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Record W4411122261 · doi:10.30683/1929-2279.2025.14.10

Evaluation of Interleukin-8 (IL-8) Levels before and after Radiotherapy in Thyroid Carcinoma Patients

2025· article· en· W4411122261 on OpenAlexvenueno aff
Zahraa Adnan Ghadhban Al Ghuraibawi

Bibliographic record

VenueJournal of cancer research updates · 2025
Typearticle
Languageen
FieldMedicine
TopicThyroid Cancer Diagnosis and Treatment
Canadian institutionsnot available
FundersUniversity of Baghdad
KeywordsThyroid carcinomaThyroidCarcinomaRadiation therapyInterleukin 18MedicineOncologyInternal medicineCytokine

Abstract

fetched live from OpenAlex

Background: Thyroid cancer is a health concern and the most common endocrine tumor in adults with a significant increase in incidence in recent years. Objective: This study investigates the role of interleukin-8 in thyroid cancer patients before and after treatment with radiotherapy and study association of it with tumor progression and inflammatory response as part of the tumor microenvironment. Subjects and Methods: A total of 45 thyroid cancer patients (21 male and 24 female) in the educational laboratories/Medical City Hospital/Baghdad from February to April 2025. participated based on the recommendations of the specialist physician after the results of clinical examination, X-ray imaging, and laboratory tests, and 18 healthy controls participated in the study. IL-8 levels were measured before and after treatment with radiotherapy using an ELISA kit, and statistical analyses were performed to assess differences between groups while taking into account demographic characteristics Result: Thyroid cancer patients showed significantly elevated levels of interleukin-8 compared to controls; IL-8 levels increased significantly after radiotherapy, indicating an enhanced inflammatory response. Conclusion: The results emphasize the potential role of interleukin-8 as a biomarker for assessing thyroid cancer progression and response to radiotherapy, highlighting its role in the tumor microenvironment and its implications for patient management.

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)0.002
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 score0.115
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.046
GPT teacher head0.424
Teacher spread0.378 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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