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Record W2908205445 · doi:10.3390/ma12010114

What’s Happening on the Other Side? Revealing Nano-Meter Scale Features of Mammalian Cells on Engineered Textured Tantalum Surfaces

2018· article· en· W2908205445 on OpenAlexafffund
Ting Y. Tsui, Megan Logan, Hassan I. Moussa, Marc G. Aucoin

Bibliographic record

VenueMaterials · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTantalumNanotechnologyMaterials scienceMicroelectronicsNanometreSiliconScanning electron microscopeNanoscopic scaleConfocalOptoelectronicsOpticsComposite material

Abstract

fetched live from OpenAlex

Advanced engineered surfaces can be used to direct cell behavior. These behaviors are typically characterized using either optical, atomic force, confocal, or electron microscopy; however, most microscopic techniques are generally restricted to observing what's happening on the "top" side or even the interior of the cell. Our group has focused on engineered surfaces typically reserved for microelectronics as potential surfaces to control cell behavior. These devices allow the exploration of novel substrates including titanium, tungsten, and tantalum intermixed with silicon oxide. Furthermore, these devices allow the exploration of the intricate patterning of surface materials and surface geometries i.e., trenches. Here we present two important advancements in our research: (1) the ability to split a fixed cell through the nucleus using an inexpensive three-point bend micro-cleaving technique and image 3D nanometer scale cellular components using high-resolution scanning electron microscopy; and (2) the observation of nanometer projections from the underbelly of a cell as it sits on top of patterned trenches on our devices. This application of a 3-point cleaving technique to visualize the underbelly of the cell is allowing a new understanding of how cells descend into surface cavities and is providing a new insight on cell migration mechanisms.

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 score0.009
Threshold uncertainty score0.609

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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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