Molecular typing of enteroviruses: comparing 5'UTR, VP1 and whole genome sequencing methods.

Enteroviruses (EV) commonly cause hand, foot and mouth disease (HFMD), and can also cause potentially fatal neurological and systemic complications. In our laboratory, sequencing 5' untranslated region (UTR) of the viral genome has been the routine method of genotyping EVs. During a recent localised outbreak of aseptic meningitis, sequencing the 5'UTR identified the causative virus as EV-A71, which did not fit with the clinical syndrome or illness severity. When genotyped using a different target gene, VP1, the result was different. This led us to evaluate the accuracy of the two different target genome regions and compare them against whole genome sequencing (WGS). We aimed to optimise the algorithm for detection and characterisation of EVs in the diagnostic laboratory. We hypothesised that VP1 and WGS genotyping would provide different results than 5'UTR in a subset of samples. Clinical samples from around New South Wales which were positive for EV by commercial polymerase chain reaction (PCR) assays were genotyped by targeting three different viral genome regions: the 5'UTR, VP1 and WGS. Sequencing was performed by Sanger and next generation sequencing. The subtyping results were compared. Of the 74/118 (63%) samples that were successfully typed using both the 5'UTR and the VP1 method, the EV typing result was identical for 46/74 (62%) samples compared to WGS as the gold standard. The same EV group but different EV types were found in 22/74 (30%) samples, and 6/74 (8%) samples belonged to different EV groups depending on typing method used. Genotyping with WGS and VP1 is more accurate than 5'UTR. Genotyping by the 5'UTR method is very sensitive, but less specific.

The dispersion-brightness relation for fast radio bursts from a wide-field survey.

Despite considerable efforts over the past decade, only 34 fast radio bursts-intense bursts of radio emission from beyond our Galaxy-have been reported1,2. Attempts to understand the population as a whole have been hindered by the highly heterogeneous nature of the searches, which have been conducted with telescopes of different sensitivities, at a range of radio frequencies, and in environments corrupted by different levels of radio-frequency interference from human activity. Searches have been further complicated by uncertain burst positions and brightnesses-a consequence of the transient nature of the sources and the poor angular resolution of the detecting instruments. The discovery of repeating bursts from one source3, and its subsequent localization4 to a dwarf galaxy at a distance of 3.7 billion light years, confirmed that the population of fast radio bursts is located at cosmological distances. However, the nature of the emission remains elusive. Here we report a well controlled, wide-field radio survey for these bursts. We found 20, none of which repeated during follow-up observations between 185-1,097 hours after the initial detections. The sample includes both the nearest and the most energetic bursts detected so far. The survey demonstrates that there is a relationship between burst dispersion and brightness and that the high-fluence bursts are the nearby analogues of the more distant events found in higher-sensitivity, narrower-field surveys5.

Development and Validation of an Infection Risk Model for Bacterial Canker of Kiwifruit, Using a Multiplication and Dispersal Concept for Forecasting Bacterial Diseases.

A weather-based disease prediction model for bacterial canker of kiwifruit (known worldwide as Psa; Pseudomonas syringae pv. actinidiae biovar 3) was developed using a new mechanistic scheme for bacterial disease forecasters, the multiplication and dispersal concept. Bacterial multiplication is estimated from a temperature function, the M index, accumulated from hourly air temperature over 3 days for hours when the leaf canopy is wet. Rainfall provides free water to move inoculum to infection sites, and the daily risk indicator, the R index, is the 3-day accumulation of the M index output on days with total rainfall >1 mm; otherwise, R is zero. The model was field-tested using potted kiwifruit trap plants exposed for discrete periods in infected kiwifruit orchards to identify when leaf infection occurred. In a 9-week study during spring, the R index predicted leaf-spot intensity with high accuracy (R2 = 93%) and, in an 82-week seasonal accuracy study, prediction of infection incidence was most accurate from spring to late summer and lower during other times. To implement the risk model for the New Zealand kiwifruit industry, a modified risk index, R', used relative humidity (RH) >81% instead of wetness, so that 2- and 6-day weather forecasts of RH could be used. Risk index values were affected by the shape of the temperature function and an alternative 'low temperature' function for the M index was identified that could be used in climates in which high temperatures are known to limit Psa development during some parts of the year. This study has shown how infection risk for bacterial diseases can be conceptualized as separate processes for temperature-dependent bacterial multiplication and rain-dependent dispersal and infection. This concept has potentially wide application for bacterial disease prediction in the same way that the infection monocycle concept has had for fungal disease prediction.

Pyrexia of unknown origin: causes, investigation and management.

Pyrexia of unknown origin (PUO) is a syndrome that has long tested the skills of physicians to achieve a diagnosis in affected patients. By definition, patients included in this syndrome will be more difficult to diagnose as they have already resisted classification during baseline investigations. Furthermore, investigation of PUO requires knowledge of many diseases across a range of clinical specialties, as well as knowledge of less commonly used investigative tools. As both society and medicine continue to change, the aetiology and epidemiology of the diseases that cause PUO also change. For these reasons, it is important for physicians to approach PUO in a logical manner, and for the causes and approach to PUO to be continuously reviewed. In this article, we review the aetiology of PUO and the diagnostic strategies that may be used to investigate it.

