Innovating manufacturing and technologies in Cumbria
Bibliographic record
Abstract
Although conventional drugs such as the penicillin’s led the golden age of antimicrobial chemotherapy, the alarming rise in antimicrobial drug resistance due to its single mode of action, means alternative approaches to infection control and disinfection needs to be rapidly considered [1]. In contrast, photoantimicrobials produce highly reactive oxygen species and thus offers both multiple and variable sites of action at the pathogenic target. This project seeks to develop a new range of near-infrared squarylium dyes capable of producing a large amount of reactive oxygen species to cause a localised photobiocidal response in pathogenic targets with potential clinical and industrial uses [2]. Preliminary microbial data highlights excellent MIC in the µM range. References: 1. Wainwright, M., et al., Photoantimicrobials-are we afraid of the light? The Lancet. Infectious diseases, 2017. 17(2): p. e49-e55. 2. Adnane, F., E. El-Zayat, and H.M. Fahmy, The combinational application of photodynamic therapy and nanotechnology in skin cancer treatment: A review. Tissue and Cell, 2022. 77: p.101856. Presentation given by University of Cumbria's Samuel Brennan, Lecturer of Chemistry. Part of an overall project: Light-Activated Lifelines: Phototherapeutic Dyes Against Antimicrobial Resistance. Some of Cumbria’s key businesses and organisations gathered at the University of Cumbria's Brampton Road campus in Carlisle for this event, hosted by the University's Research and Knowledge Exchange (RKE) colleagues. The event sought to bring together innovators, industry leaders and stakeholders to explore opportunities in manufacturing and technology across the region, and marked an important step in strengthening research and knowledge exchange opportunities between the University of Cumbria and businesses.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.046 | 0.023 |
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".