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Record W4414218258 · doi:10.1002/btm2.70061

Cell‐embedded microgels as emerging miniature <scp>3D</scp> tissue‐mimics toward biochip‐based toxicity screening

2025· article· en· W4414218258 on OpenAlex

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

VenueBioengineering & Translational Medicine · 2025
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of British Columbia
FundersKorea Institute of Science and Technology
KeywordsBiochipToxicityDrug discovery3D cell cultureIn vitroDrug developmentIn vitro toxicologyDrug delivery

Abstract

fetched live from OpenAlex

Recent developments in synthetic three-dimensional (3D) gel microenvironments for cell culture have enabled the advancement of bioengineered organ-specific cell niches that resemble the native 3D tissue architecture and mechanics. In particular, the application of 3D cell cultures based on miniaturized hydrogel scaffolds for toxicological analyses is attracting increasing interest because of their facile adaptability to on-chip systems and potential as novel in vitro screening tools. We summarize the current progress in microgel-based 3D cells integrated into biochip platforms and their utilization for the in vitro toxicity evaluation of chemicals and drug candidates. We emphasize the development of tissue-mimicking microgel systems combined with automated gel microarray chips and organ-on-a-chip devices. This review begins with the microscale hydrogel scaffolds that encapsulate mammalian cells and are used for in vitro tissue mimicry purposes. Furthermore, an overview of microgel-based tissue modeling approaches to toxicity testing and screening is provided, along with their technical advantages in drug discovery and alternatives to animal testing.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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