Extracellular vesicles as a tool for identification of new biomarkers in Chagas disease
Bibliographic record
Abstract
[eng] American tripanosomiasis or Chagas Disease (CD), caused by the parasite Trypanosoma cruzi (T. cruzi), remains one of most neglected tropical diseases. Endemic from 21 countries in Latin America, it is the most important infection in the region in terms of public health and economic impact. Updated information from the Pan American Health Organization (PAHO) indicates that 12.000 people die from CD annually in the Americas. However, these figures may be highly conservative estimates, as other studies mention that as many as 200.000 people living with T. cruzi infection may die over the next five years from heart disease and related complications. Moreover, about 70 million people are exposed to the parasite, six to seven million now live with T. cruzi infection, and 30.000 new infections occur annually in the Americas. Furthermore, in the last decades CD has become a global health concern due to the migration flows from Latin America to United States, Europe, Canada and Japan. Many challenges remain regarding CD control and prevention in endemic and non-endemic countries. There is an urgent need of more practical and useful diagnostic methods, there are no preventive vaccines, and the two available treatments present several adverse drug reactions and limited efficacy during the chronic phase of the disease . Since there are no prognosis markers, drugs should be administered to all T. cruzi infected individuals that fulfill treatment criteria. Additionally, there are no tests-of-cure either, which limits patients´ follow-up and the search of safer and more efficacious drugs. Thus, the finding of reliable biomarkers of disease progression and/or treatment response would mean the greatest leap forward in the history of CD since its discovery in 1909. In this context, research on the role of extracellular vesicles (EVs) for biomarkers discovery has grown exponentially in the last decades. EVs are small double membrane vesicles of cellular origin, present in most biological fluids and secreted by all kind of cells. The different roles of EVs are still being explored, and include multiple biological functions, such as intercellular signaling and cell-to-cell communication. As the study of EVs is an active area of research, many biomedical utilities are still being explored, such as carriers for drug and gene therapy, antigen presentation, or therapeutic properties. Importantly, EVs present a huge potential as biomarkers in clinical diagnosis: they present highly specificity and sensitivity, excellent stability, and can be easily obtained in biofluids. This thesis explores the potential of EVs secreted during T. cruzi infection as potential biomarkers for therapeutic response and disease outcome in CD.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".