Immunotherapy in mastitis: state of knowledge, research gaps and way forward
Bibliographic record
Abstract
Mastitis is an inflammatory condition that affects dairy cow's mammary glands. Traditional treatment approaches with antibiotics are increasingly leading to challenging scenarios such as antimicrobial resistance. In order to mitigate the unwanted side effects of antibiotics, alternative strategies such as those that harness the host immune system response, also known as immunotherapy, have been implemented. Immunotherapy approaches to treat bovine mastitis aims to enhance the cow's immune response against pathogens by promoting pathogen clearance, and facilitating tissue repair. Various studies have demonstrated the potential of immunotherapy for reducing the incidence, duration and severity of mastitis. Nevertheless, majority of reported therapies are lacking in specificity hampering their broad application to treat mastitis. Meanwhile, advancements in mastitis immunotherapy hold great promise for the dairy industry, with potential to provide effective and sustainable alternatives to traditional antibiotic-based approaches. This review synthesizes immunotherapy strategies, their current understanding and potential future perspectives. The future perspectives should focus on the development of precision immunotherapies tailored to address individual pathogens/group of pathogens, development of combination therapies to address antimicrobial resistance, and the integration of nano- and omics technologies. By addressing research gaps, the field of mastitis immunotherapy can make significant strides in the control, treatment and prevention of mastitis, ultimately benefiting both animal and human health/welfare, and environment health.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".