Bibliographic record
Abstract
Pathogenic bacteria are evolving resistance to conventional therapeutics at a rate which threatens our ability to reliably treat common infections, necessitating the discovery of new therapeutics.In this thesis, I look at the development of novel peptides that can be used to combat antimicrobial resistance.In Chapter 2, I employ a permutation of two known antimicrobial peptides (AMPs), Indolicidin and UyCT3, and through sequential generations of evolution, develop a peptide that can inhibit bacterial growth better than the wild type AMP.These AMPs are tested on clinically derived strains to help translate the clinical relevance of these findings.In Chapter 3, I use an orientated peptide array library (OPAL) to assist in the discovery of peptide -lactamase inhibitors against the -lactamase TEM-1.Candidates' activities were assessed for inhibition against TEM-1.These results show the significance of our findings and the robustness of the techniques that can be used for the discovery of peptide antimicrobials.I would first like to express my most sincere gratitude to both of my supervisors, Dr. Kyle Biggar, and Dr. Alex Wong.I knew being co-supervised would be a challenge but having both of you as my supervisors was an honour.To Alex: thank you for accepting me into your lab and taking
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".