Investigating the age and sex associated mucosal immune landscape of urinary bladder
Bibliographic record
Abstract
Abstract The incidence of BC is four times higher in males than females, however, females tend to present with a more aggressive disease, a poorer response to immunotherapy, and suffer worse clinical outcomes. Our recent novel findings demonstrated sexual dimorphism in the tumor immune microenvironment of non-muscle invasive bladder cancer, which is primarily treated with local immunotherapy. Investigating sexual dimorphism in the clinical journey of BC beyond the static local immune landscapes remains a challenge. With similarities in mucosal immune physiology between humans and mice, and the recently reported age-associated pathogenic stimuli independent inflammation observed female murine bladders, we conducted a comprehensive evaluation of the sex and age-related immune alterations in healthy murine bladders. Bulk-RNA sequencing and multiplex immunofluorescence based spatial immune profiling of normal murine bladders from male and female mice of the ages 3, 6, 9, 12, 15, and 18 months were performed. Preliminary findings from the transcriptomic analysis showed a highly altered immune landscape that exhibited sex differences in aged bladders. Spatial profiling of using markers specific to macrophages (CD163, CD11b), T lymphocytes (CD3, CD8), B lymphocytes (Pax5), activated dendritic cells (CD208), high endothelial venules (PNAd), inflammatory cells (Ly6G) and the PD-L1 immune checkpoint, showed sex and age associated differences. Older female mice had a higher density of tertiary lymphoid structures/lymphoid aggregates compared to both young female and male equivalents. Findings from this study will allow the appropriate modeling in pre-clinical studies evaluating immunotherapeutic agents for NMIBC treatment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".