MétaCan
Menu
Back to cohort
Record W2583496572 · doi:10.1017/s1431927616012708

Visualization of Cellular Components in a Mammalian Cell with Liquid-Cell Transmission Electron Microscopy

2017· article· en· W2583496572 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicroscopy and Microanalysis · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersMax-Planck-Gesellschaft
KeywordsTransmission electron microscopyElectron microscopeCellIntracellularMicroscopyMaterials scienceBiophysicsCell membraneVesicleChemistryMembraneNanotechnologyOpticsBiologyBiochemistryPhysics

Abstract

fetched live from OpenAlex

We present liquid-cell transmission electron microscopy (liquid-cell TEM) imaging of fixed and non-fixed prostate cancer cells (PC3 and LNCaP) with high resolution in a custom developed silicon nitride liquid cell. Fixed PC3 cells were imaged for 90-120 min without any discernable damage. High contrast on the cellular structures was obtained even at low electron doses (~2.5 e-/nm2 per image). The images show distinct structures of cell compartments (nuclei and nucleoli) and cell boundaries without any further sample embedding, dehydration, or staining. Furthermore, we observed dynamics of vesicles trafficking from the cell membrane in consecutive still frames in a non-fixed cell. Our findings show that liquid-cell TEM, operated at low electron dose, is an excellent tool to investigate dynamic events in non-fixed cells with enough spatial resolution (few nm) and natural amplitude contrast to follow key intracellular processes.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.099
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.007
GPT teacher head0.298
Teacher spread0.291 · 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