Modulating oxygen release <i>via</i> manipulated microspheres embedded in thermoresponsive hydrogels for enhanced stem cell survival under hypoxia
Bibliographic record
Abstract
) 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".