No Beneficial Effects of the Alfasigma VSL#3 Probiotic Treatment After Cervical Spinal Cord Injury in Rats
Bibliographic record
Abstract
Background: The bidirectional communication between the gastrointestinal tract and the central nervous system appears to be linked to the intestinal microbiome. Research has shown that spinal cord injury (SCI) can disrupt the gut microbiome, leading to gut dysbiosis. These changes can have several negative impacts, such as exacerbated systemic inflammation and susceptibility to infection. Probiotics administered to mice with SCI have been shown to ameliorate gut dysbiosis, confer neuroprotection, and improve locomotor recovery. However, probiotics have also produced conflicting results, making potential claims regarding the application of probiotics as a therapeutic supplement ambiguous. Objectives: This study aimed to investigate the effects of a commercially available probiotic on recovery in a rat model of cervical SCI. Methods: Rats with cervical contusion SCI received probiotics over 7 days immediately after injury, and their recovery in motor, sensory, and cognitive tasks was compared to that of untreated animals. Results: Compared with a control group, the oral administration of probiotics influenced neither motor nor cognitive outcomes. We did observe a significantly different lesion size between the two groups, where the probiotic group had less spared tissue, despite a lack of differences in functional outcomes. In addition, probiotic treatment delayed gut microbiome composition recovery to baseline levels for up to 35 days postinjury (DPI). We found significantly higher proportions of Lactobacillus in the fecal matter of SCI-vehicle rats at 35 DPI, whereas Streptococcus was higher in SCI-probiotics fecal matter at 14 and 35 DPI, and Bacillales was higher in the latter group at 35 DPI. Conclusion: Taken together, our results do not support the application of the utilized probiotic cocktail as a dietary supplement for the treatment of cervical SCI-induced gut dysbiosis and secondary complications.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".