Towards understanding the nature of high frequency backscatter from cells and tissues: an investigation of backscatter power spectra from different concentrations of cells of different sizes
Bibliographic record
Abstract
During cell death, a series of structural changes occur within the cell. We have shown that cell ensembles and tissues undergoing structural changes associated with various cell death pathways can be detected using high-frequency ultrasound. In our effort to understand better the nature of backscatter from collections of cells (which emulate tissues), we have collected raw RF backscatter data from cells of two different sizes (human acute myeloid leukemia, AML, cells and transformed prostate cells) in solutions for a series of concentrations or in pellet form. It was found that the backscatter power (as measured by the mid-band fit) increased by /spl sim/3 dB for both cell types in dilute solutions for which the volumetric concentration was doubled for a specific range of cell concentrations (which was dependent on cell size). In pellet form, the backscatter power from the prostate cell pellets was /spl sim/12-14 dB greater than the AML cell pellets. A comparison of the spectral slopes also strongly suggests a change in the scattering source contributions when the cells are in pellets: the spectral slope was negative for all concentrations for prostate cells imaged at 40 MHz, but positive when measured in pellets. This is consistent with an increased contribution to the backscatter of smaller sized scatterers (such as the cell nucleus) that manifests itself only when the cells are in pellets but not in solution. These data are compared to theoretical predictions and their significance discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".