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Record W3119791641 · doi:10.1155/2021/1340538

Progress on Preparation of pH/Temperature-Sensitive Intelligent Hydrogels and Applications in Target Transport and Controlled Release of Drugs

2021· article· en· W3119791641 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Polymer Science · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsUniversity of British Columbia
FundersFundamental Research Funds for the Central UniversitiesChongqing Municipal Education Commission
KeywordsSelf-healing hydrogelsDrug deliveryNanotechnologyMaterials scienceControlled releaseDrugPharmacologyMedicinePolymer chemistry

Abstract

fetched live from OpenAlex

Hydrogels with three-dimensional network structure, hydrophilic, and insoluble in water which are ideal carrier materials for intelligent drug delivery systems. Intelligent hydrogel has become a research frontier and hotspot because of its intelligence, high efficiency, safety, and convenience in drug controlled and prolonged release. It has a broad application prospect in the medicine and biomedicine fields and can lead the medicine fields into a new era of “precise treatment.” Based on the latest research progress, the main preparation methods of hydrogel and the development of the drug delivery system are briefly introduced. The most promising three intelligent hydrogels in the human physiological environment, namely, pH responsiveness, temperature responsiveness, and pH/temperature dual responsiveness, are emphatically reviewed. Their release mechanisms, targeting transport, and controlled-prolonged release of drug are also discussed. In addition, some suggestions for the main problems and future development were given.

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.

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)0.000
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 score0.004
Threshold uncertainty score0.260

Codex and Gemma teacher scores by category

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 head0.007
GPT teacher head0.274
Teacher spread0.267 · 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