Prevalence Of Multi Drug Resistant Bacteria On Environmental And Medical Device Surfaces Of Korea Nepal Friendship Hospital
Bibliographic record
Abstract
ABSTRACT The microbial monitoring of environmental and medical-devices’ surface is used to evaluate efficacy of routine cleaning and disinfection practises and to detect the presence of specific Nosocomial Pathogens. The prevalence of Multidrug Resistance organisms in hospital premises projects serious problems in transmitting to susceptible hosts which is difficult to treat. A cross sectional descriptive research was conducted from December 2016 to June 2017 at the pathology laboratory of Korea Nepal Friendship Hospital (KNFH). A total 140 samples were considered, encompassing the medical devices of the hospital (100), housekeeping surfaces (15) and air (25). Susceptibility test for bacterial isolates was done by disk diffusion assay. Of the total 140 samples taken and analysed, 100% showed growth positivity. In most of the swabs taken, Coagulase Negative Staphylococci was dominant, followed by Staphylococcus aureus, Streptococcus spp. Micrococcus spp., E coli, Pseudomonas spp., Bacillus spp., Acinetobacter spp., Klebsiella spp., Fungi, and least Proteus spp. The dry surfaces were dominantly contaminated by gram positive bacteria whereas moistened surfaces like wash basin were contaminated by gram negative as well as gram positive bacteria. Total 277 strains were exposed to various class of antibiotics, among the gram positive environmental isolates, Coagulase Negative Staphylococci 16 (34.78%) had highest MDR prevalence followed by Staphylococcus aureus 8 (29.62%), Streptococcus spp. 4 (12.90%), Micrococcus spp. 4 (9.30%) and no MDR was shown by any Bacillus spp isolates. Whereas, in case of gram negative, Klebsiella spp. 6 (35.29%) had highest MDR prevalence followed by Acinetobacter spp. 6 (31.57%), E. coli 8 (27.58%), Pseudomonas spp. 4 (18.18%), and lastly Proteus spp. with no MDR at all. The thick dirt covering the cotton swabs and heavy microbial load on them has displayed not only disinfecting practice but also cleaning practice is missing. Heavy contamination shows possible NIs breakout, it’s important to have routine microbial assessment with standard protocol and find ways to decrease its load.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".