Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016
ilustraciones, graficas, mapas
- Autores:
-
Tenorio Arévalo, María Caridad
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81760
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores
Antibacterianos
APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL)
Anti-Bacterial Agents
Resistencia antimicrobiana
Regresión logística
Machine Learning
Providencia rettgeri
Support Vector Machine
Antimicrobial resistance
Logistic Regression
Random Forest
WGS
- Rights
- openAccess
- License
- Atribución-SinDerivadas 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| dc.title.translated.eng.fl_str_mv |
Prediction of the resistance profile to antibiotics based on whole genome sequencing data of Colombian isolates of Providencia rettgeri during the period 2015 – 2016 |
| title |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| spellingShingle |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Antibacterianos APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) Anti-Bacterial Agents Resistencia antimicrobiana Regresión logística Machine Learning Providencia rettgeri Support Vector Machine Antimicrobial resistance Logistic Regression Random Forest WGS |
| title_short |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| title_full |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| title_fullStr |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| title_full_unstemmed |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| title_sort |
Predicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016 |
| dc.creator.fl_str_mv |
Tenorio Arévalo, María Caridad |
| dc.contributor.advisor.none.fl_str_mv |
Barreto-Hernandez, Emiliano Reguero Reza, María Teresa Jesús |
| dc.contributor.author.none.fl_str_mv |
Tenorio Arévalo, María Caridad |
| dc.contributor.researchgroup.spa.fl_str_mv |
Bioinformática |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores |
| topic |
000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores Antibacterianos APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) Anti-Bacterial Agents Resistencia antimicrobiana Regresión logística Machine Learning Providencia rettgeri Support Vector Machine Antimicrobial resistance Logistic Regression Random Forest WGS |
| dc.subject.other.none.fl_str_mv |
Antibacterianos |
| dc.subject.lemb.none.fl_str_mv |
APRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL) Anti-Bacterial Agents |
| dc.subject.proposal.spa.fl_str_mv |
Resistencia antimicrobiana Regresión logística |
| dc.subject.proposal.eng.fl_str_mv |
Machine Learning Providencia rettgeri Support Vector Machine Antimicrobial resistance Logistic Regression Random Forest |
| dc.subject.proposal.other.fl_str_mv |
WGS |
| description |
ilustraciones, graficas, mapas |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2022-07-29T16:53:04Z |
| dc.date.available.none.fl_str_mv |
2022-07-29T16:53:04Z |
| dc.type.spa.fl_str_mv |
Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
| dc.type.content.spa.fl_str_mv |
Text |
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http://purl.org/redcol/resource_type/TM |
| status_str |
acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/81760 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Nacional de Colombia |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Institucional Universidad Nacional de Colombia |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.unal.edu.co/ |
| url |
https://repositorio.unal.edu.co/handle/unal/81760 https://repositorio.unal.edu.co/ |
| identifier_str_mv |
Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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RedCol LaReferencia |
| dc.relation.references.spa.fl_str_mv |
Abdallah, M., & Balshi, A. (2018). First literature review of carbapenem-resistant Providencia. In New Microbes and New Infections (Vol. 25, pp. 16–23). Elsevier Ltd. https://doi.org/10.1016/j.nmni.2018.05.009 Adriana, L., & Buitrago, P. (2019). Análisis comparativo de los elementos genómicos de resistencia a antibióticos betalactámicos en cepas colombianas de Providencia rettgeri durante el período 2015 – 2016. Aedekerk, S., Diggle, S. P., Song, Z., Høiby, N., Cornelis, P., Williams, P., & Cámara, M. (2005). The MexGHI-OpmD multidrug efflux pump controls growth, antibiotic susceptibility and virulence in Pseudomonas aeruginosa via 4-quinolone-dependent cell-to-cell communication. Microbiology, 151(4), 1113–1125. https://doi.org/10.1099/mic.0.27631-0 Aghapour, Z., Gholizadeh, P., Ganbarov, K., Bialvaei, A. Z., Mahmood, S. S., Tanomand, A., Yousefi, M., Asgharzadeh, M., Yousefi, B., & Kafil, H. S. (2019). Molecular mechanisms related to colistin resistance in enterobacteriaceae. In Infection and Drug Resistance (Vol. 12, pp. 965–975). Dove Medical Press Ltd. https://doi.org/10.2147/IDR.S199844 Alcock, B. P., Raphenya, A. R., Lau, T. T. Y., Tsang, K. K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A.-L. V, Cheng, A. A., Liu, S., Min, S. Y., Miroshnichenko, A., Tran, H.-K., Werfalli, R. E., Nasir, J. A., Oloni, M., Speicher, D. J., Florescu, A., Singh, B., ... McArthur, A. G. (2019). CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz935 Alekshun, M. N., & Levy, S. B. (2007). Molecular Mechanisms of Antibacterial Multidrug Resistance. Cell, 128(6), 1037–1050. https://doi.org/10.1016/j.cell.2007.03.004 Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878. https://doi.org/10.15252/msb.20156651 Antunes, P., Machado, J., Sousa, J. C., & Peixe, L. (2005). Dissemination of sulfonamide resistance genes (sul1, sul2, and sul3) in Portuguese Salmonella enterica strains and relation with integrons. Antimicrobial Agents and Chemotherapy, 49(2), 836– 839. https://doi.org/10.1128/AAC.49.2.836-839.2005 Behera, R. N., Das, K., Tech, B., & Professor, A. (2017). A Survey on Machine Learning: Concept, Algorithms and Applications Machine Learning View project International Journal of Innovative Research in Computer and Communication Engineering A Survey on Machine Learning: Concept, Algorithms and Applications. Article in International Journal of Innovative Research in Computer, 1301–1309. https://doi.org/10.15680/IJIRCCE.2017 Bengoechea, J. A., Zhang, L., Toivanen, P., & Skurnik, M. (2002). Regulatory network of lipopolysaccharide O-antigen biosynthesis in Yersinia enterocolitica includes cell envelope-dependent signals. Molecular Microbiology, 44(4), 1045–1062. https://doi.org/10.1046/j.1365-2958.2002.02940.x Besier, S., Ludwig, A., Brade, V., & Wichelhaus, T. A. (2003). Molecular analysis of fusidic acid resistance in Staphylococcus aureus. Molecular Microbiology, 47(2), 463–469. https://doi.org/10.1046/j.1365-2958.2003.03307.x Bielaszewska, M., Daniel, O., Karch, H., & Mellmann, A. (2020). Dissemination of the blaCTX-M-15 gene among Enterobacteriaceae via outer membrane vesicles. The Journal of Antimicrobial Chemotherapy, 75(9), 2442–2451. https://doi.org/10.1093/jac/dkaa214 Blair, J. M. A., Richmond, G. E., & Piddock, L. J. V. (2014). Multidrug efflux pumps in Gram-negative bacteria and their role in antibiotic resistance. In Future Microbiology (Vol. 9, Issue 10, pp. 1165–1177). Future Medicine Ltd. https://doi.org/10.2217/FMB.14.66 Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O., & Piddock, L. J. V. (2015). Molecular mechanisms of antibiotic resistance. Nature Reviews Microbiology, 13(1), 42–51. https://doi.org/10.1038/nrmicro3380 Borstel, F. (1983). from a Proteus mirabilis Re-mutant. 22, 15–22. Bouziane, F., Allem, R., Sebaihia, M., Kumanski, S., Mougari, F., Sougakoff, W., Raskine, L., Yala, D., & Cambau, E. (2019). First genetic characterisation of multidrug- resistant Mycobacterium tuberculosis isolates from Algeria. Journal of Global Antimicrobial Resistance, 19, 301–307. https://doi.org/10.1016/j.jgar.2019.05.010 Brolund, A., Sundqvist, M., Kahlmeter, G., & Grape, M. (2010). Molecular Characterisation of Trimethoprim Resistance in Escherichia coli and Klebsiella pneumoniae during a Two Year Intervention on Trimethoprim Use. PLoS ONE, 5(2), e9233. https://doi.org/10.1371/journal.pone.0009233 Carvalho-Assef, A. P. D., Pereira, P. S., Albano, R. M., Beriao, G. C., Chagas, T. P. G., Timm, L. N., Da Silva, R. C. F., Falci, D. R., & Asensi, M. D. (2013). Isolation of NDM-producing Providencia rettgeri in Brazil. Journal of Antimicrobial Chemotherapy, 68(12), 2956–2957. https://doi.org/10.1093/jac/dkt298 Castanheira, M., Mills, J. C., Farrell, D. J., & Jones, R. N. (2014). Mutation-Driven β- Lactam resistance mechanisms among contemporary ceftazidime-nonsusceptible pseudomonas aeruginosa isolates from U.S. hospitals. Antimicrobial Agents and Chemotherapy, 58(11), 6844–6850. https://doi.org/10.1128/AAC.03681-14 CLSI. (n.d.). M100: Antimicrobial Susceptibility Testing Standards. 2019. Retrieved November 7, 2019, from https://clsi.org/standards/products/microbiology/documents/m100/ CLSI. (2018). Method for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically. www.clsi.org. CLSI. (2019). Performance Standards for Antimicrobial Susceptibility Testing (29th ed). Cody, A. J., Bray, J. E., Jolley, K. A., McCarthy, N. D., & Maiden, M. C. J. (2017). Core Genome Multilocus Sequence Analyses of Campylobacter jejuni and C. coli Human Disease Isolates. Journal of Clinical Microbiology, 55(7), 2086–2097. Coelho, J. R., Carriço, J. A., Knight, D., Martínez, J.-L., Morrissey, I., Oggioni, M. R., & Freitas, A. T. (2013). The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus. PLoS ONE, 8(2), e55582. https://doi.org/10.1371/journal.pone.0055582 Coyne, S., Rosenfeld, N., Lambert, T., Courvalin, P., & Périchon, B. (2010). Overexpression of resistance-nodulation-cell division pump AdeFGH confers multidrug resistance in Acinetobacter baumannii. Antimicrobial Agents and Chemotherapy, 54(10), 4389–4393. https://doi.org/10.1128/AAC.00155-10 Cui, X., Zhang, H., & Du, H. (2019). Carbapenemases in Enterobacteriaceae: Detection and Antimicrobial Therapy. Frontiers in Microbiology, 10, 1823. https://doi.org/10.3389/fmicb.2019.01823 D’Andrea, M. M., Arena, F., Pallecchi, L., & Rossolini, G. M. (2013). CTX-M-type β- lactamases: A successful story of antibiotic resistance. International Journal of Medical Microbiology, 303(6–7), 305–317. https://doi.org/10.1016/j.ijmm.2013.02.008 Dastvan, R., Fischer, A. W., Mishra, S., Meiler, J., & McHaourab, H. S. (2016). Protonation-dependent conformational dynamics of the multidrug transporter EmrE. Proceedings of the National Academy of Sciences of the United States of America, 113(5), 1220–1225. https://doi.org/10.1073/pnas.1520431113 Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies, 7(3), 1174–1179. www.ijcsit.com Didelot, X., Bowden, R., Wilson, D. J., Peto, T. E. A., & Crook, D. W. (2012). Transforming clinical microbiology with bacterial genome sequencing. In Nature Reviews Genetics (Vol. 13, Issue 9, pp. 601–612). Nat Rev Genet. https://doi.org/10.1038/nrg3226 Doménech-Sánchez, A., Hernández-Allés, S., Martínez-Martínez, L., Benedí, V. J., & Albertí, S. (1999). Identification and characterization of a new porin gene of Klebsiella pneumoniae: Its role in β-lactam antibiotic resistance. Journal of Bacteriology, 181(9), 2726–2732. https://doi.org/10.1128/jb.181.9.2726-2732.1999 Domínguez, M., Miranda, C. D., Fuentes, O., de la Fuente, M., Godoy, F. A., Bello- Toledo, H., & González-Rocha, G. (2019). Occurrence of Transferable Integrons and sul and dfr Genes Among Sulfonamide-and/or Trimethoprim-Resistant Bacteria Isolated From Chilean Salmonid Farms. Frontiers in Microbiology, 10(APR), 748. https://doi.org/10.3389/fmicb.2019.00748 EDGAR, R. (n.d.). UCLUST algorithm. 