Purified Excreted-Secreted Antigens from <i>Trypanosoma cruzi</i> Trypomastigotes as Tools for Diagnosis of Chagas' Disease
Bibliographic record
Abstract
There is currently no "gold standard" test for the diagnosis of late-stage Chagas' disease. As a result, protection of the blood supply in areas where Chagas' disease is endemic remains problematic. A panel of 709 serum samples from subjects with confirmed Chagas' disease (n = 195), healthy controls (n = 400), and patients with other parasitic diseases (n = 114) was used to assess enzyme-linked immunosorbent assays (ELISAs) based on a concentrated extract of excretory-secretory antigens from either Brazil or Tulahuen strain Trypanosoma cruzi trypomastigotes (total trypomastigote excretory-secretory antigens [TESAs]). The total TESA-based assays had excellent overall sensitivity (100%) and specificity (>94%), except for cross-reactivity with Leishmania-infected sera. In an attempt to increase the specificity of the assay, immunoaffinity chromatography was used to purify the TESA proteins (TESA(IA) proteins). By Western blotting, a series of polypeptide bands with molecular masses ranging from 60 to 220 kDa were recognized by pooled sera positive for Chagas' disease. An ELISA based on TESA(IA) proteins had a slightly lower sensitivity (98.6%) but an improved specificity (100%) compared to the sensitivity and specificity of the total TESA protein-based ELISAs. A 60-kDa polypeptide was identified as a major contributor to the cross-reactivity with Leishmania. These data suggest the need for field validation studies of TESA- and TESA(IA)-based assays in regions where Chagas' disease is endemic.
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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 | 0.000 |
| 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) | 0.000 | 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 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".