Inflammatory Biomarkers, Hematopoietic Stem Cells, and Symptoms in Breast Cancer Patients Undergoing Adjuvant Radiation Therapy
Bibliographic record
Abstract
Abstract Background Fatigue and insomnia are common symptoms experienced by breast cancer patients undergoing adjuvant radiation therapy (RT), yet the underlying mechanisms of these symptoms are unclear. In particular, the roles of hematopoietic stem cells (HSCs) and inflammatory cytokines remain to be elucidated. Methods Breast cancer patients (n = 147) completed questionnaires to longitudinally assess symptoms before, during, and after adjuvant RT. Phlebotomies were performed prior to RT, at the second and fifth treatment fractions, end of treatment (EOT), and 1 month after completing RT, assessing for CD34+, CD45+, full hematology, and 17 inflammatory cytokines. The associations between symptoms and all biomarkers were evaluated. All statistical tests were 2-sided. Results General fatigue and insomnia worsened with RT, with peak levels observed at EOT, which remained statistically significant even after controlling for anxiety and depression (P < .05 for all). CD34+, CD45+, white blood cell, and lymphocyte counts decreased, with the lowest levels also observed at EOT (P < .001). Fatigue and insomnia were associated with changes in both interferon γ-induced protein 10 (IP-10) - (P = .03 and P = .01, respectively) and tumor necrosis factor receptor II (TNF-RII) (P = .02 and P = .006, respectively), while mental fatigue was associated with increased matrix metalloproteinases-2 (MMP-2) levels (P = .03). Patients who received prior chemotherapy demonstrated statistically significantly greater severity in all symptoms, with lower baseline HSC levels. Conclusions This is the first longitudinal study to examine linkages between symptoms, HSCs, and cytokines, demonstrating that fatigue and insomnia shared associations with increasing serum levels of IP-10 and TNF-RII, and mental fatigue was associated with increasing serum levels of MMP-2. Our findings highlight opportunities for further research into mechanisms and potential interventions for these symptoms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".