An evaluation of 4 commercial assays for the detection of SARS-CoV-2 antibodies in a predominantly mildly symptomatic low prevalence Australian population.

A total of 1080 individual patient samples (158 positive serology samples from confirmed, predominantly mildly symptomatic COVID-19 patients and 922 serology negative including 496 collected pre-COVID) from four states in Australia were analysed on four commercial SARS-CoV-2 serological assays targeting antibodies to different antigens (Roche Elecsys and Abbott Architect: nucleocapsid; Diasorin Liaison and Euroimmun: spike). A subset was compared to immunofluorescent antibody (IFA) and micro-neutralisation. Sensitivity and specificity of the Roche (n = 1033), Abbott (n = 806), Diasorin (n = 1034) and Euroimmun (n = 175) were 93.7 %/99.5 %, 90.2 %/99.4 %, 88.6 %/98.6 % and 91.3 %/98.8 %, respectively. ROC analysis with specificity held at 99 % increased the sensitivity for the Roche and Abbott assays from 93.7% to 98.7% (cut-off 0.21) and 90.2 % to 94.0 % (cut-off 0.91), respectively. Overall seropositivity of samples increased from a maximum of 23 % for samples 0-7 days-post-onset of symptoms (dpos), to 61 % from samples 8-14dpos and 93 % from those >14dpos. IFA and microneutralisation values correlated best with assays targeting antibodies to spike protein with values >80 AU/mL on the Diasorin assay associated with neutralising antibody. Detectable antibody was present in 22/23 (96 %), 20/23 (87 %), 15/23 (65 %) and 9/22 (41 %) patients with samples >180dpos on the Roche, Diasorin, Abbott and microneutralisation assays respectively. Given the low prevalence in this community, two-step algorithms on initial positive results saw an increase in the positive predictive value (PPV) of positive samples (39 %-65 % to ≥98 %) for all combinations. Similarly accuracy increased from a range of 98.5 %-99.4 % to ≥99.8 % assuming a 1 % seroprevalence. Negative predictive value (NPV) was high (≥99.8 %) regardless of which assay was used initially.

Use of wound dressings with soft silicone adhesive technology.

AIM: To evaluate how pain, during and in-between dressing changes, is affected by the introduction of Mepilex Border Lite, a wound dressing manufactured by Mölnlycke Health Care using Safetac soft silicone adhesive technology, to the treatment of different types of paediatric wounds/skin injuries. METHODS: Wounds/skin injuries that met the criteria for inclusion in the study were dressed with Mepilex Border Lite. Patients were followed for six weeks or until their wounds/skin injuries had healed, whichever occurred earlier. At each dressing change, pain severity before and during dressing removal was rated by the patient and the investigator on a scale from zero (no pain at all) to ten (worst pain ever). Other variables measured included: signs of trauma to wound/skin injury and surrounding skin, the proportion of viable/non-viable tissue, the quantity and appearance of exudate, odour, and clinical signs of infection. At the final dressing change, patients and investigators completed questionnaires to rate the dressing performance. RESULTS: Mean pain severity scores were significantly lower (p < or = 0.003) at the first dressing change than at baseline. Over 99.5 per cent of the Mepilex Border Lite dressing changes were reported to be atraumatic and more than half of the wounds healed within the study period. Conformability, ease of use, ease of removal, patient comfort, and overall experience with the dressing were rated as 'good' to 'very good' at the vast majority of final visit evaluations. CONCLUSIONS: This study provides further evidence of the ability of dressings with Safetac soft silicone adhesive technology to minimise trauma and pain and demonstrates the ability of Mepilex Border Lite to overcome the clinical challenges associated with the use of dressings on the wounds/skin injuries of paediatric patients.

Achieving congruency of phylogenetic trees generated by W-curves of genomic sequences.

Comparative genomic analysis at its most fundamental level involves alignment and analysis of linear strings of DNA. Many useful and powerful tools, such as BlastN and ClustalW are able to respectively, search for, and align similar strings of DNA from a variety of species. However, interesting genomic patterns cannot be immediately visualized within the information contact embedded in long genomic strings without extensive a priori knowledge. More problematic is the question of whether we will be able to crystallize long genomic sequences and analyze their true secondary and tertiary structures. It is, of course, these putative motifs that are binding to the three-dimensional structures of proteins and inducing replication and transcription events. The W-curve is a numerical mapping algorithm that allows one to geometrically visualize the information content of genomic motifs. Patterns of ALU, LINES, SINEs, and duplication sequences may be easily visualized with the W-curve. It is our hope that this pattern recognition algorithm will lead to visualization tools to track the evolutionary history of motif patterns. The combinatorics of DNA motif crossover-recombination events will be more easily followed as we continue to sequence more and more genomes. In our laboratory we are currently collaborating with mathematicians and computer scientists to develop and test tools, such as the W-curve, for analyzing patterns of long genomic sequences. In this paper, we examine the limitations of using the W-curve to infer the phylogenetic history of species.

Higher gene duplicabilities for metabolic proteins than for nonmetabolic proteins in yeast and E. coli.

Although the evolutionary significance of gene duplication has long been appreciated, it remains unclear what factors determine gene duplicability. In this study we investigated whether metabolism is an important determinant of gene duplicability because cellular metabolism is crucial for the survival and reproduction of an organism. Using genomic data and metabolic pathway data from the yeast (Saccharomyces cerevisiae) and Escherichia coli, we found that metabolic proteins indeed tend to have higher gene duplicability than nonmetabolic proteins. Moreover, a detailed analysis of metabolic pathways in these two organisms revealed that genes in the central metabolic pathways and the catabolic pathways have, on average, higher gene duplicability than do other genes and that most genes in anabolic pathways are single-copy genes.