Identification of Deferentially Expressed Proteins in Milk during Experimental Bovine Mastitis using Difference Gel Electrophoresis
Bibliographic record
Abstract
In order to identify and understand key changes in protein profile of milk during mastitis as a guide to detecting markers for prompt management of the disease, milk from cows in which clinical mastitis were experimentally induced were subjected to a difference gel electrophoresis (DiGE) analysis. Pooled samples from 6 udders (from 6 cows) of three selected time points; 0, 81 and 312 hours post‐challenge of S. uberis mastitis were analysed. These corresponded to samples from pre‐infection, peak and resolution phase of the mastitis challenge. After preliminary sample preparation, concentration and pooling steps, samples were labeled with CyDyes® (CyDye 2, 3 and 5) after which isoelectric focusing and gel electrophoresis were carried out respectively. DiGE gels were subsequently scanned and ImageQuant, ImageJ and DeCyder ™ 2D (version 7.0) software were used to crop, obtain Jpeg images and carry out 2‐D differential analysis and processing of the images respectively. Biological Variation Analysis (BVA) software (GE Healthcare life sciences, Buckinghamshire, UK) was also used to evaluate the gels and create a gel to gel matching of spots (qualitatively and quantitatively) within the three gels produced. Overall, a total of 521 proteins spots were identified as changing significantly across the period of intramammary infection (qualitatively or quantitatively) in milk. This demonstrates the large repertoire of protein biomarker candidates available for potential employment and indicators of this disease. Some of these spots were excised for further protein identification by LC‐MS/MS analysis. Further studies are required to elucidate the merits and demerits of these changing proteins in order to identify the most suitable for clinical application in mastitis diagnosis. Support or Funding Information Zoetis, UK and TETFUND, Nigeria are acknowledged for PhD studentship funding This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.000 |
| 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) | 0.001 | 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".