Porcine stomachs with and without gastric ulcer differ in <i>Lactobacillus</i> load and strain characteristics
Bibliographic record
Abstract
Although Lactobacillus species are recognized as normal inhabitants of porcine gastric mucosa, the association of these bacteria with health status or gastric ulcer disease has never been considered. We investigated the bacterial load of Lactobacillus isolated from the antrum, corpus, and pars esophagea of stomachs with (n = 13) and without (n = 10) ulcer of the pars esophagea of slaughtered pigs. We also evaluated in vitro antagonistic properties against typical pathogens of strains isolated from stomachs without ulcer. To quantify Lactobacillus, gastric mucosa samples obtained with 5 mm biopsy punches were smeared on MRS agar and colonies were counted after 48 h of incubation under anaerobic conditions. The score of Lactobacillus was significantly greater in the antrum and corpus of stomachs without ulcer (P < 0.001 for both) when compared with stomachs with ulcer. Fingerprint profiles, obtained by repetitive sequence-based PCR using (GTG)5 primers, showed that the isolates were highly diverse. The reduction of Lactobacillus load in porcine stomachs may be a contributing factor for gastric ulcer. Strains isolated from healthy stomachs, which showed a wide spectrum of antagonistic activity against pathogens, may be viewed as an untapped source of bacteria with potential beneficial properties that deserve to be further investigated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".