The nuclear pore complex protein Tpr is a common autoantigen in sera that demonstrate nuclear envelope staining by indirect immunofluorescence
Bibliographic record
Abstract
We studied the autoantigen targets of 75 human sera that had antibodies to the nuclear envelope (NE) as identified by indirect immunofluorescence (IIF) on HEp-2 cells. Several different IIF staining patterns could be identified when antibodies to different components of the nuclear membrane (NM) and nuclear pore complexes (NuPC) were identified: a smooth membrane pattern characteristic of antibodies to nuclear lamins, a punctate pattern typical of antibodies to the nuclear pore complex and more complex patterns that included antibodies to nuclear and cytoplasmic organelles. Western immunoblotting of isolated nuclear and NE proteins and immunoprecipitation of radiolabelled recombinant proteins prepared by using the full-length cDNAs of the Translocated promoter region (Tpr), gp210 and p62 were used to identify specific autoantibody targets. Fifty-two of the 75 (70%) sera bound to Tpr, 25 (33%) bound to lamins A, B or C, 15 (20%) reacted with gp210 and none reacted with p62. Sixteen (21%) did not react with any of the NE components tested in our assays. The clinical features of 37 patients with anti-NE showed that there were 34 females and three males with an age range of 16-88 years (mean 59 years). The most frequent clinical diagnosis (9/37 = 24%) was autoimmune liver disease (ALD; two with primary biliary cirrhosis), followed by seven (19%) with systemic lupus erythematosus (SLE), four (11%) with a motor and/or sensory neuropathy, three (8%) with anti-phospholipid syndrome (APS), two with systemic sclerosis (SSc), two with Sjögren's syndrome (SjS), and others with a variety of diagnoses. This report indicates that Tpr, a component of the NuPC, is a common target of human autoantibodies that react with the NE.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".