Purification of hydroxylamine oxidase from Thiosphaera pantotropha Identification of electron acceptors that couple heterotrophic nitrification to aerobic denitrification

AbstractThiosphaera pantotropha, a Gram-negative heterotrophic nitrifying bacterium, expresses a soluble 20 kDa monomeric periplasmic hydroxylamine oxidase that differs markedly from the hydroxylamine oxidase found in autotrophic bacteria. This enzyme can use the periplasmic redox proteins, cytochrome C551 and pseudoazurin as electron acceptors, both of which can also donate electrons to denitrification enzymes. A model of electron transfer is proposed, that suggests a coupling of nitrification to denitrification and provides a mechanism by which nitrification can play a role in dissipating reductant

How accurate is patients' anatomical knowledge: a cross-sectional, questionnaire study of six patient groups and a general public sample

<p>Abstract</p> <p>Background</p> <p>Older studies have shown that patients often do not understand the terms used by doctors and many do not even have a rudimentary understanding of anatomy. The present study was designed to investigate the levels of anatomical knowledge of different patient groups and the general public in order to see whether this has improved over time and whether patients with a specific organ pathology (e.g. liver disease) have a relatively better understanding of the location of that organ.</p> <p>Methods</p> <p>Level of anatomical knowledge was assessed on a multiple-choice questionnaire, in a sample of 722 participants, comprising approximately 100 patients in each of 6 different diagnostic groups and 133 in the general population, using a between-groups, cross-sectional design. Comparisons of relative accuracy of anatomical knowledge between the present and earlier results, and across the clinical and general public groups were evaluated using Chi square tests. Associations with age and education were assessed with the Pearson correlation test and one-way analysis of variance, respectively.</p> <p>Results</p> <p>Across groups knowledge of the location of body organs was poor and has not significantly improved since an earlier equivalent study over 30 years ago (χ²= 0.04, df = 1, ns). Diagnostic groups did not differ in their overall scores but those with liver disease and diabetes were more accurate regarding the location of their respective affected organs (χ²= 18.10, p < 0.001, df = 1; χ²= 10.75, p < 0.01, df = 1). Age was significantly negatively correlated (r = -0.084, p = 0.025) and education was positively correlated with anatomical knowledge (F = 12.94, p = 0.000). Although there was no overall gender difference, women were significantly better at identifying organs on female body outlines.</p> <p>Conclusion</p> <p>Many patients and general public do not know the location of key body organs, even those in which their medical problem is located, which could have important consequences for doctor-patient communication. These results indicate that healthcare professionals still need to take care in providing organ specific information to patients and should not assume that patients have this information, even for those organs in which their medical problem is located.</p

The TatC component of the twin-arginine protein translocase functions as an obligate oligomer

The Tat protein export system translocates folded proteins across the bacterial cytoplasmic membrane and the plant thylakoid membrane. The Tat system in Escherichia coli is composed of TatA, TatB and TatC proteins. TatB and TatC form an oligomeric, multivalent receptor complex that binds Tat substrates, while multiple protomers of TatA assemble at substrate-bound TatBC receptors to facilitate substrate transport. We have addressed whether oligomerisation of TatC is an absolute requirement for operation of the Tat pathway by screening for dominant negative alleles of tatC that inactivate Tat function in the presence of wild-type tatC. Single substitutions that confer dominant negative TatC activity were localised to the periplasmic cap region. The variant TatC proteins retained the ability to interact with TatB and with a Tat substrate but were unable to support the in vivo assembly of TatA complexes. Blue-native PAGE analysis showed that the variant TatC proteins produced smaller TatBC complexes than the wild-type TatC protein. The substitutions did not alter disulphide crosslinking to neighbouring TatC molecules from positions in the periplasmic cap but abolished a substrate-induced disulphide crosslink in transmembrane helix 5 of TatC. Our findings show that TatC functions as an obligate oligomer.</p

The relationship between redox enzyme activity and electrochemical potential—cellular and mechanistic implications from protein film electrochemistry

