Standardization of <i>Trypanosoma cruzi</i> DNA extraction and purification protocol from samples collected on Whatman 903 filter paper to Chagas disease diagnosis
Bibliographic record
Abstract
Abstract Chagas disease (CD) caused by the parasite Trypanosoma cruzi , belongs to the so-called neglected diseases group. In Argentina about 1,500 children are born with congenital Chagas disease per year. The diagnosis of CD in the newborn relies on the ability to detect parasites in the blood by microscopic observation, as the serological tests are ruled out because of the presence of maternal antibodies. CD treatment is more effective during the acute phase of infection. Early diagnosis and treatment of the disease is thus very important. The Argentinian National Program for early detection of metabolic diseases uses Whatman903 filter paper for blood sampling. This type of sample collection presents many advantages as the use of low blood volumes, minimal biological risk, and easy storage and transportation. The objective of the study was to evaluate the conservation efficiency of blood samples on filter paper in order to access good sensitivity on qPCR results for the detection of T. cruzi . To standardize the procedure, negative samples of blood were infected artificially with serial dilutions of trypomastigotes forms of T. cruzi from the TcVI strain obtained by cell culture in Vero cells. Concentrations between 50000 and 5 parasites/mL were prepared and loaded in filter paper for analysis. DNA extraction was conducted by the QIAamp DNA Mini Kit from QIAGEN. For qPCR, a method based on TaqMan technology was used, with a multiplex reaction for quantification of T. cruzi satellite DNA and an internal amplification control (IAC). The detection limit found from our results was 400 parasites/mL, demonstrating that this method could be a reliable option for the diagnosis of congenital CD by the detection of T. cruzi in blood collected in filter paper.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.013 |
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".