The use of speckle decorrelation OCT to detect nanobubbles in cell pellet aggregates
Bibliographic record
Abstract
Nanobubbles (NBs) have demonstrable potential for ultrasound imaging and therapeutic applications. Recent studies have even shown their capacity for cellular internalization, which has important implications for their in-vivo stability and bioaccumulation. Traditional methods for observing NBs often involve fluorescence labelling, which can influence NB behaviour. Moreover, these methods are unsuitable for detecting intact (acoustically active) NBs within a cellular environment. This study introduces a label-free approach employing optical coherence tomography (OCT) to investigate the temporal variations in speckle intensity of the OCT backscatter signal of cells interacting with NBs. The temporal variations in the signal intensity of cell aggregates result from the motion of subcellular scatterers within the cellular environment. In this work, we investigate whether internalized NBs modify the temporal variations in the signal intensity. For our experimental imaging set-up, we used a Thorlabs MEMS-VCSEL Swept Source OCT system with a central wavelength of 1300 nm to acquire M-Mode and B-Mode acquisitions. PC3 prostate cancer cells and in-house lipid-shelled NBs were used. The sensitivity of the speckle decorrelation technique was tested on our system using an intensity autocorrelation function on polystyrene microspheres and diluted NBs. Our study demonstrates that speckle decorrelation OCT can effectively detect NBs within a compact cell pellet under specific conditions and was verified using contrast-enhanced ultrasound. This approach provides an additional optical method for NB detection within cellular environments and holds the potential for broader applications in detecting NBs in in-vivo applications.
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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 | 0.000 | 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) | 0.000 | 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".