Selection of Biomarkers from Differentially Expressed Genes in Leukocytes of Buffalos Treated with Recombinant Bovine Somatotropin: The Importance of Sample Size for Reliable Discriminating Systems
Bibliographic record
Abstract
The research on biomarkers to detect livestock treated with recombinant bovine somatotropin (rbST) is still an open issue. In fact, beyond undertaking confirmation methods, there is the need to develop simple and inexpensive screening tests. In this direction, some proposals have been forwarded, mostly involving the measurement of circulating molecules, whereas the possibility of using biomarkers related to gene expression is a field under investigation. The present study was carried out on sixteen buffalos, eight of which treated with rbST. Blood samples were collected six times during the treatment to investigate on the presence of differentially expressed genes in leukocytes. Analysis with the microarray technique was performed on two sampling moments, in order to obtain a first selection of genes. Further analysis was carried out by real time RT-PCR, in order to create a discriminating linear system. A study on the variation of the error related to the number of samples included in statistics was also performed. Results showed that, including an increasing number of samples to build the discriminating algorithm, the b-error grows and tends to stabilize on 6.5%. This study clearly shows the paramount importance of including a proper number of samples to obtain reliable algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".