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Record W2888806144 · doi:10.1080/15548627.2018.1509609

Diverse mechanisms of autophagy dysregulation and their therapeutic implications: does the shoe fit?

2018· article· en· W2888806144 on OpenAlexafffundabout
Paalini Sathiyaseelan, Katharina Rothe, Kevin Yang, Jing Xu, Norman Chow, Svetlana Bortnik, Courtney Choutka, Cally J. Ho, Xiaoyan Jiang, Sharon M. Gorski

Bibliographic record

VenueAutophagy · 2018
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyCanada's Michael Smith Genome Sciences CentreSimon Fraser University
FundersCambridge Institute for Medical Research, University of CambridgeGenome Science Institute, Boston UniversityBC Cancer AgencySimon Fraser UniversityCanadian Institutes of Health ResearchCanada's Michael Smith Genome Sciences CentreBC Cancer FoundationNew England Biolabs
KeywordsAutophagyBiologyCell biologyNeuroscienceGeneticsApoptosis

Abstract

fetched live from OpenAlex

In its third edition, the Vancouver Autophagy Symposium presented a platform for vibrant discussion on the differential roles of macroautophagy/autophagy in disease. This one-day symposium was held at the BC Cancer Research Centre in Vancouver, BC, bringing together experts in cell biology, protein biochemistry and medicinal chemistry across several different disease models and model organisms. The Vancouver Autophagy Symposium featured 2 keynote speakers that are well known for their seminal contributions to autophagy research, Dr. David Rubinsztein (Cambridge Institute for Medical Research) and Dr. Kay F. Macleod (University of Chicago). Key discussions included the context-dependent roles and mechanisms of dysregulation of autophagy in diseases and the corresponding need to consider context-dependent autophagy modulation strategies. Additional highlights included the differential roles of bulk autophagy versus selective autophagy, novel autophagy regulators, and emerging chemical tools to study autophagy inhibition. Interdisciplinary discussions focused on addressing questions such as which stage of disease to target, which type of autophagy to target and which component to target for autophagy modulation.Abbreviations: AD: Alzheimer disease; AMFR/Gp78: autocrine motility factor receptor; CCCP: carbonyl cyanide m-chlorophenylhydrazone; CML: chronic myeloid leukemia; CVB3: coxsackievirus B3; DRPLA: dentatorubral-pallidoluysian atrophy; ER: endoplasmic reticulum; ERAD: ER-associated degradation; FA: focal adhesion; HCQ: hydroxychloroquine; HD: Huntingtin disease; HIF1A/Hif1α: hypoxia inducible factor 1 subunit alpha; HTT: huntingtin; IM: imatinib mesylate; MAP1LC3B: microtubule associated protein 1 light chain 3 beta; NBR1: neighbour of BRCA1; OGA: O-GlcNAcase; PDAC: pancreatic ductal adenocarcinoma; PLEKHM1: pleckstrin homology and RUN domain containing M1; polyQ: poly-glutamine; ROS: reactive oxygen species; RP: retinitis pigmentosa; SNAP29: synaptosome associated protein 29; SPCA3: spinocerebellar ataxia type 3; TNBC: triple-negative breast cancer.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0020.006
Scholarly communication0.0080.011
Open science0.0020.003
Research integrity0.0050.013
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.025
GPT teacher head0.293
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2018
Admission routes3
Has abstractyes

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