Acoustical imaging of internal spheroid structures at a variety of frequencies
Bibliographic record
Abstract
We have previously shown that ultrasound is capable of identifying apoptosis in individual cells and cell pellets due to changes in the ultrasound attenuation, speed of sound, and backscatter. Spheroids can be used to more accurately model non-vascularized tumors due to their three-dimensional growth pattern, cell-cell interaction, disorganized growth, and development of a necrotic core when grown to sufficiently large sizes. To examine cell death in spheroids due to necrosis or chemotherapy, quantitative ultrasound methods were used on the ultrasound backscatter power spectrum throughout the spheroid. MCF7 spheroids ranging in size from 100 to 1000 μm were probed at 25 and 55 MHz using a VEVO770 high frequency ultrasound machine, and at 80 and 200 MHz using an acoustic microscope. Changes in spheroid structure as the necrotic core develops, and after it is exposed to chemotherapeutic agents were recorded and analyzed. An increase in the ultrasound backscatter amplitude from necrotic cells within the core of the spheroid versus the viable cells around the core was observed. Changes in the ultrasound backscatter were also observed for spheroids treated with chemotherapeutics to induce apoptosis. This work furthers our understanding of non-invasively identifying the viability of cancerous tumors, and the efficacy of chemotherapeutic treatments.
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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 | 0.000 |
| 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 | 0.000 |
| 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".