Serogroup-Specific Bacterial Engineered Glycoproteins as Novel Antigenic Targets for Diagnosis of Shiga Toxin-Producing-Escherichia coli-Associated Hemolytic-Uremic Syndrome
Bibliographic record
Abstract
Human infection with Shiga toxin-producing Escherichia coli (STEC) is a major cause of postdiarrheal hemolytic-uremic syndrome (HUS), a life-threatening condition characterized by hemolytic anemia, thrombocytopenia, and acute renal failure. E. coli O157:H7 is the dominant STEC serotype associated with HUS worldwide, although non-O157 STEC serogroups can cause a similar disease. The detection of anti-O157 E. coli lipopolysaccharide (LPS) antibodies in combination with stool culture and detection of free fecal Shiga toxin considerably improves the diagnosis of STEC infections. In the present study, we exploited a bacterial glycoengineering technology to develop recombinant glycoproteins consisting of the O157, O145, or O121 polysaccharide attached to a carrier protein as serogroup-specific antigens for the serological diagnosis of STEC-associated HUS. Our results demonstrate that using these antigens in indirect ELISAs (glyco-iELISAs), it is possible to clearly discriminate between STEC O157-, O145-, and O121-infected patients and healthy children, as well as to confirm the diagnosis in HUS patients for whom the classical diagnostic procedures failed. Interestingly, a specific IgM response was detected in almost all the analyzed samples, indicating that it is possible to detect the infection in the early stages of the disease. Additionally, in all the culture-positive HUS patients, the serotype identified by glyco-iELISAs was in accordance with the serotype of the isolated strain, indicating that these antigens are valuable not only for diagnosing HUS caused by the O157, O145, and O121 serogroups but also for serotyping and guiding the subsequent steps to confirm diagnosis.
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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 | 0.000 | 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 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".