2010. Retrieved March 27, 2021, from https://drive5.com/usearch/manual/uclust_algo.html Elena, B., Ayala, A., María, A., & Amórtegui, L. (n.d.). CARBAPENEMASA NUEVA DELHI TIPO 1 (NDM): DESCRIPCIÓN FENOTÍPICA, EPIDEMIOLÓGICA Y TRATAMIENTO. In Laboratorio Actual •. Retrieved October 2, 2018, from http://abj.org.co/images/revistas/vol_44/Pag. 24-31 Carbapenemasa Nueva Delhi tipo 1 (NDM) descripción fenotípica, epidemiológica y tratamiento.pdf EUCAST: Clinical breakpoints and dosing of antibiotics. (n.d.). Retrieved November 7, 2019, from http://www.eucast.org/clinical_breakpoints/ Eyre, D. W., Silva, D. De, Cole, K., Peters, J., Cole, M. J., Grad, Y. H., Demczuk, W., Martin, I., Mulvey, M. R., Crook, D. W., Walker, A. S., Peto, T. E. A., & Paul, J. (2017). WGS to predict antibiotic MICs for Neisseria gonorrhoeae. Journal of Antimicrobial Chemotherapy, 72(7), 1937–1947. https://doi.org/10.1093/jac/dkx067 Fàbrega, A., Martin, R. G., Rosner, J. L., Tavio, M. M., & Vila, J. (2010). Constitutive SoxS expression in a fluoroquinolone-resistant strain with a truncated SoxR protein and identification of a new member of the marA-soxS-rob regulon, mdtG. Antimicrobial Agents and Chemotherapy, 54(3), 1218–1225. https://doi.org/10.1128/AAC.00944-09 FDA. (n.d.). TYGACIL ® (TIGECYCLINE) FOR INJECTION Rx only. Retrieved April 8, 2021, from https://www.accessdata.fda.gov/drugsatfda_docs/label/2009/021821s016lbl.pdf Founou, R. C., Founou, L. L., Allam, M., Ismail, A., & Essack, Y. (n.d.). Whole Genome sequencing of extended spectrum β-lactamase (esBL)-producing Klebsiella pneumoniae Isolated from Hospitalized patients in KwaZulu-Natal, south Africa. Scientific Reports. https://doi.org/10.1038/s41598-019-42672-2 Freeman, Z. N., Dorus, S., & Waterfield, N. R. (2013). The KdpD/KdpE Two-Component System: Integrating K+ Homeostasis and Virulence. PLoS Pathogens, 9(3). https://doi.org/10.1371/journal.ppat.1003201 Fu, Z., Ma, Y., Chen, C., Guo, Y., Hu, F., Liu, Y., Xu, X., & Wang, M. (2016). Prevalence of fosfomycin resistance and mutations in murA, glpT, and uhpT in methicillin- resistant Staphylococcus aureus strains isolated from blood and cerebrospinal fluid samples. Frontiers in Microbiology, 6(JAN). https://doi.org/10.3389/fmicb.2015.01544 García, S., Ramírez, S. G., Luengo, J., & Herrera, F. (2016). Big Data : Preprocesamiento. Novática, 17–23. http://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/2133_Nv237-Digital- sramirez.pdf Gefen-Halevi, S., Hindiyeh, M. Y., Ben-David, D., Smollan, G., Gal-Mor, O., Azar, R., Castanheira, M., Belausov, N., Rahav, G., Tal, I., Mendelson, E., & Keller, N. (2013). Isolation of genetically unrelated bla(NDM-1)-positive Providencia rettgeri strains in Israel. Journal of Clinical Microbiology, 51(5), 1642–1643. https://doi.org/10.1128/JCM.00381-13 Ghaheri, A., Shoar, S., Naderan, M., & Hoseini, S. S. (2015). The Applications of Genetic Algorithms in Medicine. Oman Medical Journal, 30(6), 406–416. Ghotaslou, R., Yeganeh Sefidan, F., Akhi, M. T., Asgharzadeh, M., & Mohammadzadeh Asl, Y. (2017). Dissemination of Genes Encoding Aminoglycoside-Modifying Enzymes and armA among Enterobacteriaceae Isolates in Northwest Iran. Microbial Drug Resistance, 23(7), 826–832. https://doi.org/10.1089/mdr.2016.0224 Govindaswamy, A., Bajpai, V., Khurana, S., Aravinda, A., Batra, P., Malhotra, R., & Mathur, P. (2019). Prevalence and characterization of beta-lactamase-producing Escherichia coli isolates from a tertiary care hospital in India. Journal of Laboratory Physicians, 11(02), 123–127. https://doi.org/10.4103/jlp.jlp_122_18 Guidance Document on Tigecycline Dosing in association with Revision of Breakpoints for Enterobacterales and other species with an “Intermediate” category. (2018). Haidar, G., Alkroud, A., Cheng, S., Churilla, T. M., Churilla, B. M., Shields, R. K., Doi, Y., Clancy, C. J., & Nguyen, H. (2016). Association between the Presence of Aminoglycoside-Modifying Enzymes and In Vitro Activity of Gentamicin, Tobramycin, Amikacin, and Plazomicin against Klebsiella pneumoniae Carbapenemase-and Extended-Spectrum-Lactamase-Producing Enterobacter Species. https://doi.org/10.1128/AAC.00869-16 Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts ... - Aurélien Géron - Google Libros. (n.d.). Retrieved November 15, 2020, from https://books.google.com.ec/books?id=HHetDwAAQBAJ&printsec=frontcover&dq=h ands+on+machine+learning+with+scikit- learn+and+tensorflow&hl=es&sa=X&ved=2ahUKEwjU66ijiIbtAhXyxlkKHRYQBNEQ6 AEwAHoECAAQAg#v=onepage&q=hands on Machine Learning with scikit-learn and tensorflow&f=false Hirakawa, H., Nishino, K., Hirata, T., & Yamaguchi, A. (2003). Comprehensive studies of drug resistance mediated by overexpression of response regulators of two- component signal transduction systems in Escherichia coli. Journal of Bacteriology, 185(6), 1851–1856. https://doi.org/10.1128/JB.185.6.1851-1856.2003 Home - BioSample - NCBI. (n.d.). Retrieved March 27, 2021, from https://www.ncbi.nlm.nih.gov/biosample Home - Genome - NCBI. (n.d.). Retrieved March 27, 2021, from https://www.ncbi.nlm.nih.gov/genome/ Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics & Proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063 Hyun, J. C., Kavvas, E. S., Monk, J. M., & Palsson, B. O. (2020). Machine Learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens. PLoS Computational Biology, 16(3), e1007608. https://doi.org/10.1371/journal.pcbi.1007608 Iredell, J., Brown, J., & Tagg, K. (2016). Antibiotic resistance in Enterobacteriaceae: Mechanisms and clinical implications. BMJ (Online), 352(February 2016). https://doi.org/10.1136/bmj.h6420 Jabbar, H. K., & Khan, R. Z. (2015). Methods to Avoid Over-Fitting and Under-Fitting in Supervised Machine Learning (Comparative Study). December 2014, 163–172. https://doi.org/10.3850/978-981-09-5247-1_017 Jayol, A., Nordmann, P., André, C., Poirel, L., & Dubois, V. (2018). Evaluation of three broth microdilution systems to determine colistin susceptibility of Gram-negative bacilli. Journal of Antimicrobial Chemotherapy, 73(5), 1272–1278. https://doi.org/10.1093/jac/dky012 Jihye Jeon. (2015). The Strengths and Limitations of the Statistical Modeling of Complex Social Phenomenon: Focusing on SEM, Path Analysis, or Multiple Regression Models. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 9(5), 1634–1642. https://pdfs.semanticscholar.org/50a9/a4a1cf87575bbb83b43419102d09fc89f942.pd f JIM O’NEILL. (2015). TACKLING DRUG-RESISTANT INFECTIONS GLOBALLY: FINAL REPORT AND RECOMMENDATIONS. 136(1), 29–31. Jorgensen, J. H., Ferraro, M. J., Jorgensen, J. H., & Ferraro, M. J. (2009). Antimicrobial Susceptibility Testing: A Review of General Principles and Contemporary Practices. Clinical Infectious Diseases, 49(11), 1749–1755. https://doi.org/10.1086/647952 Karaiskos, I., Lagou, S., Pontikis, K., Rapti, V., & Poulakou, G. (2019). The “Old” and the “New” antibiotics for MDR Gram-negative pathogens: For whom, when, and how. In Frontiers in Public Health (Vol. 7, Issue JUN, p. 151). Frontiers Media S.A. https://doi.org/10.3389/fpubh.2019.00151 Kim, S.-Y., Park, Y.-J., Yu, J. K., & Kim, Y. S. (2011). Aminoglycoside Susceptibility Profiles of Enterobacter cloacae Isolates Harboring the aac(6’)-Ib Gene. The Korean Journal of Laboratory Medicine, 31(4), 279. https://doi.org/10.3343/KJLM.2011.31.4.279 Kobayashi, N., Nishino, K., Hirata, T., & Yamaguchi, A. (2003). Membrane topology of ABC-type macrolide antibiotic exporter MacB in Escherichia coli. FEBS Letters, 546(2–3), 241–246. https://doi.org/10.1016/S0014-5793(03)00579-9 Kolarević, S., Milovanović, D., Avdović, M., Oalđe, M., Kostić, J., Sunjog, K., Nikolić, B., Knežević-Vukčević, J., & Vuković-Gačić, B. (2016). Optimisation оf the microdilution method for detection of minimum inhibitory concentration values in selected bacteria. https://doi.org/10.5281/zenodo.48751 Köser, C. U., Ellington, M. J., Cartwright, E. J. P., Gillespie, S. H., Brown, N. M., Farrington, M., Holden, M. T. G., Dougan, G., Bentley, S. D., Parkhill, J., & Peacock, S. J. (2012). Routine Use of Microbial Whole Genome Sequencing in Diagnostic and Public Health Microbiology. PLoS Pathogens, 8(8). https://doi.org/10.1371/journal.ppat.1002824 Kotb, D. N., Mahdy, W. K., Mahmoud, M. S., & Khairy, R. M. M. (2019). Impact of co- existence of PMQR genes and QRDR mutations on fluoroquinolones resistance in Enterobacteriaceae strains isolated from community and hospital acquired UTIs. BMC Infectious Diseases, 19(1), 1–8. https://doi.org/10.1186/s12879-019-4606-y Kouchaki, S., Yang, Y. Y., Walker, T. M., Walker, A. S., Wilson, D. J., Peto, T. E. A., Crook, D. W., Clifton, D. A., Hoosdally, S. J., Gibertoni Cruz, A. L., Carter, J., Grazian, C., Kouchaki, S., Walker, T. M., Fowler, P. W., Clifton, D. A., Iqbal, Z., Hunt, M., Smith, E. G., ... Van Soolingen, D. (2019). Application of Machine Learning techniques to tuberculosis drug resistance analysis. Bioinformatics, 35(13), 2276– 2282. https://doi.org/10.1093/bioinformatics/bty949 Kumar Trivedi, M. (2015). Antibiogram, Biochemical Reactions and Biotyping of Biofield Treated <i>Providencia rettgeri</i> American Journal of Health Research, 3(6), 344. https://doi.org/10.11648/j.ajhr.20150306.15 L, D., P, N., & L, P. (2012). Association of the emerging carbapenemase NDM-1 with a bleomycin resistance protein in Enterobacteriaceae and Acinetobacter baumannii. Antimicrobial Agents and Chemotherapy, 56(4), 1693–1697. https://doi.org/10.1128/AAC.05583-11 Li, X. Z., & Nikaido, H. (2009). Efflux-mediated drug resistance in bacteria: An update. In Drugs (Vol. 69, Issue 12, pp. 1555–1623). https://doi.org/10.2165/11317030- 000000000-00000 LM, C., H, H., S, X., & FM, A. (2009). qnrD, a novel gene conferring transferable quinolone resistance in Salmonella enterica serovar Kentucky and Bovismorbificans strains of human origin. Antimicrobial Agents and Chemotherapy, 53(2), 603–608. https://doi.org/10.1128/AAC.00997-08 M, G., S, E., S, A., V, D., MA, K., E, S., & S, S. (2016). GyrA ser83 and ParC trp106 Mutations in Salmonella enterica Serovar Typhi Isolated from Typhoid Fever Patients in Tertiary Care Hospital. Journal of Clinical and Diagnostic Research : JCDR, 10(7), DC14–DC18. https://doi.org/10.7860/JCDR/2016/17677.8153 M, M. A., S, K., C, W., S, L., G, M., T, M., S, J., & TR, R. (2015). Identification of a novel mutation at the primary dimer interface of GyrA conferring fluoroquinolone resistance in Clostridium difficile. Journal of Global Antimicrobial Resistance, 3(4), 295–299. https://doi.org/10.1016/J.JGAR.2015.09.007 M, N., K, S., O, K., S, K., S, N., & R, S. (2015). Characterisation of novel mutations involved in quinolone resistance in Escherichia coli isolated from imported shrimp. International Journal of Antimicrobial Agents, 45(5), 471–476. Majlesi, A., Kakhki, R. K., Mozaffari Nejad, A. S., Mashouf, R. Y., Roointan, A., Abazari, M., & Alikhani, M. Y. (2018). Detection of plasmid-mediated quinolone resistance in clinical isolates of Enterobacteriaceae strains in Hamadan, West of Iran. Saudi Journal of Biological Sciences, 25(3), 426–430. https://doi.org/10.1016/j.sjbs.2016.11.019 Marquez-Ortiz, R. A., Haggerty, L., Sim, E. M., Duarte, C., Castro-Cardozo, B. E., Beltran, M., Saavedra, S., Vanegas, N., Escobar-Perez, J., & Petty, N. K. (2017). First Complete Providencia rettgeri Genome Sequence, the NDM-1-Producing Clinical Strain RB151. Genome Announcements, 5(3), e01472-16. https://doi.org/10.1128/genomeA.01472-16 Mazzariol, A., Kocsis, B., Koncan, R., Kocsis, E., Lanzafame, P., & Cornaglia, G. (2012). Description and plasmid characterization of qnrD determinants in Proteus mirabilis and Morganella morganii. Clinical Microbiology and Infection, 18(3), E46–E48. https://doi.org/10.1111/j.1469-0691.2011.03728.x Mbelle, N., Sekyere, J. O., Amoako, D. G., & Maningi, N. E. (2019). Genomic analysis of a multidrug-resistant clinical Providencia rettgeri (PR002) strain with the novel integron ln1483 and an A/C plasmid replicon Genetic diversity of Mycobacterium tuberculosis strains among mycobacterial isolates from symptomatic holy water attendees in Amhara region, Ethiopia View project Fluoquinolone and Ketolide Resistance in Haemophilus Parainfluenzae from Private Sector of KwaZulu-Natal, South Africa View project. https://doi.org/10.1111/nyas.14237 Misawa, K., Tarumoto, N., Tamura, S., Osa, M., Hamamoto, T., Yuki, A., Kouzaki, Y., Imai, K., Ronald, R. L., Yamaguchi, T., Murakami, T., Maesaki, S., Suzuki, Y., Kawana, A., & Maeda, T. (2018). Single nucleotide polymorphisms in genes encoding penicillin-binding proteins in β-lactamase-negative ampicillin-resistant Haemophilus influenzae in Japan. