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Record W3196430494 · doi:10.7150/thno.60679

Tryptophan metabolism is inversely regulated in the tumor and blood of patients with glioblastoma

2021· article· en· W3196430494 on OpenAlexfundno aff
Verena Panitz, Saša Končarević, Ahmed Sadik, Dennis Friedel, Tobias Bausbacher, Saskia Trump, Vadim Farztdinov, Sandra Schulz, Philipp Sievers, S. Schmidt, Ina Jürgenson, Stephan Jung, Karsten Kuhn, Irada Pflüger, Suraj Sharma, Antje Wick, Pauline Pfänder, Stefan Selzer, Felix Sahm, Andreas von Deimling, Ines Heiland, Carsten Hopf, Peter Schulz‐Knappe, Ian Pike, Michael Platten, Wolfgang Wick, Christiane A. Opitz

Bibliographic record

VenueTheranostics · 2021
Typearticle
Languageen
FieldNeuroscience
TopicTryptophan and brain disorders
Canadian institutionsnot available
FundersCommon FundNIH Office of the DirectorBundesministerium für Bildung und ForschungDeutsches KrebsforschungszentrumDeutsche ForschungsgemeinschaftDeutscher Akademischer AustauschdienstNational Institutes of HealthUniversitätsklinikum HeidelbergMinistère de l'Économie, de la Science et de l'Innovation - QuébecEuropean Commission
KeywordsAryl hydrocarbon receptorKynurenineMetaboliteCatabolismMetabolismCancer researchTryptophanChemistryKynurenine pathwayTumor progressionTumor microenvironmentInternal medicineBiologyBiochemistryMedicineAmino acidGeneTumor cellsTranscription factor

Abstract

fetched live from OpenAlex

The novel techniques we developed could support the identification of patients that may benefit from therapies targeting TCEs or AHR activation.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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 designObservational
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

Citations51
Published2021
Admission routes1
Has abstractyes

Explore more

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