Sensitive Detection of Broad-Spectrum Bacteria with Small-Molecule Fluorescent Excimer Chemosensors
Bibliographic record
Abstract
Antibiotic resistance is a major problem for world health, triggered by the unnecessary usage of broad-spectrum antibiotics on purportedly infected patients. Current clinical standards require lengthy protocols for the detection of bacterial species in sterile physiological fluids. In this work, a class of small-molecule fluorescent chemosensors termed ProxyPhos was shown to be capable of rapid, sensitive, and facile detection of broad-spectrum bacteria. The sensors act via a turn-on fluorescent excimer mechanism, where close-proximity binding of multiple sensor units amplifies a red shift emission signal. ProxyPhos sensors were able to detect down to 10 CFUs of model strains by flow cytometry assays and showed selectivity over mammalian cells in a bacterial coculture through fluorescence microscopy. The studies reveal that the zinc(II)-chelates cyclen and cyclam are novel and effective binding units for the detection of both Gram-negative and Gram-positive bacterial strains. Mode of action studies revealed that the chemosensors detect Gram-negative and Gram-positive strains with two distinct mechanisms. Preliminary studies applying ProxyPhos sensors to sterile physiological fluids (cerebrospinal fluid) in flow cytometry assays were successful. The results suggest that ProxyPhos sensors can be developed as a rapid, inexpensive, and robust tool for the “yes–no” detection of broad-spectrum bacteria in sterile fluids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".