Peripherin-Reactive Antibodies in Mouse, Rabbit, and Human Blood
Bibliographic record
Abstract
Type 1 diabetes (T1D) is an autoimmune disorder that results from the destruction of insulin-producing beta-cells in the islets of Langerhans. To date, autoimmune T-cell response and antibody reactivity to more than 20 autoantigens have been linked to this disease. Some studies have described the intermediate filament protein peripherin (PRPH) as an autoantigen associated with T1D in non-obese diabetic (NOD) mice. We evaluated immune reactivity of mouse and rabbit sera and human plasma to a 58 kDa protein expressed in RIN-m5F rat insulinoma cells. The protein was isolated using 2-DE and identified by mass spectrometry as PRPH. Antibodies from healthy humans and T1D patients, CD-1 mice, C57BL/6 mice, NOR (non-obese diabetes resistant) mice, and NOD mice reacted with PRPH on Western blots. However, antibody response to PRPH was stronger in NOD than non-autoimmune prone C57BL/6 mice. We conclude that immune reactivity to PRPH is not exclusively associated with NOD mice or human patients with T1D. Furthermore, the frequent occurrence of PRPH-reactive antibodies in mouse and human blood suggests that binding may be non-specific or could reflect the presence of natural autoantibodies against PRPH. These findings point to the need for a re-evaluation of PRPH as a T1D autoantigen in NOD mice and raise the question of the physiological relevance of such widespread immune reactivity against this peripheral nervous system protein.
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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 | 0.000 |
| 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) | 0.002 | 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".