B-076 Detection of KLHL11 Autoantibodies Using a New Cell-based Indirect Immunofluorescence Assay
Bibliographic record
Abstract
Abstract Background Autoantibodies against kelch-like protein 11 (KLHL11) were first described in 2019 as markers of paraneoplastic encephalitis. Early diagnosis and treatment are imperative to improve prognosis and outcome of affected patients. Rat brain immunohistochemistry was reported to be not useful in routine screening for KLHL11 antibodies. This study evaluates the performance of a new recombinant cell-based assay for the standardized detection of KLHL11-specific IgG and reports the characteristics of the examined patients. Methods Serum (n=9), CSF (n=5) and/or plasmapheresis (n=1) samples from 10 patients with clinical presentations compatible with anti-KLHL11 encephalitis as well as sera from 100 healthy blood donors were analyzed using a prototype indirect immunofluorescence assay (IFA; EUROIMMUN) based on recombinant HEK293 cells expressing human KLHL11. Results KLHL11 autoantibodies were detected at high titers in all samples (serum >1:1,000; CSF >1:320; plasma >1:100,000) from all (10/10) patients but in none (0/100) of the blood donors, indicating 100% sensitivity and specificity. Anti-KLHL11-positive patients had a median age of 52 years (range 27-71) and 80% were male. The most common reported clinical presentations were cerebellar syndrome (ataxia), brainstem diencephalic encephalitis, rhombencephalitis and limbic encephalitis. Testicular or ovarian tumors were found in 87.5% (7/8) of the cases with available results from malignancy screening. Conclusions The new cell-based IFA enables the sensitive and specific detection of KLHL11 autoantibodies, thus supporting the diagnosis of patients with predominantly paraneoplastic brainstem cerebellar syndrome. Future studies will evaluate assay performance in larger patient cohorts to address the reported association of KLHL11 autoimmunity with a wider spectrum of syndromes and tumors.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".