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Monitoring Targeted Drug Delivery Using Microwave Sensitive Hydrogels

2024· article· en· W4400650079 on OpenAlexafffund
Bahareh Laribi, Majid H. Mohajerani, Afsaneh Lavasanifar, Rashid Mirzavand

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
FundersAlberta Innovates
KeywordsSelf-healing hydrogelsDrug deliveryDrugMicrowaveComputer scienceBiomedical engineeringNanotechnologyMaterials sciencePharmacologyMedicineTelecommunications

Abstract

fetched live from OpenAlex

This paper investigates the feasibility of tracking the delivery of targeted drugs using microwave-sensitive hydrogels. These hydrogels that are capable of being triggered by microwaves may transform the precision and efficiency of drug delivery systems. Synthesis, characterization, and use of these materials are aimed at investigating their ability to sense external stimuli and release drugs in a controlled manner. Using microwave reflectometry, this study examines the electromagnetic properties and the response of these materials to microwave signals. In the study, microwave-sensitive alginate hydrogels were successfully detected from water, which can be used to monitor the delivery of targeted drugs in real-time. This will provide new ways to enhance treatment outcomes while minimizing undesirable outcomes. Using materials science, engineering, and pharmaceuticals interdisciplinarity, this study contributes to the advancement of precision medicine and suggests methods for improving drug administration strategies.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.249
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes2
Has abstractyes

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