Identification of protein biomarkers in cerebrospinal fluid(CSF) of Neuro Psychiatric SLE(NPSLE) patients using SOMA scan assay.
Bibliographic record
Abstract
Abstract Background Neurological manifestations of systemic lupus erythematosus (NPSLE) include a wide spectrum of symptoms like seizures, meningitis and psychosis. Clinical diagnosis of NPSLE is challenging due to the lack of objective diagnostic tests. Here, we explore if CSF proteins may have diagnostic value in NPSLE. Methods 24 CSF samples (8 healthy + 8 neuro controls (NC) + 8 NPSLE) were screened using an aptamer-based platform for about 1100 proteins. A subset of the differentially expressed CSF proteins was selected for validation in two different cohorts, Caucasian controls (N=54) & NPSLE (N= 25) and Chinese controls (N=15) & SLE (N= 17). Results The initial aptamer-based screen revealed 8 CSF proteins to be elevated in NPSLE vs healthy, at fold change >2, and p <0.05. All 8 proteins were validated by ELISA. Total IgM, Lipocalin 2, M-CSF were significantly elevated in the CSF of NPSLE when compared to controls, in both Caucasian and Chinese patients (range of fold change: 16–1.2; p < 0.05). CSF HCC-s, DAN and Angiostatin were significantly elevated in Caucasian NPSLE patients only (range of fold change: 2.3–1.8; p < 0.05). Total C3 and albumin in CSF did not show any difference in disease groups in both cohorts, as assayed by ELISA. Conclusion Several novel proteins were noted to be elevated in NPSLE CSF, relative to control subjects with other neurological symptoms. Further studies are warranted to establish the specificity of CSF IgM, Lipocalin 2, M-CSF, HCC-s, DAN and Angiostatin for NPSLE and to understand their pathogenic relevance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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".