Bibliographic record
Abstract
目的探讨脂多糖（lipopolysaccharide,LPS）对小鼠胰岛β细胞增殖的影响及NF-κB信号途径的调节作用。方法不同剂量LPS按不同时间刺激小鼠胰岛β细胞株NIT-1细胞,并使用NF-κB特异性抑制剂Bay11-7082（5μmol·L-1）进行干预。使用cell counting kit-8（CCK-8）试剂检测细胞增殖,Western blot检测NIT-1细胞磷酸化I-κBα（pI-κBα）和总I-κBα蛋白水平。结果LPS在0.1、0.5、1.0、5.0mg·L-1刺激72h对NIT-1细胞增殖有促进作用,在5.0mg·L-1浓度时促进作用减弱,在10.0mg·L-1浓度时对NIT-1细胞增殖无明显影响;NF-κB特异性抑制剂Bay11-7082可阻断LPS对NIT-1细胞增殖的促进作用;LPS刺激后60～120min,NIT-1细胞磷酸化I-κBα相对于总I-κBα蛋白水平增高;Bay11-7082阻断LPS诱导的NIT-1细胞I-κBα蛋白磷酸化。结论低剂量LPS促进NIT-1细胞增殖,NF-κB激活可能参与其过程。
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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".