Comparison of breast milk sodium‐potassium ratio, pro‐inflammatory cytokines, and somatic cell count as potential biomarkers of subclinical mastitis (623.21)
Bibliographic record
Abstract
Background: SCM is an asymptomatic inflammatory condition of the lactating breast associated with infant growth faltering. A breast milk Na/K ratio >0.6 is the current indicator for SCM whereas in cows SCC is considered the ‘gold standard’. Our objective was to determine if potential biomarkers of human SCM (SCC, pro‐inflammatory cytokines) were associated with Na/K ratio at two stages of lactation. Methods: Breast milk samples were collected from 53 lactating Mam‐Mayan women with infants < 45d (early lactation) and 52 with infants 4‐6mo (late lactation). Inductively Coupled Plasma Mass Spectrometry measured Na and K. Flow Cytometry was used to measure SCC. Luminex measured 3 pro‐inflammatory cytokines (IL‐6, IL‐8, TNF‐α). Results: One‐fifth had a breast milk Na/K ratio >0.6; an elevated Na/K ratio was more common in early lactation (26%) compared to later lactation (15%). By contrast, both IL‐8 (10.3 vs 38.3 pg/mL) and SCC (313K vs 457K cells/mL) were lower in early verses later lactation. The Na/K ratio was positively correlated with all 3 pro‐inflammatory cytokines in early lactation whereas only IL‐8 was associated in later lactation. SCC was not correlated with either Na/K ratio or pro‐inflammatory cytokines in either stage of lactation. Conclusion: Potential biomarkers of SCM vary by stage of lactation. Based on the Na/K ratio, SCM is more prevalent during early lactation. Results suggest that pro‐inflammatory cytokines may emerge as possible biomarkers and that SCC may not be a useful biomarker of SCM in lactating mothers. Grant Funding Source : McGill University International Mobility Award, NSERC
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".