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Record W4318169575 · doi:10.1002/advs.202206756

An Ionic Liquid Ablation Agent for Local Ablation and Immune Activation in Pancreatic Cancer

2023· article· en· W4318169575 on OpenAlexaff
Junming Huang, Meng Wang, Fu Zhang, Shiyi Shao, Zhuo Yao, Xinyu Zhao, Qida Hu, Tingbo Liang

Bibliographic record

VenueAdvanced Science · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsPancreas Centre (Canada)
FundersNational Key Research and Development Program of ChinaNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsImmunotherapyImmune systemCancer researchMedicineImmunogenicityImmunologyPancreatic cancerCytotoxic T cellCancerBiologyInternal medicine

Abstract

fetched live from OpenAlex

Pancreatic ductal adenocarcinoma rapidly acquires resistance to chemotherapy, remaining a fatal disease. Immunotherapy is one of the breakthroughs in cancer treatment, which includes immune checkpoint inhibitors, chimeric antigen receptor T-cell immunotherapy, and neoantigen vaccines. However, immunotherapy has not achieved satisfactory results in the treatment of pancreatic cancer. Immunogenic death comprises proinflammatory cell death, which provides a way to enhance tumor immunogenicity and promote an immune response in solid tumors. Herein, an ionic liquid ablation agent (LAA), synthesized from choline and geranic acid, which triggers necrosis-induced immunotherapy by remodeling an immunosuppressive "cold" tumor to an immune activated "hot" tumor is described. The results indicate that LAA-treated tumor cells can enhance immunogenicity, inducing dendritic cell maturation, macrophage M1 polarization, and cytotoxic T lymphocyte infiltration. The results of the present study provide a novel strategy for solid tumor immunotherapy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.266

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 communication0.0000.001
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 head0.021
GPT teacher head0.338
Teacher spread0.317 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

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