Brainstorm: A case of granulomatous encephalitis
Bibliographic record
Abstract
Background: Free-living amoebas (FLAs) can cause severe and fatal central nervous system infections that are difficult to diagnose. Methods: We present the case of a 74-year-old immunocompetent woman admitted for focal neurological symptoms with enhancing lesions in the right cerebellar hemisphere. A first cerebral biopsy showed granulomatous inflammation, but no microorganisms were identified. After transient clinical improvement, she eventually deteriorated 4 months after initial presentation, with an MRI confirming multiple new masses affecting all cerebral lobes. Results: A second brain biopsy revealed granulomatous and acute inflammation with organisms containing a large central nucleus with prominent karyosome, consistent with FLAs. Immunohistochemical and polymerase chain reaction assays performed at CDC were positive for Acanthamoeba spp, confirming the diagnosis of granulomatous amoebic encephalitis (GAE) caused by Acanthamoeba spp. The patient was treated with combination therapy recommended by CDC, but died a few days later. Upon histopathological rereview, amoebic cysts and trophozoites were identified by histochemical and immunohistochemical methods in the first cerebral biopsy. Conclusion: FLA infections can be challenging to diagnose because of the low incidence, non-specific clinical and radiological presentation, lack of accessible diagnostic tools, and clinicians’ unfamiliarity. This case highlights the importance of recognizing FLA as a potential cause of granulomatous encephalitis, even in the absence of risk factors, as early treatment might be associated with favourable outcomes in case reports. When suspected, CDC laboratories offer tests to confirm the diagnosis promptly.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".