Immunohistochemical Markers in the Diagnosis of Calcifying Pseudoneoplasm of the Neuraxis
Bibliographic record
Abstract
BACKGROUND: Calcifying pseudoneoplasm of the neuraxis (CAPNON) is a rare tumor-like lesion with unknown pathogenesis. It is likely under-reported due to diagnostic challenges including the nonspecific radiographic features, lack of diagnostic markers, and often asymptomatic nature of the lesions. METHODS: We performed detailed examination of 11 CAPNON specimens diagnosed by histopathology, with the help of electron microscopy and immunohistochemistry. RESULTS: Electron microscopy revealed the presence of fibrillary materials consistent with neurofilaments. In addition to some entrapped axons at the periphery of CAPNONs, we discovered that all specimens stained positive for neurofilament-light (NF-L) within the granular amorphous cores, but not neurofilament-phosphorylated (NF-p). CAPNONs also showed variable infiltration of CD8+ T-cells and a decreased ratio of CD4/CD8+ T-cells, suggesting an immune-mediated process in the pathogenesis of CAPNON. CONCLUSION: NF-L and CD4/CD8 immunostains may serve as diagnostic markers for CAPNON and shed light on its pathogenesis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".