The Effect of Breast Milk Storage Container on the Amount of Probiotic Microbiota in Breast Milk
Bibliographic record
Abstract
Developing babies require important bacteria from breast milk, which impact gut microbiota in adulthood and lifelong health. Breast milk also contains hundreds to thousands of different microbiomes. These microbiomes prevent infection, inflammation, organ growth, healthy microbial colonization, and aid immune maturation. The type of storage container material used influences bacterial colonization of breast milk. The aim of this study was to analyze the effect of storage containers on breast milk probiotic microbiota. The type of research is a true experiment with a pretest and posttest control group design. The probiotic microbiota studied is Lactic Acid Bacteria (LAB). The examination of the total colonization of lactic acid bacteria was carried out using the Quebec Colony Counter Unit. Data were analyzed by paired sampleT-test. The results showed that the average number of Lactic Acid Bacteria colonies stored in glass bottle is greater than the number of LAB colonies stored in plastic bag (95x106 CFU/ml vs 77x106 CFU/ml). However, statistical tests showed no significant difference between the number of BAL colonies of breast milk stored in plastic bags and breast milk stored in glass bottles.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".