<i>Lactobacillus rhamnosus</i> CRL1505 beneficially modulates the immuno-coagulative response after pneumococcal infection in immunocompromised malnourished mice
Bibliographic record
Abstract
This work evaluated the effect of orally or nasally administered Lactobacillus rhamnosus CRL1505 on the resistance of immunocompromised protein-malnourished mice to pneumococcal infection. In particular, we aimed to gain knowledge of the mechanism involved in the immunomodulatory effect of L. rhamnosus CRL1505 in malnourished hosts by evaluating its impact on the immuno-coagulative response. Malnutrition significantly increased lung tissue damage caused by Streptococcus pneumoniae infection. Lung damage was associated with a deregulated activation of coagulation and an altered inflammatory response. Pneumococcal colonization of lung and bacteremia were significantly reduced (p < 0.05) in malnourished mice receiving the CRL1505 strain. Moreover, mice repleted with supplemental L. rhamnosus CRL1505 showed the least alteration of the alveolar-capillary barrier and cell damage in lungs after the infectious challenge, especially when the CRL1505 strain was administered by nasal route. Besides, mice treated with L. rhamnosus CRL1505 showed an improved respiratory innate immune response and a lower activation of coagulation. The results of this work indicate that L. rhamnosus CRL1505 is able to beneficially modulate the inflammation-coagulation interaction after respiratory infections in malnourished hosts.
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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) | 0.001 | 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 | 0.001 |
| 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".