Determination of cell nucleus-to-cytoplasmic ratio using imaging flow cytometry and a combined ultrasound and photoacoustic technique: a comparison study
Bibliographic record
Abstract
While the nucleus-to-cytoplasmic (N:C) ratio has traditionally been used for assessing cell malignancy, most N:C measurement techniques are time-consuming and performed on thin histological sections, which prohibit assessment of three-dimensional cell structure. A combined ultrahigh frequency ultrasound (US) and photoacoustic (PA) technique was used to assess the size and N:C ratio of cultured cancer cells in three dimensions (3D). The diameters of the cells and their stained nuclei were obtained by fitting the power spectrum of backscattered US pulses and emitted PA waves, respectively, to well-established theoretical models. For comparison, an imaging flow cytometer (IFC) was also used to determine the two-dimensional cell and nucleus sizes from large cell populations using brightfield and fluorescence images, respectively. An N:C ratio was calculated for each cell using the quotient of the measured nucleus diameter and the total cell diameter. The mean N:C ratios calculated using the sound-based approach were 0.68, 0.66, and 0.54 for MCF-7, PC-3, and MDA-MB-231 cells, respectively, and were in good agreement with the corresponding values of 0.68, 0.67, and 0.68 obtained using the IFC. The combined US and PA technique, which assesses cellular N:C ratio in 3D, has potential applications in the detection of circulating tumor cells in liquid biopsies.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".