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Record W7115918246 · doi:10.1063/5.0296897

Tissue regenerative medicine: Clinical advances, challenges, and opportunities

2025· article· en· W7115918246 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

VenueAPL Bioengineering · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPluripotent Stem Cells Research
Canadian institutionsHeart and Stroke FoundationUniversity of TorontoUniversity Health Network
FundersNational Heart, Lung, and Blood InstituteCalifornia Institute for Regenerative MedicineU.S. Department of Veterans AffairsNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsRegenerative medicineBench to bedsidePrecision medicineTissue engineeringTranslational medicineBiomedicine

Abstract

fetched live from OpenAlex

Regenerative medicine is transforming how we restore tissue function, leveraging advances in cell and molecular biology, biomaterials, and engineered microenvironments. While there have been notable advances and rapid progress over the past few decades, ongoing challenges persist in the technical development and effective translation of these advancements to clinical care. This perspective highlights clinically promising examples and critically assesses present challenges in translating tissue regenerative medicine therapies from the bench to the clinic. We further examine the evolving landscape of regenerative medicine by describing strategies to optimize the cellular microenvironment, the impact of patient demographics, and the use of artificial intelligence to shape the future of this field.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.468

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 head0.056
GPT teacher head0.345
Teacher spread0.289 · 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