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Record W2508635214 · doi:10.1002/adhm.201600439

Skin Diseases Modeling using Combined Tissue Engineering and Microfluidic Technologies

2016· review· en· W2508635214 on OpenAlex
Mohammad Hossein Mohammadi, Behnaz Heidary Araghi, Vahid Beydaghi, Armin Geraili, Farshid Moradi, Parya Jafari, Mohsen Janmaleki, Karolina Papera Valente, Mohsen Akbari, Amir Sanati‐Nezhad

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

VenueAdvanced Healthcare Materials · 2016
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of VictoriaUniversity of Calgary
Fundersnot available
KeywordsMicrofluidicsTissue engineeringOrgan-on-a-chipNanotechnologyMicrofluidic chipBiomedical engineeringComputer scienceBiochemical engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

In recent years, both tissue engineering and microfluidics have significantly contributed in engineering of in vitro skin substitutes to test the penetration of chemicals or to replace damaged skins. Organ-on-chip platforms have been recently inspired by the integration of microfluidics and biomaterials in order to develop physiologically relevant disease models. However, the application of organ-on-chip on the development of skin disease models is still limited and needs to be further developed. The impact of tissue engineering, biomaterials and microfluidic platforms on the development of skin grafts and biomimetic in vitro skin models is reviewed. The integration of tissue engineering and microfluidics for the development of biomimetic skin-on-chip platforms is further discussed, not only to improve the performance of present skin models, but also for the development of novel skin disease platforms for drug screening 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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.043
GPT teacher head0.360
Teacher spread0.317 · 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