Identification of coagulase-negative Staphylococcus species by gas chromatography
Bibliographic record
Abstract
Researching the impact and epidemiology of coagulase-negative Staphylococcus species (CNS) causing intramammary infections (IMI) require their identification at the species level. Gene sequencing is the gold standard but faster and less expensive methods could be useful. The Sherlock Microbial Identification System is an automated gas chromatographic (MIS-GC) system able to speciate CNS isolates in human clinical medicine by identifying the unique cellular fatty acid patterns in bacteria cell walls. Our objective was to validate the MIS-GC method for speciating CNS responsible for IMI in dairy cows. The CNS isolates examined include 429 isolates of Staphylococcus chromogenes, 195 Staphylococcus simulans, 108 Stapylococcus xylosus, 81 Staphylococcus haemolyticus and 42 Staphylococcus epidermididis obtained from the Canadian Bovine Mastitis Research Network culture collection and speciated using rpoB gene sequencing. Isolates were harvested from apparently normal mammary quarters before and after the dry period or during lactation. CNS isolates were divided in 2 groups within species. Speciation by MIS-GC of the first group was performed with a human-source CNS fatty-acid profile library and was used to construct a bovine-source library. MIS-GC speciation of the second group was performed with the new library. Repeatability of the technique was evaluated by re-culturing and re-testing a minimum of 50 isolates of each species. Using rpoB sequencing as gold standard, sensitivities for S. chromogenes, S. simulans, S. xylosus and S. epidermidis with the human library were 63% (n=215), 58% (n=89), 40% (n=55) and 52% (n=21) respectively, and 91%, 79%, 71% and 100% with the bovine library. Repeatability was 80% (n=100), 81% (n=97), 72% (n=53). Sensitivity for S. haemolyticus was 8% with the human library and could not be included in the bovine library. Its repeatability was 90% (n=40). The final bovine library tested on a random sample of S. chromogenes gave a sensitivity of 89%.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".