Correlation between Monocyte Gene Expression and Inflammation on Brain Imaging in Patients with Solitary Cerebral Cysticercus Granuloma
Bibliographic record
Abstract
Prior work has shown that 14 monocyte genes are upregulated in patients with different forms of parenchymal neurocysticercosis, including solitary cysticercus granuloma (SCG). The aim of this study was to investigate whether changes in inflammation associated with SCG seen on follow-up brain imaging are also reflected in changes in expression of these 14 genes. Peripheral blood CD14+ monocytes were isolated from 20 patients with SCG at initial diagnosis and at clinical and imaging follow-up of 6 months or more. Expressions of 14 target monocyte genes were determined by quantitative polymerase chain reaction at each visit. At a median follow-up of 14 months, the SCG had resolved in 11 patients, was persistent in four patients, and had calcified in five patients. Edema seen in the initial imaging in 17 patients had resolved in 15 patients and was markedly reduced in two patients. The expression levels of the monocyte genes LRRFIP2, TAXIBP1, and MZB1 were significantly lower at follow-up, regardless of the status of SCG on follow-up imaging. Our findings show that expression levels of monocyte genes involved with inflammatory processes decrease in patients with SCG concomitant with follow-up imaging that reveals a reduction in inflammation as revealed by complete or near-complete resolution of edema, as well as resolution or reduction in the enhancement of the granuloma.
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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 | 0.002 |
| 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) | 0.001 | 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".