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3169-0 Mitra, S., Mukherjee, S., Naha, S., Chattopadhyay, P., Dutta, S., & Basu, S. (2019). Evaluation of co-transfer of plasmid-mediated fluoroquinolone resistance genes and bla NDM gene in Enterobacteriaceae causing neonatal septicaemia. Antimicrobial Resistance and Infection Control, 7(1), 1–15. Mohanty, S., & Mahapatra, A. (2021). In vitro activity of tigecycline against multidrug- resistant Enterobacteriaceae isolates from skin and soft tissue infections. Annals of Medicine and Surgery, 62, 228–230. https://doi.org/10.1016/J.AMSU.2021.01.010 Mohr O’hara, C., Brenner, F. W., & Miller, J. M. (2000). Classification, Identification, and Clinical Significance of Proteus, Providencia, and Morganella (Vol. 13, Issue 4). http://cmr.asm.org/ Moradigaravand, D., Palm, M., Farewell, A., Mustonen, V., Warringer, J., & Parts, L. (2018). Prediction of antibiotic resistance in Escherichia coli from large-scale pan- genome data. PLOS Computational Biology, 14(12), e1006258. Naas, T., & Nordmann, P. (1994). Analysis of a carbapenem-hydrolyzing class A β- lactamase from Enterobacter cloacae and of its LysR-type regulatory protein. Proceedings of the National Academy of Sciences of the United States of America, 91(16), 7693–7697. https://doi.org/10.1073/pnas.91.16.7693 Nagakubo, S., Nishino, K., Hirata, T., & Yamaguchi, A. (2002). The putative response regulator BaeR stimulates multidrug resistance of Escherichia coli via a novel multidrug exporter system, MdtABC. Journal of Bacteriology, 184(15), 4161–4167. https://doi.org/10.1128/JB.184.15.4161-4167.2002 Nazir, S., Dekyong, A., Fomda, B., Benazir, S., Bhat, A., & Bashir, L. (2017). Providencia Rettgeri: an Unexpected Cause of Sepsis. International Journal of Advanced Research, 5(12), 1442–1444. https://doi.org/10.21474/IJAR01/6104 Nguyen, M., Brettin, T., Long, S. W., Musser, J. M., Olsen, R. J., Olson, R., Shukla, M., Stevens, R. L., Xia, F., Yoo, H., & Davis, J. J. (2018). Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Scientific Reports, 8(1), 421. https://doi.org/10.1038/s41598-017-18972-w Nguyen, M., Wesley Long, S., McDermott, P. F., Olsen, R. J., Olson, R., Stevens, R. L., Tyson, G. H., Zhao, S., & Davisa, J. J. (2019). Using Machine Learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. Journal of Clinical Microbiology, 57(2). https://doi.org/10.1128/JCM.01260-18 Niehaus, K. E., Walker, T. M., Crook, D. W., Peto, T. E. A., & Clifton, D. A. (2014). Machine Learning for the prediction of antibacterial susceptibility in Mycobacterium tuberculosis. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 618–621. https://doi.org/10.1109/BHI.2014.6864440 Nishino, K., Senda, Y., & Yamaguchi, A. (2008). CRP regulator modulates multidrug resistance of Escherichia coli by repressing the mdtEF multidrug efflux genes. Journal of Antibiotics, 61(3), 120–127. https://doi.org/10.1038/ja.2008.120 Nordmann, P., Naas, T., & Poirel, L. (2011). Global spread of Carbapenemase-producing Enterobacteriaceae. Emerging Infectious Diseases, 17(10), 1791–1798. https://doi.org/10.3201/eid1710.110655 Olaitan, Abiola O., Morand, S., & Rolain, J.-M. (2014). Mechanisms of polymyxin resistance: acquired and intrinsic resistance in bacteria. Frontiers in Microbiology, 5, 643. https://doi.org/10.3389/fmicb.2014.00643 Olaitan, Abiola Olumuyiwa, Diene, S. M., Assous, M. V., & Rolain, J.-M. (2016). Genomic Plasticity of Multidrug-Resistant NDM-1 Positive Clinical Isolate of Providencia rettgeri. Genome Biology and Evolution, 8(3), 723–728. https://doi.org/10.1093/gbe/evv195 Olaitan, Abiola Olumuyiwa, Diene, S. M., Gupta, S. K., Adler, A., Assous, M. V., & Rolain, J. M. (2014). Genome analysis of NDM-1 producing Morganella morganii clinical isolate. Expert Review of Anti-Infective Therapy, 12(10), 1297–1305. Olivares, J., Bernardini, A., Garcia-Leon, G., Corona, F., Sanchez, M. B., & Martinez, J. L. (2013). The intrinsic resistome of bacterial pathogens. In Frontiers in Microbiology (Vol. 4, Issue APR, p. 103). Frontiers Research Foundation. https://doi.org/10.3389/fmicb.2013.00103 Olumuyiwa Olaitan, A., Diene, S. M., Victor Assous, M., & Rolain, J. M. (2016). Genomic plasticity of multidrug-resistant NDM-1 positive clinical isolate of providencia rettgeri. Genome Biology and Evolution, 8(3), 723–728. https://doi.org/10.1093/gbe/evv195 OpenSUSE. (n.d.). openSUSE - Linux OS. La mejor elección para administradores de sistemas, desarrolladores y usuarios de ordenadores de sobremesa. 2021. Retrieved March 27, 2021, from https://www.opensuse.org/ Ordóñez-díaz, K. M., Estupiñán, J. L., & Alzate, J. A. (2018). Metalobetalactamasa de tipo Nueva Delhi en Risaralda ( Colombia ): reporte de un caso. 22(1), 55–57. Ortiz, K. P. P., Segura, J. C., Bettin, L., Coriat, J., & Díez, H. (2011). recuencia de betalactamasas de espectro extendido (BLEE) en Klebsiella pneumoniae, Klebsiella oxytoca y Escherichia coli aisladas de pacientes hospitalizados en una clínica de tercer nivel en Bogotá. Ciencia Actual, 4(0), 1–9. https://doi.org/10.21500/2248468X.2285 Osei Sekyere, J., & Amoako, D. G. (2017). Genomic and phenotypic characterisation of fluoroquinolone resistance mechanisms in Enterobacteriaceae in Durban, South Africa. PLOS ONE, 12(6), e0178888. https://doi.org/10.1371/journal.pone.0178888 Ovalle, M. V., Saavedra, S. Y., González, M. N., Hidalgo, A. M., Duarte, C., & Beltrán, M. (2017). Resultados de la vigilancia nacional de resistencia antimicrobiana en infecciones asociadas a la atención en salud en enterobacterias y Gram negativos no fermentadores, Colombia 2012-2014. Biomédica, 37(4), 39. https://doi.org/http://dx.doi.org/10.7705/biomedica.v37i4.3432 Ozkaya-Parlakay, A., Gulhan, B., Kanik-Yuksek, S., Guney, D., Gonulal, D., Demirtas, G., Tezer, H., Unal, S., & Senel, E. (2020). Tigecycline therapy in pediatric patients with multidrug resistant bacteremia. Enfermedades Infecciosas y Microbiologia Clinica (English Ed.), 38(10), 471–473. https://doi.org/10.1016/j.eimce.2019.12.014 Partridge, S. R. (2015). Resistance mechanisms in Enterobacteriaceae. Pathology, 47(3), 276–284. https://doi.org/10.1097/PAT.0000000000000237 Pataki, B. Á., Matamoros, S., van der Putten, B. C. L., Remondini, D., Giampieri, E., Aytan-Aktug, D., Hendriksen, R. S., Lund, O., Csabai, I., Schultsz, C., Matamoros, S., Janes, V., Hendriksen, R. S., Lund, O., Clausen, P., Aarestrup, F. M., Koopmans, M., Pataki, B., Visontai, D., ... McDermott, P. (2020). Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with Machine Learning. Scientific Reports, 10(1), 1–9. https://doi.org/10.1038/s41598-020-71693-5 Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Cournapeau, D., Passos, A., Brucher, M., Perrot Andédouardand ́andédouard Duchesnay, M., & Perrot, M. (2011). Scikit-learn: Machine Learning in Python. In Machine Learning in Python. Journal of Machine Learning Research (Vol. 12). Microtome Pub-lishing. https://hal.inria.fr/hal-00650905v2 Pedregosa FABIANPEDREGOSA, F., Michel, V., Grisel OLIVIERGRISEL, O., Blondel, M., Prettenhofer, P., Weiss, R., Vanderplas, J., Cournapeau, D., Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B., Grisel, O., Dubourg, V., Passos, A., Brucher, M., Perrot andÉdouardand, M., Duchesnay, andÉdouard, & Duchesnay EDOUARDDUCHESNAY, Fré. (2011). Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. In Journal of Machine Learning Research (Vol. 12, Issue 85). http://scikit-learn.sourceforge.net. Pérez-Vázquez, M., Sola Campoy, P. J., Ortega, A., Bautista, V., Monzón, S., Ruiz- Carrascoso, G., Mingorance, J., González-Barberá, E. M., Gimeno, C., Aracil, B., Sáez, D., Lara, N., Fernández, S., González-López, J. J., Campos, J., Kingsley, R. A., Dougan, G., Oteo-Iglesias, J., Rodrigo, C. H., ... Suarez, C. B. (2019). Emergence of NDM-producing Klebsiella pneumoniae and Escherichia coli in Spain: phylogeny, resistome, virulence and plasmids encoding blaNDM-like genes as determined by WGS. Journal of Antimicrobial Chemotherapy, 74(12), 3489–3496. https://doi.org/10.1093/jac/dkz366 Pérez, A., Poza, M., Fernández, A., Del Carmen Fernández, M., Mallo, S., Merino, M., Rumbo-Feal, S., Cabral, M. P., & Bou, G. (2012). Involvement of the AcrAB-TolC efflux pump in the resistance, fitness, and virulence of Enterobacter cloacae. Antimicrobial Agents and Chemotherapy, 56(4), 2084–2090. https://doi.org/10.1128/AAC.05509-11 Pesesky, M. W., Hussain, T., Wallace, M., Patel, S., Andleeb, S., Burnham, C.-A. D., & Dantas, G. (2016). Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data. Frontiers in Microbiology, 7, 1887. https://doi.org/10.3389/fmicb.2016.01887 Peterson, L. R. (2008). A review of tigecycline - the first glycylcycline. International Journal of Antimicrobial Agents, 32(SUPPL. 4), S215–S222. https://doi.org/10.1016/S0924-8579(09)70005-6 Pournaras, S., Koumaki, V., Spanakis, N., Gennimata, V., & Tsakris, A. (2016). Current perspectives on tigecycline resistance in Enterobacteriaceae: susceptibility testing issues and mechanisms of resistance. International Journal of Antimicrobial Agents, 48(1), 11–18. https://doi.org/10.1016/j.ijantimicag.2016.04.017 Puértolas-Balint, F., Warsi, O., Linkevicius, M., Tang, P. C., & Andersson, D. I. (2020). Mutations that increase expression of the EmrAB-TolC efflux pump confer increased resistance to nitroxoline in Escherichia coli. Journal of Antimicrobial Chemotherapy, 75(2), 300–308. https://doi.org/10.1093/jac/dkz434 Ramirez, L. S., & Marin Castaño, D. (2009). METODOLOGIAS PARA EVALUAR IN VITRO LA ACTIVIDAD ANTIBACTERIANA DE COMPUESTOS DE ORIGEN VEGETAL Methodologies for evaluating the In vitro antibacterial activity of natural compounds of plant origin. Scientia et Technica, 42, 263–268. Ramón, J., Anaya, M., & Química, M. S. (2006). MANUAL DE TÉCNICAS BÁSICAS EN BIOLOGÍA MOLECULAR. Redgrave, L. S., Sutton, S. B., Webber, M. A., & Piddock, L. J. V. (2014). Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends in Microbiology, 22(8), 438–445. https://doi.org/10.1016/j.tim.2014.04.007 Rizzo, R., Fiannaca, A., La Rosa, M., & Urso, A. (2016). A Deep Learning Approach to DNA Sequence Classification (pp. 129–140). Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_10 Roberts, L. W., Catchpoole, E., Jennison, A. V., Bergh, H., Hume, A., Heney, C., George, N., Paterson, D. L., Schembri, M. A., Beatson, S. A., & Harris, P. N. A. (2020). Genomic analysis of carbapenemase-producing enterobacteriaceae in queensland reveals widespread transmission of blaimp-4 on an incHI2 plasmid. Microbial Genomics, 6(1). https://doi.org/10.1099/mgen.0.000321 Saad, N., Munir, T., Ansari, M., Gilani, M., Latif, M., & Haroon, A. (2016). Introduction Evaluation of phenotypic tests for detection of Amp C beta-lactamases in clinical isolates from a tertiary care hospital of Rawalpindi, Pakistan (Vol. 66, Issue 6). Saavedra-Rojas, S.-Y., Duarte-Valderrama, C., González-de-Arias, M.-N., & Ovalle- Guerro, M. V. (2013). Emergence of Providencia rettgeri NDM-1 in two departments of Colombia, 2012-2013. Enfermedades Infecciosas y Microbiologia Clinica, 35(6), doi:10.1016/j.eimc.2015.05.011. https://doi.org/10.1016/j.eimc.2015.05.011 Sagar, S., Narasimhaswamy, N., & D’Souza, J. (2017). Providencia Rettgeri: An Emerging Nosocomial Uropathogen in an Indwelling Urinary Catheterised Patient. Journal of Clinical and Diagnostic Research : JCDR, 11(6), DD01–DD02. https://doi.org/10.7860/JCDR/2017/25740.10026 Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1249 Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, 42–47. https://doi.org/10.1109/CTS.2013.6567202 Schneiders, T., Amyes, S. G. B., & Levy, S. B. (2003). Role of AcrR and RamA in Fluoroquinolone Resistance in Clinical Klebsiella pneumoniae Isolates from Singapore. Antimicrobial Agents and Chemotherapy, 47(9). Schrider, D. R., & Kern, A. D. (2018). Supervised Machine Learning for Population Genetics: A New Paradigm. Trends in Genetics, 34(4), 301–312. https://doi.org/10.1016/J.TIG.2017.12.005 Schürch, A. C., & van Schaik, W. (2017). Challenges and opportunities for whole-genome sequencing–based surveillance of antibiotic resistance. Annals of the New York Academy of Sciences, 1388(1), 108–120. https://doi.org/10.1111/nyas.13310 Sharff, A., Fanutti, C., Shi, J., Calladine, C., & Luisi, B. (2001). The role of the TolC family in protein transport and multidrug efflux from stereochemical certainty to mechanistic hypothesis. In European Journal of Biochemistry (Vol. 268, Issue 19, pp. 5011– 5026). Eur J Biochem. https://doi.org/10.1046/j.0014-2956.2001.02442.x Sharma, D., Sharma, P., & Soni, P. (2017). First case report of Providencia Rettgeri neonatal sepsis. BMC Research Notes, 10(1), 17–20. https://doi.org/10.1186/s13104-017-2866-4 Shin, S., Jeong, S. H., Lee, H., Hong, J. S., Park, M. J., & Song, W. (2018). Emergence of multidrug-resistant Providencia rettgeri isolates co-producing NDM-1 carbapenemase and PER-1 extended-spectrum β-lactamase causing a first outbreak in Korea. Annals of Clinical Microbiology and Antimicrobials, 17(1), 1–6. https://doi.org/10.1186/s12941-018-0272-y Sidjabat, H. E., Townell, N., Nimmo, G. R., George, N. M., Robson, J., Vohra, R., Davis, L., Heney, C., & Patersona, D. L. (2015). Dominance of IMP-4-producing Enterobacter cloacae among carbapenemase-producing Enterobacteriaceae in Australia. Antimicrobial Agents and Chemotherapy, 59(7), 4059–4066. https://doi.org/10.1128/AAC.04378-14 Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised Machine Learning algorithms. Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016, 1310–1315. Singh, H., Velamakanni, S., Deery, M. J., Howard, J., Wei, S. L., & Van Veen, H. W. (2016). ATP-dependent substrate transport by the ABC transporter MsbA is proton- coupled. Nature Communications, 7. https://doi.org/10.1038/ncomms12387 Sommer, C., & Gerlich, D. W. (2013). Machine Learning in cell biology – teaching computers to recognize phenotypes. Journal of Cell Science, 126(24), 5529–5539. https://doi.org/10.1242/JCS.123604 Spellberg, B., Guidos, R., Gilbert, D., Bradley, J., Boucher, H. W., Scheld, W. M., Bartlett, J. G., & Edwards, J. (2008). The epidemic of antibiotic-resistant infections: A call to action for the medical community from the infectious diseases society of America. In Clinical Infectious Diseases (Vol. 46, Issue 2, pp. 155–164). https://doi.org/10.1086/524891 Srinivasan, V. B., & Rajamohan, G. (2013). KpnEF, a new member of the Klebsiella pneumoniae cell envelope stress response regulon, is an SMR-type efflux pump involved in broad-spectrum antimicrobial resistance. Antimicrobial Agents and Chemotherapy, 57(9), 4449–4462. https://doi.org/10.1128/AAC.02284-12 Srinivasan, V. B., Singh, B. B., Priyadarshi, N., Chauhan, N. K., & Rajamohan, G. (2014). Role of novel multidrug efflux pump involved in drug resistance in Klebsiella pneumoniae. PLoS ONE, 9(5). https://doi.org/10.1371/journal.pone.0096288 Tafur, D., & Villegas, V. (2008). Mecanismos de resistencia a los antibióticos en bacterias Gram negativas. Infectio, 12(3), 217–226. https://doi.org/http://www.sld.cu/galerias/pdf/sitios/apua- cuba/mecanismos_de_resistencia_a_los_antibioticos_en_bacterias_gram_negativas .pdf Tamara, N. Q., Esthela, T. M., Pamela, C. S., Jenniffer, H. L., & Pablo, S. R. (2020). Journal of Medical Case Reports and Reviews 3:8 [2020] CARBAPENEMASE- PRODUCING ENTEROBACTERIACEAE IN PATIENTS OF A THIRD LEVEL HOSPITAL IN THE CITY OF GUAYAQUIL-ECUADOR. Journal of Medical Case Reports and Reviews, 3(08). www.jmcrr.info Tatarinova, T. V, Editors, Y. N., Raschka, S., Verdier, C. F. J. E. S. O., Hearty, J., Huffman, J., & Pajankar, A. (2000). Python 机器学习. In Astronomical Data Analysis Software and Systems IX (Vol. 216). The Comprehensive Antibiotic Resistance Database. (n.d.). Retrieved March 27, 2021, from https://card.mcmaster.ca/ Torres, E., López-Cerero, L., Rodríguez-Martínez, J. M., & Pascual, Á. (2016). Reduced Susceptibility to Cefepime in Clinical Isolates of Enterobacteriaceae Producing OXA- 1 Beta-Lactamase. Microbial Drug Resistance, 22(2), 141–146. https://doi.org/10.1089/mdr.2015.0122 Tshisevhe, V. S., Lekalakala, M. R., Tshuma, N., Janse van Rensburg, S., & Mbelle, N. (2016). Outbreak of carbapenem-resistant Providencia rettgeri in a tertiary hospital. South African Medical Journal = Suid-Afrikaanse Tydskrif Vir Geneeskunde, 107(1), 31–33. https://doi.org/10.7196/SAMJ.2016.v107.i1.12002 Van Camp, P.-J., Haslam, D. B., & Porollo, A. (2020). Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. Frontiers in Microbiology, 11, 1013. https://doi.org/10.3389/fmicb.2020.01013 van Duin, D., & Doi, Y. (2017). The global epidemiology of carbapenemase-producing Enterobacteriaceae. In Virulence (Vol. 8, Issue 4, pp. 460–469). Taylor and Francis Inc. https://doi.org/10.1080/21505594.2016.1222343 VB, S., & G, R. (2013). KpnEF, a new member of the Klebsiella pneumoniae cell envelope stress response regulon, is an SMR-type efflux pump involved in broad- spectrum antimicrobial resistance. Antimicrobial Agents and Chemotherapy, 57(9), 4449–4462. https://doi.org/10.1128/AAC.02284-12 Villalobos, A. P., Barrero, L. I., Rivera, S. M., Ovalle, M. V., & Valera, D. (2013). Vigilancia de infecciones asociadas a la atención en salud, resistencia bacteriana y consumo de antibióticos en hospitales de alta complejidad, Colombia, 2011. Biomédica, 34(0), 67. https://doi.org/10.7705/biomedica.v34i0.1698 Viviana, L., & Su, R. (2019). Caracterización de perfiles de elementos genéticos plasmídicos de aislamientos colombianos de Providencia rettgeri, causantes de IAAS. Obtenidos del Instituto Nacional de Salud, durante el periodo 2015-2016. Weinstein, M. P., Patel, J. B., Bobenchik, A. M., Campeau, S., Cullen, S. K., Galas, M. F., Gold, H., Humphries, R. M., Kirn, T. J., Lewis Ii, J. S., Limbago, B., Mathers, A. J., Mazzulli, T., Richter, S. S., Satlin, M., Schuetz, A. N., Swenson, J. M., Tamma, P. D., & Simner, P. J. (2020). M100 Performance Standards for Antimicrobial Susceptibility Testing A CLSI supplement for global application. Performance Standards for Antimicrobial Susceptibility Testing Performance Standards for Antimicrobial Susceptibility Testing. Weiss, S. J., Mansell, T. J., Mortazavi, P., Knight, R., & Gill, R. T. (2016). Parallel Mapping of Antibiotic Resistance Alleles in Escherichia coli. PLOS ONE, 11(1), e0146916. https://doi.org/10.1371/journal.pone.0146916 Welcome to Python.org. (n.d.). Retrieved March 27, 2021, from https://www.python.org/ Weston, N., Sharma, P., Ricci, V., & Piddock, L. J. V. (2017). Regulation of the AcrAB- TolC efflux pump in Enterobacteriaceae. Research in Microbiology, 1–7. https://doi.org/10.1016/j.resmic.2017.10.005 Weston, N., Sharma, P., Ricci, V., & Piddock, L. J. V. (2018). Regulation of the AcrAB- TolC efflux pump in Enterobacteriaceae. Research in Microbiology, 169(7–8), 425– 431. https://doi.org/10.1016/j.resmic.2017.10.005 WHO | Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. (2017). WHO. Wu, L. T., Tsou, M. F., Wu, H. J., Chen, H. E., Chuang, Y. C., & Yu, W. L. (2004). Survey of CTX-M-3 extended-spectrum β-lactamase (ESBL) among cefotaxime-resistant Serratia marcescens at a medical center in middle Taiwan. Diagnostic Microbiology and Infectious Disease, 49(2), 125–129. https://doi.org/10.1016/j.diagmicrobio.2004.02.004 Yang, Y., Niehaus, K. E., Walker, T. M., Iqbal, Z., Walker, A. S., Wilson, D. J., Peto, T. E. A., Crook, D. W., Smith, E. G., Zhu, T., & Clifton, D. A. (2018). Machine Learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics, 34(10), 1666–1671. https://doi.org/10.1093/bioinformatics/btx801 |
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Universidad Nacional de Colombia |
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Bogotá - Ciencias - Maestría en Ciencias - Microbiología |
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Instituto de Biotecnología (IBUN) |
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Facultad de Ciencias |
