Distribution of intranasal instillations in mice: effects of volume, time, body position, and anesthesia
Bibliographic record
Abstract
Intranasal instillation techniques are used to deliver various substances to the upper and lower respiratory tract (URT and LRT) in mice. Here, we quantify the relative distribution achieved with intranasal delivery of a nonabsorbable tracer, (99m)Tc-labeled sulfide-colloid. Relative distribution was determined by killing mice after instillation and quantifying the radioactivity in dissected tissues using gamma scintigraphy. A significant effect of delivery volume on relative distribution was observed when animals were killed 5 min after instillation delivered under gas anesthesia. With a delivery volume of 5 microl, no radiation was detected in the LRT; this increased to a maximum of 55.7 +/- 2.5% distribution to the LRT when 50 microl were delivered. The majority of radiation not detected in the LRT was found in the URT. Over the course of the following 1 h, radiation in the LRT remained constant, while that in the URT decreased and appeared in the gastrointestinal tract. Instillation of 25 microl into anesthetized mice resulted in 30.1 +/- 6.9% distribution to the LRT, while only 5.3 +/- 1.5% (P < 0.05) of the same volume was detected in the LRT of awake mice. Varying the body position of mice did not affect relative distribution. When using intranasal instillation, the relative distribution between the URT and LRT and the gastrointestinal tract is heavily influenced by delivery volume and level of anesthesia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".