Neuropathological Variability within a Spectrum of <scp>NMDAR</scp>‐Encephalitis
Bibliographic record
Abstract
OBJECTIVE: To describe the neuropathological features of N-methyl-D-aspartate receptor (NMDAR)-encephalitis in an archival autopsy cohort. METHODS: We examined four autopsies from patients with NMDAR-encephalitis; two patients were untreated, three had comorbidities: small cell lung cancer, brain post-transplant lymphoproliferative disease (PTLD), and overlapping demyelination. RESULTS: B cells/plasma cells in basal ganglia, amygdala, and hippocampus with surrounding white matter. The hippocampi showed a significant decrease of NMDAR-immunoreactivity that correlated with disease severity. The patient with NMDAR-encephalitis and immunosuppression for kidney transplantation developed a brain monomorphic PTLD. Inflammatory changes were compatible with NMDAR-encephalitis. Additionally, plasma cells accumulated in the vicinity of the necrotic tumor along with macrophages and activated microglia that strongly expressed pro-inflammatory activation markers HLA-DR, CD68, and IL18. The fourth patient developed demyelinating lesions in the setting of a relapse 4 years after NMDAR-encephalitis. These lesions exhibited the hallmarks of classic multiple sclerosis with radially expanding lesions and remyelinated shadow plaques without complement or immunoglobulin deposition, compatible with a pattern I demyelination. INTERPRETATION: The topographic distribution of inflammation in patients with NMDAR-encephalitis reflects the clinical symptoms of movement disorders, abnormal behavior, and memory dysfunction with inflammation dominantly observed in basal ganglia, amygdala, and hippocampus, and loss of NMDAR-immunoreactivity correlates with disease severity. Co-occurring pathologies influence the spatial distribution, composition, and intensity of inflammation, which may modify patients' clinical presentation and outcome. ANN NEUROL 2021;90:725-737.
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 | 0.000 | 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".