Identification of Novel Diagnostic Serum Biomarkers for Chagas' Disease in Asymptomatic Subjects by Mass Spectrometric Profiling
Bibliographic record
Abstract
More than 10 million people are thought to be infected with Trypanosoma cruzi, primarily in the Americas. The clinical manifestations of Chagas' disease (CD) are variable, but most subjects remain asymptomatic for decades. Only 15 to 30% eventually develop terminal complications. All current diagnostic tests have limitations. New approaches are needed for blood bank screening as well as for improved diagnosis and prognosis. Sera from subjects with asymptomatic CD (n = 131) were compared to those from uninfected controls (n = 164) and subjects with other parasitic diseases (n = 140), using protein array mass spectrometry. To identify biomarkers associated with CD, sera were fractionated by anion-exchange chromatography and bound to two commercial ProteinChip array chemistries: WCX2 and IMAC3. Multiple candidate biomarkers were found in CD sera (3 to 75.4 kDa). Algorithms employing 3 to 5 of these biomarkers achieved up to 100% sensitivity and 98% specificity for CD. The biomarkers most useful for diagnosis were identified and validated. These included MIP1 alpha, C3a anaphylatoxin, and unusually truncated forms of fibronectin, apolipoprotein A1 (ApoA1), and C3. An antipeptide antiserum against the 28.9-kDa C terminus of the fibronectin fragment achieved good specificity (90%) for CD in a Western blot format. We identified full-length ApoA1 (28.1 kDa), the major structural and functional protein component of high-density lipoprotein (HDL), as an important negative biomarker for CD, and relatively little full-length ApoA1 was detected in CD sera. This work provides proof of principle that both platform-dependent (i.e., mass spectrometry-based) and platform-independent (i.e., Western blot) tests can be generated using high-throughput mass profiling.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".