Meta-analysis of NAD(P)(H) quantification results exhibits variability across mammalian tissues
Bibliographic record
Abstract
Abstract Nicotinamide Adenine Dinucleotide (NAD+) plays an important role in energy metabolism and signaling pathways controlling crucial cellular functions. The increased interest in NAD+ metabolism and NAD+-boosting therapies has reinforced the necessity for accurate NAD+ quantification. To examine the published NAD(P)(H) measures across mammalian tissues, we performed a meta-analysis of the existing data. An Ovid MEDLINE database search identified articles with NAD(P)(H) quantification results obtained from mammalian tissues published between 1961 and 2021. We screened 4890 records and extracted quantitative data, as well as the quantification methods, pre-analytical conditions, and subject characteristics. The extracted physiological NAD(P)(H) concentrations in various tissues from mice, rats, and humans, revealed an important inter- and intra-method variability that extended to recent publications. This highlights the relatively poor potential for cross-experimental analyses for NAD(P)(H) quantitative data and the importance of standardization for NAD(P)(H) quantification methods and pre-analytical procedures for future preclinical and clinical studies.
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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 | 0.029 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".