Effect of Ethanol/Trisodium Citrate Lock on Microorganisms Causing Hemodialysis Catheter-Related Infections
Bibliographic record
Abstract
PURPOSE: This in vitro study tested the effectiveness of a novel 30% ethanol/4% trisodium citrate (TSC) lock solution against the most common pathogens causing hemodialysis catheter-related infections. METHODS: Clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) (n = 4), methicillin-sensitive S. aureus (MSSA) (n = 8), methicillin-resistant Staphylococcus epidermidis (MRSE) (n = 8), Pseudomonas aeruginosa (n = 4) and Escherichia coli (n = 4) were tested in duplicate. Bacterial suspensions of each isolate were made in a control solution of normal saline and Mueller-Hinton broth (MHB), and in a lock solution of ethanol 30%, TSC 4% and MHB. Suspensions were incubated at 37 degrees C for 48 h. Colony counts were determined from samples collected at t = 0 h (before exposure to the ethanol/TSC lock), t = 1 h (one hour after exposure to the ethanol/TSC lock), t = 24 h and t = 48 h. To confirm the absence of viable organisms in the lock solution, the remaining volume at 48 h was filtered through a 0.45 microm filter. The filter was rinsed with 15 mL sterile water and plated on tryptic soy agar (TSA). RESULTS: All controls demonstrated significant growth over 48 h. In the lock solutions, initial inocula were reduced to 0 viable colonies by t = 1 h (6-log kill), and there was no growth at t = 24 and 48 h. Filtering of lock solutions also showed no growth. These results were consistent among duplicates of all isolates. CONCLUSIONS: The 30% ethanol/4% TSC lock solution consistently eradicated MRSA, MSSA, MRSE, P. aeruginosa and E. coli within 1 h of exposure. Experiments are currently underway to test this novel lock solution on preventing biofilm production by these pathogens.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".