Efficacy of common hospital biocides with biofilms of multi-drug resistant clinical isolates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The hospital environment is particularly susceptible to contamination by bacterial pathogens that grow on surfaces in biofilms. The effects of hospital biocides on two nosocomial pathogens, meticillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa, growing as free-floating (planktonic) and adherent biofilm populations (sessile) were examined. Clinical isolates of MRSA and P. aeruginosa were grown as biofilms on discs of materials found in the hospital environment (stainless steel, glass, polyethylene and Teflon) and treated with three commonly used hospital biocides containing benzalkonium chloride (1 % w/v), chlorhexidine gluconate (4 % w/v) and triclosan (1 % w/v). Cell viability following biocide treatment was determined using an XTT assay and the LIVE/DEAD BacLight Bacterial Viability kit. The minimum bactericidal concentration (MBC) of all biocides for planktonic populations of both organisms was considerably less than the concentration recommended for use by the manufacturer. However, when isolates were grown as biofilms, the biocides were ineffective at killing bacteria at the concentrations recommended for use. Following biocide treatment, 0-11 % of cells in MRSA biofilms survived, and up to 80 % of cells in P. aeruginosa biofilms survived. This study suggests that although biocides may be effective against planktonic populations of bacteria, some biocides currently used in hospitals are ineffective against nosocomial pathogens growing as biofilms attached to surfaces and fail to control this reservoir for hospital-acquired infection.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it