[Whole cell protein profiling characterization of Corynebacterium diphtheriae clinical isolates collected from various infections].
Bibliographic record
Abstract
INTRODUCTION: Corynebacterium diphtheriae can cause various infections such as diphtheria, wound infections, septic arthritis, bacteraemia and endocarditis. Different virulence properties of the isolates might be related to different virulence factors expressed by the isolates. The objective of this study was to explore whether whole cell protein profiling might be useful in prediction of pathogenic properties of C. diphtheriae isolates. METHODS: C. disphtheriae isolates collected from diphtheria, invasive and local infections and from asymptomatic carriers in Poland, France, New Caledonia and Canada in 1950-2014 were investigated using whole cell protein profile analysis. RESULTS: All the examined isolates were divided into two clades: A and B with similarity about 47%, but clade B was represented by only one isolate. The clade A was divided in two subclades A.I NS .II with similarity 53,2% and then into four groups: A.Ia, A.Ib, A.Ic and A.Id. The comparative analysis did not distinguish clearly toxigenic and nontoxigenic isolates as well as invasive and noninvasive isolates. CONCLUSIONS: Whole cell protein profile analysis of C. diphtheria exhibits good concordance with other genotyping methods but this method is not able to distinguish clearly invasive from non-invasive isolates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".