The Use of the λ Bacteriophage to Neutralize the Pathogenic Effects of Shiga Toxins from Escherichia Coli to Combat Antimicrobial Resistance: A Research Protocol
Bibliographic record
Abstract
Introduction: Resistance to antibiotics is becoming a global health crisis. Certain strains of Escherichia coli bacteria are resistant to antibiotic drugs and thrive without competition. Pathogenic effects of E. coli are caused by Shiga toxins they produce. Instead of attempting to kill the pathogens, we propose altering the bacterial genome of E. coli, causing the expression of globotriaosylceramide (Gb3) receptors, effectively neutralizing produced toxins. Methods: Phages are introduced to cultured E. coli cells and incorporate their DNA into the bacterial genome. Mutation vectors are transformed into E. coli cells using electroporation, causing expression of Gb3 receptors and preventing the lysing of the cell. Next, exposure to UV light causes phages to enter the lytic cycle, and mutated phages can be collected. Then, E. coli cells will be administered to 28 Wistar rats, and phage treatments causing expression of Gb3 receptors on E. coli cells will be administered to the treatment group. Rats in both groups will be monitored for symptomatology of E. coli poisoning, and stool samples will be collected and analyzed for quantities of Shiga toxins. Anticipated Results: We anticipate that phage-treated E. coli cells will express Gb3 receptors. We expect that, in the control group, symptoms of E. coli poisoning and quantities of Shiga toxins will increase over the duration of the study. Upon adequate expression of Gb3 receptors, we expect symptoms of E. coli poisoning and quantities of Shiga toxins to be lower in the treatment group than in the control group throughout the study. Discussion: Confirmation of our anticipated results through immunofluorescence spectra visualizing Gb3 receptors, histogram plots of symptoms, and lateral flow assays detecting quantities of Shiga toxins will prove our methods to be a valuable asset for decreasing the effects of E. coli poisoning and related diseases. Conclusion: Antibiotic resistance is becoming a serious threat. As antibiotic drugs are designed to kill bacteria, we propose an alternative method to neutralizing the pathogenic effects caused by E. coli through the incorporation of Gb3 receptors, and allowing the proliferation of the bacteria. This suggests an approach to slowing the acceleration of antibiotic resistance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".