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Record W3075109078 · doi:10.2144/btn-2020-0071

An Optimized Procedure For Quantitative Analysis of Mitophagy With the Mtkeima System Using Flow Cytometry

2020· article· en· W3075109078 on OpenAlexafffund
Nathaniel J. Winsor, Samuel A. Killackey, Dana J. Philpott, Stephen E. Girardin

Bibliographic record

VenueBioTechniques · 2020
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMitophagyFlow cytometryBiologyQuantitative analysis (chemistry)CytometryComputational biologyCell biologyComputer scienceAutophagyChemistryMolecular biologyGeneticsApoptosisChromatography

Abstract

fetched live from OpenAlex

Mitophagy is the process by which mitochondria are selectively targeted and removed via autophagic machinery to maintain mitochondrial homeostasis in the cell. Recently, flow cytometry-based assays that utilize the fluorescent mtKeima reporter system have allowed for quantitative assessment of mitophagy at a single-cell level. However, clear guidelines for appropriate flow cytometry workflow and downstream analysis are lacking and studies using flow cytometry in mtKeima-expressing cells often display incorrect and arbitrary binary mitophagic or nonmitophagic cutoffs that prevent proper quantitative analyses. In this paper we propose a novel method of mtKeima data analysis that preserves subtle differences present within flow cytometry data in a manner that ensures reproducibility.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.043
GPT teacher head0.349
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations9
Published2020
Admission routes2
Has abstractyes

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