Morphometric Analysis of Larval Rostellar Hooks in Taenia multiceps of Sheep in Iran and Its Association with Mitochondrial Gene Variability.
Bibliographic record
Abstract
BACKGROUND: The purposes of the present study were morphometric characterization of rostellar hooks of Taenia multiceps and to investigate the association of hook length variation and the variability within two mitochondrial genes of sheep isolates of the parasite. METHODS: Up to 4500 sheep brains were examined for the presence of C. cerebralis. Biometric characters based on the larval rostellar hook size were measured for each individual isolate. Representative mitochondrial CO1 and 12S rRNA gene sequences for each of the isolates were obtained from NCBI GenBank. Morphometric and genetic data were analyzed using cluster analysis, Interclass Correlation Coefficient (ICC) and random effects model. RESULTS: One hundred and fourteen sheep (2.5%) were found infected with the coenuri. The minimum and maximum number of scoleces per cyst was 40 and 550 respectively. Each scolex contained 22-27 hooks arranged in two rows of large and small hooks. The average total length of the large and small hooks was 158.9 and 112.1 μm, respectively. Using ICC, statistically significant clusters of different hook sizes were identified within the isolates. The length of the large and small hooks was significantly associated with the variability in mitochondrial 12S rRNA gene. CONCLUSION: Taenia multiceps, is a relatively important zoonotic infection in Iranian sheep with the prevalence rate of 2.5%. Hook length analysis revealed statistically significant difference among individual isolates. Associations between the rostellar hook length and variability in the mitochondrial 12S rRNA was documented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".