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TO INVESTIGATE THE EFFECT OF THE PASTEURISATION PROCESS ON TRACE ELEMENTS IN DONOR BREAST MILK

2016· article· en· W2507446257 on OpenAlexaff
Nor Mohd Taufek, David Cartwright, Amitha K. Hewavitharana, Pieter Koorts, Helen McConachy, Karen Whitfield, Mark R. Davies

Bibliographic record

VenueArchives of Disease in Childhood · 2016
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsMedicinePasteurizationTRACE (psycholinguistics)Breast milkProcess (computing)Food scienceBiochemistry

Abstract

fetched live from OpenAlex

AIM: To investigate the effect of the pasteurisation process on trace elements in donor breast milk. METHOD: Premature infants often receive donor breast milk when the mother is unable to produce sufficient breast milk. It is widely accepted that donor milk has considerable advantages over formula milk.1 The Royal Brisbane and Women's Hospital (RBWH) has a milk bank that receives milk donated by women which undergoes a pasteurisation process.2 This study investigated the effect of pasteurisation on a range of trace elements in donor milk.A total of 14 participants who donated to the milk bank were recruited in this study. A 2 ml sample was collected pre- and post- pasteurisation, and frozen at -80 °C. Post-natal age of the milk was documented. Inductively-coupled plasma mass-spectrometry was used to analyse the following trace elements - zinc (Zn), copper (Cu), selenium (Se), manganese (Mn), iodine (I), iron (Fe), molybdenum (Mo) and bromine (Br). The study received ethical approval from RBWH and The University of Queensland Ethics Committee. RESULTS: No significant difference was found between the levels of any of the trace elements tested pre- and post-pasteurisation. The following p-values were calculated - Zn (0.82), Cu (0.80), Se (0.97), Mn (0.63), I (0.99), Fe (0.05), Mo (0.41), Br (0.59). The following ranges in mcg/L of trace elements were calculated - Zn (365.4-5460.0), Cu (157.6-820.5), Se (10.6-23.7), Mn (0.55-3.24), I (66.4-215.3), Fe (101.5-473.1), Mo (0.20-5.45), Br (704.9-3379.0). Spearman's rank correlation analysis showed significant correlations between post-natal age of milk and trace elements - Zn (ρ=-0.578), Se (ρ=-0.627). Fe (ρ=-0.704), and Mo (ρ=-0.534). No significant correlation was found for Cu, Mn, I, and Br. CONCLUSION: This study found that the pasteurisation process had minimal effect on trace element levels in donor breast milk. However, it was noted that there was a correlation between post-natal age of donor milk and Zn, Se, Fe and Mo. Further work is needed to establish factors that may influence levels of trace elements in donor milk such as post-natal age.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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".

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Citations4
Published2016
Admission routes1
Has abstractyes

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