Probiotic Studies in Neonatal Mice Using Gavage
Bibliographic record
Abstract
Adult mouse models have been widely used to understand the mechanism behind disease progression in humans. The applicability of studies done in adult mouse models to neonatal diseases is limited. To better understand disease progression, host responses and long-term impact of interventions in neonates, a neonatal mouse model likely is a better fit. The sparse use of neonatal mouse models can in part be attributed to the technical difficulties of working with these small animals. A neonatal mouse model was developed to determine the effects of probiotic administration in early life and to specifically assess the ability to establish colonization in the newborn mouse intestinal tract. Specifically, to assess probiotic colonization in the neonatal mouse, Lactobacillus plantarum (LP) was delivered directly into the neonatal mouse gastrointestinal tract. To this end, LP was administered to mice by feeding through intra-esophageal (IE) gavage. A highly reproducible method was developed to standardize the process of IE gavage that allows an accurate administration of probiotic dosages while minimizing trauma, an aspect particularly important given the fragility of newborn mice. Limitations of this process include possibilities of esophageal irritation or damage and aspiration if gavaged incorrectly. This approach represents an improvement on current practices because IE gavage into the distal esophagus reduces the chances of aspiration. Following gavage, the colonization profile of the probiotic was traced using quantitative polymerase chain reaction (qPCR) of the extracted intestinal DNA with LP specific primers. Different litter settings and cage management techniques were used to assess the potential for colonization-spread. The protocol details the intricacies of IE neonatal mouse gavage and subsequent colonization quantification with LP.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".