Eukaryotic Translation Initiation Factor 5A Small Interference RNA–Liposome Complexes Reduce Inflammation and Increase Survival in Murine Models of Severe Sepsis and Acute Lung Injury
Bibliographic record
Abstract
BACKGROUND: Many novel therapeutics have failed to reduce all-cause mortality associated with severe sepsis. Eukaryotic translation initiation factor 5A (eIF5A) is a regulator of apoptosis as well as inflammatory cell activation, making it a potential target for sepsis therapy. METHODS: In a murine model of severe sepsis, mice were intraperitoneally challenged with lipopolysaccharide (LPS). Mice were treated both before and after LPS challenge with liposome complexes containing either an eIF5A-specific or control small interference RNA (siRNA), and both survival and serum concentrations of inflammatory cytokines were monitored. The ability of eIF5A siRNA to reduce inflammatory cytokines was also tested in a model of acute lung injury established by intranasal administration of LPS to mice. RESULTS: There was a statistically significant increase in the rate of survival for mice intraperitoneally challenged with LPS that received eIF5A siRNA, compared with that noted for mice that received control siRNA (71% vs. 5%; P< .001), as well as a reduction in cytokine expression in serum. Concentrations of proinflammatory cytokines were also reduced in the lung homogenates and serum of mice that were intranasally challenged with LPS and received eIF5A siRNA (P< or = .05). CONCLUSIONS: eIF5A siRNA-liposome complexes reduced inflammation and contributed to increased survival in a model of severe sepsis, decreased inflammation in a model of acute lung injury, and should be considered for clinical use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".