Conventional frequency evaluation of tumor cell death in response to treatment <i>in vivo</i>.
Bibliographic record
Abstract
In this study, we investigate for the first time the potential to quantify tumor responses to therapy in vivo, using spectral and signal envelope statistics analysis of conventional frequency ultrasound data. Tumors were grown in SCID mice using a human prostate cell line (PC-3) and treated in three groups consisting of combinations of ultrasonically stimulated micro-bubble anti-angiogenic treatment and 8 Gy doses of single fraction 100 kVp X-ray radiation. Data collection consisted of tumor images in addition to radiofrequency data prior to treatment administration and at 24 h following. Analysis of the normalized power spectra of 10-MHz data yielded an increase in mid-band fit of approximately 3.8±0.7, 4.6±0.8, and 5.8±0.7 dBr compared to data acquired before treatment. Spectral slopes were relatively invariant. However, the 0-MHz intercept followed the trend of the mid-band fit. Signal envelope changes were compatible with observations made from spectral data and linked to correlative increases in cell death. The aim of this research is to investigate the potential of conventional frequency ultrasound to noninvasively quantify response to cancer treatments, laying the groundwork for future investigation into the viability of 10-MHz ultrasound to be employed in a clinical setting.
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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 | 0.000 |
| 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".