Antineuronal antibodies in idiopathic achalasia and gastro-oesophageal reflux disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: The precise aetiology of achalasia is unknown although autoimmunity has been implicated and is supported by several studies. We screened sera from patients with achalasia or gastro-oesophageal reflux disease (GORD) to test for circulating antimyenteric neuronal antibodies. METHODS: Serum was obtained from 45 individuals with achalasia, 16 with GORD, and 22 normal controls. Serum was used in immunohistochemistry to label whole mount preparations of ileum and oesophagus of the guinea pig and mouse. Also, sections of superior cervical and dorsal root ganglia, and spinal cord were examined. RESULTS: Positive immunostaining of the myenteric plexus was detected in significantly more achalasia and GORD samples than control samples (achalasia, p<0.001; GORD, p<0.01), and immunoreactivity was significantly more intense with achalasia and GORD serum samples than controls (achalasia, p<0.01; GORD, p<0.05). There was no correlation between intensity of immunoreactivity and duration of achalasia symptoms. In most cases, achalasia and GORD sera stained all ileal submucosal and myenteric neurones, and oesophageal neurones. Immunostaining was not species specific; however, immunostaining was largely specific for enteric neurones. Western blot analysis failed to reveal specific myenteric neuronal proteins that were labelled by antibodies in achalasia or GORD serum. CONCLUSIONS: These data suggest that antineuronal antibodies are generated in response to tissue damage or some other secondary phenomenon in achalasia and GORD. We conclude that antineuronal antibodies found in the serum of patients with achalasia represent an epiphenomenon and not a causative factor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".