Chronic ethanol consumption induces micturition dysfunction and alters the oxidative state of the urinary bladder
Bibliographic record
Abstract
Oxidative stress is pointed out as a major mechanism by which ethanol induces functional and structural changes in distinctive tissues. We evaluated whether ethanol consumption would increase oxidative stress and cause micturition dysfunction. Male C57BL/6J mice were treated with 20% ethanol (v/v) for 10 weeks. Our findings showed that chronic ethanol consumption reduced micturition spots and urinary volume in conscious mice, whereas in anaesthetized animals cystometric analysis revealed reduced basal pressure and increased capacity, threshold pressure, and maximum voiding. Treatment with ethanol reduced the contraction induced by carbachol in isolated bladders. Chronic ethanol consumption increased the levels of oxidant molecules and thiobarbituric acid reactive species in the mouse bladder. Upregulation of Nox2 was detected in the bladder of ethanol-treated mice. Increased activity of both superoxide dismutase and catalase were detected in the mouse bladder after treatment with ethanol. Conversely, decreased levels of reduced glutathione were detected in the bladder of ethanol-treated mice. The present study first demonstrated that chronic ethanol consumption induced micturition dysfunction and that this response was accompanied by increased levels of oxidant molecules in the mousebladder. These findings suggest that ethanol consumption is a risk factor for vesical dysfunction.
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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".