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Record W4285095558 · doi:10.3233/jhd-220536

Recent Microscopy Advances and the Applications to Huntington’s Disease Research

2022· review· en· W4285095558 on OpenAlexafffund
Mouhanad Babi, Kaitlyn Neuman, Christina Peng, Tamara Maiuri, Celeste Suart, Ray Truant

Bibliographic record

VenueJournal of Huntington s Disease · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchKrembil Foundation
KeywordsHuntingtinContext (archaeology)MicroscopyPolyglutamine tractHuntingtin ProteinStructural biologyComputational biologyBiologyNanotechnologyCell biologyChemistryBiophysicsPhysicsBiochemistryMaterials scienceOptics

Abstract

fetched live from OpenAlex

Huntingtin is a 3144 amino acid protein defined as a scaffold protein with many intracellular locations that suggest functions in these compartments. Expansion of the CAG DNA tract in the huntingtin first exon is the cause of Huntington's disease. An important tool in understanding the biological functions of huntingtin is molecular imaging at the single-cell level by microscopy and nanoscopy. The evolution of these technologies has accelerated since the Nobel Prize in Chemistry was awarded in 2014 for super-resolution nanoscopy. We are in a new era of light imaging at the single-cell level, not just for protein location, but also for protein conformation and biochemical function. Large-scale microscopy-based screening is also being accelerated by a coincident development of machine-based learning that offers a framework for truly unbiased data acquisition and analysis at very large scales. This review will summarize the newest technologies in light, electron, and atomic force microscopy in the context of unique challenges with huntingtin cell biology and biochemistry.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.040
GPT teacher head0.419
Teacher spread0.379 · 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.

Study designNot applicable
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

Citations2
Published2022
Admission routes2
Has abstractyes

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