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Effect of sodium tripolyphosphate concentration and simulated gastrointestinal fluids on release profile of paracetamol from chitosan microsphere

2018· article· en· W2795303830 on OpenAlexfundno aff
Kamarza Mulia, Andrie Andrie, Elsa Anisa Krisanti

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsnot available
FundersUniversitas IndonesiaMallinckrodt PharmaceuticalsUnited States Agency for International Development
KeywordsChitosanChemistryChromatographyMicrosphereSodiumPolymerControlled releaseNuclear chemistryChemical engineeringOrganic chemistryMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

The problem to overcome in oral drug administration is the significant pH changes present in the human digestive system. In this study, ionotropic gelation method employing 2-8% (w/v) tripolyphosphate solutions were used to crosslink chitosan microspheres for a controlled release of paracetamol as a model drug. The release profiles of paracetamol from chitosan microspheres were determined using simulated gastrointestinal fluids having pH values of 1.2, 6.8, and 7.4. The results showed that the paracetamol loading and the encapsulation efficiency values increased with increasing concentration of tripolyphosphate solutions used in the preparation step. Paracetamol released at pH 1.2 and 6.8 buffer solutions was significantly higher than that at pH 7.4; also, more paracetamol was released in the presence of α-amylase and β-glucosidase enzymes. The release profiles showed zero-order release behaviour up to 8 hours where the highest drug release was 39% of the paracetamol loaded in the chitosan microspheres, indicating a strong crosslinking between chitosan and TPP anions. The relatively low accumulated drug release could be compensated by employing suitable enzymes, lower TPP solution concentration, and addition of other biodegradable polymer to reduce the TPP crosslink.

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)0.001
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 score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.318
Teacher spread0.295 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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