Characterization of the gut microbiome in a porcine model of thoracic spinal cord injury
Bibliographic record
Abstract
BACKGROUND: The gut microbiome is a diverse network of bacteria which inhabit our digestive tract and is crucial for efficient cellular metabolism, nutrient absorption, and immune system development. Spinal cord injury (SCI) disrupts autonomic function below the level of injury and can alter the composition of the gut microbiome. Studies in rodent models have shown that SCI-induced bacterial imbalances in the gut can exacerbate the spinal cord damage and impair recovery. In this study we, for the first time, characterized the composition of the gut microbiome in a Yucatan minipig SCI model. We compared the relative abundance of the most dominant bacterial phyla in control samples to those collected from animals who underwent a contusion-compression SCI at the 2nd or 10th Thoracic level. RESULTS: We identify specific bacterial fluctuations that are unique to SCI animals, which were not found in uninjured animals given the same dietary regimen or antibiotic administration. Further, we identified a specific time-frame, "SCI-acute stage", during which many of these bacterial fluctuations occur before returning to "baseline" levels. CONCLUSION: This work presents a dynamic view of the microbiome changes that accompany SCI, establishes a resource for future studies and to understand the changes that occur to gut microbiota after spinal cord injury and may point to a potential therapeutic target for future treatment.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".