Guidelines in Designing a Universal Primer Mixture to Probe and Quantify Antibiotic-Resistant Genes Using the Polymerase Chain Reaction (PCR)
Bibliographic record
Abstract
Multidrug resistance efflux pumps (MDREPs) in biofilm communities have become an increasingly expensive problem in clinical settings. Polymerase chain reaction (PCR)-based detection can be used to diagnose and characterize these genes, but this requires effective primer design to minimize false positives and negatives in test conclusions. A universal primer approach has previously been used to detect conserved core genes but not for accessory genes such as MDREPs. This study describes a guideline for the design of primers used in the detection of MDREP genes and an optimization approach for creating primers by using multiple sequence alignments to target conserved regions in silico, progressing from in silico to in vitro to generate working primers. Using this approach, this paper was able to generate primers to target sugE, a small multidrug resistance (SMR) protein found in microbial species. Primers were tested positively against synthetic DNA sequences but were inconsistent with DNA extracted from the organism of interest. Primer design informs the shortfalls of this detection technique and the difficulty in characterizing such genomic elements.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.020 |
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".