Extracellular Vesicles from Different Sources of Mesenchymal Stromal Cells Have Distinct Effects on Lung and Distal Organs in Experimental Sepsis
Bibliographic record
Abstract
The effects of the administration of mesenchymal stromal cells (MSC) may vary according to the source. We hypothesized that MSC-derived extracellular vesicles (EVs) obtained from bone marrow (BM), adipose (AD), or lung (L) tissues may also lead to different effects in sepsis. We profiled the proteome from EVs as a first step toward understanding their mechanisms of action. Polymicrobial sepsis was induced in C57BL/6 mice by cecal ligation and puncture (SEPSIS) and SHAM (control) animals only underwent laparotomy. Twenty-four hours after surgery, animals in the SEPSIS group were randomized to receive saline or 3 × 106 MSC-derived EVs from BM, AD, or L. The diffuse alveolar damage was decreased with EVs from all three sources. In kidneys, BM-, AD-, and L-EVs reduced edema and expression of interleukin-18. Kidney injury molecule-1 expression decreased only in BM- and L-EVs groups. In the liver, only BM-EVs reduced congestion and cell infiltration. The size and number of EVs from different sources were not different, but the proteome of the EVs differed. BM-EVs were enriched for anti-inflammatory proteins compared with AD-EVs and L-EVs. In conclusion, BM-EVs were associated with less organ damage compared with the other sources of EVs, which may be related to differences detected in their proteome.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".