The promise and challenges of RNA-targeted therapeutics in preventive cardiology
Bibliographic record
Abstract
Despite landmark achievements made in the past 50 years to understand, treat, and prevent atherosclerotic cardiovascular diseases (ACVD), preventing heart attacks and strokes in high-risk patients still remains very challenging for clinicians. Our pharmacological arsenal of preventive therapies like statins and medicines targeting high blood pressure are effective in reducing the burden of ACVD, but they leave behind an important ‘residual risk’. Over the years, it has become increasingly recognized that our sedentary lifestyle and toxic food environment, our exposure to chronic stress, tobacco smoke and poor air quality contribute greatly to this so-called residual risk. The rise in the prevalence of cardiometabolic disease such as type 2 diabetes (T2D) and non-alcoholic fatty liver diseases (NAFLD) combined with systemic inflammation have also been shown to increase ACVD risk burden in the general population. From a pathophysiological standpoint, apolipoprotein B (apoB)-containing lipoproteins are the major drivers of ACVD and we have yet to find the threshold at which further lowering blood levels of apoB-containing lipoproteins is not beneficial for cardiovascular health.1 In the majority of patients, the most abundant atherogenic apoB-containing lipoproteins in the bloodstream include triglyceride-rich lipoproteins such as very-low-density lipoproteins (VLDLs) and their remnants, low-density lipoproteins (LDLs) and lipoprotein(a) (Lp[a]). ApoB-containing lipoproteins are the major culprit in ACVD and since most clinicians have limited options to target factors contributing to ACVD residual risk, developing therapies that will further reduce all apoB-containing lipoproteins is likely to represent the most effective and achievable strategy to prevent ACVD in high-risk patients.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".