Use of next generation sequencing to investigate the microbiota of experimentally induced wounds and the effect of bandaging in horses
Bibliographic record
Abstract
OBJECTIVES: To use next generation sequencing to characterize the microbiota of horses during healing of skin wounds in two anatomical locations (body and limb) known to present different healing patterns; and to investigate the impact of bandaging on bacterial communities of skin wounds located on the limbs of horses. METHODS: Full-thickness skin wounds were created on the distal extremity of both thoracic limbs and on one lateral mid-thoracic wall of four healthy horses. Limb wounds were randomly assigned to bandaging or not. A full-thickness sample was collected with a biopsy punch from intact thorax and limb skin (T0) and from the margin of one wound per site (thorax, unbandaged limb, bandaged limb) 1 week (T1) and 2 weeks (T2) postoperatively, and at full healing (T3). Thoracic skin samples obtained from three healthy horses were included in the analysis as controls. RESULTS: Anatomic location (thorax vs. limb) significantly influenced bacterial composition of equine skin and healing wounds. Fusobacterium and Actinobacillus were strongly associated with limb wounds during the initial phases of healing. Bandaging had a significant impact on the microbiota during the healing process. The skin microbiota after healing was more similar to samples from controls, demonstrating the resilience and stability of the environment. CONCLUSIONS: Equine skin microbiota is a rich and stable environment that is disturbed by wounding, but returns to its previous stage after full healing. Anatomic location significantly influences bacterial composition of the equine skin during wound healing. Bandaging has a significant impact on the skin microbiota of horses during the healing process. Results of this study provide new insight for a better understanding of the contribution of bacteria to wound healing in horses and may facilitate the future development of therapeutic strategies using commensal bacteria.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".