Different loci and mRNA copy number of the increased serum survival gene of <i>Escherichia coli</i>
Bibliographic record
Abstract
The increased serum survival gene (iss) has been identified as a virulence trait associated with the virulence of Escherichia coli, causing colibacillosis in poultry. However, it remains unclear as to whether iss mRNA copy number and sequence affect virulence. To examine these influences, we assessed the presence of iss, sequence analysis, iss mRNA copy number, and serum resistance. The iss gene was detected in 88 (all) E. coli isolates from different sources, and sequencing identified 16 alleles (32 different loci) and 10 amino acid sequences (10 different loci). Nested polymerase chain reaction improved iss detection. The isolates from sick chickens had >68% livability in serum resistance tests and higher iss mRNA copy number. The iss mRNA copy number highly correlated with mortality and E. coli livability. Student's t tests confirmed the relationship between the different loci to iss transcription, serum resistance, and virulence. These data suggest that iss mRNA copy number and different loci affect the virulence and serum resistance. These findings could be useful in further studies on the prevalence of iss among E. coli isolates and other virulence factors.
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 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 | 0.000 | 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) | 0.001 | 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".