Correlations between functional Interleukin-1 and changes in fatigue and quality of life in children and adolescents with cancer.
Bibliographic record
Abstract
95 Background: Several studies have explored the hypothesis of involvement of the immune system in the cancer-related fatigue (CRF) and quality of life in adults with cancer via release of cytokines. However, how the immune system may produce these outcomes remains a largely unanswered question. Furthermore, research on fatigue in children and adolescents with cancer has included primarily self-reports of this symptom, with scarce although increasing data exploring biologic variables related to this symptom. Methods: To comprehensively examine the influence of cytokines in the fatigue and health related quality of life (HRQOL) in children and adolescents with cancer, we investigated the plasma levels of pro and anti-inflamatory cytokines and its correlation with fatigue and HRQOL in children and adolescents with cancer. Plasma levels of 6 cytokines were measured via flow cytometry. Results: The final sample consisted of 33 hospitalized children and adolescents aged between 8 and 18 years. We found a correlation between general fatigue and IL-1β (r = –0.361, p = 0.039) and correlation with quality of life was noted with IL-1β (r = –0.382, p = 0.028). The results of this research indicate that there may be a relationship between cytokines and fatigue, and HRQOL in children and adolescents with cancer. When analyzed the correlation between fatigue and some confounding variables found the link between general fatigue and neutrophils (r = –0.360, p = 0.040). This correlation is a novel found in this population under cancer condition, implicating a possible interleukin-hematological pathway for CRF in oncology. Conclusions: Our study confirms the potential role of cytokines in developing cancer-related symptoms, thus supporting the utility of the neuroimmunological approach to the discovery of biomarkers linked to the clusters of cancer-related symptoms.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".