Long-term Effects on the Histology and Function of Livers and Spleens in Rats after 33% Toploading of PEG-PLA-nano Artificial Red Blood Cells
Bibliographic record
Abstract
This study is to investigate the long-term effects of nanodimension PEG-PLA artificial red blood cells containing hemoglobin and red blood cell enzymes on the liver and spleen after 1/3 blood volume top loading in rats. The experimental rats received one of the following infusions: Nano artificial red blood cells in Ringer lactate, Ringer lactate, stroma-free hemoglobin, polyhemoglobin, and autologous rat whole blood. Blood samples were taken before infusions and on days 1, 7, and 21 after infusions for analysis. Nano artificial red blood cells, polyhemoglobin, Ringer lactate and rat red blood cells did not have any significant adverse effects on alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, creatine kinase, amylase and creatine kinase. On the other hand, stroma-free hemoglobin induced significant adverse effects on liver as shown by elevation in alanine aminotransferase and aspartate aminotransferase throughout the 21 days. On day 21 after infusions rats were sacrificed and livers and spleens were excised for histological examination. Nano artificial red blood cells, polyhemoglobin, Ringer lactate and rat red blood cells did not cause any abnormalities in the microscopic histology of the livers and spleens. In the stroma-free hemoglobin group the livers showed accumulation of hemoglobin in central veins and sinusoids, and hepatic steatosis. In conclusion, injected nano artificial red blood cells can be efficiently metabolized and removed by the reticuloendothelial system, and do not have any biochemical or histological adverse effects on the livers or the spleens.
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 | 0.001 |
| 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".