Review: Soluble innate immune pattern-recognition proteins for clearing dying cells and cellular components: implications on exacerbating or resolving inflammation
Bibliographic record
Abstract
Soluble innate immune pattern-recognition proteins (sPRPs) identify non-self or altered-self molecular patterns. Dying cells often display altered-self arrays of molecules on their surfaces. Hence, sPRPs are ideal for recognizing these cells and their components. Dying cell surfaces often contain, or allow the access to different lipids, intracellular glycoproteins and nucleic acids such as DNA at different stages of cell death. These are considered as 'eat me' signals that replace the native 'don't eat me' signals such as CD31, CD47 present on the live cells. A programmed cell death process such as apoptosis also generates cell surface blebs that contain intracellular components. These blebs are easily released for effective clearance or signalling. During late stages of cell death, soluble components are also released that act as 'find me' signal (e.g. LysoPC, nucleotides). The sPRPs such as collectins, ficolins, pentraxins, sCD14, MFG-E8, natural IgM and C1q can effectively identify some of these specific molecular patterns. The biological end-point is different depending on sPRP, tissue, stage of apoptosis and the type of cell death. The sPRPs that reside in the immune-privileged surfaces such as lungs often act as opsonins and enhance a silent clearance of dying cells and cellular material by macrophages and other phagocytic cells. Although the recognition of these materials by complement-activating proteins could amplify the opsonic signal, this pathway may aggravate inflammation. Clear understanding of the involvement of specific sPRPs in cell death and subsequent clearance of dying cell and their components is essential for devising appropriate treatment strategies for diseases involving infection, inflammation and auto-antibody generation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".