Extending immunofluorescence detection limits in whole paraffin‐embedded formalin fixed tissues using hyperspectral confocal fluorescence imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A major problem in microscopic imaging of ex vivo tissue sections stained with fluorescent agents (e.g. antibodies, peptides) is the confounding presence of background tissue autofluorescence. Autofluorescence limits (1) the accuracy of differentiating background signals from single and multiple fluorescence labels and (2) reliable quantification of fluorescent signals. Advanced techniques such as hyperspectral imaging and spectral unmixing can be applied to essentially remove this autofluorescent signal contribution, and this work attempts to quantify the effectiveness of autofluorescence spectral unmixing in a tumour xenograft model. Whole-specimen single-channel fluorescence images were acquired using excitation wavelengths of 488 nm (producing high autofluorescence) and 568 nm (producing negligible autofluorescence). These single-channel data sets are quantified against hyperspectral images acquired at 488 nm using a prototype whole-slide hyperspectral fluorescence scanner developed in our facility. The development and further refinement of this instrument will improve the quantification of weak fluorescent signals in fluorescence microscopy studies of ex vivo tissues in both preclinical and clinical applications.
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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 | 0.000 | 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 it