Existence and significance of antibody against the inward rectifying potassium channel KIR4.1 in patients with multiple sclerosis in western part of Turkey (P5.219)
Bibliographic record
Abstract
Objective: To examine the frequency of KIR4.1 binding IgG in serum of patients with clinically isolated syndrome (CIS) and MS to assess whether the antibody has any effect on clinical and paraclinical parameters. Background : Recent studies suggest that potassium channel KIR4.1 could be a target of the antibody response in a subgroup of patients with multiple sclerosis (MS). Yet, there is a debate in the current literature. Methods: Using ELISA with a KIR4.1 peptide, we tested serum from 31 patients (11 pts with CIS and 20 pts with relapsing remitting (RR) MS) and from 16 controls. Results: 13 of 31 (41.9[percnt]) patients and 12 of 16 (75[percnt]) controls were KIR4.1 antibody positive by ELISA. 27.3[percnt] out of patients with CIS and 50[percnt] out of patients with RRMS were KIR4.1 antibody positive. There was no correlation among KIR4.1-IgG, the disease duration (p=0.857), expanded disability status scale (EDSS) (p=0.372), the cerebrospinal fluid Immunoglobulin G index (p=0.325), the cerebrospinal immunoglubulin M index(p=0.717), the presence of gadolinum enhancing lesions (p=0.358) and total numebr of T2 lesions (p=0.457). Conclusion: Although serum antibodies against KIR4.1 are found in approximately half of the patients in the present study, our results suggest that the presence of these antibodies in the serum does not have clinical and paraclinical significance. Study Supported by:None
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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) | 0.001 | 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 | 0.001 | 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".