Murine and Human Cathelicidins Contribute Differently to Hallmarks of Mastitis Induced by Pathogenic Prototheca bovis Algae
Bibliographic record
Abstract
Prototheca bovis (formerly P. zopfii genotype-II) is an opportunistic, achlorophyllous alga that causes mastitis in cows and skin disease in cats and dogs, as well as cutaneous lesions in both immunocompetent and immunosuppressed humans. Antifungal medications are commonly ineffective. This study aimed to investigate innate immune responses contributed by cathelicidins to P. bovis in the mammary gland using a mastitis model in mice deficient in the sole murine cathelicidin (Camp). We determined P. bovis caused acute mastitis in mice and induced Camp gene transcription. Whereas Camp-/- and Camp+/+ littermates had similar local algae burden, Camp+/+ mice produced more pro-inflammatory cytokines, TNF α and Cxcl-1. Likewise, Camp+/+ bone marrow-derived macrophages were more responsive to P. bovis, producing more TNF α and Cxcl-1. Human cathelicidin (LL-37) exhibited a different effect against P. bovis; it had direct algicidal activity against P. bovis and lowered TNF α, Cxcl-1, and IL-1β production in both cultured murine macrophages and mammary epithelial cells exposed to the pathogenic algae. In conclusion, cathelicidins were involved in protothecosis pathogenesis, with unique roles among the diverse peptide family. Whereas endogenous cathelicidin (Camp) was key in mammary gland innate defense against P. bovis, human LL-37 had algicidal and immunomodulatory functions.
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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".