Improvement of Labeling Efficiency of Glycoprotein-Liposome Conjugates with Iodine-125 by Using Bolton-Hunter Reagent and Their Distribution in Mice
Bibliographic record
Abstract
著者らはすでに, 機能性複合糖質の代表格である細胞膜面上の糖蛋白質の糖鎖機能を模倣した新規なドラッグデリバリーシステムの開発を目指して, 蛋白質を介したリポソームへの糖鎖の導入方法を検討し, リポソーム膜面上に結合した糖蛋白質の糖鎖構造改変とレクチン結合活性ならびに体内動態特性との関連について明らかにしてきた。これまでの体内動態実験では, 125I標識複合体を用いてきたが, 通常, 糖蛋白質の125I標識率はきわめて低いため, その125I標識率を高めるための工夫が必要となってきた。そこで, 本研究ではBolton-Hunter試薬 (以下, BHRと略す) を用いて, 標識率の改善を試みるとともに, レクチン結合活性および生体内動態への影響について検討した。一般的には先にBHRを125I標識し, 125I-BHRを蛋白質に導入する方法をとるが, 今回は先にリポソーム中のアミノ基にBHRを反応させ, p-ヒドロキシフェニルプロピル基を導入した後にクロラミンT法による125I標識を行う方法をとった。結果として, BHR処理により複合体の125I標識率は顕著に上昇した。また, BHR処理による複合体のレクチン結合活性への影響は見られなかった。しかしながら, 生体内動態は両者で顕著な差が見られた。すなわち, 肝臓への取込みが高まり, その結果腎臓への排泄が速められた。以上の結果より, 糖蛋白質結合リポソームの放射性ヨード標識には, 新たなる方法を開発する必要があろう。
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".