Multidrug-resistant Pseudomonas isolated from water at primary health care centers in Gaza, Palestine: a cross-sectional study
Bibliographic record
Abstract
Introduction Water contaminated with drug-resistant Pseudomonas spp. poses a serious risk for nosocomial transmission, especially in patients with wounds or implants prone to biofilm formation. This is particularly concerning in Gaza, where the ongoing war since October 2023 has severely impacted healthcare and water infrastructure, increasing the risk of infection in overcrowded health facilities. This study aimed to investigate the antibiotic resistance profile, Multiple Antibiotic Resistance Index (MARI), and genetic determinants of Pseudomonas spp. isolated from water in Primary Healthcare Centers (PHCs). Methods A total of 64 water samples were collected from five PHCs across Gaza (April–August 2022). Isolates were identified using standard microbiological techniques. Antibiotic susceptibility was tested via the Kirby-Bauer disk diffusion method; MARI was calculated, and PCR was used to detect NDM genes. Results Pseudomonas spp. were isolated from 59.4% of samples (desalinated: 48.3%; municipal: 68.6%). High resistance was observed to imipenem (84%) and piperacillin (84%), followed by aztreonam (31.6%) and gentamicin (28.9%). The average MARI was 0.4. NDM genes were detected in a subset of isolates. Conclusion The presence of multidrug-resistant, NDM-producing Pseudomonas in both RO and municipal water highlights the urgent need for water safety, screening, and infection control in conflict-affected healthcare settings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".