL3, WTAP and RBM15 were further verified by quantitative reverse transcription PCR (qRT-PCR). The constructed nomogram model showed high accuracy in predicting death. These three genes are mainly involved in chemokine signaling pathway, differentiation of monocytes and T cells, and phagocytosis of immune cells. The analysis showed that a high m6A score subtype is linked to lower immunosuppression and higher survival rates in clinical samples, suggesting better immune responses and outcomes for these patients. Finally, the protective effect of METTL3 in sepsis was demonstrated in mouse sepsis model applied with METTL3 inhibitor, by conducting cell flow cytometry analysis, enzyme-linked immunosorbent assay (ELISA) and hematoxylin-eosin (HE) staining. In conclusion, these findings provide potential biomarkers and targets for early precision diagnosis and treatment.