Gene-Based Targeting for Treatment of Hypercholesterolemia and Prevention of Atherosclerotic Cardiovascular Disease
Bibliographic record
Abstract
Familial hypercholesterolemia, characterized by high levels of LDL cholesterol is a monogenic metabolic disorder linked to atherosclerotic cardiovascular disease. Several genetic mutations in genes such as LDLR and PSK9 are thought to cause familial hypercholesterolemia. This ultimately causes dysfunction of the LDLR pathway, increasing circulating LDL levels. Current treatment revolves around decreasing LDL cholesterol levels in the blood. Statins are the current gold standard while sterol absorption inhibitors (ezetimibe) and PCSK9 inhibitors (evolocumab) are either used as primary treatment in individuals with statin intolerance, or supplementary treatment otherwise. However, CRISPR-Cas9 offers an upstream therapeutic intervention, inducing mutations in pathogenic genes (such as LDLR) to upregulate or downregulate gene expression and subsequently reduce LDL cholesterol levels. This technology thus has great potential as a two-fold treatment option, offering an effective solution for familial hypercholesterolemia, while simultaneously diminishing the risk of atherosclerotic cardiovascular disease. A limitation to current research exists in the sole analysis of monogenic factors in genetic screening, thus polygenic, multifactorial analyses are required to assess more holistic treatment plans. Furthermore, there exist current limitations to CRISPR-Cas9 including ethical limitations and social considerations revolving around the perpetuation of social inequities through technology necessitating future research on global health policies.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".