Lactobacillus-Based Probiotics Reduce the Adverse Effects of Stress in Rodents: A Meta-analysis
Bibliographic record
Abstract
Lactobacillus species play a critical role in the bidirectional communication between the gut and the brain. Consequently, they have the potential to aid in the treatment of psychological disorders. The impact of Lactobacillus supplementation on the stress responses triggering psychological disorders has not been systematically reviewed. Therefore, the aim of this meta-analysis is to summarize the body of research assessing the effects of Lactobacillus-based probiotics in rodents that underwent an experimental stress treatment or not. The duration of immobility in a Forced Swim Test (FST) was the outcome used to measure changes induced by various treatments. Four online databases were systematically searched for relevant studies published in English. Fourteen studies meeting the criteria were included in the meta-analysis. The effects of probiotic supplementation and stress treatment on the duration of immobility in the FST were analyzed using a generalized linear mixed model. Publication bias was evaluated by funnel plots. Our analysis shows that Lactobacillus-based probiotic supplements significantly reduce immobility in the FST (P < 0.001) in stressed rodents. However, probiotics did not affect the rodents that did not undergo the stress treatment (P = 0.168). These findings provide a better understanding of the potential of Lactobacillus-based probiotics for the management of stress-induced behavior.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".