Injection of rat mesenchymal stem cells leads to their homing and differentiation in the liver in a blunt liver trauma model
Bibliographic record
Abstract
Background: The liver heals remarkably after different forms of injury. However, healing can be lengthy following high-grade injuries. We hypothesize that injected mesenchymal stem cells (MSCs) could locate in the liver and differentiate into hepatocytes after blunt trauma using a rat liver trauma model. Methods: Blunt liver trauma was induced in Lewis rats. MSCs were transfected with LacZ retrovirus so that they express the beta-galactosidase enzyme, giving their nuclei a blue color on light microscopy. Each rat received a dose of MSCs ( n = 6 × 10 6 ) within 24 h of trauma. Through different steps of the experiment, the route of injection was the tail vein (TV) in nine rats, the portal vein (PV) in 19 rats, and directly to the injured liver (DI) in four rats. Rats were euthanized at 48 h or 7 days after the injection of MSCs. Livers were harvested and examined under light microscopy to identify the MSCs. Results: Liver sections showed localization and migration of MSCs to trauma sites in the PV group euthanized at 48 h (3/10 rats). Some MSCs differentiated into hepatocytes. Similar findings were present in 1/9 rats euthanized at 7 days in the PV group. There was no evidence of MSC localization in TV and DI groups. Discussion: MSCs can locate and differentiate into liver-like cells in blunt liver injury. Portal vein injection of MSC has emerged as the most effective method of delivery to the liver among the other methods. Optimizing homing to injured tissue and evaluating differentiated stem cell functionality are future areas of research.
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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) | 0.002 | 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".