Biology subjects

Clarke, S.

Publications and source records attributed to Clarke, S..

20 records · Page 2Linked to original sources

bioRxiv2020

Complete genome sequencing of Acinetobacter baumannii AC1633 and Acinetobacter nosocomialis AC1530 unveils a large multidrug resistant plasmid encoding the NDM-1 and OXA-58 carbapenemases

Carbapenem-resistant Acinetobacter spp. are considered priority drug-resistant human pathogenic bacteria. The genomes of two carbapenem-resistant Acinetobacter spp. clinical isolates obtained from the same tertiary hospital in Terengganu, Malaysia, namely A. baumannii AC1633 and A. nosocomialis AC1530, were sequenced. Both isolates were found to harbor the carbapenemase genes blaNDM-1 and blaOXA-58 in a large (ca. 170 kb) plasmid designated pAC1633-1 and pAC1530, respectively, that also encodes genes that confer resistance to aminoglycosides, sulfonamides, and macrolides. The two plasmids were almost identical except for the insertion of ISAba11 and an IS4 family element in pAC1633-1, and ISAba11 along with relBE toxin-antitoxin genes flanked by inversely orientated pdif (XerC/XerD) recombination sites in pAC1530. The blaNDM-1 gene was encoded in a Tn125 composite transposon structure flanked by ISAba125 whereas blaOXA-58 was flanked by ISAba11 and ISAba3 downstream and a partial ISAba3 element upstream within a pdif module. The presence of conjugative genes in plasmids pAC1633-1/pAC1530 and their discovery in two distinct species of Acinetobacter from the same hospital are suggestive of conjugative transfer but mating experiments failed to demonstrate transmissibility under standard laboratory conditions. Comparative sequence analysis strongly inferred that pAC1633-1/pAC1530 was derived from two separate plasmids in an IS1006-mediated recombination or transposition event. A. baumannii AC1633 also harbored three other plasmids designated pAC1633-2, pAC1633-3 and pAC1633-4. Both pAC1633-3 and pAC1633-4 are cryptic plasmids whereas pAC1633-2 is a 12,651 bp plasmid of the GR8/GR23 Rep3-superfamily group that encodes the tetA(39) tetracycline resistance determinant in a pdif module.

microbiology↗

bioRxiv2019

Computer-assisted EEG diagnostic review for idiopathic generalized epilepsy

Epilepsy diagnosis can be costly, time-consuming and not uncommonly inaccurate. The reference standard diagnostic monitoring is continuous video-EEG monitoring, ideally capturing all events or concordant interictal discharges. Automating EEG data review would save time and resources, thus enabling more people to receive reference standard monitoring and also potentially herald a more quantitative approach to therapeutic outcomes. There is substantial research into automated detection of seizures and epileptic activity from EEG. However, automated detection software is not widely used in the clinic; and, despite numerous published algorithms, few methods have regulatory approval for detecting epileptic activity from EEG.\n\nThis study reports on a deep learning algorithm for computer-assisted EEG review. Deep, convolutional neural networks were trained to detect epileptic discharges using a pre-existing dataset of over 6000 labelled events in a cohort of 103 patients with idiopathic generalized epilepsy (IGE). Patients underwent 24-hour ambulatory outpatient EEG, and all data was curated and confirmed independently by two epilepsy specialists (Seneviratne et al, 2016). The resulting automated detection algorithm was then used to review diagnostic scalp-EEG for seven patients (four with IGE and three with events mimicking seizures) to validate performance in a clinical setting.\n\nThe automated detection algorithm showed state-of-the-art performance for detecting epileptic activity from clinical EEG, with mean sensitivity of >95% and corresponding mean false positive rate of 1 detection per minute. Importantly, diagnostic case studies showed that the automated detection algorithm reduced human review time by 80%-99%, without compromising event detection or diagnostic accuracy. The presented results demonstrate that computer-assisted review can increase the speed and accuracy of EEG assessment and has the potential to greatly improve therapeutic outcomes.

neuroscience↗