Urinary cytokines/chemokines after magnetic resonance-guided high intensity focused ultrasound for palliative treatment of painful bone metastases
Bibliographic record
Abstract
BACKGROUND: Pain is experienced by 50-75% of patients with bone metastases, representing a major source of morbidity amongst cancer patients. Magnetic resonance-guided high intensity focused ultrasound (MRgHIFU) is a new, non-invasive, outpatient treatment modality for painful bone metastases. The aim of this study was to analyze urinary cytokines/chemokines pattern after MRgHIFU for palliative treatment of painful bone metastases. The findings were compared to the cytokines/chemokines pattern post single 8 Gy fraction radiation from our previous study. METHODS: Urine samples were collected from patients with painful bone metastases 3 days before and 2 days after treatment with MRgHIFU. Each urine sample was tested for pro-inflammatory cytokines and anti-inflammatory cytokines. Patients received teaching on how to collect urine samples on their own. The Millipore Milliplex 42-Plex Cytokine/Chemokine Kit™ was used to measure urinary levels of a panel of cytokines/chemokines. RESULTS: Ten patients were enrolled for the study. The following 15 cytokines were above the level of detection (LOD) in at least 50% of patients at both pre MRgHIFU and post MRgHIFU: EGF, eotaxin, Fit-3 ligand, fractalkine, G-CSF, GRO, IFNα2, IL-1ra, IL-8, IP-10, MCP-1, PDGF-AA, RANTES, sIL-2Rα, and VEGF. Nine urinary cytokines significantly decreased post MRgHIFU, namely, eotaxin, GRO, IL-8, IL-13, IP-10, MCP-1, MIP-1β, RANTES, and sIL-2Rα. In addition, there were significant differences between post MRgHIFU and post-8 Gy fraction radiation in most urinary cytokines. CONCLUSIONS: Nine urinary cytokines significantly reduced post-MRgHIFU in patients with painful bone metastases. The significance of cytokines/chemokines pattern for palliative treatment of painful bone metastases is still unknown.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".