Salmonella Fimbriae: What is the Clue to Their Hairdo?
Bibliographic record
Abstract
Fimbriae are important virulence factors for Salmonella pathogenesis. They mediate adhesion to host cells (including plants), food, stainless steel and much more. The fimbrial systems are organised in gene clusters of four to fifteen genes that code for structural, assembly and regulatory proteins. There are three kinds of fimbriae depending on their mode of assembly. The chaperone/usher (CU) fimbriae use a dedicated chaperone and usher protein to coordinate the subunit biogenesis on the cell surface. The curli fimbriae are assembled by nucleation/precipitation pathway. The type IV fimbria assembly requires a transmembrane apparatus and ATP to energise the reaction. Several fimbriae are conserved among Salmonella serovars, while some are present in a limited set or only specific serovars. Expression and regulation of fimbrial genes are not well understood, and most Salmonella fimbriae are poorly expressed during in vitro culture, which further complicates research concerning their regulation and role during infection. However, Salmonella fim gene cluster, coding for type-1 fimbriae, was widely studied and presents its own set of regulators. Investigating fimbrial distribution, expression and regulation will further elucidate their roles in bacterial pathogenesis and host specificity. Furthermore, fimbriae are important for developing efficient diagnostic tests and antimicrobial strategies against Salmonella.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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".