Imaging Mass Cytometry Immune Profiling of Hunner Lesions in a Convenience Sample of Patients With Interstitial Cystitis/Bladder Pain Syndrome
Bibliographic record
Abstract
PURPOSE: A comprehensive spatial immune profile of Hunner lesions (HLs) in interstitial cystitis/bladder pain syndrome (IC/BPS) is absent from the literature. Here, we leveraged imaging mass cytometry, a multiplex imaging platform, with novel computational pipelines to evaluate the immune in situ microenvironment of HL-IC/BPS. MATERIALS AND METHODS: Formalin-fixed paraffin-embedded HL tissue samples retrospectively collected from 10 patients with HL-IC/BPS were stained using a cocktail of 20-metal conjugated antibodies designed to profile both the innate and adaptive immune system. Imaging data were acquired using the Hyperion Imaging System. Data were visualized and processed using computational machine learning pipelines to resolve general immune complexity and spatial relationships in HL. RESULTS: macrophages within lesions. Computational analysis also demonstrated quantifiable methods to differentiate HL-IC/BPS patients based on differences in immune cell agglomeration within a lesion. CONCLUSIONS: Here, we demonstrate the use of highly multiplexed imaging in combination with novel analysis pipelines as a feasible method to understand the spatial organization of HLs. This pilot study suggests that these methods will be useful to prospectively characterize and evaluate the local immune microenvironment in HL-IC/BPS and could uncover mechanisms of disease pathogenesis.
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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 | 0.001 |
| 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".