G-CSF Enhances Resolution of Staphylococcus aureus Wound Infection in an Age-Dependent Manner
Bibliographic record
Abstract
This study tested the hypothesis that heightened bacterial colonization and delayed wound closure in aged mice could be attenuated by granulocyte colony-stimulating factor (G-CSF) treatment. Previously, we reported that aged mice had elevated bacterial levels, protracted wound closure, and reduced wound neutrophil accumulation after Staphylococcus aureus wound infection relative to young mice. In aseptic wound models, G-CSF treatment improved wound closure in aged mice to rates observed in young mice. Given these data, our objective was to determine if G-CSF could restore age-associated differences in wound bacterial burden and closure by increasing wound neutrophil recruitment. Young (3- to 4-month) and aged (18- to 20-month) BALB/c mice received three dorsal subcutaneous injections of G-CSF (250 ng/50 μL per injection) or saline control (50 μL per injection) 30 min after wound infection. Mice were killed at days 3 and 7 after wound infection, and bacterial colonization, wound size, wound leukocyte accumulation, and peripheral blood were evaluated. At days 3 and 7 after wound infection, bacterial colonization was significantly reduced in G-CSF-treated aged mice to levels observed in saline-treated young animals. Wound size was reduced in G-CSF-treated aged animals, with no effect on wound size in G-CSF-treated young mice. Local G-CSF treatment significantly enhanced neutrophil wound accumulation in aged mice, whereas there was no G-CSF-induced change in young mice. These data demonstrate that G-CSF enhances bacterial clearance and wound closure in an age-dependent manner. Moreover, G-CSF may be of therapeutic potential in the setting of postoperative wound infection or chronic nonhealing wounds in elderly patients.
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) | 0.000 | 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".