Bibliographic record
Abstract
Dietary niacin deficiency, and pharmacological excesses of nicotinic acid or nicotinamide, have dramatic effects on cellular NAD pools, ADP-ribose metabolism, tissue function and health. ADP-ribose metabolism is providing new targets for pharmacological intervention, and it is important to consider how the supply of vitamin B3 may directly influence ADP-ribosylation reactions, or create interactions with other drugs designed to influence these pathways. In addition to its redox roles, NAD+ is used as a substrate for mono-, poly- and cyclic ADP-ribose formation. During niacin deficiency, not all of these processes can be maintained, and dramatic changes in tissue function and clinical condition take place. Conversely, these reactions may be differentially enhanced by pharmacological intakes of vitamin B3, and potentially by changing expression of specific NAD generating enzymes. A wide range of metabolic changes can take place following pharmacological supplementation of nicotinic acid or nicotinamide. As niacin status decreases towards a deficient state, the function of other types of pharmaceutical agents may be modified, including those that target ADP-ribosylation reactions, apoptosis and inflammation. This article will explore what is known and yet to be learned about the response of tissues, cells and subcellular compartments to excessive and limiting supplies of niacin, and will discuss the etiology of the resulting pathologies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| 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".