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Record W2921523274 · doi:10.21037/pm.2019.02.02

Comparison and culturing different types of cells from fresh breast milk with different culture medium

2019· article· en· W2921523274 on OpenAlexaff
Chuanqing Tang, Qi Zhou, Chunmei Lu, Man Xiong, Shookim Lee

Bibliographic record

VenuePediatric Medicine · 2019
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsMount Sinai Hospital
Fundersnot available
KeywordsFlow cytometryBreast milkMesenchymal stem cellColostrumLactationAndrologyCell cultureBiologyChemistryFood scienceImmunologyAntibodyCell biologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Background: To investigate the properties of breast milk cells at different stages and culture different types of cells from fresh breast milk. Methods: Cell concentration and viability were analyzed after being isolated from fresh breast milk. Cell properties were tested by flow cytometry with different cell surface markers. Different types of breast milk cells were cultured with different medium. After being purified, the cells were identified by flow cytometry and immunofluorescence. Results: The concentration of breast milk cells decreased with lactation time. The viability of breast milk cells decreased gradually in both colostrum and transitional milk (C and T milk) group and mature milk group, and there was no significant difference in two groups over time. The expression levels of different cell surface markers were higher in C and T milk than mature milk. Using three different culturing media, we could get different types of cells including immune cells, mammary epithelial cells (MECs), mesenchymal stem cells (MSCs) and breast milk stem cells (BSCs) with different efficiency between C and T milk and mature milk. Conclusions: C and T milk contains more cells than mature milk, especially immunocytes. It is feasible to culture MECs with F medium, MSCs with M medium or F medium, and BSCs with CM medium.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.618

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.012
GPT teacher head0.269
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, 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

Citations9
Published2019
Admission routes1
Has abstractyes

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