PCR‐based ELISA technique for malaria diagnosis of specimens from Thailand
Bibliographic record
Abstract
We performed a field evaluation of polymerase chain reaction (PCR)-based enzyme-linked immuno-sorbent assays (ELISA) for the diagnosis of malaria. A commercially available PCR-ELISA microplate hybridization (MPH) assay was used. Blood specimens were collected from 300 volunteers seeking care at malaria clinics in Thailand. Examination of 200 high power fields by Giemsa-stained thick and thin smear (GTTS) revealed 51 P. falciparum (Pf), 45 P. vivax (Pv), seven mixed Pf-Pv infections. These plus a random sample of 48 GTTS-negative specimens were selected for this study. All 151 specimens were processed for parasite DNA extraction and assayed by PCR-MPH. The target DNA sequence of the 18S small subunit ribosomal RNA (SSUrRNA) gene was amplified by PCR and hybridized with species-specific probes for Pf, Pv, P. malariae (Pm) and P. ovale (Po) immobilized in the wells of the microtiter plate and detected by colorimetric assay. Colour development was assessed at an optical density (OD) of 405 nm. An absorbance reading of > or = 0.1 was used as a positive cut-off. In comparison with GTTS results, PCR-MPH sensitivity was 91.4% (53/58, 95% CI 84.2-98.6) for Pf, 94.2% (49/52, 87.9-100) for Pv and specificity was 95.8% (46/48, 95% CI 90.2-100). There was statistically significant positive correlation between parasite densities < or = 7000/microl blood and absorbance reading, suggestive of PCR-MPH being semiquantitative. PCR-MPH also detected additional Pf and Pv cases as well as Pm and Po.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".