Ultrasound Imaging of Apoptosis: DNA-Damage Effects Visualized
Bibliographic record
Abstract
This chapter presents a new method for the detection of DNA damage that leads to the physiological process of apoptosis which is a normal programmed cell-death response to sufficiently toxic levels of DNA damage. The high-frequency ultrasonic detection of programmed cell death has been demonstrated in vitro, in situ, and in vivo using a variety of different apoptosis inducing methods (1,2). This method provides a useful image based adjunct for the detection of programmed cell death in a laboratory setting as well as being a powerful potential clinical tool which can be used to monitor tumour responses to treatment. Typical low-frequency medical ultrasound imaging devices operate at 1–10 MHz, provide mainly low resolution structural information, and predominantly are used in obstetrics and cardiology. In contrast, high-frequency ultrasound imaging devices operate at 30–50 MHz and offer increased resolution as well as the emerging capability to detect cells and tissues in different physiological states including those undergoing programmed cell death, or apoptosis (1–4). Data collected to date indicates that this capability of high-frequency ultrasound is based on interactions of high-frequency ultrasound waves with the chromosomal nuclear material in cells, which undergoes structural changes of condensation and subsequent fragmentation with the process of programmed cell death (1–2,4). We have demonstrated this process experimentally in vitro, in situ, and in vivo using a number of different systems in which apoptosis is induced with physiological stimuli, chemotherapeutic drugs, radiation, or photodynamic therapy. The ultrasound-based approach to detecting apoptosis has a number of potential applications which range from embryological studies of development where apoptosis plays an important role, to assessing organ viability for the purposes of transplantation, again a situation where the presence of programmed cell death is correlated to clinical outcome (5).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".