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Record W4410526227 · doi:10.3934/dcdss.2025076

Bifurcation analysis on immunotherapy of a tumor model without treatment

2025· article· en· W4410526227 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDiscrete and Continuous Dynamical Systems - S · 2025
Typearticle
Languageen
FieldMathematics
TopicMathematical Biology Tumor Growth
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsImmunotherapyBifurcationMedicineComputer scienceInternal medicinePhysicsCancerNonlinear system

Abstract

fetched live from OpenAlex

Immunotherapy is one of the most methodologies developed recently for reducing and alleviating the dangerous growth of tumors. In this paper, we explore bifurcation analysis on a tumor mathematical model including CD4$ ^+ $ T cells and cytokines. The case without treatment is studied in order to estimate the limitation of a successful therapy. Particular attention is focused on the existence of equilibrium solutions and their stability. Bifurcation diagrams are used to consider the inter-individual variability for a non-treatment situation, showing the existence of oscillating behaviors due to Hopf bifurcation. It is shown that patients with very low tumor antigenicity experience tumors expanding to its maximum size, regardless of whether the tumor destruction rate and cytokine production rate are high. Numerical simulations are present to illustrate the theoretical predictions.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.904
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.015
GPT teacher head0.300
Teacher spread0.285 · 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