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Record W4361000827 · doi:10.1002/adhm.202203172

Recent Advances in Organ‐on‐Chips Integrated with Bioprinting Technologies for Drug Screening

2023· review· en· W4361000827 on OpenAlex
Nima Tabatabaei Rezaei, Hitendra Kumar, Hongqun Liu, Samuel S. Lee, Simon S. Park, Keekyoung Kim

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 · 2023
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMicrofluidicsNanotechnologyOrgan-on-a-chipComputer scienceBiochemical engineeringMicrofluidic chipMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Currently, the demand for more reliable drug screening devices has made scientists and researchers develop novel potential approaches to offer an alternative to animal studies. Organ-on-chips are newly emerged platforms for drug screening and disease metabolism investigation. These microfluidic devices attempt to recapitulate the physiological and biological properties of different organs and tissues using human-derived cells. Recently, the synergistic combination of additive manufacturing and microfluidics has shown a promising impact on improving a wide array of biological models. In this review, different methods are classified using bioprinting to achieve the relevant biomimetic models in organ-on-chips, boosting the efficiency of these devices to produce more reliable data for drug investigations. In addition to the tissue models, the influence of additive manufacturing on microfluidic chip fabrication is discussed, and their biomedical applications are reviewed.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.081
GPT teacher head0.398
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