Monocyte Gene Expression Distinguishes Enhancing Brain Parenchymal Cysticercal Granulomas From Tuberculomas
Bibliographic record
Abstract
BACKGROUND: In patients with enhancing brain parenchymal lesions, parenchymal neurocysticercosis (pNCC) is often difficult to distinguish from tuberculoma, necessitating biopsy or empirical therapy. METHODS: In a prospective study, peripheral blood monocytes were isolated from patients with definitive pNCC (n = 39) and brain tuberculomas (n = 20). Patients with tuberculomas were diagnosed by the presence of concurrent systemic tuberculosis (n = 7), pathological or bacteriological confirmation (n = 5), and resolution of typical brain lesions following a therapeutic trial of antituberculous therapy (n = 8). Expressions of 14 NCC-associated monocyte genes were determined by quantitative polymerase chain reaction and analyzed for diagnostic usefulness between the 2 groups. RESULTS: Expression of 7 genes (TAX1BP1, RAP1A, PLCG2, TOR3A, GBP1P1, LRRFIP2, and FEZ2) was significantly higher in pNCC patients than in tuberculoma patients, with TAX1BP1 and RAP1A expressions more than 22- and 5-fold higher in pNCC patients. TAX1BP1 had the highest sensitivity of 66.7% at a specificity of 100% in discriminating pNCC from tuberculoma. A combination of TAX1BP1 and RAP1A increased the sensitivity to 84.6%, and including GBP1P1 with TAX1BP1 and RAP1A further increased sensitivity to 87.2% while maintaining specificity of 100%. CONCLUSIONS: Expression of a panel of genes in blood monocytes distinguishes pNCC from brain tuberculomas in patients with enhancing brain lesions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".