Optical and photoacoustic radiofrequency spectroscopic analysis for detecting red blood cell death
Bibliographic record
Abstract
Under stress, red blood cells (RBCs) undergo programmed cell death (eryptosis). One of the signaling molecules for eryptosis, sphingomyelinase (SMase), plays an important role in monitoring the efficacy of vascular targeted cancer therapy. The high optical absorption of erythrocytes coupled with the changes of eryptotic RBCs makes RBCs ideal targets for the photoacoustic (PA) detection and characterization of vascular treatments. In this work, experiments characterizing eryptosis were performed: PA detection of high frequencies (>100 MHz) that enabled analysis at the single-cell level and of low frequencies (21 MHz) that enabled analysis at the RBC ensemble level. Ultrasound spectral analysis was performed on control and SMase-treated RBCs. Spectral unmixing was applied to quantify methemoglobin production as a by-product of RBC death. Validation was performed using a blood gas analyzer and optical spectrometry. Our results indicate that PA radiofrequency spectra could be used to differentiate the biochemically induced morphological changes as RBCs lose their native biconcave shape, and release hemoglobin into the surroundings. Spectral unmixing revealed a 7% increase in methemoglobin content for SMase-treated samples due to the oxidative stress on the RBCs. These findings suggest that PA spectral analysis of RBC death can potentially serve as a biomarker of the efficacy of vascular targeted cancer therapies.
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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 | 0.001 |
| 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 | 0.001 | 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".