Scanless confocal light sheet microscopy for high-content and high-throughput imaging of cells and microorganisms
Bibliographic record
Abstract
Fluorescent Confocal Microscopy (FCM) has revolutionized biology and medicine by enabling high-resolution, three-dimensional imaging of cells and tissues, advancing our understanding of disease mechanisms and facilitating the development of targeted therapies. However, effective utilization of FCM in many fields is limited by photobleaching/phototoxicity and its relatively slow imaging speed due to point-by-point scanning. Here we report on development of a scanless confocal microscopy technique with light sheet excitation. The core innovation behind this work is utilization of the broadband nature of fluorescence expression to eliminate the need for scanning light across the sample which, in return, significantly lowers the instrumentation complexity and the cost of the system. With such an approach, scanless performance is achieved by taking advantage of a narrow slit (optical encoding in one sample direction) and spectral encoding of the broadband fluorescent emission (spectral encoding in other sample direction). Results from Zemax numerical modeling, experimental characterization tests, and confocal imaging of HeLa and HEK293 cells will be presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".