Early interactions of<i>Entamoeba histolytica</i>trophozoites with parenchymal and inflammatory cells in the hamster liver: an immunocytochemical study
Bibliographic record
Abstract
We studied the early in situ interactions of live and fixed Entamoeba histolytica trophozoites with hamster hepatic parenchymal and inflammatory cells using immunoperoxidase and immunoelectronmicroscopy. Close contact between trophozoites and endothelial cells and the diffusion of amoebic molecules from trophozoites towards nearby endothelial cells and distant hepatocytes were observed. The inflammatory cells around the amoebae and the remnants of parenchymal cells and hepatocytes located close to the lesion had a positive stain for amoebic molecules. In the amoebae, at the ultrastructural level, molecules were attached to the membranes and inside the vesicles. These molecules were apparently released into the space formed between the parasite and the endothelial cells. The endothelial cells and the nearby and distant hepatocytes captured amoebic molecules, and later they became necrotic. Contrarily, when fixed amoebae were inoculated, amoebic molecules were captured by endothelial cells and polymorphonuclear (PMN) leukocytes, but neither suffered any damage. In this work, we are presenting evidence clearly showing that some molecules of the amoeba can diffuse away long distances causing cytotoxic effects and even necrosis on hepatic cells of hamster liver without the need of the trophozoite being in close contact with the target cells. They also may promote lytic or proinflammatory effects by inducing the secretion of enzymes or cytokines in other nonparenchymal cells, like PMN leukocytes and endothelial cells. Our results suggest that the accepted mechanisms of cytotoxicity by amoebae are not exclusively restricted to the following sequence: adhesion, phagocytosis, and necrosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".