Genomic insights into in-ICU emergence of last-resort antimicrobial resistance in a rare, carbapenem resistant, ST16 Klebsiella pneumoniae strain from Jodhpur, India.

OBJECTIVES: To investigate the genomic differences between two extensively drug resistant, ST16 strains of Klebsiella pneumoniae recovered from patients in the same ICU, one of which was colistin resistant. METHODS: Antimicrobial susceptibilities of the isolates were determined using VITEK-2. Hybrid assemblies for both strains were generated using Oxford Nanopore and Illumina technologies. The sequence type, capsule type, O-locus type, antimicrobial resistance determinants and plasmids carried by the isolates were inferred from the genome sequence. The phylogenetic placement, antimicrobial resistance, and virulence determinants of the isolates relative to a collection (n = 871) of ST16 isolates were assessed. RESULTS: Both BC16, a colistin-resistant blood stream isolate and U23, a colistin-sensitive urinary isolate displayed near-identical antimicrobial resistance profiles and genome sequences with varying plasmid profiles. The BC16 genome only had 21 SNPs relative to U23 and belonged to the same capsule, O-antigen locus and multi-locus sequence types. The mgrB locus in BC16 was disrupted by an IS5 element. Phylogenetically, U23 and BC16 were placed on a clade with 4 strains belonging to K-type K48 and O-type O2a as opposed to majority (n = 807) of the strains (K-type K51 and O-type O3b). CONCLUSIONS: BC16 was a colistin resistant derivative of U23, which evolved colistin resistance by an IS5-mediated disruption of the mgrB locus, likely during treatment of the index patient with colistin in the ICU. The strains belong to a rare subtype of ST16 with unique capsular and O-antigen types underscoring the utility of genomic surveillance networks and open-access genomic surveillance data in tracking problem clones.

Noninvasive blood glucose sensing using near infra-red spectroscopy and artificial neural networks based on inverse delayed function model of neuron.

In this paper, a non-invasive blood glucose sensing system is presented using near infra-red(NIR) spectroscopy. The signal from the NIR optodes is processed using artificial neural networks (ANN) to estimate the glucose level in blood. In order to obtain accurate values of the synaptic weights of the ANN, inverse delayed (ID) function model of neuron has been used. The ANN model has been implemented on field programmable gate array (FPGA). Error in estimating glucose levels using ANN based on ID function model of neuron implemented on FPGA, came out to be 1.02 mg/dl using 15 hidden neurons in the hidden layer as against 5.48 mg/dl using ANN based on conventional neuron model.