Peptide discovery across the spectrum of neuroinflammation; microglia and astrocyte phenotypical targeting, mediation, and mechanistic understanding
Bibliographic record
Abstract
Uncontrolled and chronic inflammatory states in the Central Nervous System (CNS) are the hallmark of neurodegenerative pathology and every injury or stroke-related insult. The key mediators of these neuroinflammatory states are glial cells known as microglia, the resident immune cell at the core of the inflammatory event, and astroglia, which encapsulate inflammatory insults in proteoglycan-rich scar tissue. Since the majority of neuroinflammation is exclusively based on the responses of said glia, their phenotypes have been identified to be on an inflammatory spectrum encompassing developmental, homeostatic, and reparative behaviors as opposed to their ability to affect devastating cell death cascades and scar tissue formation. Recently, research groups have focused on peptide discovery to identify these phenotypes, find novel mechanisms, and mediate or re-engineer their actions. Peptides retain the diverse function of proteins but significantly reduce the activity dependence on delicate 3D structures. Several peptides targeting unique phenotypes of microglia and astroglia have been identified, along with several capable of mediating deleterious behaviors or promoting beneficial outcomes in the context of neuroinflammation. A comprehensive review of the peptides unique to microglia and astroglia will be provided along with their primary discovery methodologies, including top-down approaches using known biomolecules and naïve strategies using peptide and phage libraries.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".