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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 OpenAlexaff
Guojun Chen, Zhitong Chen, Zejun Wang, Richard Obenchain, Di Wen, Hongjun Li, Richard E. Wirz, Zhen Gu

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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

Citations97
Published2021
Admission routes1
Has abstractyes

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