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Record W2326233272 · doi:10.1080/07373937.2016.1141781

Spray and freeze drying of human milk on the retention of immunoglobulins (IgA, IgG, IgM)

2016· article· en· W2326233272 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDrying Technology · 2016
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsUniversité LavalCentre hospitalier de l'Université LavalCentre hospitalier universitaire de Québec
FundersConsejo Nacional de Ciencia y TecnologíaUniversité Laval
KeywordsSpray dryingFreeze-dryingRelative humidityChemistryHumidityFood scienceChromatographyAntibodyMoistureImmunologyBiology

Abstract

fetched live from OpenAlex

Several freeze-drying and spray-drying methods were investigated in relation to the retention of immunoglobulins (Ig) A, IgG, and IgM. Spray drying produced human milk powders with 2% humidity and a good retention of IgG (>88%) and IgM (∼70%). However, only 38% of IgA remained after spray drying. For freeze drying, only the highest heating plate temperature used in this study (40°C) brought IgA content down to 55% in powder with 1.75% residual humidity, whereas milk samples undergoing lower temperatures had higher preservation rates (75% for IgA and 80% for IgG and IgM) and higher residual moisture contents. From these results, it can be concluded that IgA is the most sensitive Ig lost during drying processing of human milk. The best method to generate human milk powders without a significant loss of Ig was thus freeze drying at 30°C heating plate temperature, which accelerated the process compared to lower processing temperatures, but still had good overall Ig retention.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.028
GPT teacher head0.283
Teacher spread0.256 · 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