Performance of a RT-PCR for the Diagnostic of Loiasis in an Endemic Area in Gabon
Bibliographic record
Abstract
Loa loa infection is a growing public health issue in the endemic area where two-thirds of infected individuals have no detectable circulating microfilaræmia. Conventional microscopic-based diagnosis on the visualisation of the filarial worm is limited. Molecular tests are known to be sensitive, precise and fast. Here we evaluated the performance of a Real Time PCR assay for the diagnostic of Loa loa infection in blood sample from an endemic area of Gabon. Blood samples were analyzed by microscopy. Proven loiasis was defined by a positive conventional parasitological assay and subconjunctival migration of an adult worm. DNA from blood samples was extracted and tested by RT-PCR targeting the gene coding the Loa loa-15 kDa polyprotein antigen. Microscopic analyses identified 25/545 (4.59%) microfilaremic individuals. Thirty (30/545) samples were RT-PCR positive. According to the classification of infection cases, 26 individuals were positive for proven loiase and 4 individuals for non-loiasis; the test showed a sensitivity of 15.90% (95% CI [12.6 - 17.75]) and a specificity of 99.0% (95% CI [97.6 - 99.7]) for the diagnosis of proven loiase. The evaluated RT-PCR targeting the 15 kDa gene protein detected all microfilaremic cases but only a few amicrofilaremic ones. It is specific to Loa loa infection and might be used for the screening of at-risk populations in epidemiological and/or pretreatment surveys.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".