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Record W3126501026 · doi:10.1073/pnas.2020395118

IL17A critically shapes the transcriptional program of fibroblasts in pancreatic cancer and switches on their protumorigenic functions

2021· article· en· W3126501026 on OpenAlexaff
Gianluca Mucciolo, Claudia Curcio, Cecilia Roux, Wanda Y. Li, Michela Capello, Roberta Curto, Roberto Chiarle, Daniele Giordano, Maria Antonietta Satolli, Rita T. Lawlor, Aldo Scarpa, Pavol Lukáč, Dmitry Stakheev, Paolo Provero, Luca Vannucci, Tak W. Mak, Francesco Novelli, Paola Cappello

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
FundersUniversità degli Studi di TorinoAkademie Věd České RepublikyAssociazione Italiana per la Ricerca sul CancroUniversity of TokyoFondazione Nadia ValsecchiFondazione CRT
KeywordsPancreatic cancerCell biologyCancerCancer researchBiologyChemistryNanotechnologyComputational biologyMaterials scienceGenetics

Abstract

fetched live from OpenAlex

Significance There are controversial data about the protumoral role of pancreatic cancer stroma, and dissecting multiple aspects will help to develop effective tailored therapies. Interleukin-17A (IL17A) has been reported to accelerate pancreatic acinar–ductal metaplasia, be important for maintaining stem-like cancer cells, and recruit immunosuppressive granulocytes into the tumor. Here we unveil a relationship between IL17A and pancreatic stromal cells, which strongly modifies their gene and protein expression profiles. Ablation of IL17A, in fact, modifies the cytokines/factors released by tumor fibroblasts by limiting T cell immunosuppression. IL17A inhibition may represent an important tool for designing novel combined therapeutic approaches.

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 score0.002
Threshold uncertainty score0.008

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.073
GPT teacher head0.375
Teacher spread0.302 · 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

Citations49
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicPancreatic and Hepatic Oncology ResearchFrench-language works237,207