From Infection to Infertility: Diagnostic, Therapeutic, and Molecular Perspectives on Postpartum Metritis and Endometritis in Dairy Cows
Bibliographic record
Abstract
Postpartum uterine diseases such as metritis and endometritis impair reproductive performance and cause substantial economic losses in dairy cows worldwide. The multifactorial etiology, involving polymicrobial infections and complex host immune responses, poses diagnostic and therapeutic challenges. Traditional treatments rely on antibiotics, e.g., cephalosporins like ceftiofur and cephapirin, with broad-spectrum efficacy. However, emerging antimicrobial resistance, biofilm formation by pathogens such as Trueperella pyogenes, Fusobacterium necrophorum, and Escherichia coli, and bacterial virulence factors have reduced effectiveness of conventional therapies. Advances in systems biology, particularly proteomics, metabolomics, and microRNA (miRNA) profiling, have provided unprecedented insights into the molecular mechanisms underpinning uterine disease pathophysiology. Proteomic analyses reveal dynamic changes in inflammatory proteins and immune pathways, whereas metabolomics highlight shifts in energy metabolism and bacterial–host interactions. Furthermore, miRNAs have critical roles in post-transcriptional gene regulation affecting immune modulation, inflammation, and tissue repair, and also in modulating neutrophil function and inflammatory signaling. Uterine inflammation not only disrupts local tissue homeostasis but also compromises early embryo development by altering endometrial receptivity, cytokine milieu, and oocyte quality. Integration of multi-omics approaches, combined with improved diagnostics and adjunct therapies—including micronutrient supplementation and immunomodulators—offers promising avenues for enhancing disease management and fertility in dairy herds. This review synthesizes current knowledge on proteomics, metabolomics, and miRNAs in postpartum uterine diseases and highlights future directions for research and clinical applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".