Standardization of a SYBR Green Based Real-Time PCR System for Detection and Molecular Quantification of Babesia bovis and B. bigemina in Water Buffaloes (Bubalus bubalis)
Bibliographic record
Abstract
Water buffalo (Bubalus bubalis) is a potential reservoir for Babesia bovis and B. bigemina in tropical regions, but the epidemiological evidence of their reservoir competence is limited, especially due to the lack of diagnostic tests capable of detecting and quantifying the low-level parasitemia present in the carrier animals. In this paper we present the standardization process of a SYBR Green based real-time PCR system (qPCR), consisting of two single qPCR assays, for the detection and quantification of B. bovis and/or B. bigemina. Both assays were optimized in similar protocols, including reagent concentrations and thermocycling parameters, so it is possible its use as a multiple qPCR in a single run. Both single assays showed a suitable analytical performance, especially by allowing detection of a greater number of carrier animals when compared with nested PCR assays (nPCR) against a reference panel of 60 DNA samples extracted from blood of both, infected- and non-infected buffaloes. Furthermore, a mathematical algorithm to convert the qPCR outcomes in percent of infected red blood cell was used, and was found that the estimated parasitemia in carrier buffaloes within the reference sample panels were close to those described in carrier cattle. This method could be a useful tool for epidemiological studies on the participation of the bubaline specie in the epidemic process of bovine babesiosis.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".