Rapid and accurate diagnosis of brain abscess caused by <i>Nocardia asiatica</i> with a combination of Ziehl‐Neelsen staining and metagenomics next‐generation sequencing
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early and accurate diagnosis is vital in cerebral nocardiosis, a very rare and infectious disease associated with a high mortality rate. Herein, we report a case that a patient with brain abscess was swiftly diagnosed and successfully treated. METHODS: We report a case of a 61-year-old woman with a brain abscess caused by Nocardia asiatica, diagnosed by a combination of Ziehl-Neelsen staining and metagenomics next-generation sequencing (mNGS). RESULTS: A 61-year-old woman with left breast cancer resection, diabetes mellitus and a 7-month discontinuous cough and fever was admitted to our hospital. On the third day of hospitalization, she experienced a sudden loss of consciousness and was diagnosed with a brain abscess and a pathological change in cerebral mass on brain magnetic resonance imaging (MRI). Due to the failure of culturing any microorganisms from the pup, the dissected sample from the patient with Ziehl-Neelsen staining tested positive for acid-fast bacilli and was subjected to mNGS. The pathogen was identified as N. asiatica and the patient was treated accordingly with linezolid and trimethoprim-sulfamethoxazole until complete recovery was confirmed by the follow-up cerebral MRI. CONCLUSIONS: This is the first case report of a brain abscess caused by N. asiatica being swiftly diagnosed by a combination of Ziehl-Neelsen staining and mNGS. This rapid diagnosis allowed us to successfully treat this rare infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".