The role of <scp>SDF</scp>‐1 in homing of human adipose‐derived stem cells
Bibliographic record
Abstract
One of the putative pathophysiological mechanisms of chronic wounds is a disturbed homing of stem cells. In this project, the stromal cell-derived factor 1 (SDF-1)/C-X-C chemokine receptor (CXCR) 4 and SDF-1/CXCR7 pathway were focused in human adipose-derived stem cells (ASCs). ASCs were incubated with acute (AWF) or chronic wound fluid (CWF) to analyze their effects by quantitative real-time polymerase chain reaction (SDF-1, CXCR4, CXCR7, TIMP3), enzyme-linked immunosorbent assay (SDF-1 in WFs and supernatant), and transwell migration assay with/without antagonization. Whereas SDF-1 amounted 73.5 pg/mL in AWF, it could not be detected in CWF. Incubation with AWF led to a significant enhancement (129.7 pg/mL vs. 95.5 pg/mL), whereas CWF resulted in a significant reduction (30 pg/mL vs. 95.5 pg/mL) of SDF-1 in ASC supernatant. The SDF-1 receptor CXCR7 was detected on ASCs. AWF but not CWF significantly induced ASC migration, which was inhibited by CXCR4 and CXCR7 antagonists. Expressions of SDF-1, CXCR4, and CXCR7 were significantly stimulated by AWF while TIMP3 expression was reduced. In conclusion, an uncontrolled inflammation in the chronic wound environment, indicated by a reduced SDF-1 expression, resulted in a decreased ASC migration. A disturbed SDF-1/CXCR4 as well as SDF-1/CXCR7 pathway seems to play an important role in the impaired healing of chronic wounds.
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".