All-optical Reflection-mode Microscopic Histology of Unstained Human Tissues
Bibliographic record
Abstract
Surgical oncologists depend heavily on visual field acuity during cancer resection surgeries for in-situ margin assessment. Clinicians must wait up to two weeks for results from a pathology lab to confirm a post-operative diagnosis, potentially resulting in subsequent treatments. Currently, there are no clinical tools that can visualize diagnostically pertinent tissue information in-situ. Here, we present the first microscopy capable of non-contact label-free visualization of human cellular morphology in a reflection-mode apparatus. This is possible with the recently reported imaging modality called photoacoustic remote sensing microscopy which enables non-contact detection of optical absorption contrast. By taking advantage of the 266-nanometer optical absorption peak of DNA, photoacoustic remote sensing is efficacious in recovering qualitatively similar nuclear information in comparison to that provided by the hematoxylin stain in the gold-standard hematoxylin and eosin (H&E) prepared samples. A photoacoustic remote sensing system was employed utilizing a 266-nanometer pulsed excitation beam to induce photoacoustic pressures within the sample resulting in refractive index modulation of the optical absorber. A 1310-nanometer continuous-wave interrogation beam detects these perturbed regions as back reflected intensity variations due to the changes in the local optical properties. Using this technique, clinically useful histologic images of human tissue samples including breast cancer (invasive ductal carcinoma), tonsil, gastrointestinal, and pancreatic tissue images were formed. These were qualitatively comparable to standard H&E prepared samples.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".