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Atribución-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Barreto-Hernandez, Emilianof221480a6c3375402380114095237e76Reguero Reza, María Teresa Jesús9ba54bc0326cdc978d84a9a9130f2d0cTenorio Arévalo, María Caridad4574fff32ba4cb9c7f18cd446deda838Bioinformática2022-07-29T16:53:04Z2022-07-29T16:53:04Z2021https://repositorio.unal.edu.co/handle/unal/81760Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, graficas, mapasLa resistencia a los antibióticos es considerada una de las amenazas más urgentes de la salud pública mundial. Actualmente obtener resultados fenotípicos de esa resistencia por los métodos convencionales basados en cultivos toma mucho tiempo. La secuenciación de genoma completo (WGS) supera estas limitaciones ya que permite inferir el comportamiento fenotípico mediante la identificación de elementos de resistencia a antibióticos en el genoma en menor tiempo, sin embargo, aún no se ha conseguido una predicción óptima de estos perfiles. Los métodos de Machine Learning facilitan esta optimización, por lo tanto, el objetivo de este trabajo fue implementar un modelo de predicción de resistencia a antibióticos utilizando métodos de Machine Learning a partir de datos de WGS de 521 Enterobacterales que incluye 28 aislamientos colombianos de Providencia rettgeri. Para la predicción se utilizaron tres métodos: a) Regresión Logística (RL), b) Support Vector Machine (SVM) y c) Random Forest (RF) y tres métodos de selección de características: 1) Eliminación recursiva de características (RFECV), 2) regularización L1 y 3) Feature importance. Se desarrollaron modelos de predicción a 10 antibióticos, con una exactitud promedio del 88% (IC 95% ± 6) y exactitudes individuales de 89% (IC 95% ± 7), 93% (IC 95% ± 5), 90% (IC 95% ± 7), 93% (IC 95% ± 6), 81% (IC 95% ± 12), 93% (IC 95% ± 8), 81% (IC 95% ± 10), 79% (IC 95% ± 9), 86% (IC 95% ± 9) y 93% (IC 95% ± 5) para amikacina, ciprofloxacina, trimetropim/sulfometoxazol, tetraciclina, tigeciclina, colistina, ceftazidima, cefepime, imipenem y meropenem, respectivamente. Los métodos que permitieron obtener estos desempeños corresponden a RL y SVM con los métodos de selección de características RFECV y regularización L1. Estos hallazgos señalan que los modelos construidos pueden predecir con exactitud elevada la resistencia a antibióticos de diferentes especies de bacterias y apoya la idea de que pueden convertirse en una herramienta potencial para el diagnóstico clínico. (Texto tomado de la fuente)Antibiotic resistance is considered one of the most urgent threats to global public health. Due to the public health risk, there are several methods for obtained phenotypic results. However, conventional methods take days or weeks. Whole-genome sequencing (WGS) overcomes these limitations by estimating phenotypic behavior and identifying antibiotic resistance elements in the genome in a faster way. However, information about the optimal prediction of these profiles is still scarce. The project aim was to implement an antibiotic resistance prediction model using Machine Learning methods, using WGS data of 521 Enterobacterales isolates, including 28 Providencia rettgeri isolates sequenced in Colombia. The Machine Learning methods used were a) Logistic Regression (RL), b) Support Vector Machine (SVM), and c) Random Forest (RF). Also, the following feature selection methods were applied: 1) recursive feature elimination (RFECV), 2) L1 regularization, and 3) feature importance. Finally, prediction models were developed for 10 antibiotics, with a mean accuracy of 88% (IC 95% ± 6) and individual accuracies of 89% (IC 95% ± 7), 93% (IC 95% ± 5), 90% (IC 95% ± 7), 93% (IC 95% ± 6), 81% (IC 95% ± 12), 93% (IC 95% ± 8) 81% (IC 95% ± 10), 79% (IC 95% ± 9), 86% (IC 95% ± 9) and 93% (IC 95% ± 5), for amikacin, ciprofloxacin, trimethoprim/sulfamethoxazole, tetracycline, tigecycline, colistin, ceftazidime, cefepime, imipenem and meropenem respectively. These performances correspond to RL and SVM, using RFECV and L1 as regularization feature selection methods. These findings indicate that these models could accurately predict antibiotic resistance from different Enterobacteriaceae species and could be a potential tool for clinical diagnosis.MaestríaMagíster en Ciencias - MicrobiologíaBiología molecular de agentes infecciosos167 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ciencias - Maestría en Ciencias - MicrobiologíaInstituto de Biotecnología (IBUN)Facultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadoresAntibacterianosAPRENDIZAJE AUTOMATICO (INTELIGENCIA ARTIFICIAL)Anti-Bacterial AgentsResistencia antimicrobianaRegresión logísticaMachine LearningProvidencia rettgeriSupport Vector MachineAntimicrobial resistanceLogistic RegressionRandom ForestWGSPredicción del perfil de resistencia a antibióticos a partir de datos de secuenciación del genoma completo de aislamientos colombianos de Providencia rettgeri comprendidos en el período 2015 – 2016Prediction of the resistance profile to antibiotics based on whole genome sequencing data of Colombian isolates of Providencia rettgeri during the period 2015 – 2016Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMRedColLaReferenciaAbdallah, M., & Balshi, A. (2018). First literature review of carbapenem-resistant Providencia. In New Microbes and New Infections (Vol. 25, pp. 16–23). Elsevier Ltd. https://doi.org/10.1016/j.nmni.2018.05.009Adriana, L., & Buitrago, P. (2019). Análisis comparativo de los elementos genómicos de resistencia a antibióticos betalactámicos en cepas colombianas de Providencia rettgeri durante el período 2015 – 2016.Aedekerk, S., Diggle, S. P., Song, Z., Høiby, N., Cornelis, P., Williams, P., & Cámara, M. (2005). The MexGHI-OpmD multidrug efflux pump controls growth, antibiotic susceptibility and virulence in Pseudomonas aeruginosa via 4-quinolone-dependent cell-to-cell communication. Microbiology, 151(4), 1113–1125. https://doi.org/10.1099/mic.0.27631-0Aghapour, Z., Gholizadeh, P., Ganbarov, K., Bialvaei, A. Z., Mahmood, S. S., Tanomand, A., Yousefi, M., Asgharzadeh, M., Yousefi, B., & Kafil, H. S. (2019). Molecular mechanisms related to colistin resistance in enterobacteriaceae. In Infection and Drug Resistance (Vol. 12, pp. 965–975). Dove Medical Press Ltd. https://doi.org/10.2147/IDR.S199844Alcock, B. P., Raphenya, A. R., Lau, T. T. Y., Tsang, K. K., Bouchard, M., Edalatmand, A., Huynh, W., Nguyen, A.-L. V, Cheng, A. A., Liu, S., Min, S. Y., Miroshnichenko, A., Tran, H.-K., Werfalli, R. E., Nasir, J. A., Oloni, M., Speicher, D. J., Florescu, A., Singh, B., ... McArthur, A. G. (2019). CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Research. https://doi.org/10.1093/nar/gkz935Alekshun, M. N., & Levy, S. B. (2007). Molecular Mechanisms of Antibacterial Multidrug Resistance. Cell, 128(6), 1037–1050. https://doi.org/10.1016/j.cell.2007.03.004Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878. https://doi.org/10.15252/msb.20156651Antunes, P., Machado, J., Sousa, J. C., & Peixe, L. (2005). Dissemination of sulfonamide resistance genes (sul1, sul2, and sul3) in Portuguese Salmonella enterica strains and relation with integrons. Antimicrobial Agents and Chemotherapy, 49(2), 836– 839. https://doi.org/10.1128/AAC.49.2.836-839.2005Behera, R. N., Das, K., Tech, B., & Professor, A. (2017). A Survey on Machine Learning: Concept, Algorithms and Applications Machine Learning View project International Journal of Innovative Research in Computer and Communication Engineering A Survey on Machine Learning: Concept, Algorithms and Applications. Article in International Journal of Innovative Research in Computer, 1301–1309. https://doi.org/10.15680/IJIRCCE.2017Bengoechea, J. A., Zhang, L., Toivanen, P., & Skurnik, M. (2002). Regulatory network of lipopolysaccharide O-antigen biosynthesis in Yersinia enterocolitica includes cell envelope-dependent signals. Molecular Microbiology, 44(4), 1045–1062. https://doi.org/10.1046/j.1365-2958.2002.02940.xBesier, S., Ludwig, A., Brade, V., & Wichelhaus, T. A. (2003). Molecular analysis of fusidic acid resistance in Staphylococcus aureus. Molecular Microbiology, 47(2), 463–469. https://doi.org/10.1046/j.1365-2958.2003.03307.xBielaszewska, M., Daniel, O., Karch, H., & Mellmann, A. (2020). Dissemination of the blaCTX-M-15 gene among Enterobacteriaceae via outer membrane vesicles. The Journal of Antimicrobial Chemotherapy, 75(9), 2442–2451. https://doi.org/10.1093/jac/dkaa214Blair, J. M. A., Richmond, G. E., & Piddock, L. J. V. (2014). Multidrug efflux pumps in Gram-negative bacteria and their role in antibiotic resistance. In Future Microbiology (Vol. 9, Issue 10, pp. 1165–1177). Future Medicine Ltd. https://doi.org/10.2217/FMB.14.66Blair, J. M. A., Webber, M. A., Baylay, A. J., Ogbolu, D. O., & Piddock, L. J. V. (2015). Molecular mechanisms of antibiotic resistance. Nature Reviews Microbiology, 13(1), 42–51. https://doi.org/10.1038/nrmicro3380Borstel, F. (1983). from a Proteus mirabilis Re-mutant. 22, 15–22.Bouziane, F., Allem, R., Sebaihia, M., Kumanski, S., Mougari, F., Sougakoff, W., Raskine, L., Yala, D., & Cambau, E. (2019). First genetic characterisation of multidrug- resistant Mycobacterium tuberculosis isolates from Algeria. Journal of Global Antimicrobial Resistance, 19, 301–307. https://doi.org/10.1016/j.jgar.2019.05.010Brolund, A., Sundqvist, M., Kahlmeter, G., & Grape, M. (2010). Molecular Characterisation of Trimethoprim Resistance in Escherichia coli and Klebsiella pneumoniae during a Two Year Intervention on Trimethoprim Use. PLoS ONE, 5(2), e9233. https://doi.org/10.1371/journal.pone.0009233Carvalho-Assef, A. P. D., Pereira, P. S., Albano, R. M., Beriao, G. C., Chagas, T. P. G., Timm, L. N., Da Silva, R. C. F., Falci, D. R., & Asensi, M. D. (2013). Isolation of NDM-producing Providencia rettgeri in Brazil. Journal of Antimicrobial Chemotherapy, 68(12), 2956–2957. https://doi.org/10.1093/jac/dkt298Castanheira, M., Mills, J. C., Farrell, D. J., & Jones, R. N. (2014). Mutation-Driven β- Lactam resistance mechanisms among contemporary ceftazidime-nonsusceptible pseudomonas aeruginosa isolates from U.S. hospitals. Antimicrobial Agents and Chemotherapy, 58(11), 6844–6850. https://doi.org/10.1128/AAC.03681-14CLSI. (n.d.). M100: Antimicrobial Susceptibility Testing Standards. 2019. Retrieved November 7, 2019, from https://clsi.org/standards/products/microbiology/documents/m100/CLSI. (2018). Method for Dilution Antimicrobial Susceptibility Tests for Bacteria That Grow Aerobically. www.clsi.org.CLSI. (2019). Performance Standards for Antimicrobial Susceptibility Testing (29th ed).Cody, A. J., Bray, J. E., Jolley, K. A., McCarthy, N. D., & Maiden, M. C. J. (2017). Core Genome Multilocus Sequence Analyses of Campylobacter jejuni and C. coli Human Disease Isolates. Journal of Clinical Microbiology, 55(7), 2086–2097.Coelho, J. R., Carriço, J. A., Knight, D., Martínez, J.-L., Morrissey, I., Oggioni, M. R., & Freitas, A. T. (2013). The Use of Machine Learning Methodologies to Analyse Antibiotic and Biocide Susceptibility in Staphylococcus aureus. PLoS ONE, 8(2), e55582. https://doi.org/10.1371/journal.pone.0055582Coyne, S., Rosenfeld, N., Lambert, T., Courvalin, P., & Périchon, B. (2010). Overexpression of resistance-nodulation-cell division pump AdeFGH confers multidrug resistance in Acinetobacter baumannii. Antimicrobial Agents and Chemotherapy, 54(10), 4389–4393. https://doi.org/10.1128/AAC.00155-10Cui, X., Zhang, H., & Du, H. (2019). Carbapenemases in Enterobacteriaceae: Detection and Antimicrobial Therapy. Frontiers in Microbiology, 10, 1823. https://doi.org/10.3389/fmicb.2019.01823D’Andrea, M. M., Arena, F., Pallecchi, L., & Rossolini, G. M. (2013). CTX-M-type β- lactamases: A successful story of antibiotic resistance. International Journal of Medical Microbiology, 303(6–7), 305–317. https://doi.org/10.1016/j.ijmm.2013.02.008Dastvan, R., Fischer, A. W., Mishra, S., Meiler, J., & McHaourab, H. S. (2016). Protonation-dependent conformational dynamics of the multidrug transporter EmrE. Proceedings of the National Academy of Sciences of the United States of America, 113(5), 1220–1225. https://doi.org/10.1073/pnas.1520431113Dey, A. (2016). Machine Learning Algorithms: A Review. International Journal of Computer Science and Information Technologies, 7(3), 1174–1179. www.ijcsit.comDidelot, X., Bowden, R., Wilson, D. J., Peto, T. E. A., & Crook, D. W. (2012). Transforming clinical microbiology with bacterial genome sequencing. In Nature Reviews Genetics (Vol. 13, Issue 9, pp. 601–612). Nat Rev Genet. https://doi.org/10.1038/nrg3226Doménech-Sánchez, A., Hernández-Allés, S., Martínez-Martínez, L., Benedí, V. J., & Albertí, S. (1999). Identification and characterization of a new porin gene of Klebsiella pneumoniae: Its role in β-lactam antibiotic resistance. Journal of Bacteriology, 181(9), 2726–2732. https://doi.org/10.1128/jb.181.9.2726-2732.1999Domínguez, M., Miranda, C. D., Fuentes, O., de la Fuente, M., Godoy, F. A., Bello- Toledo, H., & González-Rocha, G. (2019). Occurrence of Transferable Integrons and sul and dfr Genes Among Sulfonamide-and/or Trimethoprim-Resistant Bacteria Isolated From Chilean Salmonid Farms. Frontiers in Microbiology, 10(APR), 748. https://doi.org/10.3389/fmicb.2019.00748EDGAR, R. (n.d.). UCLUST algorithm. 