Harnessing gut microbiota to mitigate <i>Salmonella</i> Dublin: Lessons from <i>S</i> . Typhimurium
Bibliographic record
Abstract
The emerging cattle-adapted pathogen, Salmonella Dublin, threatens the global cattle industry by causing high mortality in calves and reduced production efficiency in cows. Due to limited therapeutic options, there is a need for novel interventions to mitigate S. Dublin. In the inflamed gut, Salmonella Typhimurium, and possibly S. Dublin, gain a metabolic advantage by utilizing niche nutrients during anaerobic respiration. S. Dublin invades intestinal epithelial cells using genes encoded by Salmonella pathogenicity island 1 (SPI-1), initiating systemic disease and chronic infection. Propionate, a microbial fermentation product, inhibits SPI-1 transcription, presenting an opportunity to prevent infection. Lactobacilli endogenous to the small intestine of calves may be leveraged to inhibit S. Dublin invasion and growth through propionate synthesis and nutrient blocking, respectively. Here, we discuss critical knowledge gaps of S. Dublin pathogenesis while offering data-driven insights for the development of sustainable microbial-based interventions to mitigate S. Dublin in cattle.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".