Protein–protein coupling and its application to functional red cell substitutes
Bibliographic record
Abstract
The need for an alternative to red cells for oxygen transport in transfusions has led to the creation of hemoglobin-based oxygen carriers, materials produced by chemical modification or genetic engineering of human or bovine hemoglobin. Modifications of the native proteins are necessitated by the spontaneous dissociation of the functional hemoglobin tetramers (alpha(2)beta(2)) into non-functional alphabeta dimers. Based on clinical observations of hypertension resulting from some of these materials, it was proposed that the stabilized tetramers are sufficiently small to extravasate through blood vessels and scavenge nitric oxide, depleting the endothelium of the signal for smooth muscle relaxation. In order to increase size and minimize extravasation while maintaining structure and function, methods for producing larger entities through protein-protein conjugation were developed. Approaches have included the use of nonspecific reagents that polymerize proteins (e.g., polyglutaraldehyde), conjugation to polyethylene glycol, expression of naturally occurring multimers and the use of selective reagents, which is the focus of this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".