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Record W4285219474 · doi:10.5267/j.ccl.2022.6.003

Experimental study on the interaction of insulin with apatitic calcium phosphates analogous to bone mineral: adsorption and release

2022· article· en· W4285219474 on OpenAlexvenueno aff
Abdelhadi El Rhilassi, M. Bennani-Ziatni

Bibliographic record

VenueCurrent Chemistry Letters · 2022
Typearticle
Languageen
FieldMedicine
TopicAlkaline Phosphatase Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryAdsorptionApatiteLangmuirCalciumSimulated body fluidPhosphateInorganic chemistryFreundlich equationChemical engineeringKineticsCalcium carbonateInsulinNuclear chemistryMineralogyOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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)0.000
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 score0.012
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.038
GPT teacher head0.332
Teacher spread0.294 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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