MP62-19 MUSCARINIC RECEPTOR EXPRESSION IN SPINAL CORD TRANSECTED RATS WITH EARLY ANTICHOLINERGIC TREATMENT
Bibliographic record
Abstract
many troublesome symptoms including urgency, frequency and incomplete emptying.Urine contains circulating miRNAs, and previously we established the protocols for urinary exosome isolation and miRNA profiling.Here we evaluated the suitability of urinary miRNAs for noninvasive diagnosis of functional changes in the bladder wall during obstruction.METHODS: We isolated total urinary RNA and exosomes from 50 ml of starting urine and profiled miRNAs of exosomes and total urine using NanoString nCounter Human miRNA Expression Assay.RESULTS: Using the samples of 42 patients and 12 controls we determined the expression profiles of 800 human miRNAs.We detected 320 urinary miRNAs, 16 of which were present in all tested samples.Hierarchical clustering of samples revealed a correlation between the urinary miRNAs and the symptoms of LUTD.In particular, hsa-miR-376a-3p, hsa-miR-196a-5p und hsa-miR-363-3p were more abundant in BPO patients samples compared to controls.Interestingly, in controls the expression of selected miRNAs was dependent on the subjects age.Exosomal miRNAs were more abundant in young controls, while hsa-miR-301a-5p, hsa-miR-301b-3p and hsa-miR-376a-3p were only detected in older controls (above 45 y.o.) and elderly BPO patients.We investigated the urinary exosome abundance and miRNA profiles in two age-matched groups of healthy subjects.CONCLUSIONS: Based on our results, we can select a small panel of representative miRNAs, which can be further explored to develop a non-invasive diagnostic test for BOO.The age-related discrepancy in the urinary miRNA content observed in this study points to the importance of selecting appropriate, age-matched controls.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".