A modular Raman microspectroscopy system for biological tissue analysis
Bibliographic record
Abstract
Raman spectroscopy has been used as a sensitive tool for studying biological tissue and evaluating disease. In many applications, microscopic level resolution spectral analysis is desirable. And this has been performed mostly by expensive commercial confocal micro-Raman systems. In this research, we present a simple method for building an economical and modular Raman microspectroscopy system that combines a microscope with a Raman spectrometer using an optical fiber bundle. The bundle with a circular collection end is positioned at an image plane of the microscope to collect Raman signals from the interested micro-location on the sample. The light delivery end is specially configured so that its 37 fibers are arranged along a straight line to fit into the spectrometer entrance slit. This configuration improves light collection efficiency and maintains high spectral resolution. To battle the great background autofluorescence and Raman signals that could originate from the microscope slides and optics due to the non-confocal set-up of our simplified system, conventional normal-incident illumination is replaced by oblique illumination at 45° degrees and the microscope slides are coated with gold. We demonstrated the usefulness of the system by measuring micro-Raman spectra from different skin layers on vertical sections of normal skin tissue samples.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".