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Record W4386989285 · doi:10.1093/pch/pxad055.067

67 Infant Health Status Impacts the Neutrophil Phenotypes and Polyunsaturated Fatty Acids Composition in Human Milk

2023· article· en· W4386989285 on OpenAlexaboutno aff
Rana Badewy, Michael Glogauer, Amir Azarpazhooh, Howard C. Tenenbaum, Kristin L. Connor, Michael J. Sgro, Richard P. Bazinet, Noah Fine, Chunxiang Sun, Sourav Saha, Jim Yuan Lai

Bibliographic record

VenuePaediatrics & Child Health · 2023
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsnot available
Fundersnot available
KeywordsBreastfeedingMedicineLactationPhysiologyProspective cohort studyPolyunsaturated fatty acidFatty acidImmunologyBreast milkPregnancyInternal medicinePediatricsBiologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background The abundance of human milk leukocytes, including neutrophils, changes in response to the health status of mothers and their infants. Elevated leukocyte counts occur during infant infection and return to baseline levels upon recovery. However, it is unclear how infant health status can affect the neutrophil phenotypes and the concentration of fatty acids in their mothers’ human milk. Objectives This study investigates the association between infant health status and human milk neutrophil counts and activation state, and fatty acid levels, in human milk. Design/Methods This is a prospective cohort study of 50 healthy breastfeeding mothers recruited from St. Michael’s hospital, in Toronto, Ontario, who were followed up from 2-4 weeks until 4 months postpartum. Human milk samples were collected from participants and data regarding infant health status were collected from self-reported questionnaires completed by participants at both timepoints, then imputed based on whether infants had taken antibiotics, had been hospitalized, or had been diagnosed with a medical condition, at least 2 weeks prior to samples collection. Neutrophils were quantified using flow cytometry and labelled with a panel of antibodies to detect specific cluster of differentiation (CD) biomarkers. Fatty acids were identified and quantified using a gas chromatography-flame ionization detector (GC-FID). Linear mixed-effects models were used to evaluate the correlation between infant health status and changes in neutrophil counts, along with their expressed CD markers, and fatty acids composition in human milk during lactation. Results The CD markers were classified into 4 categories based on their function: degranulation/activation markers (CD63, CD64, and CD66a), an immunoregulation marker (CD16), adhesion markers (CD11b, CD18), and a lipopolysaccharide receptor (CD14). Human milk from mothers whose infants had a health condition had neutrophils which significantly expressed elevated levels of the CD64 biomarker (β: 85.65, p=0.009 in adjusted models), compared to human milk from mothers of healthy infants. However, there were no significant associations between infant health status, absolute hmPMN counts, andother CD biomarkers (p>0.05). There were also elevated levels of arachidonic acid (β: 0.12, p=0.013) and C22:5n-6 (β: 0.06, p=0.015) fatty acids in the human milk of mothers whose infants had a health condition, compared with human milk from mothers of healthy infants, after adjusting for maternal age, post-pregnancy body mass index, infant sex, and infant feeding pattern. Conclusion This study demonstrates that infant health status is associated with changes in human milk immunological components, suggesting an inflammatory protective response mechanism in the mammary glands triggered by infants infection. How these alterations can affect infant health outcomes in the short and longer terms need critical consideration.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.306
Teacher spread0.289 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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