Functional Nucleic Acids as Bacterial Biosensors: A Focus on <i>Clostridioides difficile</i> Infection
Bibliographic record
Abstract
Abstract Clostridioides difficile (CD) is a Gram‐positive, anaerobic, and spore‐forming bacillus that colonizes the human gut and causes a range of diseases, such as pseudomembranous colitis and antibiotic‐associated diarrhea, that are generally known as CD infection (CDI). Rapid and accurate detection of CDI with high sensitivity and specificity is crucial for patient treatment, infection control, and epidemiological monitoring. Current diagnostic methods for CDI have several limitations, such as high cost, long turnaround time, suboptimal sensitivity, and the need for specialized equipment. Hence, novel detection methods that can overcome these limitations are needed. Functional nucleic acids (FNAs) are a promising class of molecular recognition element (MRE) that can be incorporated into biosensors for detecting infectious pathogens. Several FNAs have been developed for detecting CD. In this review, an overview of CD, CDI, and current diagnostic methods for CDI and their drawbacks are provided. Furthermore, the design principles and working mechanisms of FNAs as well as their applications for the detection of pathogenic bacteria, including CD, are discussed. The potential for developing point‐of‐care paper sensors using currently available CD‐selective FNAs is also highlighted.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".