A study of PCSK9 in glucose and insulin homeostasis
Bibliographic record
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is thought to account for >30% of all deaths today.Patients at very high risk, who suffer from ASCVD and comorbidities, such as diabetes, are projected to have 10-year event risks between 26% to 43%.Considering that lowdensity lipoprotein cholesterol (LDLc) is the main driver of ASCVD and that reducing its circulating levels was demonstrated to be beneficial in reducing clinical events, new guidelines required patients to reduce their LDLc levels to very low levels.Therefore, therapeutic agents are currently being developed in order to decrease this societal predicament.One of the very promising agents is directed towards lowering proprotein convertase subtilisin/kexin type 9 (PCSK9) synthesis or circulating levels.Indeed, clinical trials on these PCSK9-inhibitors demonstrated safety and beneficial effects on cardiovascular outcomes.Several research groups are interested in determining whether PCSK9 may be important for other processes than cholesterol homeostasis.perseverant and enthusiastic about science.This experience has led me to work alongside wonderful people everyday, learn and grow from challenges and failures and travel abroad to meet scientists from around the world, several of which have become close friends.I would also like to thank Dr. Annik Prat for being a strong example of a critical scientist, with immense care for excellent quality science.Annik and Nabil have proven to be essential pillars for this long journey through the thesis and I thank them for being there for me.
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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 | 0.001 | 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 | 0.000 | 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".