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Record W4406911732 · doi:10.1038/s41598-025-87608-1

Comparative analysis of salivary cytokine profiles in newly diagnosed pediatric patients with cancer and healthy children

2025· article· en· W4406911732 on OpenAlexaff
Nora Fritschi, Cornelia Filippi, Nicole Ritz, Urs Simmen, Katrin Scheinemann, Andreas Filippi, Tamara Diesch

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsMcMaster UniversityMcMaster Children's Hospital
Fundersnot available
KeywordsMedicineCytokinePediatric cancerCancerImmunologyPediatricsBioinformaticsInternal medicineBiology

Abstract

fetched live from OpenAlex

Salivary cytokines have the potential to serve as biomarkers for evaluating cancer progression and treatment response in specific cancer types. This study explored salivary cytokine profiles in pediatric cancer patients and healthy controls, examining changes during chemotherapy. We conducted a prospective study involving newly diagnosed cancer patients and healthy controls under 19 years old. Saliva samples were collected at diagnosis, and three and six months post-diagnosis for cancer patients, while healthy controls provided samples at a single time point. Cytokine levels were analyzed using Luminex technology. Our study included 19 cancer patients (10 with leukemia, 5 with lymphoma, and 4 with solid tumors) and 128 healthy controls aged 4 to 18 years. At diagnosis, patients with leukemia and solid tumors showed elevated levels of interferon-γ, interleukin (IL)-1α, IL-1β, IL-4, IL-5, IL-8, IL-10, and tumor necrosis factor. After three months, IL-6, IL-10, and inducible protein-10 levels significantly increased, while IL-1α, IL-1β, and IL-8 rose by six months. These findings indicate that salivary cytokines are elevated at diagnosis and during initial treatment phases in pediatric cancer patients, highlighting saliva's potential as a noninvasive medium for early detection of systemic diseases in children.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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 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

Citations5
Published2025
Admission routes1
Has abstractyes

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