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Record W4393044211 · doi:10.1002/smll.202309495

Pulsed Photothermal Therapy of Solid Tumors as a Precondition for Immunotherapy

2024· article· en· W4393044211 on OpenAlexafffund
Negin Farivar, Nastaran Khazamipour, Morgan E. Roberts, Irina Nelepcu, Mona Marzban, Alireza Moeen, Htoo Zarni Oo, Nader Al Nakouzi, Casper Dolleris, Peter C. Black, Mads Daugaard

Bibliographic record

VenueSmall · 2024
Typearticle
Languageen
FieldEngineering
TopicNanoplatforms for cancer theranostics
Canadian institutionsDoug Bragg Enterprises (Canada)Prevention of Organ FailureUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMitacs
KeywordsPhotothermal therapyCD80ImmunotherapyCancer researchImmunogenic cell deathImmune systemMaterials scienceHyperthermiaCD86Cytotoxic T cellMedicineT cellImmunologyBiologyNanotechnologyCD40In vitroInternal medicine

Abstract

fetched live from OpenAlex

Photothermal therapy (PTT) refers to the use of plasmonic nanoparticles to convert electromagnetic radiation in the near infrared region to heat and kill tumor cells. Continuous wave lasers have been used clinically to induce PTT, but the treatment is associated with heat-induced tissue damage that limits usability. Here, the engineering and validation of a novel long-pulsed laser device able to induce selective and localized mild hyperthermia in tumors while reducing the heat affected zone and unwanted damage to surrounding tissue are reported. Long-pulsed PTT induces acute necrotic cell death in heat affected areas and the release of tumor associated antigens. This antigen release triggers maturation and stimulation of CD80/CD86 in dendritic cells in vivo that primes a cytotoxic T cell response. Accordingly, long-pulsed PTT enhances the therapeutic effects of immune checkpoint inhibition and increases survival of mice with bladder cancer. Combined, the data promote long-pulsed PTT as a safe and effective strategy for enhancing therapeutic responses to immune checkpoint inhibitors while minimizing unwanted tissue damage.

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 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 score0.053
Threshold uncertainty score0.542

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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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 teacher head, 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

Citations19
Published2024
Admission routes2
Has abstractyes

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