Investigation of the Food-Transmitted Parasites Trichinella spp. and Alaria spp. in Wild Boars in Greece by Classical and Molecular Methods and Development of a Novel Real-Time PCR for Alaria spp. Detection
Bibliographic record
Abstract
Foodborne parasitic diseases represent a major threat to public health. Trichinellosis, caused by the nematode parasite Trichinella spp., is one of the most important foodborne diseases, while alariosis, caused by the trematode parasite Alaria spp., is less common in humans, and rare cases have been reported only in the USA and Canada. Both parasites can infect humans via the consumption of raw or undercooked wild boar meat. In order to investigate the prevalence of these parasites in wild boar meat in Greece, samples from the diaphragm pillars and the region of the mandibular angle from 128 wild boars, hunted in Greece, were collected. The samples were examined by classical parasitological (compression, artificial digestion, and Alaria spp. migration) and by molecular (real-time PCR) methods. For Trichinella spp. an existent real-time PCR detecting all species likely to be present in Greece was applied, while for Alaria spp. a real-time PCR was developed, employing an LNA TaqMan probe targeting the large subunit ribosomal RNA gene. All examined wild boar samples from Greece resulted negative for Trichinella and Alaria species, indicating a low prevalence of infection in the examined population. The novel real-time PCR for Alaria spp. has 81.5% amplification efficiency and is able to detect 0.12 larvae per 50 g of tissue and could be utilized as a complementary to AMT diagnostic tool in surveillance.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".