β-Lactamase-Responsive Probe for Efficient Photodynamic Therapy of Drug-Resistant Bacterial Infection
Bibliographic record
Abstract
Several photosensitizers have recently been proposed as novel approaches against β-lactamase-producing drug-resistant bacteria. However, these reported photosensitizers are rarely used for accurate recognition of drug-resistant bacteria. To tackle this challenge, the structurally modified photosensitizer CySG-2 based on a lipophilic cationic heptamethine indocyanine near-infrared (NIR) dye (IR-780) and an important synthesis intermediate of cephalosporin antibiotic (GCLE) not only achieved the accurate recognition of TEM-1 methicillin-resistant Staphylococcus aureus (MRSA) successfully but also achieved antimicrobial photodynamic therapy (aPDT) in animal models infected by drug-resistant bacteria. Accurate enzyme recognition and efficient photodynamic therapy capabilities allow CySG-2 to achieve one stone with two birds. In addition, CySG-2 could also promote the eradication of internalized MRSA by facilitating the autophagy process, which is synergistic with its capacity of inducing reactive oxygen species generation under NIR laser irradiation for aPDT. Collectively, it is an effective multifunctional photosensitizer with the potential ability to guide the optimal use of different antibiotics and apply them in clinical treatment.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".