Nodding syndrome is unlikely to be an autoimmune reaction to leiomodin-1 after infection by Onchocerca volvulus
Bibliographic record
Abstract
Nodding syndrome is a neurological disease of children in northern Uganda. Infection with the nematode parasite Onchocerca volvulus has been epidemiologically implicated as the cause of the disease. It has been proposed that an autoantibody directed against the human protein leiomodin-1 cross reacts with a tropomyosin-like nematode protein, thus suggesting that nodding syndrome is an autoimmune brain disease due to extra-cerebral parasitism. This hypothesis is dependent on constitutive neuronal expression of leiomodin-1. We tested this hypothesis by studying the distribution of leiomodin-1 in the normal human brain and other human tissues using immunohistochemistry. We found that immunostaining for leiomodin-1 follows a smooth muscle cell specific pattern. In the brain, it is confined to the smooth muscle cells of cerebral blood vessels and is not generally present in neurons or glia. However, immunoreactivity was identified in human Purkinje cell membrane and the body wall of C. elegans (as a proxy for Onchocerca volvulus) but only when immunostained with an antibody recognizing the N-terminal of leiomodin-1. Homology between leiomodin-1 and tropomodulin, specifically at the N-terminus, could explain why leiomodin-1 antibody cross reactivity between human Purkinje cells and C. elegans. However, we cannot provide proof confirming that the immunoreactivity in the membranes of Purkinje cells is specifically caused by the expression of tropomodulin. To overcome this limitation, further investigations using additional immunohistochemical and biochemical studies are required to corroborate our findings and provide more comprehensive evidence. Nevertheless, our findings do not support to the autoimmunity hypothesis involving Onchocerca volvulus and leiomodin-1. To gain a more comprehensive understanding of the cause and pathogenesis of NS, it is essential to explore alternative hypotheses.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".