Bibliographic record
Abstract
The goal of drugs is to modulate the activity of particular gene products in order to change the physiological state of an organism from disease to health. Because of the complexity of development and metabolism, it is difficult to extrapolate from in vitro experimental results using purified reagents, to predict the actual change in behavior of an entire biological system in vivo. Metabolic control analysis provides a theoretical framework for understanding the basis of this apparent paradox. In practice, genetics provides a unique opportunity to observe the behavior of whole systems in the presence of different amounts of a particular gene product, resulting from varying genomic sequences of alleles governing the expression or activity of that gene product. This is most evident in the case of monogenic disorders, in which severe mutations in a gene lead to clear effects on gene product activity, and strong causal genotype/phenotype correlations can be inferred. Among the many therapeutic targets currently in development for treatment of dyslipidemia, a major risk factor for cardiovascular disease, there is particular interest in those targets whose coding genes are associated with molecularly characterized monogenic human conditions. Herein a selection of these genetic disorders, especially those involving HDL and LDL cholesterol, is discussed in the context of ongoing or potential therapeutic development programs. This article also includes recent patent review coverage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".