Imaging Cytometry of Human Leukocytes with Third Harmonic Generation Microscopy
Why this work is in the frame
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Bibliographic record
Abstract
Based on third-harmonic-generation (THG) microscopy and a k-means clustering algorithm, we developed a label-free imaging cytometry method to differentiate and determine the types of human leukocytes. According to the size and average intensity of cells in THG images, in a two-dimensional scatter plot, the neutrophils, monocytes, and lymphocytes in peripheral blood samples from healthy volunteers were clustered into three differentiable groups. Using these features in THG images, we could count the number of each of the three leukocyte types both in vitro and in vivo. The THG imaging-based counting results agreed well with conventional blood count results. In the future, we believe that the combination of this THG microscopy-based imaging cytometry approach with advanced texture analysis of sub-cellular features can differentiate and count more types of blood cells with smaller quantities of blood.
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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 | 0.001 | 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 it