Frequencies of activated T cell populations increase in breast milk of HCMV-seropositive mothers during local HCMV reactivation
Bibliographic record
Abstract
Background Human cytomegalovirus (HCMV) can reactivate in the mammary gland during lactation and is shed into breast milk of nearly every HCMV-IgG-seropositive mother of a preterm infant. Dynamics of breast milk leukocytes during lactation, as well as blood leukocytes and the comparison between both in the context of HCMV reactivation is not well understood. Methods Here, we present the BlooMil study that aimed at comparing changes of immune cells in blood and breast milk from HCMV-seropositive- vs -seronegative mothers, collected at four time ranges up to two months post-partum. Viral load was monitored by qPCR and nested PCR. Multiparameter flow cytometry was used to identify leukocyte subsets. Results CD3+ T cell frequencies were found to increase rapidly in HCMV-seropositive mothers’ milk, while they remained unchanged in matched blood samples, and in both blood and breast milk of HCMV-seronegatives. The activation marker HLA-DR was more strongly expressed on CD4+ and CD8+ T cells in all breast milk samples than matched blood samples, but HCMV-seropositive mothers displayed a significant increase of HLA-DR+ CD4+ and HLA-DR+ CD8+ T cells during lactation. The CD4+/CD8+ T cell ratio was lower in breast milk of HCMV-seropositive mothers than in the blood. HCMV-specific CD8+ T cell frequencies (recognizing pp65 or IE1) were elevated in breast milk relative to blood, which might be due to clonal expansion of these cells during local HCMV reactivation. Breast milk contained very low frequencies of naïve T cells with no significant differences depending on serostatus. Conclusion Taken together, we conclude that the distribution of breast milk leukocyte populations is different from blood leukocytes and may contribute to the decrease of breast milk viral load in the late phase of HCMV reactivation in the mammary gland.
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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) | 0.002 | 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".