Precision autophagy: Will the next wave of selective autophagy markers and specific autophagy inhibitors feed clinical pipelines?
Bibliographic record
Abstract
Research presented at the Vancouver Autophagy Symposium (VAS) 2014 suggests that autophagy's influence on health and disease depends on tight regulation and precision targeting of substrates. Discussions recognized a pressing need for robust biomarkers that accurately assess the clinical utility of modulating autophagy in disease contexts. Biomarker discovery could flow from investigations of context-dependent triggers, sensors, and adaptors that tailor the autophagy machinery to achieve target specificity. In his keynote address, Dr. Vojo Deretic (University of New Mexico) described the discovery of a cargo receptor family that utilizes peptide motif-based cargo recognition, a mechanism that may be more precise than generic substrate tagging. The keynote by Dr. Alec Kimmelman (Harvard Medical School) emphasized that unbiased screens for novel selective autophagy factors may accelerate the development of autophagy-based therapies. Using a quantitative proteomics screen for de novo identification of autophagosome substrates in pancreatic cancer, Kimmelman's group discovered a new type of selective autophagy that regulates bioavailable iron. Additional presentations revealed novel autophagy regulators and receptors in metabolic diseases, proteinopathies, and cancer, and outlined the development of specific autophagy inhibitors and treatment regimens that combine autophagy modulation with anticancer therapies. VAS 2014 stimulated interdisciplinary discussions focused on the development of biomarkers, drugs, and preclinical models to facilitate clinical translation of key autophagy discoveries.
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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 | 0.026 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".