Possible Impact of Peripheral Inflammatory Factors and Interleukin-1β (IL-1β) on Cognitive Functioning in Progressive Supranuclear Palsy–Richardson Syndrome (PSP-RS) and Progressive Supranuclear Palsy–Predominant Parkinsonism (PSP-P)
Bibliographic record
Abstract
Progressive supranuclear palsy (PSP) is a tauopathic atypical parkinsonian syndrome. Recent studies suggest that inflammation may play a role in PSP pathogenesis, highlighting markers like the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and cytokines such as IL-1β and IL-6. This study aimed to assess the relationship between peripheral inflammatory markers and psychological abnormalities in PSP-RS and PSP-P patients. The study included 24 participants: 12 with PSP-RS, 12 with PSP-P, and 12 controls. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA); however, the executive functions were evaluated using the Frontal Assessment Battery (FAB), while inflammatory markers such as IL-1β, IL6, NLR, and PLR were measured. The parameter correlation was executed using Spearman's correlation (rs). The analysis revealed significant negative correlations between NLR and MoCA (rs = -0.48), as well as between PLR and MoCA (rs = -0.60). The negative correlation between IL-1β and MoCA was statistically significant but relatively weak. This study highlights the relevance of inflammatory markers such as NLR and PLR in reflecting cognitive decline in PSP patients, with IL-1β potentially playing a protective role in cognitive function.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".