Data for the manuscript: Fecal Transplant Prevents Gut Dysbiosis and Anxiety-like Behaviour After Spinal Cord Injury in Rats
Bibliographic record
Abstract
STUDY PURPOSE: To establish a model of anxiety following a cervical contusion spinal cord injury (SCI) in rats and to determine whether the microbiota play a role in the observed behavioural changes. DATA COLLECTED: This dataset includes n=57 rats from 2 experiments. Experiment 1: sham (underwent surgery with no SCI) n=6; unilateral cervical contusion SCI n=6. Experiment 2: Healthy (no operation, no gavage) n=10; Sham n=11; SCI (gavaged with a control solution) n=10; SCI-FMT (gavaged with fecal microbiota transplant solution) n=14. A subset of subjects from experiment 2 were used for the fecal 16s rRNA analysis (healthy n = 10; SCI-FMT n = 10; sham n = 5; SCI n = 5). Fecal matter for the 16s rRNA analysis were collected before injury, 3 days after injury and 4 weeks after injury. A PICRUST analysis was performed to infer the functional pathways involved using the 16s rRNA gene data. Rats were assessed on a battery of behavioural tests: the light-dark box, the cylinder test, the sucrose preference test, the elevated plus maze and the open field. Lesion size was calculated as the percentage of damaged tissue area throughout the rostral-caudal extension of the injury site. PRIMARY CONCLUSION: Treatment with a fecal microbiota transplant in the acute post-injury period prevents spinal cord injury-induced gut dysbiosis as well as the development of anxiety-like behaviour.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.381 | 0.094 |
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".