Milk Mineral and Cytokine Concentrations Differ in Guatemalan Mothers with Subclinical Mastitis (SCM) by Stage of Lactation
Bibliographic record
Abstract
Background Little is known about the impact of SCM on the immunological and mineral concentrations of human breast milk. Our objective was to determine if minerals and cytokines differed in mothers with and without SCM during 3 stages of lactation. Methods Transitional milk (TM: 5‐17d, n=21), early mature milk (EMM: 18‐46d, n=32) and mature milk (MM: 109‐187d, n=59) samples were collected from Mam ‐Mayan women. Inductively Coupled Plasma Mass Spectrometry was used to analyze the concentration of 13 minerals (Na, K, Ca, Mg, Mn, Zn, Cu, Cr, Sr, Se, Rb, Fe, P) and immunoassay with Luminex was used to determine the concentration of 4 cytokines (IL‐1β, IL‐6, IL‐8, TNF‐α). Results Prevalences of SCM, using Na/K > 0.6, were 26.3% in TM, 15.6% in EMM and 8.9% in MM. Na, K, P, Cu, Fe, Rb, Zn and IL6 were higher in TM and EMM whereas Mg was higher in MM, as was IL‐8. SCM was associated with changes in P and Se and with the presence of 3 cytokines (IL‐6, IL‐8, and TNF‐α) in TM only. Regression analyses for each mineral showed that cytokines were associated with higher milk mineral concentrations: IL‐1β with P, Fe and Mn; IL‐6 with Na, K, Ca and Cu; IL‐8 with Zn; and TNF‐α with Na, Mn and Se. Conclusion Milk minerals and cytokines concentrations vary by lactation stages. The cytokines are associated with changes in milk mineral concentrations. Our finding suggests that IL‐6 is associated with elevated Na/K ratio. Funding McGill University International Mobility Award
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 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".