Quantitative ultrasound microscopy imaging of cells
Bibliographic record
Abstract
Using high-frequency ultrasound, two approaches were used to quantify eukaryotic cell properties. One approach (10-100 MHz) is model-based wherein live cells of known number and volume density are placed in a mixture of bovine plasma and thrombin to form a clot. Backscatter coefficient estimates are modeled against a concentric sphere scattering model to yield cell and nucleus diameters as well as density and speed of cytoplasm and nucleus of Chinese hamster ovary (CHO) and 3T3 fibroblast cells. Estimated cell and nucleus diameters were consistent with direct light microscope measures [CHO cell: 13 microns, nucleus: 6.6 microns and 3T3 cell: 23 microns, nucleus: 13 microns]. For CHO cells, both density and speed of the cytoplasm were less than those of the nucleus. For the 3T3 cells, both density and speed of the cytoplasm were not significantly different from those of the nucleus. The other approach (65-170 MHz) is a direct through-transmission round-trip measure (RF echo reflected from strong reflector), yielding estimates of attenuation and speed of cytoplasm and nucleus of MAT B III mammary adenocarcinoma cell. Speed of cytoplasm was not significantly different from that of the nucleus. The attenuation of the cytoplasm and nucleus were 0.61-0.77 and 0.94 dB/cm-MHz, respectively. [Supported by NIH R01CA111289; l'Ecole Centrale de Lille for TA; Canadian National Science and Engineering Research Council for LAW]
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".