Tetraspanins Associated With oxLDL and IgG Mediated Phagocytosis In Human U937 Macrophages
Bibliographic record
Abstract
One of the crucial key targets in treatment of diseases are cell surface proteins, such as receptor complexes, and their associated signaling pathways. The Fc receptor is one of the most important phagocytic receptors of the cells of immune system. The ligand of the Fc gamma receptor is immunoglobulin G (IgG), which triggers the engulfment of foreign molecules coated by antibodies by a process called phagocytosis. A Specialized subset of cells including macrophages engulfs foreign particles by the Fc receptor. Another phagocytic receptor of macrophages is the CD36 receptor, which binds the ligand oxLDL and is known to be involved in the development of atherosclerotic lesions in the arteries. A few members of the Tetraspanin proteins have been found to be associated with theses receptors in macrophages. Tetraspanins may act as “molecular facilitators” grouping specific cell-surface proteins and thus increasing the formation and stability of functional signaling complexes. There is a significant amount of research done on the receptors of the surface of macrophages, however, the proteins associated with these receptors, their potential signaling pathways and the mechanisms involved are not yet fully understood. This thesis aims to investigate the presence and potential functional role of the specific Tetraspanin isoforms in Fc and CD36 mediated phagocytosis. Silencing RNA, quantitative assays of phagocytosis, and laser scanning confocal microscopy were used to test the phagocytic efficiency of macrophages in IgG and oxLDL mediated phagocytosis. Understanding the regulatory roles of Tetraspanins can provide insight into various immune diseases.
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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) | 0.000 | 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".