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Record W2900521017 · doi:10.1002/adfm.201806200

Ultralight Conductive and Elastic Aerogel for Skeletal Muscle Atrophy Regeneration

2018· article· en· W2900521017 on OpenAlexaff
Leyu Wang, Xingying Zhang, Yutong He, Ying Wang, Wen Zhong, Kibret Mequanint, Xiaozhong Qiu, Malcolm Xing

Bibliographic record

VenueAdvanced Functional Materials · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsWestern UniversityUniversity of Manitoba
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceAerogelC2C12MyocyteSkeletal muscleBiomedical engineeringBiophysicsComposite materialMyogenesisAnatomyInternal medicineMedicineBiology

Abstract

fetched live from OpenAlex

Abstract Cell‐free materials that can transmit both mechanical and electrical stimulations provide a promising strategy for myoinjury repair. Skeletal muscle is sensitive to electrical stimulation (ES), and accordingly, materials are required to transmit the electrical signal while maintaining their elasticity to build the cellular communication network in denervated muscle for retarding muscle atrophy. Here, tannic acid functionalized with methacrylate group (TA–MA), dopamine, and hydrothermal reduction are employed in multistep base reduction to fabricate a polydopamine (PDA)/reduced graphite oxide (rGO) aerogel. This mussel‐inspired PDA/rGO aerogel possesses good conductivity, electromechanical stability, and appropriate Young's modulus, which are favorable for the growth and differentiation of C2C12 myoblasts. After the cell‐free PDA/rGO aerogel‐transplanted denervated muscle is loaded with cyclic ES for 3 weeks, the mean muscle fiber size increases by 90% and the maximum contraction force of denervated muscle elevates by 50%, accompanied with a slight inflammation infiltration in muscle. In conclusion, PDA/rGO aerogel is a safe and effective implant for retarding the disuse muscle atrophy.

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 score0.034
Threshold uncertainty score0.780

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 head0.015
GPT teacher head0.221
Teacher spread0.207 · 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

Citations58
Published2018
Admission routes1
Has abstractyes

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