The life and death of single cells: an acoustic microscopy investigation
Bibliographic record
Abstract
Changes in cell structure as tumours respond to cancer therapies can be detected using high frequency ultrasound (20-60 MHz). However, it is unknown how single cell variations influence the ultrasound signal as these ultrasound frequencies cannot resolve individual cells. Acoustic microscopy uses ultra-high frequency ultrasound (over 100 MHz) to quantify the structural and mechanical properties of single cells. The backscattered echoes from the cell membrane and substrate were used to determine the thickness, sound speed, density, bulk modulus and attenuation of breast cancer cells undergoing various biological processes, such as mitosis (cell division) and apoptosis (programmed death). Moreover, measurements were made to examine differences between malignant and benign breast tumour cells. Statistically significant changes in the thickness, sound speed and bulk modulus (2%), and attenuation (60%) between malignant and benign cells, and thickness (70%) and attenuation (21%) between cells undergoing mitosis and apoptosis were observed. Differences in bulk modulus (4%) and attenuation (30%) were observed between early and late stage apoptosis, which can be attributed to the irreversible changes in cell structure during cell death. In summary, acoustic microscopy can be used to probe single cells and help understand their contribution to ultrasonic changes in tumours responding to chemotherapy 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".