Bibliographic record
Abstract
细胞和生物材料表面的甲氧基聚乙二醇(mPEG)修饰是一项具有广阔应用前景的非药理性免疫调节技术, 但由于细胞的不稳定性, 目前对其表面与血浆间相互作用的认识还不够深入. 本实验采用相对稳定的乳胶微球作为模型, 来研究其表面对血浆蛋白的吸附. 结果显示, 血浆总蛋白的吸附水平随着mPEG修饰浓度和分子量的增加而降低, 未修饰的乳胶微球表面蛋白吸附量为(159.9±6.4) ng/cm2, mPEG 修饰后, 蛋白吸附量分别下降至(18.4±0.8) ng/cm2(1 mmol/L 2 kD SCmPEG)和(52.3±5.3) ng/cm2(1 mmol/L 20 kD SCmPEG). 聚炳烯酰胺凝胶电泳(SDS-PAGE)和同位素标记相对和绝对定量的质谱技术(iTRAQ-MS)分析表明, 在PEG 修饰微球表面的蛋白质吸附层中, 参与免疫系统活化的蛋白质丰度明显下降. 因此, 免疫修饰细胞和材料的生物学效应是通过降低抗原性及减弱表面与大分子间相互作用来实现的.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".