Progranulin in frontotemporal lobar degeneration and neuroinflammation
Bibliographic record
Abstract
Progranulin (PGRN) is a pleiotropic protein that has gained the attention of the neuroscience community with recent discoveries of mutations in the gene for PGRN that cause frontotemporal lobar degeneration (FTLD). Pathogenic mutations in PGRN result in null alleles, and the disease is likely the result of haploinsufficiency. Little is known about the normal function of PGRN in the central nervous system apart from a role in brain development. It is expressed by microglia and neurons. In the periphery, PGRN is involved in wound repair and inflammation. High PGRN expression has been associated with more aggressive growth of various tumors. The properties of full length PGRN are distinct from those of proteolytically derived peptides, referred to as granulins (GRNs). While PGRN has trophic properties, GRNs are more akin to inflammatory mediators such as cytokines. Loss of the neurotrophic properties of PGRN may play a role in selective neuronal degeneration in FTLD, but neuroinflammation may also be important. Gene expression studies suggest that PGRN is up-regulated in a variety of neuroinflammatory conditions, and increased PGRN expression by microglia may play a pivotal role in the response to brain injury, neuroinflammation and neurodegeneration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".