2010. Retrieved March 27, 2021, from https://drive5.com/usearch/manual/uclust_algo.htmlElena, B., Ayala, A., María, A., & Amórtegui, L. (n.d.). CARBAPENEMASA NUEVA DELHI TIPO 1 (NDM): DESCRIPCIÓN FENOTÍPICA, EPIDEMIOLÓGICA Y TRATAMIENTO. In Laboratorio Actual •. Retrieved October 2, 2018, from http://abj.org.co/images/revistas/vol_44/Pag. 24-31 Carbapenemasa Nueva Delhi tipo 1 (NDM) descripción fenotípica, epidemiológica y tratamiento.pdfEUCAST: Clinical breakpoints and dosing of antibiotics. (n.d.). Retrieved November 7, 2019, from http://www.eucast.org/clinical_breakpoints/Eyre, D. W., Silva, D. De, Cole, K., Peters, J., Cole, M. J., Grad, Y. H., Demczuk, W., Martin, I., Mulvey, M. R., Crook, D. W., Walker, A. S., Peto, T. E. A., & Paul, J. (2017). WGS to predict antibiotic MICs for Neisseria gonorrhoeae. Journal of Antimicrobial Chemotherapy, 72(7), 1937–1947. https://doi.org/10.1093/jac/dkx067Fàbrega, A., Martin, R. G., Rosner, J. L., Tavio, M. M., & Vila, J. (2010). Constitutive SoxS expression in a fluoroquinolone-resistant strain with a truncated SoxR protein and identification of a new member of the marA-soxS-rob regulon, mdtG. Antimicrobial Agents and Chemotherapy, 54(3), 1218–1225. https://doi.org/10.1128/AAC.00944-09FDA. (n.d.). TYGACIL ® (TIGECYCLINE) FOR INJECTION Rx only. Retrieved April 8, 2021, from https://www.accessdata.fda.gov/drugsatfda_docs/label/2009/021821s016lbl.pdfFounou, R. C., Founou, L. L., Allam, M., Ismail, A., & Essack, Y. (n.d.). Whole Genome sequencing of extended spectrum β-lactamase (esBL)-producing Klebsiella pneumoniae Isolated from Hospitalized patients in KwaZulu-Natal, south Africa. Scientific Reports. https://doi.org/10.1038/s41598-019-42672-2Freeman, Z. N., Dorus, S., & Waterfield, N. R. (2013). The KdpD/KdpE Two-Component System: Integrating K+ Homeostasis and Virulence. PLoS Pathogens, 9(3). https://doi.org/10.1371/journal.ppat.1003201Fu, Z., Ma, Y., Chen, C., Guo, Y., Hu, F., Liu, Y., Xu, X., & Wang, M. (2016). Prevalence of fosfomycin resistance and mutations in murA, glpT, and uhpT in methicillin- resistant Staphylococcus aureus strains isolated from blood and cerebrospinal fluid samples. Frontiers in Microbiology, 6(JAN). https://doi.org/10.3389/fmicb.2015.01544García, S., Ramírez, S. G., Luengo, J., & Herrera, F. (2016). Big Data : Preprocesamiento. Novática, 17–23. http://sci2s.ugr.es/sites/default/files/ficherosPublicaciones/2133_Nv237-Digital- sramirez.pdfGefen-Halevi, S., Hindiyeh, M. Y., Ben-David, D., Smollan, G., Gal-Mor, O., Azar, R., Castanheira, M., Belausov, N., Rahav, G., Tal, I., Mendelson, E., & Keller, N. (2013). Isolation of genetically unrelated bla(NDM-1)-positive Providencia rettgeri strains in Israel. Journal of Clinical Microbiology, 51(5), 1642–1643. https://doi.org/10.1128/JCM.00381-13Ghaheri, A., Shoar, S., Naderan, M., & Hoseini, S. S. (2015). The Applications of Genetic Algorithms in Medicine. Oman Medical Journal, 30(6), 406–416.Ghotaslou, R., Yeganeh Sefidan, F., Akhi, M. T., Asgharzadeh, M., & Mohammadzadeh Asl, Y. (2017). Dissemination of Genes Encoding Aminoglycoside-Modifying Enzymes and armA among Enterobacteriaceae Isolates in Northwest Iran. Microbial Drug Resistance, 23(7), 826–832. https://doi.org/10.1089/mdr.2016.0224Govindaswamy, A., Bajpai, V., Khurana, S., Aravinda, A., Batra, P., Malhotra, R., & Mathur, P. (2019). Prevalence and characterization of beta-lactamase-producing Escherichia coli isolates from a tertiary care hospital in India. Journal of Laboratory Physicians, 11(02), 123–127. https://doi.org/10.4103/jlp.jlp_122_18Guidance Document on Tigecycline Dosing in association with Revision of Breakpoints for Enterobacterales and other species with an “Intermediate” category. (2018).Haidar, G., Alkroud, A., Cheng, S., Churilla, T. M., Churilla, B. M., Shields, R. K., Doi, Y., Clancy, C. J., & Nguyen, H. (2016). Association between the Presence of Aminoglycoside-Modifying Enzymes and In Vitro Activity of Gentamicin, Tobramycin, Amikacin, and Plazomicin against Klebsiella pneumoniae Carbapenemase-and Extended-Spectrum-Lactamase-Producing Enterobacter Species. https://doi.org/10.1128/AAC.00869-16Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts ... - Aurélien Géron - Google Libros. (n.d.). Retrieved November 15, 2020, from https://books.google.com.ec/books?id=HHetDwAAQBAJ&printsec=frontcover&dq=h ands+on+machine+learning+with+scikit- learn+and+tensorflow&hl=es&sa=X&ved=2ahUKEwjU66ijiIbtAhXyxlkKHRYQBNEQ6 AEwAHoECAAQAg#v=onepage&q=hands on Machine Learning with scikit-learn and tensorflow&f=falseHirakawa, H., Nishino, K., Hirata, T., & Yamaguchi, A. (2003). Comprehensive studies of drug resistance mediated by overexpression of response regulators of two- component signal transduction systems in Escherichia coli. Journal of Bacteriology, 185(6), 1851–1856. https://doi.org/10.1128/JB.185.6.1851-1856.2003Home - BioSample - NCBI. (n.d.). Retrieved March 27, 2021, from https://www.ncbi.nlm.nih.gov/biosampleHome - Genome - NCBI. (n.d.). Retrieved March 27, 2021, from https://www.ncbi.nlm.nih.gov/genome/Huang, S., Cai, N., Pacheco, P. P., Narrandes, S., Wang, Y., & Xu, W. (2018). Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics & Proteomics, 15(1), 41–51. https://doi.org/10.21873/cgp.20063Hyun, J. C., Kavvas, E. S., Monk, J. M., & Palsson, B. O. (2020). Machine Learning with random subspace ensembles identifies antimicrobial resistance determinants from pan-genomes of three pathogens. PLoS Computational Biology, 16(3), e1007608. https://doi.org/10.1371/journal.pcbi.1007608Iredell, J., Brown, J., & Tagg, K. (2016). Antibiotic resistance in Enterobacteriaceae: Mechanisms and clinical implications. BMJ (Online), 352(February 2016). https://doi.org/10.1136/bmj.h6420Jabbar, H. K., & Khan, R. Z. (2015). Methods to Avoid Over-Fitting and Under-Fitting in Supervised Machine Learning (Comparative Study). December 2014, 163–172. https://doi.org/10.3850/978-981-09-5247-1_017Jayol, A., Nordmann, P., André, C., Poirel, L., & Dubois, V. (2018). Evaluation of three broth microdilution systems to determine colistin susceptibility of Gram-negative bacilli. Journal of Antimicrobial Chemotherapy, 73(5), 1272–1278. https://doi.org/10.1093/jac/dky012Jihye Jeon. (2015). The Strengths and Limitations of the Statistical Modeling of Complex Social Phenomenon: Focusing on SEM, Path Analysis, or Multiple Regression Models. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, 9(5), 1634–1642. https://pdfs.semanticscholar.org/50a9/a4a1cf87575bbb83b43419102d09fc89f942.pd fJIM O’NEILL. (2015). TACKLING DRUG-RESISTANT INFECTIONS GLOBALLY: FINAL REPORT AND RECOMMENDATIONS. 136(1), 29–31.Jorgensen, J. H., Ferraro, M. J., Jorgensen, J. H., & Ferraro, M. J. (2009). Antimicrobial Susceptibility Testing: A Review of General Principles and Contemporary Practices. Clinical Infectious Diseases, 49(11), 1749–1755. https://doi.org/10.1086/647952Karaiskos, I., Lagou, S., Pontikis, K., Rapti, V., & Poulakou, G. (2019). The “Old” and the “New” antibiotics for MDR Gram-negative pathogens: For whom, when, and how. In Frontiers in Public Health (Vol. 7, Issue JUN, p. 151). Frontiers Media S.A. https://doi.org/10.3389/fpubh.2019.00151Kim, S.-Y., Park, Y.-J., Yu, J. K., & Kim, Y. S. (2011). Aminoglycoside Susceptibility Profiles of Enterobacter cloacae Isolates Harboring the aac(6’)-Ib Gene. The Korean Journal of Laboratory Medicine, 31(4), 279. https://doi.org/10.3343/KJLM.2011.31.4.279Kobayashi, N., Nishino, K., Hirata, T., & Yamaguchi, A. (2003). Membrane topology of ABC-type macrolide antibiotic exporter MacB in Escherichia coli. FEBS Letters, 546(2–3), 241–246. https://doi.org/10.1016/S0014-5793(03)00579-9Kolarević, S., Milovanović, D., Avdović, M., Oalđe, M., Kostić, J., Sunjog, K., Nikolić, B., Knežević-Vukčević, J., & Vuković-Gačić, B. (2016). Optimisation оf the microdilution method for detection of minimum inhibitory concentration values in selected bacteria. https://doi.org/10.5281/zenodo.48751Köser, C. U., Ellington, M. J., Cartwright, E. J. P., Gillespie, S. H., Brown, N. M., Farrington, M., Holden, M. T. G., Dougan, G., Bentley, S. D., Parkhill, J., & Peacock, S. J. (2012). Routine Use of Microbial Whole Genome Sequencing in Diagnostic and Public Health Microbiology. PLoS Pathogens, 8(8). https://doi.org/10.1371/journal.ppat.1002824Kotb, D. N., Mahdy, W. K., Mahmoud, M. S., & Khairy, R. M. M. (2019). Impact of co- existence of PMQR genes and QRDR mutations on fluoroquinolones resistance in Enterobacteriaceae strains isolated from community and hospital acquired UTIs. BMC Infectious Diseases, 19(1), 1–8. https://doi.org/10.1186/s12879-019-4606-yKouchaki, S., Yang, Y. Y., Walker, T. M., Walker, A. S., Wilson, D. J., Peto, T. E. A., Crook, D. W., Clifton, D. A., Hoosdally, S. J., Gibertoni Cruz, A. L., Carter, J., Grazian, C., Kouchaki, S., Walker, T. M., Fowler, P. W., Clifton, D. A., Iqbal, Z., Hunt, M., Smith, E. G., ... Van Soolingen, D. (2019). Application of Machine Learning techniques to tuberculosis drug resistance analysis. Bioinformatics, 35(13), 2276– 2282. https://doi.org/10.1093/bioinformatics/bty949Kumar Trivedi, M. (2015). Antibiogram, Biochemical Reactions and Biotyping of Biofield Treated <i>Providencia rettgeri</i> American Journal of Health Research, 3(6), 344. https://doi.org/10.11648/j.ajhr.20150306.15L, D., P, N., & L, P. (2012). Association of the emerging carbapenemase NDM-1 with a bleomycin resistance protein in Enterobacteriaceae and Acinetobacter baumannii. Antimicrobial Agents and Chemotherapy, 56(4), 1693–1697. https://doi.org/10.1128/AAC.05583-11Li, X. Z., & Nikaido, H. (2009). Efflux-mediated drug resistance in bacteria: An update. In Drugs (Vol. 69, Issue 12, pp. 1555–1623). https://doi.org/10.2165/11317030- 000000000-00000LM, C., H, H., S, X., & FM, A. (2009). qnrD, a novel gene conferring transferable quinolone resistance in Salmonella enterica serovar Kentucky and Bovismorbificans strains of human origin. Antimicrobial Agents and Chemotherapy, 53(2), 603–608. https://doi.org/10.1128/AAC.00997-08M, G., S, E., S, A., V, D., MA, K., E, S., & S, S. (2016). GyrA ser83 and ParC trp106 Mutations in Salmonella enterica Serovar Typhi Isolated from Typhoid Fever Patients in Tertiary Care Hospital. Journal of Clinical and Diagnostic Research : JCDR, 10(7), DC14–DC18. https://doi.org/10.7860/JCDR/2016/17677.8153M, M. A., S, K., C, W., S, L., G, M., T, M., S, J., & TR, R. (2015). Identification of a novel mutation at the primary dimer interface of GyrA conferring fluoroquinolone resistance in Clostridium difficile. Journal of Global Antimicrobial Resistance, 3(4), 295–299. https://doi.org/10.1016/J.JGAR.2015.09.007M, N., K, S., O, K., S, K., S, N., & R, S. (2015). Characterisation