Bacteriophages: biosensing tools for multi-drug resistant pathogens
Bibliographic record
Abstract
Pathogen detection is of utmost importance in many sectors, such as in the food industry, environmental quality control, clinical diagnostics, bio-defence and counter-terrorism. Failure to appropriately, and specifically, detect pathogenic bacteria can lead to serious consequences, and may ultimately be lethal. Public safety, new legislation, recent outbreaks in food contamination, and the ever-increasing prevalence of multidrug-resistant infections have fostered a worldwide research effort targeting novel biosensing strategies. This review concerns phage-based analytical and biosensing methods targeted towards theranostic applications. We discuss and review phage-based assays, notably phage amplification, reporter phage, phage lysis, and bioluminescence assays for the detection of bacterial species, as well as phage-based biosensors, including optical (comprising SPR sensors and fiber optic assays), electrochemical (comprising amperometric, potentiometric, and impedimetric sensors), acoustic wave and magnetoelastic sensors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".