Mechanism and biological significance of erythrocyte homoaggregation (rouleaux formation): temperature-dependent entropic liberation of water from cells and macromolecules
Bibliographic record
Abstract
In the pre-antibiotic era, infections were usually more frequent and serious than today. Robin Fåhraeus (1888-1958) examined the erythrocyte sedimentation rate (ESR) test for infections, which was normally carried out in vitro with freshly drawn blood. His extensive studies on the mechanism and physiological significance of the enhanced sedimentation of erythrocyte aggregates (rouleaux) in disease included in vivo simulation. This led him to propose an explanation for the finding of long white strips ("fibrin coagula") within the blood vessels of those who had died from infections. The surge of serious infections in pandemic times has likely kindled a reemergence. He further speculated that (i) the weak aggregation of red blood cells (RBCs) followed the liberation of water molecules from their surfaces, and (ii) the importance of their aggregation, which was induced by changes in serum proteins (not necessarily antibodies), extended beyond the clinic. In modern times these changes have led to immunologically significant entropic interpretations of infection-associated aggregations, whether cellular (e.g., RBC) or molecular (i.e., macromolecular polymerizations). Thus, rouleaux formation displays a process at the cellular level that can proceed in parallel at a less visible macromolecular level. It has been proposed that, when intracellular, aggregations would discriminate between self and not-self proteins in the crowded cytosol. Favoured by an associated pyrexia, this could lead, by mechanisms to be determined, to the preferential loading of peptides from proteins deemed "foreign" for presentation as major histocompatibility complexes (MHCs) to specific clones of immune cells.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".