FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387305887> ?p ?o ?g. }

• W4387305887 abstract "Abstract Background The growing prevalence of carbapenem resistance has caused an increasing number of bacterial isolates with multi-drug resistance features, especially in the Enterobacteriaceae family. Klebsiella pneumoniae , as one of the important members of the Enterobacteriaceae family, causes serious infections, which has attracted the attention of scientists due to the emergence of hypervirulent pathotypes with increasing antibiotic resistance and has been raised as a major concern worldwide. Early detection of this new super bacterium and its antibiotic resistance is of great help in reducing mortality and costs. The lack of new antibiotic options underscores the need to optimize current diagnostics. Therefore, this study was designed to leverage machine-learning approach for optimized selection of crucial antibiotics to reduce the experiments needed for the detection of pathotypes and genes’ presence in two classical and hypervirulent K. pneumoniae pathotypes. Methods 341 non-duplicate clinical isolates of K. pneumoniae were collected from five university hospitals in Tehran and Qazvin, Iran. Pathotype differentiation of classical (c Kp ) and hypervirulent K . pneumoniae (hv Kp ) was done by PCR method by two molecular biomarkers including iuc and iut . After identifying the phenotypic antibiotic resistance, the presence of antibiotic resistance genes was detected by PCR method. Then, the relevance of resistance/susceptibility of the antibiotics and presence of pathotypes, aerobactin, and beta-lactamase genes was investigated and analyzed using five supervised machine learning algorithms by selecting crucial antibiotics through feature selection methods. Results Among the 341 K . pneumoniae isolates, 102 and 239 isolates were hv Kp and c Kp respectively. The highest rate of antibiotic resistance after ampicillin (100%) was related to cefotaxime (76.2%) and the lowest rate of resistance was found in meropenem (24.3%). Imipenem, Meropenem, Aztreonam, Ceftazidime, Ceftriaxone, and Gentamicin are crucial antibiotics for detection of the pathotypes and the aerobactin genes. Moreover, Cefotaxime, Ciprofloxacin, Cefepime, Meropenem, and Imipenem are essential for detection of the beta-lactamase genes. Conclusion Implementing a machine learning approach including various feature selection methods and algorithms, results in less-required experiments on more limited antibiotics to detect genes and pathotypes. Our findings reveal that using machine learning in the prediction of the presence of genes and pathotypes of clinical isolates was a suitable method in terms of rapidity and cost-efficiency on top of accuracy." @default.
• W4387305887 created "2023-10-04" @default.
• W4387305887 creator A5000011303 @default.
• W4387305887 creator A5007581456 @default.
• W4387305887 creator A5009967079 @default.
• W4387305887 creator A5021102688 @default.
• W4387305887 creator A5041350029 @default.
• W4387305887 creator A5049813798 @default.
• W4387305887 creator A5050406011 @default.
• W4387305887 creator A5081831181 @default.
• W4387305887 creator A5092994046 @default.
• W4387305887 date "2023-10-02" @default.
• W4387305887 modified "2023-10-10" @default.
• W4387305887 title "Reducing work-load of pathotype and gene detection in Klebsiella pneumoniae by leveraging machine learning" @default.
• W4387305887 cites W1973184605 @default.
• W4387305887 cites W1973676386 @default.
• W4387305887 cites W2001619934 @default.
• W4387305887 cites W2017337590 @default.
• W4387305887 cites W2024070582 @default.
• W4387305887 cites W2053394749 @default.
• W4387305887 cites W205803544 @default.
• W4387305887 cites W2060515872 @default.
• W4387305887 cites W2072692157 @default.
• W4387305887 cites W2080392558 @default.
• W4387305887 cites W2128428930 @default.
• W4387305887 cites W2135337280 @default.
• W4387305887 cites W2137591261 @default.
• W4387305887 cites W2141973118 @default.
• W4387305887 cites W2142343447 @default.
• W4387305887 cites W2145915746 @default.
• W4387305887 cites W2242354492 @default.
• W4387305887 cites W2320916269 @default.
