Identification of candidate serum biomarkers for schistosomiasis infection using mass spectrometric approaches
Bibliographic record
Abstract
Schistosome infections, caused by a family of helminth parasites, are major neglected tropical diseases (NTDs) that have serious implications for socio-economic development in many tropical countries. These debilitating and chronic diseases are endemic in more than 70 countries but diagnostic tools are limited and new tests are necessary to limit morbidity and mortality. The development of such tests may be facilitated by the identification of disease-specific biomarkers that are positive early in infection, that can distinguish between acute and chronic infection and that can serve as validation of cure. This thesis work has two major goals: (a) to identify stage-specific proteomic patterns during early (3 week), acute (6 week) and chronic (12 week) Schistosoma mansoni infection in a mouse model; (b) to profile the sera of humans infected by S. mansoni, or S. haematobium and compare these patterns with the protein profile of healthy human serum. To achieve these goals, proteomic analyses were performed on mouse and human sera using several different mass spectrometry (MS) methodologies. These investigations not only identified large numbers of host proteins/protein peaks that are up- or down-regulated in infected sera (mouse and human) but also found large numbers of schistosome-origin proteins in the serum of infected mice. The presence of host proteins, such as transferrin and alpha 1- antitrypsin, as well as one of the schistosome proteins, glutathione S-transferase (GST), were confirmed by Western blot. In addition to these disease- and stage-specific protein profiles, we also identified a number of host proteins that may individually have potential as novel diagnostic tests for schistosome infections in humans, including apolipoprotein A-I (Apo A-I: up-regulated in chronic S. haematobium infected patients) and carbonic anhydrase 1 (CA1: down-regulated in chronic S. mansoni infected patients). If confirmed in larger field studies, these novel biomarkers, and others yet to be identified from our large MS databases, have the potential to contribute significantly to schistosomiasis detection and subsequent eradication efforts. â
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".