The guinea-pig is a poor animal model for studies of niacin deficiency and presents challenges in any study using purified diets
Bibliographic record
Abstract
The guinea-pig was previously reported as being sensitive to a niacin-deficient (ND), high-protein diet, suggesting that it is a suitable model for the low tryptophan to NAD+ conversion observed in human subjects. However, these studies were based on growth rates and mortality. The objective of the present study was to determine whether guinea-pigs are suitable for ND studies based on measurements of blood and bone marrow NAD+. Using a 20 % casein diet, ND decreased blood NAD+ after 4 weeks, but this parameter returned to normal after 9 weeks of feeding, while bone marrow was decreased by 35 % at this time point. Using a 15 % casein diet, 7 weeks of ND caused 44 and 42 % decreases in blood and bone marrow NAD+. Using a 10 % casein diet, ND decreased NAD+ by 32 % in blood and 62 % in bone marrow at 7 weeks. Growth rates were directly related to the dietary tryptophan content, with the lowest growth rates seen with the 10 % casein diet. Changes in guinea-pig NAD+ are comparable with the rat model at similar levels of dietary tryptophan, while mortality rates were dramatically higher in the guinea-pig model. The present study concludes that mortality in ND guinea-pigs is not indicative of poor tryptophan conversion, but is due to environmental stresses in guinea-pigs that are not observed with rats. We conclude that guinea-pigs are not suitable for research on niacin deficiency and they present challenges for any study requiring purified diets and wire-bottomed cages.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".