Mucormycosis in Australia: contemporary epidemiology and outcomes.

Mucormycosis is the second most common cause of invasive mould infection and causes disease in diverse hosts, including those who are immuno-competent. We conducted a multicentre retrospective study of proven and probable cases of mucormycosis diagnosed between 2004-2012 to determine the epidemiology and outcome determinants in Australia. Seventy-four cases were identified (63 proven, 11 probable). The majority (54.1%) were caused by Rhizopus spp. Patients who sustained trauma were more likely to have non-Rhizopus infections relative to patients without trauma (OR 9.0, p 0.001, 95% CI 2.1-42.8). Haematological malignancy (48.6%), chemotherapy (42.9%), corticosteroids (52.7%), diabetes mellitus (27%) and trauma (22.9%) were the most common co-morbidities or risk factors. Rheumatological/autoimmune disorders occurred in nine (12.1%) instances. Eight (10.8%) cases had no underlying co-morbidity and were more likely to have associated trauma (7/8; 87.5% versus 10/66; 15.2%; p <0.001). Disseminated infection was common (39.2%). Apophysomyces spp. and Saksenaea spp. caused infection in immuno-competent hosts, most frequently associated with trauma and affected sites other than lung and sinuses. The 180-day mortality was 56.7%. The strongest predictors of mortality were rheumatological/autoimmune disorder (OR = 24.0, p 0.038 95% CI 1.2-481.4), haematological malignancy (OR = 7.7, p 0.001, 95% CI 2.3-25.2) and admission to intensive care unit (OR = 4.2, p 0.02, 95% CI 1.3-13.8). Most deaths occurred within one month. Thereafter we observed divergence in survival between the haematological and non-haematological populations (p 0.006). The mortality of mucormycosis remains particularly high in the immuno-compromised host. Underlying rheumatological/autoimmune disorders are a previously under-appreciated risk for infection and poor outcome.

Invasive infections due to filamentous fungi other than Aspergillus: epidemiology and determinants of mortality.

The epidemiology of invasive fungal disease (IFD) due to filamentous fungi other than Aspergillus may be changing. We analysed clinical, microbiological and outcome data in Australian patients to determine the predisposing factors and identify determinants of mortality. Proven and probable non-Aspergillus mould infections (defined according to modified European Organization for Research and Treatment of Cancer/Mycoses Study Group criteria) from 2004 to 2012 were evaluated in a multicentre study. Variables associated with infection and mortality were determined. Of 162 episodes of non-Aspergillus IFD, 145 (89.5%) were proven infections and 17 (10.5%) were probable infections. The pathogens included 29 fungal species/species complexes; mucormycetes (45.7%) and Scedosporium species (33.3%) were most common. The commonest comorbidities were haematological malignancies (HMs) (46.3%) diabetes mellitus (23.5%), and chronic pulmonary disease (16%); antecedent trauma was present in 21% of cases. Twenty-five (15.4%) patients had no immunocompromised status or comorbidity, and were more likely to have acquired infection following major trauma (p <0.01); 61 (37.7%) of cases affected patients without HMs or transplantation. Antifungal therapy was administered to 93.2% of patients (median 68 days, interquartile range 19-275), and adjunctive surgery was performed in 58.6%. The all-cause 90-day mortality was 44.4%; HMs and intensive-care admission were the strongest predictors of death (both p <0.001). Survival varied by fungal group, with the risk of death being significantly lower in patients with dematiaceous mould infections than in patients with other non-Aspergillus mould infections. Non-Aspergillus IFD affected diverse patient groups, including non-immunocompromised hosts and those outside traditional risk groups; therefore, definitions of IFD in these patients are required. Given the high mortality, increased recognition of infections and accurate identification of the causative agent are required.

Vitamin E correlates inversely with non-transferrin-bound iron in sickle cell disease.

Decreased serum vitamin E levels are found in homozygous sickle cell disease (SCD). Excessive transfusions may lead high non-transferrin-bound iron (NTBI). Hypothesizing a relationship between the two, vitamin E (measured using high performance liquid chromatography) was significantly lower in 30 SCD patients than in 30 age-/sex-matched controls (P < 0.001), but NTBI (bleomycin assay) was higher (P < 0.001). Vitamin E was lower in 10 transfused patients than in 20 non-transfused patients (P < 0.001) with a significant inverse correlation between the NTBI and vitamin E (r = -0.58, P < 0.001). NTBI associated with iron overload in SCD may increase the potential for oxidative damage and low vitamin E activity may compound this effect.

ANN modeling of the penetration across a polydimethylsiloxane membrane from theoretically derived molecular descriptors.

