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Record W1994490806 · doi:10.1038/srep06313

Remote Control of Tissue Interactions via Engineered Photo-switchable Cell Surfaces

2014· article· en· W1994490806 on OpenAlexfundno aff
Wei Luo, Abigail Pulsipher, Debjit Dutta, Brian M. Lamb, Muhammad N. Yousaf

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNational Cancer InstituteNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsTissue engineeringRegenerative medicineLiposomeDrug deliveryCellSurface engineeringNanotechnologyMaterials scienceStem cellBiomedical engineeringFlow cytometryCell cultureCell biologyChemistryBiologyEngineeringImmunology

Abstract

fetched live from OpenAlex

We report a general cell surface molecular engineering strategy via liposome fusion delivery to create a dual photo-active and bio-orthogonal cell surface for remote controlled spatial and temporal manipulation of microtissue assembly and disassembly. Cell surface tailoring of chemoselective functional groups was achieved by a liposome fusion delivery method and quantified by flow cytometry and characterized by a new cell surface lipid pull down mass spectrometry strategy. Dynamic co-culture spheroid tissue assembly in solution and co-culture tissue multilayer assembly on materials was demonstrated by an intercellular photo-oxime ligation that could be remotely cleaved and disassembled on demand. Spatial and temporal control of microtissue structures containing multiple cell types was demonstrated by the generation of patterned multilayers for controlling stem cell differentiation. Remote control of cell interactions via cell surface engineering that allows for real-time manipulation of tissue dynamics may provide tools with the scope to answer fundamental questions of cell communication and initiate new biotechnologies ranging from imaging probes to drug delivery vehicles to regenerative medicine, inexpensive bioreactor technology and tissue engineering therapies.

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)0.000
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 score0.404
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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)0.0010.000

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations50
Published2014
Admission routes1
Has abstractyes

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