of novel mutations involved in quinolone resistance in Escherichia coli isolated from imported shrimp. International Journal of Antimicrobial Agents, 45(5), 471–476.Majlesi, A., Kakhki, R. K., Mozaffari Nejad, A. S., Mashouf, R. Y., Roointan, A., Abazari, M., & Alikhani, M. Y. (2018). Detection of plasmid-mediated quinolone resistance in clinical isolates of Enterobacteriaceae strains in Hamadan, West of Iran. Saudi Journal of Biological Sciences, 25(3), 426–430. https://doi.org/10.1016/j.sjbs.2016.11.019Marquez-Ortiz, R. A., Haggerty, L., Sim, E. M., Duarte, C., Castro-Cardozo, B. E., Beltran, M., Saavedra, S., Vanegas, N., Escobar-Perez, J., & Petty, N. K. (2017). First Complete Providencia rettgeri Genome Sequence, the NDM-1-Producing Clinical Strain RB151. Genome Announcements, 5(3), e01472-16. https://doi.org/10.1128/genomeA.01472-16Mazzariol, A., Kocsis, B., Koncan, R., Kocsis, E., Lanzafame, P., & Cornaglia, G. (2012). Description and plasmid characterization of qnrD determinants in Proteus mirabilis and Morganella morganii. Clinical Microbiology and Infection, 18(3), E46–E48. https://doi.org/10.1111/j.1469-0691.2011.03728.xMbelle, N., Sekyere, J. O., Amoako, D. G., & Maningi, N. E. (2019). Genomic analysis of a multidrug-resistant clinical Providencia rettgeri (PR002) strain with the novel integron ln1483 and an A/C plasmid replicon Genetic diversity of Mycobacterium tuberculosis strains among mycobacterial isolates from symptomatic holy water attendees in Amhara region, Ethiopia View project Fluoquinolone and Ketolide Resistance in Haemophilus Parainfluenzae from Private Sector of KwaZulu-Natal, South Africa View project. https://doi.org/10.1111/nyas.14237Misawa, K., Tarumoto, N., Tamura, S., Osa, M., Hamamoto, T., Yuki, A., Kouzaki, Y., Imai, K., Ronald, R. L., Yamaguchi, T., Murakami, T., Maesaki, S., Suzuki, Y., Kawana, A., & Maeda, T. (2018). Single nucleotide polymorphisms in genes encoding penicillin-binding proteins in β-lactamase-negative ampicillin-resistant Haemophilus influenzae in Japan. BMC Research Notes, 11(1). https://doi.org/10.1186/s13104-018-3169-0Mitra, S., Mukherjee, S., Naha, S., Chattopadhyay, P., Dutta, S., & Basu, S. (2019). Evaluation of co-transfer of plasmid-mediated fluoroquinolone resistance genes and bla NDM gene in Enterobacteriaceae causing neonatal septicaemia. Antimicrobial Resistance and Infection Control, 7(1), 1–15.Mohanty, S., & Mahapatra, A. (2021). In vitro activity of tigecycline against multidrug- resistant Enterobacteriaceae isolates from skin and soft tissue infections. Annals of Medicine and Surgery, 62, 228–230. https://doi.org/10.1016/J.AMSU.2021.01.010Mohr O’hara, C., Brenner, F. W., & Miller, J. M. (2000). Classification, Identification, and Clinical Significance of Proteus, Providencia, and Morganella (Vol. 13, Issue 4). http://cmr.asm.org/Moradigaravand, D., Palm, M., Farewell, A., Mustonen, V., Warringer, J., & Parts, L. (2018). Prediction of antibiotic resistance in Escherichia coli from large-scale pan- genome data. PLOS Computational Biology, 14(12), e1006258.Naas, T., & Nordmann, P. (1994). Analysis of a carbapenem-hydrolyzing class A β- lactamase from Enterobacter cloacae and of its LysR-type regulatory protein. Proceedings of the National Academy of Sciences of the United States of America, 91(16), 7693–7697. https://doi.org/10.1073/pnas.91.16.7693Nagakubo, S., Nishino, K., Hirata, T., & Yamaguchi, A. (2002). The putative response regulator BaeR stimulates multidrug resistance of Escherichia coli via a novel multidrug exporter system, MdtABC. Journal of Bacteriology, 184(15), 4161–4167. https://doi.org/10.1128/JB.184.15.4161-4167.2002Nazir, S., Dekyong, A., Fomda, B., Benazir, S., Bhat, A., & Bashir, L. (2017). Providencia Rettgeri: an Unexpected Cause of Sepsis. International Journal of Advanced Research, 5(12), 1442–1444. https://doi.org/10.21474/IJAR01/6104Nguyen, M., Brettin, T., Long, S. W., Musser, J. M., Olsen, R. J., Olson, R., Shukla, M., Stevens, R. L., Xia, F., Yoo, H., & Davis, J. J. (2018). Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Scientific Reports, 8(1), 421. https://doi.org/10.1038/s41598-017-18972-wNguyen, M., Wesley Long, S., McDermott, P. F., Olsen, R. J., Olson, R., Stevens, R. L., Tyson, G. H., Zhao, S., & Davisa, J. J. (2019). Using Machine Learning to predict antimicrobial MICs and associated genomic features for nontyphoidal Salmonella. Journal of Clinical Microbiology, 57(2). https://doi.org/10.1128/JCM.01260-18Niehaus, K. E., Walker, T. M., Crook, D. W., Peto, T. E. A., & Clifton, D. A. (2014). Machine Learning for the prediction of antibacterial susceptibility in Mycobacterium tuberculosis. IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 618–621. https://doi.org/10.1109/BHI.2014.6864440Nishino, K., Senda, Y., & Yamaguchi, A. (2008). CRP regulator modulates multidrug resistance of Escherichia coli by repressing the mdtEF multidrug efflux genes. Journal of Antibiotics, 61(3), 120–127. https://doi.org/10.1038/ja.2008.120Nordmann, P., Naas, T., & Poirel, L. (2011). Global spread of Carbapenemase-producing Enterobacteriaceae. Emerging Infectious Diseases, 17(10), 1791–1798. https://doi.org/10.3201/eid1710.110655Olaitan, Abiola O., Morand, S., & Rolain, J.-M. (2014). Mechanisms of polymyxin resistance: acquired and intrinsic resistance in bacteria. Frontiers in Microbiology, 5, 643. https://doi.org/10.3389/fmicb.2014.00643Olaitan, Abiola Olumuyiwa, Diene, S. M., Assous, M. V., & Rolain, J.-M. (2016). Genomic Plasticity of Multidrug-Resistant NDM-1 Positive Clinical Isolate of Providencia rettgeri. Genome Biology and Evolution, 8(3), 723–728. https://doi.org/10.1093/gbe/evv195Olaitan, Abiola Olumuyiwa, Diene, S. M., Gupta, S. K., Adler, A., Assous, M. V., & Rolain, J. M. (2014). Genome analysis of NDM-1 producing Morganella morganii clinical isolate. Expert Review of Anti-Infective Therapy, 12(10), 1297–1305.Olivares, J., Bernardini, A., Garcia-Leon, G., Corona, F., Sanchez, M. B., & Martinez, J. L. (2013). The intrinsic resistome of bacterial pathogens. In Frontiers in Microbiology (Vol. 4, Issue APR, p. 103). Frontiers Research Foundation. https://doi.org/10.3389/fmicb.2013.00103Olumuyiwa Olaitan, A., Diene, S. M., Victor Assous, M., & Rolain, J. M. (2016). Genomic plasticity of multidrug-resistant NDM-1 positive clinical isolate of providencia rettgeri. Genome Biology and Evolution, 8(3), 723–728. https://doi.org/10.1093/gbe/evv195OpenSUSE. (n.d.). openSUSE - Linux OS. La mejor elección para administradores de sistemas, desarrolladores y usuarios de ordenadores de sobremesa. 2021. Retrieved March 27, 2021, from https://www.opensuse.org/Ordóñez-díaz, K. M., Estupiñán, J. L., & Alzate, J. A. (2018). Metalobetalactamasa de tipo Nueva Delhi en Risaralda ( Colombia ): reporte de un caso. 22(1), 55–57.Ortiz, K. P. P., Segura, J. C., Bettin, L., Coriat, J., & Díez, H. (2011). recuencia de betalactamasas de espectro extendido (BLEE) en Klebsiella pneumoniae, Klebsiella oxytoca y Escherichia coli aisladas de pacientes hospitalizados en una clínica de tercer nivel en Bogotá. Ciencia Actual, 4(0), 1–9. https://doi.org/10.21500/2248468X.2285Osei Sekyere, J., & Amoako, D. G. (2017). Genomic and phenotypic characterisation of fluoroquinolone resistance mechanisms in Enterobacteriaceae in Durban, South Africa. PLOS ONE, 12(6), e0178888. https://doi.org/10.1371/journal.pone.0178888Ovalle, M. V., Saavedra, S. Y., González, M. N., Hidalgo, A. M., Duarte, C., & Beltrán, M. (2017). Resultados de la vigilancia nacional de resistencia antimicrobiana en infecciones asociadas a la atención en salud en enterobacterias y Gram negativos no fermentadores, Colombia 2012-2014. Biomédica, 37(4), 39. https://doi.org/http://dx.doi.org/10.7705/biomedica.v37i4.3432Ozkaya-Parlakay, A., Gulhan, B., Kanik-Yuksek, S., Guney, D., Gonulal, D., Demirtas, G., Tezer, H., Unal, S., & Senel, E. (2020). Tigecycline therapy in pediatric patients with multidrug resistant bacteremia. Enfermedades Infecciosas y Microbiologia Clinica (English Ed.), 38(10), 471–473. https://doi.org/10.1016/j.eimce.2019.12.014Partridge, S. R. (2015). Resistance mechanisms in Enterobacteriaceae. Pathology, 47(3), 276–284. https://doi.org/10.1097/PAT.0000000000000237Pataki, B. Á., Matamoros, S., van der Putten, B. C. L., Remondini, D., Giampieri, E., Aytan-Aktug, D., Hendriksen, R. S., Lund, O., Csabai, I., Schultsz, C., Matamoros, S., Janes, V., Hendriksen, R. S., Lund, O., Clausen, P., Aarestrup, F. M., Koopmans, M., Pataki, B., Visontai, D., ... McDermott, P. (2020). Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with Machine Learning. Scientific Reports, 10(1), 1–9. https://doi.org/10.1038/s41598-020-71693-5Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Cournapeau, D., Passos, A., Brucher, M., Perrot Andédouardand ́andédouard Duchesnay, M., & Perrot, M. (2011). Scikit-learn: Machine Learning in Python. In Machine Learning in Python. Journal of Machine Learning Research (Vol. 12). Microtome Pub-lishing. https://hal.inria.fr/hal-00650905v2Pedregosa FABIANPEDREGOSA, F., Michel, V., Grisel OLIVIERGRISEL, O., Blondel, M., Prettenhofer, P., Weiss, R., Vanderplas, J., Cournapeau, D., Pedregosa, F., Varoquaux, G., Gramfort, A., Thirion, B., Grisel, O., Dubourg, V., Passos, A., Brucher, M., Perrot andÉdouardand, M., Duchesnay, andÉdouard, & Duchesnay EDOUARDDUCHESNAY, Fré. (2011). Scikit-learn: Machine Learning in Python Gaël Varoquaux Bertrand Thirion Vincent Dubourg Alexandre Passos PEDREGOSA, VAROQUAUX, GRAMFORT ET AL. Matthieu Perrot. In Journal of Machine Learning Research (Vol. 12, Issue 85). http://scikit-learn.sourceforge.net.Pérez-Vázquez, M., Sola Campoy, P. J., Ortega, A., Bautista, V., Monzón, S., Ruiz- Carrascoso, G., Mingorance, J., González-Barberá, E. M., Gimeno, C., Aracil, B., Sáez, D., Lara, N., Fernández, S., González-López, J. J., Campos, J., Kingsley, R. A., Dougan, G., Oteo-Iglesias, J., Rodrigo, C. H., ... Suarez, C. B. (2019). Emergence of NDM-producing Klebsiella pneumoniae and Escherichia coli in Spain: phylogeny, resistome, virulence and plasmids encoding blaNDM-like genes as determined by WGS. Journal of Antimicrobial Chemotherapy, 74(12), 3489–3496. https://doi.org/10.1093/jac/dkz366Pérez, A., Poza, M., Fernández, A., Del Carmen Fernández, M., Mallo, S., Merino, M., Rumbo-Feal, S., Cabral, M. P., & Bou, G. (2012). Involvement of the AcrAB-TolC efflux pump in the resistance, fitness, and virulence of Enterobacter cloacae. Antimicrobial Agents and Chemotherapy, 56(4), 2084–2090. https://doi.org/10.1128/AAC.05509-11Pesesky, M. W., Hussain, T., Wallace, M., Patel, S., Andleeb, S., Burnham, C.-A. D., & Dantas, G. (2016). Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data. Frontiers in Microbiology, 7, 1887. https://doi.org/10.3389/fmicb.2016.01887Peterson, L. R. (2008). A review of tigecycline - the first glycylcycline. International Journal of Antimicrobial Agents, 32(SUPPL. 