Polyethylene Glycol Conjugation Enhances the Nitrite Reductase Activity of Native and Cross-Linked Hemoglobin
Bibliographic record
Abstract
Although stabilized hemoglobins have been evaluated as oxygen-carrying replacements for red cells in transfusions, in vivo evaluations have noted that these materials are associated with vasoactivity, a serious complication. Scavenging of endogenous nitric oxide by the deoxyheme sites of the stabilized proteins is one likely source of vasoactivity. Recent reports indicate that modification of cell-free hemoglobin derivatives with multiple chains of polyethylene glycol (PEG) suppresses vasoactivity. Gladwin and co-workers observed that the nitrite reductase activity of hemoglobin serves as a major endogenous source of nitric oxide. If PEG conjugation leads to enhanced nitrite reductase activity, this could compensate for scavenged endogenous nitric oxide. To test this possibility, the rates of conversion of nitrite ion to nitric oxide by altered hemoglobins with and without PEG were measured at 25 degrees C. Fumaryl (alpha99-alpha99) cross-linked hemoglobin reacts with nitrite with a bimolecular rate constant of 0.52 M (-1) s (-1), which is comparable to that associated with native hemoglobin (0.25 M (-1) s (-1)). Addition of PEG chains to the cross-linked hemoglobin at beta-Cys93 (alphaalpha-Hb-PEG5K 2) results in a material that produces nitric oxide much more rapidly ( k = 1.41 M (-1) s (-1)). R-State-stabilized hemoglobins with multiple PEG chains (Hb-PEG5K 2 and Hb-PEG5K 6) react 10 times faster with nitrite to produce nitric oxide than does native hemoglobin ( k = 2.5 and 2.4 M (-1) s (-1), respectively). These results, showing enhanced production of nitric oxide resulting from an increased proportion of the protein residing in the R-state, are consistent with the decrease in vasoactivity associated with PEG conjugation.
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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) | 0.000 | 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".