Peroxisomes as emerging clinical targets in neuroinflammatory diseases
Bibliographic record
Abstract
Peroxisomes are membrane-bounded organelles that contribute to a range of physiological functions in eukaryotic cells. In the central nervous system (CNS), peroxisomes are implicated in several vital homeostatic functions including, but not limited to, reactive oxygen species signaling and homeostasis; generation of critical myelin sheath components (including ether phospholipids); biosynthesis of neuroprotective docosahexaenoic acid; breakdown of neurotoxic metabolites (such as very-long chain fatty acids); and, intriguingly, glial activation and response to inflammatory stimuli. Indeed, peroxisomes play a critical role in modulating inflammatory responses and are key regulators of the mitochondrial antiviral signaling (MAVS) protein-mediated response to infections. The importance of peroxisomes in CNS physiology is exemplified by the peroxisome biogenesis disorders (PBDs), a spectrum of inherited disorders of peroxisome assembly and/or abundance, that are characterized in part by neurological manifestations ranging from severe cerebral malformations to vision and hearing loss, depending on the individual disorder. Recently, peroxisome dysfunction has been implicated in neurological diseases associated with neuroinflammation including Alzheimer's disease, amyotrophic lateral sclerosis, multiple sclerosis, and Parkinson's disease while also contributing to the pathogenesis of neurotropic viruses including SARS-CoV-2, Human Pegivirus, HIV-1 and Zika virus. In the present review, we examine the diverse roles that peroxisomes serve in CNS health before reviewing more recent studies investigating peroxisome dysfunction in inflammatory brain disorders and also highlight potential peroxisomal targets for diagnostic biomarkers and therapeutic interventions.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".