Effects of mesenchymal stromal cells on human umbilical vein endothelial cells in in vitro sepsis models
Bibliographic record
Abstract
Septic shock is a medical emergency that, despite the medical advances that have been made, still remains a major cause of hospital deaths. Cell therapy is an innovative field of research that could provide a therapy for sepsis. Mesenchymal stromal cells (MSC) are promising in cell therapy and more importantly for sepsis because of their immunosuppressive capabilities [ 1 ]. MSC have been shown by several groups to have a positive effect against sepsis in vivo [ 2 – 4 ]. It has been predicted that the MSC interact with macrophage to release IL-10 that in turns reduces inflammation [ 4 ]. Other groups have focused on the use of stimulated MSC to ameliorate their immunosuppressive capabilities [ 5 ]. The main stimulation of MSC has been the use of inflammatory stimulants like IFNγ. Our work focuses on the identification of effective MSC donors, whether primed with IFNγ or naïve, and the development of in vitro models that will predict how an MSC donor will act in vivo . We also want to eliminate the use of cells completely and use their secreted microvesicles as a therapy. The hypothesis is that the in vitro models will eliminate a noneffective MSC donor and allow us to identify the MSC donor that will have the greatest effect.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".