Erythrocyte aggregation in relation to plasma proteins and lipids
Bibliographic record
Abstract
In static or low-flow conditions erythrocytes form linear or three-dimensional aggregates with characteristic face-to-face morphology, similar to a stack of coins, often called rouleaux formation. This aggregation is reversible and shear dependent (i.e. dispersed at high shear and reformed at low shear or stasis) and caused by a variety of macromolecules present in the blood plasma. The plasma protein fibrinogen is the major plasma component promoting red blood cell (RBC) aggregation in blood, with an almost linear relationship between aggregate size and plasma fibrinogen concentration. However, other plasma proteins are also reported to increase RBC aggregation, e.g. α2-macroglobulin, immunoglobulin M or G. In addition, there is evidence, that plasma lipids like cholesterol or triglyceride may influence the aggregation of erythrocytes. In this study we evaluated whether there is an independent influence of proteins and lipids on the RBC aggregation. Using a regression analysis, we analyzed the correlation between the fibrinogen-, α2-macrogobulin-, immunoglobulin M-, Antithrombin III-, Protein C-, Factor VIII-, total cholesterol- and triglyceride concentration with RBC aggregation in blood samples from 2717 apparently healthy subjects or patients. An univariate analysis showed, that the only variable which correlates on a biologically relevant level is fibrinogen ( r = 0.46). The multiple correlation coefficient corresponded to r mult = 0.589 what indicated that nearly 59% of the variation of the erythrocyte aggregation can be explained by the influencing factors used in this model. This clearly showed that there are additional factors which are involved in the process of erythrocyte aggregation and still are under discussion.
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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".