Rat model of experimentally induced abacterial prostatitis
Bibliographic record
Abstract
BACKGROUND: An experimental model in rats was developed to investigate the significance of mucosal integrity in abacterial prostatitis. METHODS: Ethanol was instilled into the ventral prostates of male rats to reduce mucosal integrity; dinitrobenzenesulfonic acid (DNBS) was added as an irritant to induce inflammation. Controls received no treatment, ethanol only, DNBS only, or a suspension of bacteria. After various time points, rats were sacrificed, and their prostates were assayed for gross morphology, histological appearance, and cytokine levels. RESULTS: Prostates subjected to ethanol plus DNBS showed significant inflammation, most notably after 12, 24, and 48 hr. Inflammation judged by gross and histological observations and interleukin-1beta levels correlated well at these times. Rats given only ethanol, DNBS, or no treatment, acting as negative controls, displayed little or no inflammation; rats given a bacterial suspension, acting as positive controls, showed inflammation consistent with past studies. Cytokine assays revealed raised interleukin-1beta levels in this model, while tumor necrosis factor-alpha remained at a basal level. CONCLUSIONS: The loss of an intact mucosal surface in the prostate resulted in inflammation caused by an irritant. Interleukin-1beta appears to play a role in this inflammation, while tumor necrosis factor-alpha does not.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".