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Record W2103322166 · doi:10.1039/c4py00631c

Schiff based injectable hydrogel for in situ pH-triggered delivery of doxorubicin for breast tumor treatment

2014· article· en· W2103322166 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

VenuePolymer Chemistry · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsHealth CanadaUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
FundersPeking University
KeywordsDoxorubicinChemistryConjugated systemChitosanDrug deliveryTumor microenvironmentSchiff baseNuclear chemistryPharmacologyBiophysicsCancer researchPolymer chemistryChemotherapyTumor cellsBiochemistrySurgeryOrganic chemistryMedicinePolymer

Abstract

fetched live from OpenAlex

In this study, we report a facile approach to develop an injectable hydrogel with an in situ and pH sensitive drug delivery system for cancer treatment. The hydrogel was based on modified chitosan and alginate. We conjugated doxorubicin (DOX) to succinated chitosan (S-chi) via a Schiff base between a ketone group in the DOX and an amine group in the S-chi, which led to a pH sensitive release of DOX upon the stimulus of an acidic tumor microenvironment. Hydrogel formed in minutes while DOX conjugated S-chi was mixed with oxidized alginate. The hydrogel structure was characterized by cryo-imaging, FTIR and a rheology test. The DOX release profiles were tested in response to different pH values. The MTT assay showed a low toxicity of the hydrogel. The gel in turn inhibited the growth of tumor cells MCF-7 effectively when loaded with DOX. Finally, the DOX laden hydrogel was injected into the xenograft breast tumor model and significantly inhibited tumor growth.

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 categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.223
Teacher spread0.214 · 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