Amyloid processing in <scp>COVID</scp>‐19‐associated neurological syndromes
Bibliographic record
Abstract
Abstract SARS‐CoV‐2 infection can damage the nervous system with multiple neurological manifestations described. However, there is limited understanding of the mechanisms underlying COVID‐19 neurological injury. This is a cross‐sectional exploratory prospective biomarker cohort study of 21 patients with COVID‐19 neurological syndromes (Guillain–Barre Syndrome [GBS], encephalitis, encephalopathy, acute disseminated encephalomyelitis [ADEM], intracranial hypertension, and central pain syndrome) and 23 healthy COVID‐19 negative controls. We measured cerebrospinal fluid (CSF) and serum biomarkers of amyloid processing, neuronal injury (neurofilament light), astrocyte activation (GFAp), and neuroinflammation (tissue necrosis factor [TNF] ɑ, interleukin [IL]‐6, IL‐1β, IL‐8). Patients with COVID‐19 neurological syndromes had significantly reduced CSF soluble amyloid precursor protein (sAPP)‐ɑ ( p = 0.004) and sAPPβ ( p = 0.03) as well as amyloid β (Aβ) 40 ( p = 5.2 × 10 −8 ), Aβ42 ( p = 3.5 × 10 −7 ), and Aβ42/Aβ40 ratio ( p = 0.005) compared to controls. Patients with COVID‐19 neurological syndromes showed significantly increased neurofilament light (NfL, p = 0.001) and this negatively correlated with sAPPɑ and sAPPβ. Conversely, GFAp was significantly reduced in COVID‐19 neurological syndromes ( p = 0.0001) and this positively correlated with sAPPɑ and sAPPβ. COVID‐19 neurological patients also displayed significantly increased CSF proinflammatory cytokines and these negatively correlated with sAPPɑ and sAPPβ. A sensitivity analysis of COVID‐19‐associated GBS revealed a non‐significant trend toward greater impairment of amyloid processing in COVID‐19 central than peripheral neurological syndromes. This pilot study raises the possibility that patients with COVID‐19‐associated neurological syndromes exhibit impaired amyloid processing. Altered amyloid processing was linked to neuronal injury and neuroinflammation but reduced astrocyte activation. image
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".