Quantification of Anti-GP50, Anti-rT24H, and Anti-sTs18var1 Antibodies to Identify Viable Infection in Patients with Neurocysticercosis
Bibliographic record
Abstract
Identifying viable infections in neurocysticercosis (NCC) is crucial for treatment. Neuroimaging is the primary diagnostic tool, but it is not widely available. Moreover, in many cases, imaging diagnosis is not pathognomonic and requires serological confirmation. The serological assay of choice, enzyme-linked immunoelectrotransfer blot using lentil lectin-purified glycoprotein (LLGP-EITB) Taenia solium antigens to detect specific antibodies, exhibits high predictive values for the presence of viable NCC when the results are positive for multiple (>3) antibody bands; it also exhibits high predictive values for the absence of viable infection when the results are negative or the test reacts to a single antibody band. However, its interpretation in terms of viable infection is limited in cases with two or three positive bands (intermediate results), which occur in one-quarter of patients with NCC. The quantification of specific antibodies could allow for the identification of viable infections. Using a multi-antigen, quantitative multiplex bead assay, antibody levels were measured against Taenia solium proteins rGP50, rT24H, and sTs18var1 in 94 patients with intermediate LLGP-EITB results. The antibody-to-rT24H (25.96 versus 5.49; P = 0.0048) and antibody-to-sTs18var1 ratios (3.62 versus 1.37; P = 0.0083) were higher in subjects with viable cysticerci than in controls. Patients with high antibody levels against the proteins rT24H and sTs18var1 were 5.4 times more likely to have a viable infection than those with low antibody levels. The quantification of antibodies against rT24H and sTs18var1 can help define a viable NCC infection.
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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 | 0.001 | 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 | 0.001 | 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".