Bibliographic record
Abstract
Cationic antimicrobial peptides (cAMPs) are of interest as a possible solution to the problem of bacterial resistance to antibiotics. In order to facilitate identification of useful cAMPs, the mechanism of action in killing prokaryotic cells is of interest. Robert Hancock (UBC) has proposed that charge interaction between cAMPs and the negatively-charged bacterial membrane is a major factor that contributes to the disruption of the bacterial membrane. The goal of this thesis is to contribute to the development of a method based on scanning transmission X-ray microscopy (STXM) to investigate the electrostatic interaction hypothesis as the method by which cAMPS interact with negatively charged bacterial membranes, using fluorescence microscopy (FM) as a guide. Methods were developed to generate phase-segregated lipid bilayers as model membranes on the silicon nitride membranes. C 1s, N 1s and O 1s X-ray absorption spectra of 3 lipid species - 1,2-di-(9Z-octadecenoyl)-sn-glycero-3-phosphocholine (DOPC), 1,2-dioctadecanoyl-sn-glycero-3-phosphocholine (DSPC), and 1,2-di-(9Z-octadecenoyl)-3-trimethylammonium-propane (chloride salt) (DOTAP) were obtained to be used as reference standards in future studies. FM and STXM were used to map the saturated and unsaturated domains in dried lipid bilayers exhibiting phase segregation. Attempts were also made to image the lipid bilayers under hydrated conditions using both static and flow cells. Efforts to develop a flow cell for STXM using 3D printing are outlined. The potential to evolve this line of research to enable systematic studies of protein and peptide interactions with lipid bilayers under static and dynamic conditions is discussed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".