Reflection mode polarimetry guides laser mass spectrometry to diagnostically important regions of human breast cancer tissue
Bibliographic record
Abstract
To enhance the clinical utility of mass spectrometry (MS), lengthy dwell times on less informative regions of patient specimens (e.g., adipose tissue in breast) must be minimized. Additionally, a promising variant of MS known as picosecond infrared laser MS (PIRL-MS) faces further challenges, namely, lipid contamination when probing adipose tissue. Here we demonstrate on several thick non-sectioned resected human breast specimens (healthy and malignant) that reflection-mode polarimetric imaging can robustly guide PIRL-MS toward regions devoid of significant fat content to (1) avoid signal contamination and (2) shorten overall MS analysis times. Through polarimetric targeting of non-fat regions, PIRL-MS sampling revealed feature-rich spectral signatures including several known breast cancer markers. Polarimetric guidance mapping was enabled by circular degree-of-polarization (DOP) imaging via both Stokes and Mueller matrix polarimetry. These results suggest a potential synergistic hybrid approach employing polarimetry as a wide-field-imaging guidance tool to optimize efficient probing of tissue molecular content using MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".