Marker-independent imaging reveals a correlation of fibrotic and epigenetic alterations in endometriosis
Bibliographic record
Abstract
Abstract Introduction Endometriosis describes the presence of endometrial glands outside of the uterus and can cause various symptoms such as chronic pelvic pain, hypermenorrhea and infertility. These complications pose an extreme burden on the patients, especially as up to date, the average time until diagnosis can consume several years and requires invasive laparoscopy. Objectives The aim of this study is to molecularly characterize endometrium and endometriosis using marker-independent Raman microspectroscopy to identify potential biomarkers and validate its diagnostic potential. Methods After histopathological characterization of tissue sections of human endometrium and endometriosis, Raman microspectroscopy was performed on the gland region. Multivariate analysis of the hyperspectral maps was used to localize major subcellular structures and further decipher their molecular composition. Samples from different anatomical regions and throughout all menstrual cycle phases were analyzed. Results Raman imaging enabled label-free visualization of tissue morphology and submolecular tissue characterization. Distinct differences between endometrium and endometriosis were found for collagen type I and nuclear signatures. Spectral deconvolution allowed identification of a Raman biomarker indicative of fibrotic changes in endometriosis samples. Additionally, a significant increase in epigenetic 5mC foci and an increased signal intensity relevant for methylations was detected in nuclei of endometriosis. Furthermore, a neural network-based classification of Raman data resulted in high accuracies in discriminating endometrial and peritoneal tissue from endometriosis. Conclusion The non-destructive approach by hyperspectral Raman imaging enabled for molecular sensitive characterization of endometriotic lesions which could not only be an asset in complementing histopathological tissue evaluation but combined with data-driven classification models support in situ tissue diagnosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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 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".