Detection and Differentiation of <i>Entamoeba histolytica</i> and <i>Entamoeba dispar</i> Isolates in Clinical Samples by PCR and Enzyme-Linked Immunosorbent Assay
Bibliographic record
Abstract
Differential diagnosis of Entamoeba histolytica (pathogenic) and Entamoeba dispar (nonpathogenic), which are two morphologically identical species of amebae, is essential both for treatment decision and public health knowledge. The study reported here was designed to choose a reference differentiation technique. Stool samples (n = 95) were tested by microscopy, TechLab enzyme-linked immunosorbent assays (ELISAs), and an in-house PCR. The target for the PCR amplification was a small region (135 bp) of the SSU rRNA selected to increase the sensitivity of the test. Sixty-eight specimens tested positive by PCR: 2 for E. histolytica and 66 for E. dispar. For detection of E. dispar, ELISA performance was lower than that of microscopy in this reference context, while PCR was much more sensitive than microscopy. Given the low proportion of E. histolytica cases, test performance for this species is difficult to assess. However, for differentiation, PCR performed well on simulated samples, while ELISA gave a discordant result for one of the two samples PCR positive for E. histolytica during the study. This report also confirms that E. dispar infection is significantly higher among travelers and underlines the possibility of acquiring E. histolytica infection in regions that are not areas of endemicity. Because of its lower sensitivity, the interest of ELISA for Entamoeba detection and differentiation in stools seems questionable in nontropical regions. On the other hand, results suggest that PCR should be useful as a reference test for sensitive differentiation of both species and to contribute to physicians' decision in treatment of E. histolytica- or E. dispar-infected patients.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".