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Record W2790727988 · doi:10.1016/j.celrep.2018.02.081

Silk Fibroin Films Facilitate Single-Step Targeted Expression of Optogenetic Proteins

2018· article· en· W2790727988 on OpenAlexfundno aff
Skyler L. Jackman, Christopher H. Chen, Selmaan N. Chettih, Shay Q. Neufeld, Iain R. Drew, Chimuanya K. Agba, Isabella Flaquer, Alexis N. Stefano, Thomas J. Kennedy, Justine E. Belinsky, Keiramarie Roberston, Celia Beron, Bernardo L. Sabatini, Christopher D. Harvey, Wade G. Regehr

Bibliographic record

VenueCell Reports · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilk-based biomaterials and applications
Canadian institutionsnot available
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Mental HealthCanadian Institutes of Health ResearchUniversity of PennsylvaniaNew York Stem Cell FoundationNancy Lurie Marks Family FoundationNational Institutes of HealthCenter for Outcomes Research and Evaluation, Yale School of MedicineNational Science Foundation
KeywordsOptogeneticsFibroinSILKProtein expressionNanotechnologyCell biologyBiological neural networkMaterials scienceBiologyNeuroscienceGeneticsGene

Abstract

fetched live from OpenAlex

Optical methods of interrogating neural circuits have emerged as powerful tools for understanding how the brain drives behaviors. Optogenetic proteins are widely used to control neuronal activity, while genetically encoded fluorescent reporters are used to monitor activity. These proteins are often expressed by injecting viruses, which frequently leads to inconsistent experiments due to misalignment of expression and optical components. Here, we describe how silk fibroin films simplify optogenetic experiments by providing targeted delivery of viruses. Films composed of silk fibroin and virus are applied to the surface of implantable optical components. After surgery, silk releases the virus to transduce nearby cells and provide localized expression around optical fibers and endoscopes. Silk films can also be used to express genetically encoded sensors in large cortical regions by using cranial windows coated with a silk/virus mixture. The ease of use and improved performance provided by silk make this a promising approach for optogenetic studies.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.023
GPT teacher head0.240
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2018
Admission routes1
Has abstractyes

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