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Record W3198897601 · doi:10.1126/sciadv.abg5686

Portable air-fed cold atmospheric plasma device for postsurgical cancer treatment

2021· article· en· W3198897601 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

VenueScience Advances · 2021
Typearticle
Languageen
FieldMedicine
TopicPlasma Applications and Diagnostics
Canadian institutionsMcGill University Health Centre
FundersNational Cancer Institute
KeywordsMedicineMelanomaCancerBreast cancerAtmospheric-pressure plasmaImmune systemCancer researchAtmospheric airAtmospheric pressureCancer cellSurgeryPlasmaInternal medicineImmunology

Abstract

fetched live from OpenAlex

Surgery represents the major option for treating most solid tumors. Despite continuous improvements in surgical techniques, cancer recurrence after surgical resection remains the most common cause of treatment failure. Here, we report cold atmospheric plasma (CAP)–mediated postsurgical cancer treatment, using a portable air-fed CAP (aCAP) device. The aCAP device we developed uses the local ambient air as the source gas to generate cold plasma discharge with only joule energy level electrical input, thus providing a device that is simple and highly tunable for a wide range of biomedical applications. We demonstrate that local aCAP treatment on residual tumor cells at the surgical cavities effectively induces cancer immunogenic cell death in situ and evokes strong T cell–mediated immune responses to combat the residual tumor cells. In both 4T1 breast tumor and B16F10 melanoma models, aCAP treatment after incomplete tumor resection contributes to inhibiting tumor growth and prolonging survival.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.575
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.334
Teacher spread0.314 · 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