Serum-Positive and -Negative AQP4 Antibody NMO in Chinese Patients
Bibliographic record
Abstract
OBJECTIVE: To compare the clinical features of our sero-negative and sero-positive neuromyelitis optica (NMO) patients. METHODS: Thirty-nine patients with NMO were recruited and analyzed retrospectively. Serum aquaporin 4 (AQP4) antibody status was determined by a cell-based assay. For the sero-negative patients, cerebrospinal fluid (CSF) and serum samples were re-tested using the cell-based assay and an indirect immunofluorescence assay. RESULTS: By the cell-based assay, 30 patients (76.92%, 30/39), were positive for AQP4 antibodies in serum and 37 patients (94.9%, 37/39), had a CSF-positive antibody status. Seven NMO patients (17.9%, 7/39) were sero-negative by the cell-based assay but demonstrated positive CSF results. By indirect immunofluorescence, the remaining two patients, who had no AQP4 antibodies in serum or CSF by the cell-based assay, were positive for IgG antibodies in serum, which selectively targeted the central nervous system microvessels, pia, subpia, Virchow-Robin space, kidney, and stomach. There were no significant differences between the sero-positive and sero-negative NMO groups among their demographic and clinical data. CONCLUSIONS: Repeating the test using a different assay or CSF is helpful to clarify whether sero-negative NMO patients do in fact carry AQP4 antibodies.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".