Dynamic Expression of IL-6 Trans-Signalling Molecules in the Lungs of Preterm Baboons Undergoing Mechanical Ventilation
Bibliographic record
Abstract
BACKGROUND: Interleukin (IL)-6, when complexed with soluble IL-6 receptor (sIL-6R), has emerged as an important modulator of chemokine expression and leukocyte recruitment during inflammation and in this state can be specifically antagonised by soluble gp130 (sgp130). The expression of these modifiers of IL-6 activity during ventilator-induced inflammation remains poorly understood. OBJECTIVES: To ascertain the expression pattern of IL-6, sIL-6R and sgp130 in response to mechanical ventilation in the preterm neonatal lung and define its relationship to associated markers of inflammation. METHODS: Inflammatory cell recruitment and expression of IL-6, sIL-6R, sgp130, IL-8 and monocyte chemotactic protein-1 (MCP-1) were quantified in tracheal aspirate fluid collected over a 14-day period from preterm (125 days) baboons undergoing mechanical ventilation. RESULTS: Over the period of ventilation, the ratio of agonistic IL-6/sIL-6R increased 4.3-fold between days 3 and 10-11 (p < 0.01) while the ratio of antagonistic sgp130/IL-6 decreased 2.6-fold over the same period (p < 0.05). Over the same period, the relative numbers of neutrophils compared to mononuclear cells shifted from an excess of 1.8 on day 1 to 0.6 on day 14 (p < 0.01). Both IL-8 and MCP-1 were elevated between days 1 and 10-11 of ventilation (p < 0.01). CONCLUSIONS: In the ventilated preterm baboon lung, expression of sIL-6R and dynamic modulation of sgp130 expression appear to modulate the activity and inflammatory potential of IL-6.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".