Oxygen Delivery by Biopolymeric Scaffolds to Enhance Tissue Regeneration
Bibliographic record
Abstract
Oxygen plays a vital role in tissue regeneration as it is essential for various cellular processes, such as metabolism, growth, and repair. Consequently, scientists have been exploring ways to enhance cells’ access to oxygen through scaffolds for more effective and accurate tissue reconstruction. A critical need remains for a comprehensive investigation into the fabrication of oxygen-generating scaffolds (OGSCs), targeted tissues, and the underlying biological signaling pathways as well as the challenges related to their application, which have not yet been fully explored in the existing literature. According to the results, 3D printing was the most effective fabrication technique for developing OGSCs. Electrospinning and cryogelation were also identified as other valuable techniques. Among the oxygen sources, CaO 2 was the most effective, especially when combined with catalase, which enhances oxygen generation. The production of H 2 O 2 during oxygen generation presented a significant challenge due to its cytotoxic effects; however, catalase helped mitigate H 2 O 2 levels within the body. OGSC development has mainly focused on applications in the bone, heart, skin, and cartilage. It was concluded that the impact of oxygen on biological activities varies depending on the tissue type. It was also inferred that excessive oxygen generation can potentially lead to hyperoxia and disrupt critical signaling pathways. Notably, oxygen generation in cartilage has shown an adverse biological effect. The primary limitation of OGSCs remains the lack of precise control over the level of oxygen generated. To summarize, OGSCs demonstrated a strong potential in tissue regeneration.
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 | 0.001 | 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".