New Insights Into Shiga Toxigenic<i>Escherichia coli</i>Pathogenesis: When Less Is More
Bibliographic record
Abstract
(See the major article by Russo et al on pages 1271–9.) Hemorrhagic colitis due to Shiga toxin–producing Escherichia coli (STEC) is perhaps the most feared foodborne infection in the industrialized world. The Centers for Disease Control estimates that there are 265 000 cases of STEC infection in the United States each year [1]. Whereas hemorrhagic colitis is a painful and often severe illness, the greatest concern with STEC infections is hemolytic-uremic syndrome (HUS), which develops in 5%–10% of patients and can cause permanent and occasionally fatal renal or neurologic sequelae. Some of the greatest frustrations with STEC infections are the lack of demonstrated effectiveness of antibiotics or other targeted treatments and their well-demonstrated ability to cause outbreaks because of contamination of seemingly innocuous foods (like sprouts, ready-to-eat salads, and tree nuts), in addition to ground beef, where these infections were first identified. The first reported serotype of STEC, and still the most common cause of hemorrhagic colitis and HUS, is O157:H7. Most O157:H7 isolates carry a specific complement of virulence factors within a locus of enterocyte effacement (LEE). The proteins encoded on the LEE allow O157:H7 to adhere intimately to intestinal epithelial cells and produce cytoskeletal changes in the cells, which greatly enhance its pathogenicity. However, the deadliest virulence factors of O157:H7 are its Shiga toxins, Stx1 and Stx2 (formerly called Shiga-like toxins). For E. coli to be called EHEC, it must possess the LEE and Stx1 and/or Stx2; those that express Stx1 and/or Stx2 without the LEE are referred to as STEC. In general, it is believed that non-EHEC STEC are less virulent than O157:H7, but some (like the O104:H4 enteroaggregative STEC that caused a large European outbreak in 2011) have alternative virulence factors that lead to severe illness [2].
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.019 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".