In protein film electrochemistry a redox protein of interest is studied as an electroactive film adsorbed on an electrode surface. For redox enzymes this configuration allows quantification of the relationship between catalytic activity and electrochemical potential. Considered as a function of enzyme environment, i.e., pH, substrate concentration etc., the activity–potential relationship provides a fingerprint of activity unique to a given enzyme. Here we consider the nature of the activity–potential relationship in terms of both its cellular impact and its origin in the structure and catalytic mechanism of the enzyme. We propose that the activity–potential relationship of a redox enzyme is tuned to facilitate cellular function and highlight opportunities to test this hypothesis through computational, structural, biochemical and cellular studies

Prediction of reader estimates of mammographic density using convolutional neural networks.

Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not yet captured by hand-crafted algorithms. With deep learning, it may be possible to encapsulate this knowledge in an automatic method. We have built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms. The CNNs are trained using whole-image mammograms, each labeled with the average VAS score of two independent readers. Each CNN learns a mapping between mammographic appearance and VAS score so that at test time, they can predict VAS score for an unseen image. Networks were trained using 67,520 mammographic images from 16,968 women and for model selection we used a dataset of 73,128 images. Two case-control sets of contralateral mammograms of screen detected cancers and prior images of women with cancers detected subsequently, matched to controls on age, menopausal status, parity, HRT and BMI, were used for evaluating performance on breast cancer prediction. In the case-control sets, odd ratios of cancer in the highest versus lowest quintile of percentage density were 2.49 (95% CI: 1.59 to 3.96) for screen-detected cancers and 4.16 (2.53 to 6.82) for priors, with matched concordance indices of 0.587 (0.542 to 0.627) and 0.616 (0.578 to 0.655), respectively. There was no significant difference between reader VAS and predicted VAS for the prior test set (likelihood ratio chi square, p = 0.134 ). Our fully automated method shows promising results for cancer risk prediction and is comparable with human performance.This paper presents independent research funded by NIHR under its Programme Grants for Applied Research programme (reference number RP-PG-0707-10031: “Improvement in risk prediction, early detection and prevention of breast cancer”) with additional funding from the Prevent Breast Cancer Appeal and supported by the NIHR Manchester Biomedical Research Centre Award No. IS-BRC-1215-20007

First-in-human technique translation of oxygen-enhanced MRI to an MR Linac system in patients with head and neck cancer

BACKGROUND AND PURPOSE: Tumour hypoxia is prognostic in head and neck cancer (HNC), associated with poor loco-regional control, poor survival and treatment resistance. The advent of hybrid MRI - radiotherapy linear accelerator or 'MR Linac' systems - could permit imaging for treatment adaptation based on hypoxic status. We sought to develop oxygen-enhanced MRI (OE-MRI) in HNC and translate the technique onto an MR Linac system. MATERIALS AND METHODS: MRI sequences were developed in phantoms and 15 healthy participants. Next, 14 HNC patients (with 21 primary or local nodal tumours) were evaluated. Baseline tissue longitudinal relaxation time (T1) was measured alongside the change in 1/T1 (termed ΔR1) between air and oxygen gas breathing phases. We compared results from 1.5 T diagnostic MR and MR Linac systems. RESULTS: Baseline T1 had excellent repeatability in phantoms, healthy participants and patients on both systems. Cohort nasal concha oxygen-induced ΔR1 significantly increased (p < 0.0001) in healthy participants demonstrating OE-MRI feasibility. ΔR1 repeatability coefficients (RC) were 0.023-0.040 s-1 across both MR systems. The tumour ΔR1 RC was 0.013 s-1 and the within-subject coefficient of variation (wCV) was 25% on the diagnostic MR. Tumour ΔR1 RC was 0.020 s-1 and wCV was 33% on the MR Linac. ΔR1 magnitude and time-course trends were similar on both systems. CONCLUSION: We demonstrate first-in-human translation of volumetric, dynamic OE-MRI onto an MR Linac system, yielding repeatable hypoxia biomarkers. Data were equivalent on the diagnostic MR and MR Linac systems. OE-MRI has potential to guide future clinical trials of biology guided adaptive radiotherapy