Polyhemoglobin with Different Percentage of Tetrameric Hemoglobin and Effects on Vasoactivity and Electrocardiogram
Bibliographic record
Abstract
There has been considerable discussions on why some types of haemoglobin-based blood substitutes increase vasoactivity whereas a very few others do not. In this study, we prepare four different types of PolyHb each containing different percentage of tetrameric hemoglobin using glutaraldehyde crosslinking and characterized to ensure that they all have the same oxygen affinity. Thus the preparations are prepared from the same chemical method and have the same oxygen affinity. We infused these in the form of 1/6 volume toploading into anesthetized rats to simulate the use of blood substitutes in surgery. Mean arterial pressure (MAP) increased immediately after injection of PolyHb containing 38% or 78% of tetrameric hemoglobin. However, there was no significant increase in blood pressure with the injection of PolyHb containing 16% or 0.4% tetrameric hemoglobin. In electrocardiogram (ECG) study, we observe that high percentage (78%) of tetrameric hemoglobin causes marked changes in ECG immediately after infusion. Injection of PolyHb containing 16% or 38% of tetrameric hemoglobin resulted in minimal elevation of the ST segment. Infusion of PolyHb containing 0.4% of tetrameric hemoglobin did not result in any changes.
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 | 0.000 | 0.000 |
| 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".