Bibliographic record
Abstract
Objective To study the effect of PAI-1 (plasinogen activator inhibitor-1) in mesangial proliferative glomerulonephritis (MsPGN). Methods The rut model of MsPGN was induced by injecting anti-thy1.1 monoclonal antibody. 18 rats were randomly assigned to three groups, including control group(control antibody, mouse Gamma Globulin, West Grove, PA, USA, 1 time/per week, 4 weeks, n=6), multiple injection group(anti-thy1,1 monoclonal antibody,1mg/kg, Cedarlane, Ontario, Canada, 1 time/per week, 4 weeks, n=6) and single anti-thy1.1 monoclonal antibody injection group (n=6). After 8 weeks, Picro-Sirius Red stained was applied to measured colla-genⅠ ,Ⅲ in the kidney. Expression of PAI-1 in kidney was analyzed by western-blotting and real-time PCR. Results Picro-Sirius Red stained results revealed significant accumulation of collagens Ⅰ and Ⅲ in multiple injection group, compared with control group and single an-tibody injection group(P<0.01). Western-blotting and real time PCR results showed expression of PAI-1 in multiple injection group was higher than that in control group and single antibody injection(P<0.01). The expression of collagens Ⅰ and Ⅲ in multiple injection group was correlated with the expression of PAI-1-mRNA in renal(r=0.71, P<0.01). Conclusions The 4 dose anti-thy1 monoclonal anti-body injection can induce persistent MsPGN. PAI-1 play an important role in the fibrosis of persistent MsPGN . Key words: Plasminogen activator inhibitor 1 ; Glomerulonephritis,membranoproliferative/PA
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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 | 0.001 |
| 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".