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Record W4400986061 · doi:10.53555/sfs.v10i1.2907

Formulation And Characterization Of Mucoadhesive Tablets For Prolonged Drug Release

2023· article· en· W4400986061 on OpenAlexvenueno aff
Mohini Rithoriya, Akash Yadav, Dinesh Kumar Jain

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicAdvanced Drug Delivery Systems
Canadian institutionsnot available
Fundersnot available
KeywordsImmediate releaseDrugPharmacologyMaterials scienceMedicine

Abstract

fetched live from OpenAlex

This study focuses on the formulation and evaluation of atenolol mucoadhesive tablets, aiming to enhance drug absorption and bioavailability. Hypertension, affecting a significant portion of the adult population, can lead to severe cardiovascular conditions. Atenolol, a beta-blocker, effectively manages hypertension and other heart-related issues. The study used mucoadhesive polymers like hydroxypropyl methylcellulose (HPMC), tamarind gum, and badam gum to prepare the tablets via direct compression. The formulation process included optimizing drug release kinetics and mucoadhesive strength. Pre-compression parameters such as angle of repose, bulk density, and compressibility index were evaluated. Post-compression evaluations covered organoleptic properties, hardness, friability, weight variation, and in-vitro dissolution. Stability studies followed ICH guidelines to ensure long-term efficacy and safety. Key findings include consistent drug content, adequate hardness, and desirable swelling behavior. The optimized formulation demonstrated prolonged drug release and significant mucoadhesive properties, confirming its potential for improved therapeutic applications. Statistical analyses validated the formulation's consistency and reliability.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.315
GPT teacher head0.410
Teacher spread0.095 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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