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Record W4414358099 · doi:10.1039/d5bm00480b

Modulating oxygen release <i>via</i> manipulated microspheres embedded in thermoresponsive hydrogels for enhanced stem cell survival under hypoxia

2025· article· en· W4414358099 on OpenAlexaff
Jiyeon Lee, Ki Wan Bong, Soo‐Chang Song

Bibliographic record

VenueBiomaterials Science · 2025
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsNexen (Canada)
FundersNational Research Foundation of KoreaKorea Institute of Science and Technology
KeywordsSelf-healing hydrogelsMesenchymal stem cellHypoxia (environmental)Stem cellAngiogenesisOxygenViability assayParacrine signalling

Abstract

fetched live from OpenAlex

) content, we fabricated calcium peroxide-loaded (CPO) microspheres with distinct oxygen release profiles and incorporated them into the PPZ hydrogel, forming a hydrogel based oxygen delivery platform, termed OxyCellgel. This platform, composed solely of PPZ and CPO microspheres, allows for precise control over oxygen release rates and amounts, enabling adaptation to both mild and severe hypoxic environments. The interaction between the microspheres and hydrogel matrix facilitated uniform and sustained oxygen release. Subsequently, human mesenchymal stem cells (hMSCs) were co-delivered with this OxyCellgel system to evaluate cell viability and function under hypoxic conditions. The system significantly enhanced the survival and proliferation of hMSCs and promoted angiogenesis through their paracrine effects under hypoxia. Notably, hMSCs co-encapsulated with OxyCellgel showed markedly improved viability under hypoxic conditions compared to controls. This study presents a hydrogel-based oxygen delivery platform with controllable release kinetics as a promising strategy to improve the efficacy of stem cell-based therapies under diverse hypoxic conditions.

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)0.002
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 score0.031
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.282
Teacher spread0.263 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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