Comparison of Whole Genome Sequencing versus Standard Molecular Diagnostics for Species Identification in the Leishmania Viannia Subgenus
Bibliographic record
Abstract
The prognosis and treatment of New World tegumentary leishmaniasis is dependent on the infecting species, yet such species identification in the Leishmania Viannia subgenus poses a diagnostic challenge. Currently, speciation relies on standard molecular techniques such as restriction fragment length polymorphism (RFLP) analysis, and Sanger sequencing (SS). Whole-genome sequencing (WGS) is a robust and increasingly cost-efficient tool that may improve Leishmania species identification. We evaluated WGS versus standard RFLP-SS for species identification in three reference and five clinical strains of Leishmania Viannia spp. Internal transcribed spacer1 (its1), cysteine proteinase b (cpb), and heat shock protein 70 (hsp70) polymerase chain reaction-restriction fragment length polymorphism (RFLP) was performed, followed by SS of the its2, cpb, hsp70, and mannose phosphate isomerase (mpi) loci. After de novo assembly, sequences were mapped, and homology compared with both reference strains and reference genomes on National Center for Biotechnology Information. All American Type Culture Collection strains were confirmed to be single-species of L. V. braziliensis, L. V. guyanensis, or L. V. panamensis by WGS. Conversely, RFLP-SS was able to definitively identify one of three isolates to the species level. Clinical samples were identified as either single-species (N = 3), mixed (N = 1), or hybrid (N = 1) infections by WGS, while standard molecular diagnosis required multi-target composite analysis for identification due to loci-dependent results by RFLP-SS. We have corroborated the utility of WGS as a diagnostic tool to speciate members of the L. Viannia subgenus and to discriminate between mixed and hybrid infections. WGS is a potentially useful complement to multistaged RFLP-SS for species identification in Leishmania infections.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".