The potential of proteomics for in-depth molecular investigations of progressive supranuclear palsy
Bibliographic record
Abstract
INTRODUCTION: Progressive supranuclear palsy (PSP) is a rare neurodegenerative disorder. The lack of comprehension about the pathogenesis of the disease, its heterogeneity, and the complex clinical evaluation in early stages, limit the development of effective treatments for PSP patients and highlight the need of further research on the field. AREAS COVERED: In this work, we review the current knowledge of the physio- and neuropathology of PSP, its clinical features, diagnosis markers, and treatment options. We also compare the proteomic-based studies done to date in brain tissues as well as in cerebrospinal fluid and other non-cerebral samples, briefly describing the proteomic approach used and the biological findings obtained in each study. EXPERT OPINION: PSP is a complex neurodegenerative disorder marked by tau aggregation, glial dysfunction, and neuroinflammation. Although advances in neuroimaging and biofluid biomarkers have improved PSP diagnostic accuracy, no disease-modifying therapies are currently available. Promising avenues such as tau PET tracers, seed amplification assays, and advanced proteomic-based approaches are enhancing our ability to detect disease-specific tau pathology and hold the potential to provide novel biomarkers for earlier and more precise clinical diagnosis and treatment development that could transform the landscape of PSP.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".