Advances in gene-targeted diagnostics for pathogenic <i>Escherichia coli</i>
Bibliographic record
Abstract
(PEC) strains are important pathogens that causes a variety of infectious diseases in humans. Traditional bacterial culture and biochemical identification methods are time-consuming and lack specificity, making rapid and accurate diagnosis challenging. Molecular detection techniques, with their high sensitivity, specificity, and rapidity, have become increasingly important for identifying, typing, and tracing PEC. This review systematically sorts out the main target genes currently used for molecular detection of PEC, including virulence genes and marker genes of intestinal PEC and extraintestinal PEC. It also details the progress of mainstream molecular assays, such as polymerase chain reaction and its derivatives, multiple isothermal amplification technologies, biosensors, genome sequencing, integrated devices, and emerging detection methods. Despite significant advancements, challenges remain in differentiating live from dead bacteria, handling complex sample matrices, standardization, and cost control. Future developments in PEC molecular testing will focus on integrating macro-genomics, biosensors, and artificial intelligence to achieve more automated, intelligent, high-throughput, and field-deployable solutions, thereby providing robust support for disease prevention, control, and food safety assurance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".