4), S215–S222. https://doi.org/10.1016/S0924-8579(09)70005-6Pournaras, S., Koumaki, V., Spanakis, N., Gennimata, V., & Tsakris, A. (2016). Current perspectives on tigecycline resistance in Enterobacteriaceae: susceptibility testing issues and mechanisms of resistance. International Journal of Antimicrobial Agents, 48(1), 11–18. https://doi.org/10.1016/j.ijantimicag.2016.04.017Puértolas-Balint, F., Warsi, O., Linkevicius, M., Tang, P. C., & Andersson, D. I. (2020). Mutations that increase expression of the EmrAB-TolC efflux pump confer increased resistance to nitroxoline in Escherichia coli. Journal of Antimicrobial Chemotherapy, 75(2), 300–308. https://doi.org/10.1093/jac/dkz434Ramirez, L. S., & Marin Castaño, D. (2009). METODOLOGIAS PARA EVALUAR IN VITRO LA ACTIVIDAD ANTIBACTERIANA DE COMPUESTOS DE ORIGEN VEGETAL Methodologies for evaluating the In vitro antibacterial activity of natural compounds of plant origin. Scientia et Technica, 42, 263–268.Ramón, J., Anaya, M., & Química, M. S. (2006). MANUAL DE TÉCNICAS BÁSICAS EN BIOLOGÍA MOLECULAR.Redgrave, L. S., Sutton, S. B., Webber, M. A., & Piddock, L. J. V. (2014). Fluoroquinolone resistance: mechanisms, impact on bacteria, and role in evolutionary success. Trends in Microbiology, 22(8), 438–445. https://doi.org/10.1016/j.tim.2014.04.007Rizzo, R., Fiannaca, A., La Rosa, M., & Urso, A. (2016). A Deep Learning Approach to DNA Sequence Classification (pp. 129–140). Springer, Cham. https://doi.org/10.1007/978-3-319-44332-4_10Roberts, L. W., Catchpoole, E., Jennison, A. V., Bergh, H., Hume, A., Heney, C., George, N., Paterson, D. L., Schembri, M. A., Beatson, S. A., & Harris, P. N. A. (2020). Genomic analysis of carbapenemase-producing enterobacteriaceae in queensland reveals widespread transmission of blaimp-4 on an incHI2 plasmid. Microbial Genomics, 6(1). https://doi.org/10.1099/mgen.0.000321Saad, N., Munir, T., Ansari, M., Gilani, M., Latif, M., & Haroon, A. (2016). Introduction Evaluation of phenotypic tests for detection of Amp C beta-lactamases in clinical isolates from a tertiary care hospital of Rawalpindi, Pakistan (Vol. 66, Issue 6).Saavedra-Rojas, S.-Y., Duarte-Valderrama, C., González-de-Arias, M.-N., & Ovalle- Guerro, M. V. (2013). Emergence of Providencia rettgeri NDM-1 in two departments of Colombia, 2012-2013. Enfermedades Infecciosas y Microbiologia Clinica, 35(6), doi:10.1016/j.eimc.2015.05.011. https://doi.org/10.1016/j.eimc.2015.05.011Sagar, S., Narasimhaswamy, N., & D’Souza, J. (2017). Providencia Rettgeri: An Emerging Nosocomial Uropathogen in an Indwelling Urinary Catheterised Patient. Journal of Clinical and Diagnostic Research : JCDR, 11(6), DD01–DD02. https://doi.org/10.7860/JCDR/2017/25740.10026Sagi, O., & Rokach, L. (2018). Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4). https://doi.org/10.1002/widm.1249Sagiroglu, S., & Sinanc, D. (2013). Big data: A review. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, 42–47. https://doi.org/10.1109/CTS.2013.6567202Schneiders, T., Amyes, S. G. B., & Levy, S. B. (2003). Role of AcrR and RamA in Fluoroquinolone Resistance in Clinical Klebsiella pneumoniae Isolates from Singapore. Antimicrobial Agents and Chemotherapy, 47(9).Schrider, D. R., & Kern, A. D. (2018). Supervised Machine Learning for Population Genetics: A New Paradigm. Trends in Genetics, 34(4), 301–312. https://doi.org/10.1016/J.TIG.2017.12.005Schürch, A. C., & van Schaik, W. (2017). Challenges and opportunities for whole-genome sequencing–based surveillance of antibiotic resistance. Annals of the New York Academy of Sciences, 1388(1), 108–120. https://doi.org/10.1111/nyas.13310Sharff, A., Fanutti, C., Shi, J., Calladine, C., & Luisi, B. (2001). The role of the TolC family in protein transport and multidrug efflux from stereochemical certainty to mechanistic hypothesis. In European Journal of Biochemistry (Vol. 268, Issue 19, pp. 5011– 5026). Eur J Biochem. https://doi.org/10.1046/j.0014-2956.2001.02442.xSharma, D., Sharma, P., & Soni, P. (2017). First case report of Providencia Rettgeri neonatal sepsis. BMC Research Notes, 10(1), 17–20. https://doi.org/10.1186/s13104-017-2866-4Shin, S., Jeong, S. H., Lee, H., Hong, J. S., Park, M. J., & Song, W. (2018). Emergence of multidrug-resistant Providencia rettgeri isolates co-producing NDM-1 carbapenemase and PER-1 extended-spectrum β-lactamase causing a first outbreak in Korea. Annals of Clinical Microbiology and Antimicrobials, 17(1), 1–6. https://doi.org/10.1186/s12941-018-0272-ySidjabat, H. E., Townell, N., Nimmo, G. R., George, N. M., Robson, J., Vohra, R., Davis, L., Heney, C., & Patersona, D. L. (2015). Dominance of IMP-4-producing Enterobacter cloacae among carbapenemase-producing Enterobacteriaceae in Australia. Antimicrobial Agents and Chemotherapy, 59(7), 4059–4066. https://doi.org/10.1128/AAC.04378-14Singh, A., Thakur, N., & Sharma, A. (2016). A review of supervised Machine Learning algorithms. Proceedings of the 10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, INDIACom 2016, 1310–1315.Singh, H., Velamakanni, S., Deery, M. J., Howard, J., Wei, S. L., & Van Veen, H. W. (2016). ATP-dependent substrate transport by the ABC transporter MsbA is proton- coupled. Nature Communications, 7. https://doi.org/10.1038/ncomms12387Sommer, C., & Gerlich, D. W. (2013). Machine Learning in cell biology – teaching computers to recognize phenotypes. Journal of Cell Science, 126(24), 5529–5539. https://doi.org/10.1242/JCS.123604Spellberg, B., Guidos, R., Gilbert, D., Bradley, J., Boucher, H. W., Scheld, W. M., Bartlett, J. G., & Edwards, J. (2008). The epidemic of antibiotic-resistant infections: A call to action for the medical community from the infectious diseases society of America. In Clinical Infectious Diseases (Vol. 46, Issue 2, pp. 155–164). https://doi.org/10.1086/524891Srinivasan, V. B., & Rajamohan, G. (2013). KpnEF, a new member of the Klebsiella pneumoniae cell envelope stress response regulon, is an SMR-type efflux pump involved in broad-spectrum antimicrobial resistance. Antimicrobial Agents and Chemotherapy, 57(9), 4449–4462. https://doi.org/10.1128/AAC.02284-12Srinivasan, V. B., Singh, B. B., Priyadarshi, N., Chauhan, N. K., & Rajamohan, G. (2014). Role of novel multidrug efflux pump involved in drug resistance in Klebsiella pneumoniae. PLoS ONE, 9(5). https://doi.org/10.1371/journal.pone.0096288Tafur, D., & Villegas, V. (2008). Mecanismos de resistencia a los antibióticos en bacterias Gram negativas. Infectio, 12(3), 217–226. https://doi.org/http://www.sld.cu/galerias/pdf/sitios/apua- cuba/mecanismos_de_resistencia_a_los_antibioticos_en_bacterias_gram_negativas .pdfTamara, N. Q., Esthela, T. M., Pamela, C. S., Jenniffer, H. L., & Pablo, S. R. (2020). Journal of Medical Case Reports and Reviews 3:8 [2020] CARBAPENEMASE- PRODUCING ENTEROBACTERIACEAE IN PATIENTS OF A THIRD LEVEL HOSPITAL IN THE CITY OF GUAYAQUIL-ECUADOR. Journal of Medical Case Reports and Reviews, 3(08). www.jmcrr.infoTatarinova, T. V, Editors, Y. N., Raschka, S., Verdier, C. F. J. E. S. O., Hearty, J., Huffman, J., & Pajankar, A. (2000). Python 机器学习. In Astronomical Data Analysis Software and Systems IX (Vol. 216).The Comprehensive Antibiotic Resistance Database. (n.d.). Retrieved March 27, 2021, from https://card.mcmaster.ca/Torres, E., López-Cerero, L., Rodríguez-Martínez, J. M., & Pascual, Á. (2016). Reduced Susceptibility to Cefepime in Clinical Isolates of Enterobacteriaceae Producing OXA- 1 Beta-Lactamase. Microbial Drug Resistance, 22(2), 141–146. https://doi.org/10.1089/mdr.2015.0122Tshisevhe, V. S., Lekalakala, M. R., Tshuma, N., Janse van Rensburg, S., & Mbelle, N. (2016). Outbreak of carbapenem-resistant Providencia rettgeri in a tertiary hospital. South African Medical Journal = Suid-Afrikaanse Tydskrif Vir Geneeskunde, 107(1), 31–33. https://doi.org/10.7196/SAMJ.2016.v107.i1.12002Van Camp, P.-J., Haslam, D. B., & Porollo, A. (2020). Prediction of Antimicrobial Resistance in Gram-Negative Bacteria From Whole-Genome Sequencing Data. Frontiers in Microbiology, 11, 1013. https://doi.org/10.3389/fmicb.2020.01013van Duin, D., & Doi, Y. (2017). The global epidemiology of carbapenemase-producing Enterobacteriaceae. In Virulence (Vol. 8, Issue 4, pp. 460–469). Taylor and Francis Inc. https://doi.org/10.1080/21505594.2016.1222343VB, S., & G, R. (2013). KpnEF, a new member of the Klebsiella pneumoniae cell envelope stress response regulon, is an SMR-type efflux pump involved in broad- spectrum antimicrobial resistance. Antimicrobial Agents and Chemotherapy, 57(9), 4449–4462. https://doi.org/10.1128/AAC.02284-12Villalobos, A. P., Barrero, L. I., Rivera, S. M., Ovalle, M. V., & Valera, D. (2013). Vigilancia de infecciones asociadas a la atención en salud, resistencia bacteriana y consumo de antibióticos en hospitales de alta complejidad, Colombia, 2011. Biomédica, 34(0), 67. https://doi.org/10.7705/biomedica.v34i0.1698Viviana, L., & Su, R. (2019). Caracterización de perfiles de elementos genéticos plasmídicos de aislamientos colombianos de Providencia rettgeri, causantes de IAAS. Obtenidos del Instituto Nacional de Salud, durante el periodo 2015-2016.Weinstein, M. P., Patel, J. B., Bobenchik, A. M., Campeau, S., Cullen, S. K., Galas, M. F., Gold, H., Humphries, R. M., Kirn, T. J., Lewis Ii, J. S., Limbago, B., Mathers, A. J., Mazzulli, T., Richter, S. S., Satlin, M., Schuetz, A. N., Swenson, J. M., Tamma, P. D., & Simner, P. J. (2020). M100 Performance Standards for Antimicrobial Susceptibility Testing A CLSI supplement for global application. Performance Standards for Antimicrobial Susceptibility Testing Performance Standards for Antimicrobial Susceptibility Testing.Weiss, S. J., Mansell, T. J., Mortazavi, P., Knight, R., & Gill, R. T. (2016). Parallel Mapping of Antibiotic Resistance Alleles in Escherichia coli. PLOS ONE, 11(1), e0146916. https://doi.org/10.1371/journal.pone.0146916Welcome to Python.org. (n.d.). Retrieved March 27, 2021, from https://www.python.org/Weston, N., Sharma, P., Ricci, V., & Piddock, L. J. V. (2017). Regulation of the AcrAB- TolC efflux pump in Enterobacteriaceae. Research in Microbiology, 1–7. https://doi.org/10.1016/j.resmic.2017.10.005Weston, N., Sharma, P., Ricci, V., & Piddock, L. J. V. (2018). Regulation of the AcrAB- TolC efflux pump in Enterobacteriaceae. Research in Microbiology, 169(7–8), 425– 431. https://doi.org/10.1016/j.resmic.2017.10.005WHO | Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics. (2017). WHO.Wu, L. T., Tsou, M. F., Wu, H. J., Chen, H. E., Chuang, Y. C., & Yu, W. L. (2004). Survey of CTX-M-3 extended-spectrum β-lactamase (ESBL) among cefotaxime-resistant Serratia marcescens at a medical center in middle Taiwan. Diagnostic Microbiology and Infectious Disease, 49(2), 125–129. https://doi.org/10.1016/j.diagmicrobio.2004.02.004Yang, Y., Niehaus, K. E., Walker, T. M., Iqbal, Z., Walker, A. S., Wilson, D. J., Peto, T. E. A., Crook, D. W., Smith, E. G., Zhu, T., & Clifton, D. A. (2018). Machine Learning for classifying tuberculosis drug-resistance from DNA sequencing data. Bioinformatics, 34(10), 1666–1671. https://doi.org/10.1093/bioinformatics/btx801EstudiantesInvestigadoresPúblico generalORIGINAL0104797576.2022.pdf0104797576.2022.pdfapplication/pdf5625810https://repositorio.unal.edu.co/bitstream/unal/81760/3/0104797576.2022.pdfd73d6662fb92f1e2b78e92842c5c5977MD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81760/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL0104797576.2022.pdf.jpg0104797576.2022.pdf.jpgGenerated Thumbnailimage/jpeg6036https://repositorio.unal.edu.co/bitstream/unal/81760/5/0104797576.2022.pdf.jpge1b6ecc01502e6fd8ec667fe7f64ae18MD55unal/81760oai:repositorio.unal.edu.co:unal/817602023-08-06 23:03:45.299Repositorio Institucional Universidad Nacional de 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