Human mesenchymal stem cells attenuate early damage in a ventilated pig model of acute lung injury
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Acute lung injury/acute respiratory distress syndrome (ALI/ARDS) is a major cause of global morbidity and mortality. Mesenchymal stem cells (MSC) have shown promise in treating inflammatory lung conditions. We hypothesised that human MSC (hMSC) can improve ALI/ARDS through their anti-inflammatory actions. We subjected pigs (n=6) to intravenous oleic acid (OA) injury, ventilation and hMSC infusion, while the controls (n=5) had intravenous OA, ventilation and an infusion vehicle control. hMSC were infused 1h after the administration of OA. The animals were monitored for additional 4h. Nuclear translocation of nuclear factor-light chain enhancer of activated B cells (NF-κB), a transcription factor that mediates several inflammatory pathways was reduced in hMSC treated pigs compared to controls (p=0.04). There was no significant difference in lung injury, assessed by histological scoring in hMSC treated pigs versus controls (p=0.063). There was no difference in neutrophil counts between hMSC-treated pigs and controls. Within 4h, there was no difference in the levels of IL-10 and IL-8 pre- and post-treatment with hMSC. In addition, there was no difference in hemodynamics, lung mechanics or arterial blood gases between hMSC treated animals and controls. Subsequent studies are required to determine if the observed decrease in inflammatory transcription factors will translate into improvement in inflammation and in physiological parameters over the long term.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it