Analysis of ultrasound backscatter from ensembles of cells and isolated nuclei
Bibliographic record
Abstract
We have previously shown that the intensity of the ultrasound backscatter from cells ensembles undergoing apoptosis increases and shifts in their normalized power spectra are detected when compared to the backscatter from non-apoptotic cells. The etiology of these changes is unknown. During apoptosis many cellular changes occur, perhaps the most striking being the condensation and subsequent fragmentation of the cell nucleus. In this set of experiments have exposed either whole Acute Myeloid Leukemia (AML) cells or nuclei isolated from AML cells to different ionic strengths known to induce specific and reproducible cellular and nuclear changes. Ultrasound images and rf backscatter data were collected and analyzed at the different ionic strengths, and electron micrographs were made. Exposing cells to higher ionic strengths increased the ultrasound backscatter by 12 dB, but exposing the nuclei to the same experimental conditions decreased the backscatter by 23 dB. Furthermore, while the spectral slopes of the rf backscatter were similar for cells and nuclei at physiological saline, at increased concentrations the slope increased for the nuclei but decreased for the cells. The paper discusses the implications and significance of the findings. In conclusion, disruptions in cell and nuclear structure induced by exposure to strong ionic environments can greatly alter the ultrasound backscatter signal characteristics.
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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 | 0.000 |
| 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".