Survival and Interplay of γ-Aminobutyric Acid-Producing Psychobiotic Candidates with the Gut Microbiota in a Continuous Model of the Human Colon
Bibliographic record
Abstract
Over decades, probiotic research has focused on their benefits to gut health. Recently, the gut microbiota has been proven to share bidirectional connections with the brain through the gut–brain axis. Therefore, the manipulation of this axis via probiotics has garnered interest. We have recently isolated and characterized in vitro probiotic candidates producing γ-aminobutyric acid (GABA), a major neuromodulator of the enteric nervous system. This study investigates the growth and competitiveness of selected GABA-producing probiotic candidates (Bifidobacterium animalis, Streptococcus thermophilus, and Lactobacillus delbrueckii subsp. bulgaricus) in the presence of human gut microbiota ex vivo in a model mimicking physiological and microbiological conditions of the human proximal colon. Supplementation with GABA-producing probiotic candidates did not affect the overall gut microbiota diversity over 48 h of treatment. However, these candidates modulated the microbiota composition, especially by increasing the Bacteroidetes population, a key gut microbe associated with anti-inflammatory activities. The level of microbiota-generated SCFAs within 12 h of treatment was also increased, compared to the control group. Results from this study demonstrate the probiotic potential of the tested GABA-producing bacteria and their impact on gut microbiota structure and metabolism, suggesting their suitability for gut health-promoting applications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".