Bibliographic record
Abstract
PURPOSE OF REVIEW: Gastrointestinal disease caused by Salmonella species leads to significant morbidity and mortality worldwide. The use of various animal models has greatly advanced understanding of Salmonella pathogenesis at intestinal and systemic sites. This review will emphasize recent advances in the understanding of intestinal Salmonella infections. RECENT FINDINGS: Recent research has focused on bacterial products and the host pathogen recognition receptors involved in the activation of immune pathways. In particular, activation of Toll-like receptor 5 and Ipaf by Salmonella flagellin has been a major finding. The discovery of cryptopatches as novel lymphoid follicles and the characterization of intestinal dendritic cell populations have been examined in the context of Salmonella infections. The development and use of the streptomycin pretreated mouse model of enterocolitis has allowed researchers to probe the host factors contributing to intestinal immunopathology. Furthermore, the analysis of microbiota in Salmonella infections has provided new insights regarding the role of inflammation in gastrointestinal diseases. In addition, the contributions of specific Salmonella type 3-secreted effectors to the establishment and modulation of inflammation have been further refined. SUMMARY: New advances in animal models have allowed researchers to further define the contribution of specific bacterial and host factors involved in Salmonella-induced enterocolitis.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".