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Record W1581690061 · doi:10.18433/j37w2v

Co-encapsulation of a Drug with a Protein in Erythrocytes for Improved Drug Loading and Release: Phenytoin and Bovine Serum Albumin (BSA)

2011· article· en· W1581690061 on OpenAlexvenueno aff
Mehrdad Hamidi, Kourosh Azimi, Soliman Mohammadi‐Samani

Bibliographic record

VenueJournal of Pharmacy & Pharmaceutical Sciences · 2011
Typearticle
Languageen
FieldMedicine
TopicErythrocyte Function and Pathophysiology
Canadian institutionsnot available
Fundersnot available
KeywordsDrugBovine serum albuminChemistryIn vitroAlbuminSerum albuminSwellingPharmacologyPhenytoinErythrocyte fragilityChromatographyMaterials scienceHemolysisBiochemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

The aim of the present study was to use a novel protein co-encapsulation method to prepare phenytoin-loaded human erythrocytes with improved loading parameters and release profiles. Carrier erythrocytes were prepared using the hypotonic pre-swelling method. A series of in vitro characterization tests were carried out on the carrier cells, including loading parameters, drug and hemoglobin release, hematological indices, particle size analysis, osmotic fragility, turbulence fragility, and scanning electron microscopy (SEM). Co-encapsulation with bovine serum albumin (BSA) resulted in about 8-times higher drug loading in erythrocytes, with biphasic release trend instead of triphasic in the case of drug alone loading. In comparison to the normal unloaded cells, MCH and MCHC indices were decreased in the case of both drug and drug/protein loading, apparent cell sizes were unchanged, cell shapes were changed to spherical rather than biconcave discoid, and the osmotic as well as turbulence fragilities were higher in the case of drug/protein but were unchanged in the case of drug alone loading. The most profound finding of this study was the possibility of achieving remarkably higher drug loading and more controllable drug release profile in the case of drug/protein loading, with no unwanted in vitro characteristics change.

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 score0.026
Threshold uncertainty score0.441

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 head0.060
GPT teacher head0.355
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

Citations24
Published2011
Admission routes1
Has abstractyes

Explore more

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