Natural defense by saliva and mucosa against oral infection by<i>Leptospira</i>
Bibliographic record
Abstract
Leptospirosis caused by drinking water has not been as frequently reported as percutaneous infection. Resistance to oral infection by pathogenic Leptospira was examined in an experimental hamster infection model. The results suggested some natural defenses against oral infection by Leptospira. First, we found that characteristic linear agglutination of Leptospira rapidly occurs when mixed with human saliva. That human saliva attenuated the infectivity of the treated leptospires by its agglutination activity suggested saliva to be the first line of defense against oral infection by leptospires. Second, only 10(1) Leptospira organisms caused death after submucosal injection into oral mucosa in hamsters, but oral infection with drinking water containing 10(5) organisms/mL did not cause death. This result showed that the mucosa plays the role of a physical barrier. Third, hamsters intragastrically infected by leptospires, with doses lethal to hamsters in oral infection, showed no signs of illness, which suggested that gastric acid plays an important role in preventing oral infection. Based on these results, saliva, mucosa, and gastric acid make up a natural defense, which confers high resistance to hosts against oral infection by leptospires.
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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 | 0.000 | 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) | 0.001 | 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".