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Record W3208681660 · doi:10.1002/pi.6320

The unexplored potential of gas‐responsive polymers in drug delivery: progress, challenges and outlook

2021· article· en· W3208681660 on OpenAlexafffund
Hui Wen Yong, Ashok Kakkar

Bibliographic record

VenuePolymer International · 2021
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanomedicineNanotechnologyDrug deliveryDrugBiochemical engineeringMaterials scienceChemistryNanoparticlePharmacologyMedicineEngineering

Abstract

fetched live from OpenAlex

Abstract Targeted drug delivery based on polymeric nanoparticles has been a long‐standing interest in nanomedicine for its beneficial traits including controlled and localized drug release. Gas‐responsive polymers offer an advantageous platform and have been slowly gaining attention in spatially locating and displaying unique interactions of specific responsive chemical entities in polymeric chains with endogenous gaseous stimuli. In this review, we highlight recent developments in polymeric nanoformulations with stimulant chemical entities for gasotransmittors such as NO, CO, H2S, SO2, O2 and CO2 in enhancing efficacy in therapeutic interventions. We underline some challenges and limitations of exploring these systems for clinical applications, and how we can further tap into the potential of these emerging materials. © 2021 Society of Industrial Chemistry.

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.015
GPT teacher head0.261
Teacher spread0.245 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations15
Published2021
Admission routes2
Has abstractyes

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