Binding of acellular, native and cross-linked human hemoglobins to haptoglobin: enhanced distribution and clearance in the rat
Bibliographic record
Abstract
It is well established that hemoglobin resulting from red cell lysis binds to haptoglobin in plasma to form a complex. The increased molecular size precludes its filtration by the kidneys, redirecting it toward hepatocellular entry. Chemically cross-linked hemoglobins are designed to be resistant to renal excretion, even in the absence of haptoglobin. The manner in which binding to haptoglobin influences the pharmacokinetics of acellular cross-linked and native hemoglobins was investigated after intravenous injection of radiolabeled native human hemoglobin and trimesyl-(Lys82)beta-(Lys82)beta cross-linked human hemoglobin, at trace doses, into rats. Under these conditions, there is sufficient plasma haptoglobin for binding with hemoglobin. In vitro binding assayed by size-exclusion chromatography for bound and free hemoglobin revealed that, at <8 muM hemoglobin, native human hemoglobin was completely bound to rat haptoglobin, whereas only approximately 30% of trimesyl-(Lys82)beta-(Lys82)beta cross-linked hemoglobin was bound. Plasma disappearance of low doses (0.31 mumol/kg) of native and cross-linked hemoglobins was monoexponential (half-life = 23 and 33 min, respectively). The volume of distribution (40 vs. 19 ml/kg) and plasma clearance (1.22 vs. 0.4 ml.min(-1).kg(-1)) were higher for native than for cross-linked hemoglobin. Native and cross-linked human hemoglobins were found primarily in the liver, and not in the kidney, heart, lung, or spleen, mostly as degradation products. These pharmacokinetic findings suggest that the binding of hemoglobin to haptoglobin enhances its hepatocellular entry, clearance, and distribution.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".