A quantitative structure-permeability relationship was developed using Artificial Neural Network (ANN) modeling to study penetration across a polydimethylsiloxane membrane. A set of 254 compounds and their experimentally derived maximum steady state flux values used in this study was gathered from the literature. A total of 42 molecular descriptors were calculated for each compound. A genetic algorithm was used to select important molecular descriptors and supervised ANN was used to correlate selected descriptors with the experimentally derived maximum steady-state flux through the polydimethylsiloxane membrane (log J). Calculated molecular descriptors were used as the ANN's inputs and log J as the output. Developed model indicates that molecular shape and size, inter-molecular interactions, hydrogen-bonding capacity of drugs, and conformational stability could be used to predict drug absorption through skin. A 12-descriptor nonlinear computational neural network model has been developed for the estimation of log J values for a data set of 254 drugs. Described model does not require experimental parameters and could potentially provide useful prediction of membrane penetration of new drugs and reduce the need for actual compound synthesis and flux measurements.

Theoretically-derived molecular descriptors important in human intestinal absorption.

A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.

Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine and incorporate both literature-based and experimental data to solve problems. The various applications of ANNs can be summarised into classification or pattern recognition, prediction and modeling. Supervised 'associating networks can be applied in pharmaceutical fields as an alternative to conventional response surface methodology. Unsupervised feature-extracting networks represent an alternative to principal component analysis. Non-adaptive unsupervised networks are able to reconstruct their patterns when presented with noisy samples and can be used for image recognition. The potential applications of ANN methodology in the pharmaceutical sciences range from interpretation of analytical data, drug and dosage form design through biopharmacy to clinical pharmacy.

The power of support groups: influence on infant feeding trends in New Zealand.

By outlining infant feeding trends and the essential characteristics of support groups, this paper reveals how important such groups and their development have been in shaping the history of infant feeding in twentieth-century New Zealand. The paper draws, in particular, on the histories, growth, and influence of the Royal New Zealand Plunket Society, Parents Centre New Zealand, and La Leche League New Zealand. It demonstrates the importance of such middle-class groups in changing practices and attitudes within society. In general, this paper is a call for recognition of the importance of lay support groups in the improvement of health outcomes.

Effect of nucleotide substrate binding on the pKa of catalytic residues in barnase.

A thermodynamic cycle is used to describe barnase catalysis, which considers explicitly the presence of different ionic states of the catalytic residues Glu-73 and His-102 in barnase during the enzyme-substrate recognition process. Reinterpretation of published experimental data using rate equations derived from this cycle provides estimates of the ionization constants of these catalytic side chains, in the free enzyme and in the barnase-GpA complex. In addition, the electrostatic properties of the barnase-d(CGAC) crystal complex and of a barnase-5'3'(AAGAAp)-O-methyl ester modeled complex are investigated by means of a continuum approach to account for solvent polarization effects. Taking GpA as a reference substrate, it is shown that increasing the length of the bound nucleotide induces pKa shifts in the catalytic side chains, which modulate the fraction of enzyme in the correct ionic form for achieving the transesterification reaction. The computed results are in good agreement with the experimental variation of the optimum pH of barnase activity. The present analysis underscores the influence of pH effects on the kcat and KM kinetic constants of barnase and provides the basic formalism for linking the effective kinetic parameters, which usually depend on the pH, to the theoretical estimates of the true kinetic constants.

Stimulus properties of some analogues of 4-methylaminorex.

The stimulus properties of aminorex and analogues of 4-methylaminorex, namely (4S,5S)-4-methylaminorex, N-methyl-(4S,5S)-4-methylaminorex, and the regioisomeric (R)- and (S)-2-amino-4-phenyl-2-oxazoline (rexamino) were compared in rats trained to distinguish (S)-amphetamine (1 mg/kg) from saline. The first three compounds, aminorex, (4S,5S)-4-methylaminorex, and N-methyl-(4S,5S)-4-methylaminorex shared discriminative stimulus effects with amphetamine, although the stimulus properties for racemic aminorex were less than those of the other two compounds. The two regioisomers, (R)- and (S)-rexamino, produced only partial generalisation to the amphetamine.

Relation of personality and attentional factors to cognitive deficits in human immunodeficiency virus-infected subjects.

In view of the evidence that patients with human immunodeficiency virus infection experience reactive depression and anxiety, it is important to determine whether these factors might account for some of the cognitive deficiencies observed in this group, as is often the case in psychiatric populations. An extensive battery of cognitive, personality, and attention tests was administered to 26 patients who tested positive for the human immunodeficiency virus. In this group were patients who demonstrated no symptoms, patients who had acquired immunodeficiency syndrome-related complex, and patients who had acquired immunodeficiency syndrome. Pearson Product Moment correlations were computed between scores on the three types of measures. The results of this correlational study suggest that cognitive decline in patients infected with human immunodeficiency virus is independent of mood and attentional changes.