Harnessing Mycobacterium bovis BCG Trained Immunity to\nControl Human and Bovine Babesiosis
Bibliographic record
Abstract
Babesiosis is a disease caused by tickborne hemoprotozoan apicomplexan parasites of the genus Babesia that negatively impacts public health and food security worldwide. Development of effective and sustainable vaccines against babesiosis is currently hindered in part by the absence of definitive host correlates of protection. Despite that, studies in Babesia microti and Babesia bovis, major causative agents of human and bovine babesiosis, respectively, suggest that early activation of innate immune responses is crucial for vertebrates to survive acute infection. Trained immunity (TI) is defined as the development of memory in vertebrate innate immune cells, allowing more efficient responses to subsequent specific and non-specific challenges. Considering that Mycobacterium bovis bacillus Calmette-Guerin (BCG), a widely used anti-tuberculosis attenuated vaccine, induces strong TI pro-inflammatory responses, we hypothesize that BCG TI may protect vertebrates against acute babesiosis. This premise is supported by early investigations demonstrating that BCG inoculation protects mice against experimental B. microti infection and recent observations that BCG vaccination decreases the severity of malaria in children infected with Plasmodium falciparum, a Babesia-related parasite. We also discuss the potential use of TI in conjunction with recombinant BCG vaccines expressing Babesia immunogens. In conclusion, by concentrating on human and bovine babesiosis, herein we intend to raise awareness of BCG TI as a strategy to efficiently control Babesia infection.
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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".