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  1. Ravanfar SA, Aziz MA, Saud HM, Abdullah JO
    Curr Genet, 2015 Nov;61(4):653-63.
    PMID: 25986972 DOI: 10.1007/s00294-015-0494-x
    An efficient system for shoot regeneration and Agrobacterium tumefaciens-mediated transformation of Brassica oleracea cv. Green Marvel cultivar is described. This study focuses on developing shoot regeneration from hypocotyl explants of broccoli cv. Green Marvel using thidiazuron (TDZ), zeatin, and kinetin, the optimization of factors affecting Agrobacterium-mediated transformation of the hypocotyl explants with heat-resistant cDNA, followed by the confirmation of transgenicity of the regenerants. High shoot regeneration was observed in 0.05-0.1 mg dm(-3) TDZ. TDZ at 0.1 mg dm(-3) produced among the highest percentage of shoot regeneration (96.67 %) and mean number of shoot formation (6.17). The highest percentage (13.33 %) and mean number (0.17) of putative transformant production were on hypocotyl explants subjected to preculture on shoot regeneration medium (SRM) with 200 µM acetosyringone. On optimization of bacterial density and inoculation time, the highest percentage and mean number of putative transformant production were on hypocotyl explants inoculated with a bacterial dilution of 1:5 for 30 min. Polymerase chain reaction (PCR) assay indicated a transformation efficiency of 8.33 %. The luciferase assay showed stable integration of the Arabidopsis thaliana HSP101 (AtHSP101) cDNA in the transgenic broccoli regenerants. Three out of five transgenic lines confirmed through PCR showed positive hybridization bands of the AtHSP101 cDNA through Southern blot analysis. The presence of AtHSP101 transcripts in the three transgenic broccoli lines indicated by reverse transcription-PCR (RT-PCR) confirmed the expression of the gene. In conclusion, an improved regeneration system has been established from hypocotyl explants of broccoli followed by successful transformation with AtHSP101 for resistance to high temperature.
  2. Lau HJ, Lim CH, Foo SC, Tan HS
    Curr Genet, 2021 Jun;67(3):421-429.
    PMID: 33585980 DOI: 10.1007/s00294-021-01156-5
    Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.
  3. Wong BC, Abu Bakar J, Dhanoa A, Tan HS
    Curr Genet, 2022 Feb;68(1):27-38.
    PMID: 34505182 DOI: 10.1007/s00294-021-01212-0
    The development of nucleic-acid-based antimicrobials such as RNA-cleaving DNAzyme (RCD), a short catalytically active nucleic acid, is a promising alternative to the current antibiotics. The current rapid spread of antimicrobial resistance (AMR) in bacteria renders some antibiotics useless against bacterial infection, thus creating the need for alternative antimicrobials such as DNAzymes. This review summarizes recent advances in the use of RCD as a diagnostic and therapeutic agent against AMR. Firstly, the recent diagnostic application of RCD for the detection of bacterial cells and the associated resistant gene(s) is discussed. The next section summarises the therapeutic application of RCD in AMR bacterial infections which includes direct targeting of the resistant genes and indirect targeting of AMR-associated genes. Finally, this review extends the discussion to challenges of utilizing RCD in real-life applications, and the potential of combining both diagnostic and therapeutic applications of RCD into a single agent as a theranostic agent.
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