Effect of Flash-Heat Treatment on Immunoglobulins in Breast Milk
Bibliographic record
Abstract
BACKGROUND: Heat-treated expressed breast milk is recommended by the World Health Organization as an option to reduce vertical HIV transmission in resource-poor regions. Flash-heat (FH) is a low technology pasteurization method developed for home use, but its effect on quantity and quality of breast milk immunoglobulins is unknown. OBJECTIVE: To evaluate FH's effect on breast milk immunoglobulin levels and antigen-binding capacity. DESIGN/METHODS: Fifty HIV+ mothers in South Africa provided breast milk. Part of each sample served as an unheated control; the remainder was flash-heated. Total and antigen-specific immunoglobulin A (IgA) and immunoglobulin G (IgG) were measured by enzyme-linked immunosorbent assay. Paired t test was performed on log-transformed data. RESULTS: FH significantly decreased total IgA and IgG concentrations [geometric mean (geometric SD) 318.0 (1.9) vs. 398.2 (1.9) microg/mL and 89.1 (2.7) vs. 133.3 (2.5) microg/mL, P < 0.001 each]. Similar decreases in anti-HIV-1 gp120 IgG, anti-pneumococcal polysaccharide, and anti-poliovirus IgA occurred (P < 0.001 each). Although the latter was most affected, FH retained 66% of the antigen-binding ability. In contrast, binding capacity of IgA and IgG to influenza increased after FH (P = 0.029 and 0.025, respectively). CONCLUSIONS: Most breast milk immunoglobulin activity survives FH, suggesting flash-heated breast milk is immunologically superior to breast milk substitutes. Clinical significance of this decreased immunoglobulin activity needs evaluation in prospective trials.
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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".