The bioconjugation mechanism of purine cross-linkers affects microstructure and cell response to ultra rapidly gelling purine–chitosan sponges
Bibliographic record
Abstract
C-NMR revealed the formation of a quadruplex structure among the four GDP molecules confined between the chitosan backbone. This resulted from the ability of guanine to form intermolecular hydrogen bonds, while adenosine in ADP lacks this capacity. The formation of a more organized structure in GDP-chitosan sponges also increased the crystallinity of the sponge as shown by X-ray diffraction data. Further, physicochemical analyses with SEM and μCT indicated a more open-pore architecture and increased porosity. Although an active population of encapsulated cells was maintained in all chitosan sponges overtime, the GDP-based sponges provided a 6-fold increase in the activity of MC-3T3 cells and significantly enhanced their proliferation due to a more appropriate microstructure. Overall, these findings suggest that slight changes in the chemical structure of the cross-linker in the preparation of chitosan-based biomaterials will have a significant impact on the structural properties of the chitosan. This important parameter can be utilized to modulate cell response and to understand the cell signaling pathway of chitosan-based biomaterials in the context of their applications in tissue engineering.
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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".