Progranulin: normal function and role in neurodegeneration
Bibliographic record
Abstract
Progranulin (PGRN) is a multifunctional protein that has attracted significant attention in the neuroscience community following the recent discovery of PGRN mutations in some cases of frontotemporal dementia. Most of the pathogenic mutations result in null alleles, and it is thought that frontotemporal dementia in these families results from PGRN haploinsufficiency. The neuropathology associated with PGRN mutations is characterized by the presence of tau-negative, ubiquitin-immunoreactive neuronal inclusions (frontotemporal lobar degeneration with ubiquitinated inclusions) that are also positive for the transactivation response DNA binding protein with M(r) 43 kD. The clinical phenotype includes behavioral abnormalities, language disorders and parkinsonism but not motor neuron disease. There is significant clinical variation between families with different PGRN mutations and among members of individual families. The normal function of PGRN is complex, with the full-length form of the protein having trophic and anti-inflammatory activity, whereas proteolytic cleavage generates granulin peptides that promote inflammatory activity. In the periphery, PGRN functions in wound healing responses and modulates inflammatory events. In the CNS, PGRN is expressed by neurons and microglia; consequently, reduced levels of PGRN could affect both neuronal survival and CNS inflammatory processes. In this review, we discuss current knowledge of the molecular genetics, neuropathology, clinical phenotype and functional aspects of PGRN in the context of neurodegenerative disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".