A fibre-optic biosensor for detection of microbial contamination
Bibliographic record
Abstract
A fibre-optic biosensor is described for detection of genomic target sequences from Escherichia coli. A small portion of the LacZ DNA sequence is the basis for selection of DNA probe molecules that are produced by automated nucleic acid synthesis on the surface of optical fibres. Fluorescent intercalating agents are used to report the presence of hybridization events with target strands. This work reviews the fundamental design criteria for development of nucleic acid biosensors and reports a preliminary exploration of the use of the biosensor for detection of sequences that mark the presence of E. coli. The research work includes consideration of the length of the strands and non-selective binding interactions that can potentially block the selective chemistry or create background signals. The biosensors were able to detect genomic targets from E. coli at a picomole level in a time of a few minutes, and dozens of cycles of use have been demonstrated. In a step towards the preparation of a completely self-contained sensor technology, a new intercalating dye known as SYBR 101 (Molecular Probes, Inc.) has been end-labelled to the LacZ nucleic acid probe, to examine whether dye tethered onto an oligonucleotide terminus could fluorimetrically transduce the formation of hybrids. The results obtained from experiments in solution indicate that the use of tethered dye provides fluorescence signals that are due to hybridization, and that this process is functional even in the presence of a high concentration of non-selective background DNA obtained from sonicated salmon sperm. Key words: biosensor, DNA, fibre optic, hybridization, fluorescence, pathogen, E. coli.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".