Experimental study on the interaction of insulin with apatitic calcium phosphates analogous to bone mineral: adsorption and release
Bibliographic record
Abstract
The present work investigated the interaction of human insulin with synthesized poorly crystalline apatitic calcium phosphates containing simultaneously HPO42- and CO32- ions in various amount. The adsorption kinetics is very fast, while the release kinetics is generally slow. The chemical composition of apatite has an influence on both the adsorption and release processes. The experimental results show that the percentage of insulin adsorption and release decreased with the increase of the content of carbonate. The equilibrium adsorption data are fitted into Langmuir, Freundlich, Elovich, Temkin, and Dubinin–Radushkevich isotherms. The Langmuir model is best suited with a maximum monolayer adsorption capacity of 33.20 and 25.08 mg/g at 310 K corresponding to the carbonated and octocalcium phosphate apatite respectively. Isotherms parameters have revealed that the adsorption of insulin on these apatites is a feasible, spontaneous, and exothermic process. Fourier-transforms infrared confirm the fixation of insulin on non-carbonated and carbonated apatite. The adsorption and release of insulin molecules can be well described as an ions exchange-reaction between species in the hydrated layer of apatite and other species in solution. All of these results suggested that apatitic calcium phosphates can be used as systems for insulin delivery.
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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 | 0.001 |
| 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 | 0.001 |
| 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".