Improved PCR-Based Detection of Soil Transmitted Helminth Infections Using a Next-Generation Sequencing Approach to Assay Design
Bibliographic record
Abstract
BACKGROUND: The soil transmitted helminths are a group of parasitic worms responsible for extensive morbidity in many of the world's most economically depressed locations. With growing emphasis on disease mapping and eradication, the availability of accurate and cost-effective diagnostic measures is of paramount importance to global control and elimination efforts. While real-time PCR-based molecular detection assays have shown great promise, to date, these assays have utilized sub-optimal targets. By performing next-generation sequencing-based repeat analyses, we have identified high copy-number, non-coding DNA sequences from a series of soil transmitted pathogens. We have used these repetitive DNA elements as targets in the development of novel, multi-parallel, PCR-based diagnostic assays. METHODOLOGY/PRINCIPAL FINDINGS: Utilizing next-generation sequencing and the Galaxy-based RepeatExplorer web server, we performed repeat DNA analysis on five species of soil transmitted helminths (Necator americanus, Ancylostoma duodenale, Trichuris trichiura, Ascaris lumbricoides, and Strongyloides stercoralis). Employing high copy-number, non-coding repeat DNA sequences as targets, novel real-time PCR assays were designed, and assays were tested against established molecular detection methods. Each assay provided consistent detection of genomic DNA at quantities of 2 fg or less, demonstrated species-specificity, and showed an improved limit of detection over the existing, proven PCR-based assay. CONCLUSIONS/SIGNIFICANCE: The utilization of next-generation sequencing-based repeat DNA analysis methodologies for the identification of molecular diagnostic targets has the ability to improve assay species-specificity and limits of detection. By exploiting such high copy-number repeat sequences, the assays described here will facilitate soil transmitted helminth diagnostic efforts. We recommend similar analyses when designing PCR-based diagnostic tests for the detection of other eukaryotic pathogens.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".