• W4387305887 cites W2754494334 @default.
• W4387305887 cites W2791729250 @default.
• W4387305887 cites W2793582850 @default.
• W4387305887 cites W2806978311 @default.
• W4387305887 cites W2891497705 @default.
• W4387305887 cites W2901155787 @default.
• W4387305887 cites W2942675595 @default.
• W4387305887 cites W3005968613 @default.
• W4387305887 cites W3006176809 @default.
• W4387305887 cites W3033955832 @default.
• W4387305887 cites W3036473471 @default.
• W4387305887 cites W3043625533 @default.
• W4387305887 cites W3092019212 @default.
• W4387305887 cites W3107246561 @default.
• W4387305887 cites W3185516026 @default.
• W4387305887 cites W3187743275 @default.
• W4387305887 cites W4205344436 @default.
• W4387305887 cites W4212975450 @default.
• W4387305887 cites W4214821016 @default.
• W4387305887 cites W4234749634 @default.
• W4387305887 cites W4239510810 @default.
• W4387305887 cites W4280562914 @default.
• W4387305887 doi "https://doi.org/10.1101/2023.10.02.560438" @default.
• W4387305887 hasPublicationYear "2023" @default.
• W4387305887 type Work @default.
• W4387305887 citedByCount "0" @default.
• W4387305887 crossrefType "posted-content" @default.
• W4387305887 hasAuthorship W4387305887A5000011303 @default.
• W4387305887 hasAuthorship W4387305887A5007581456 @default.
• W4387305887 hasAuthorship W4387305887A5009967079 @default.
• W4387305887 hasAuthorship W4387305887A5021102688 @default.
• W4387305887 hasAuthorship W4387305887A5041350029 @default.
• W4387305887 hasAuthorship W4387305887A5049813798 @default.
• W4387305887 hasAuthorship W4387305887A5050406011 @default.
• W4387305887 hasAuthorship W4387305887A5081831181 @default.
• W4387305887 hasAuthorship W4387305887A5092994046 @default.
• W4387305887 hasBestOaLocation W43873058871 @default.
• W4387305887 hasConcept C104317684 @default.
• W4387305887 hasConcept C114851261 @default.
• W4387305887 hasConcept C2777058267 @default.
• W4387305887 hasConcept C2778910516 @default.
• W4387305887 hasConcept C2779631663 @default.
• W4387305887 hasConcept C2780416669 @default.
• W4387305887 hasConcept C501593827 @default.
• W4387305887 hasConcept C54355233 @default.
• W4387305887 hasConcept C547475151 @default.
• W4387305887 hasConcept C86803240 @default.
• W4387305887 hasConcept C89423630 @default.
• W4387305887 hasConcept C94665300 @default.
• W4387305887 hasConceptScore W4387305887C104317684 @default.
• W4387305887 hasConceptScore W4387305887C114851261 @default.
• W4387305887 hasConceptScore W4387305887C2777058267 @default.
• W4387305887 hasConceptScore W4387305887C2778910516 @default.
• W4387305887 hasConceptScore W4387305887C2779631663 @default.
• W4387305887 hasConceptScore W4387305887C2780416669 @default.
• W4387305887 hasConceptScore W4387305887C501593827 @default.
• W4387305887 hasConceptScore W4387305887C54355233 @default.
• W4387305887 hasConceptScore W4387305887C547475151 @default.
• W4387305887 hasConceptScore W4387305887C86803240 @default.
• W4387305887 hasConceptScore W4387305887C89423630 @default.
• W4387305887 hasConceptScore W4387305887C94665300 @default.
• W4387305887 hasLocation W43873058871 @default.
• W4387305887 hasOpenAccess W4387305887 @default.
• W4387305887 hasPrimaryLocation W43873058871 @default.
• W4387305887 hasRelatedWork W2066004548 @default.
• W4387305887 hasRelatedWork W2101777980 @default.
• W4387305887 hasRelatedWork W2154430824 @default.
• W4387305887 hasRelatedWork W2199149111 @default.

• next