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Record W4312053323 · doi:10.1063/5.0125692

Biomaterials for enhanced immunotherapy

2022· review· en· W4312053323 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAPL Bioengineering · 2022
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsÉcole de Technologie SupérieureCentre Hospitalier de l’Université de Montréal
FundersInstitute of Cancer ResearchNatural Sciences and Engineering Research Council of CanadaInstitut Du Cancer de Montréal
KeywordsImmunotherapyMedicineCancer immunotherapyImmune systemDrug deliveryCancerCancer treatmentImmunologyNanotechnologyInternal medicineMaterials science

Abstract

fetched live from OpenAlex

Cancer immunotherapies have revolutionized the treatment of numerous cancers, with exciting results often superior to conventional treatments, such as surgery and chemotherapy. Despite this success, limitations such as limited treatment persistence and toxic side effects remain to be addressed to further improve treatment efficacy. Biomaterials offer numerous advantages in the concentration, localization and controlled release of drugs, cancer antigens, and immune cells in order to improve the efficacy of these immunotherapies. This review summarizes and highlights the most recent advances in the use of biomaterials for immunotherapies including drug delivery and cancer vaccines, with a particular focus on biomaterials for immune cell delivery.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.304
Teacher spread0.265 · 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