Search

Background: Knee osteoarthritis (OA) is a common and important cause of pain and disability, but interventions aimed at modifying structures visible on imaging have been disappointing. While OA affects the whole joint, synovitis and effusion have been recognised as having a role in the pathogenesis of OA. Krill oil reduces knee pain and systemic inflammation and could be used for targeting inflammatory mechanisms of OA.Methods/design: We will recruit 260 patients with clinical knee OA, significant knee pain and effusion-synovitis present on MRI in five Australian cities (Hobart, Melbourne, Sydney, Adelaide and Perth). These patients will be randomly allocated to the two arms of the study, receiving 2 g/day krill oil or inert placebo daily for 6 months. MRI of the study knee will be performed at screening and after 6 months. Knee symptoms, function and MRI structural abnormalities will be assessed using validated methods. Safety data will be recorded. Primary outcomes are absolute change in knee pain (assessed by visual analog score) and change in size of knee effusion-synovitis over 24 weeks. Secondary outcomes include improvement in knee pain over 4, 8, 12, 16 and 20 weeks. The primary analyses will be intention-to-treat analyses of primary and secondary outcomes. Per protocol analyses adjusting for missing data and for treatment compliance will be performed as the secondary analyses.Discussion: This study will provide high-quality evidence to assess whether krill oil 2 g/day reduces pain and effusion-synovitis size in older adults with clinical knee OA and knee effusion-synovitis. If krill oil is effective and confirmed to be safe, we will provide compelling evidence that krill oil improves pain and function, changes disease trajectory and slows disease progression in OA. Given the lack of approved therapies for slowing disease progression in OA, and moderate cost of krill oil, these findings will be readily translated into clinical practice.Trial Registration: Australian New Zealand Clinical Trials Registry, ACTRN12616000726459. Registered on 02 June 2016. Universal Trial Number (UTN) U1111-1181-7087. [ABSTRACT FROM AUTHOR]

Background: Diagnostic testing using PCR is a fundamental component of COVID-19 pandemic control. Criteria for determining who should be tested by PCR vary between countries, and ultimately depend on resource constraints and public health objectives. Decisions are often based on sets of symptoms in individuals presenting to health services, as well as demographic variables, such as age, and travel history. The objective of this study was to determine the sensitivity and specificity of sets of symptoms used for triaging individuals for confirmatory testing, with the aim of optimising public health decision making under different scenarios., Methods: Data from the first wave of COVID-19 in New Zealand were analysed; comprising 1153 PCR-confirmed and 4750 symptomatic PCR negative individuals. Data were analysed using Multiple Correspondence Analysis (MCA), automated search algorithms, Bayesian Latent Class Analysis, Decision Tree Analysis and Random Forest (RF) machine learning., Results: Clinical criteria used to guide who should be tested by PCR were based on a set of mostly respiratory symptoms: a new or worsening cough, sore throat, shortness of breath, coryza, anosmia, with or without fever. This set has relatively high sensitivity (> 90%) but low specificity (< 10%), using PCR as a quasi-gold standard. In contrast, a group of mostly non-respiratory symptoms, including weakness, muscle pain, joint pain, headache, anosmia and ageusia, explained more variance in the MCA and were associated with higher specificity, at the cost of reduced sensitivity. Using RF models, the incorporation of 15 common symptoms, age, sex and prioritised ethnicity provided algorithms that were both sensitive and specific (> 85% for both) for predicting PCR outcomes., Conclusions:  If predominantly respiratory symptoms are used for test-triaging,  a large proportion of the individuals being tested may not have COVID-19. This could overwhelm testing capacity and hinder attempts to trace and eliminate infection. Specificity can be increased using alternative rules based on sets of symptoms informed by multivariate analysis and automated search algorithms, albeit at the cost of sensitivity. Both sensitivity and specificity can be improved through machine learning algorithms, incorporating symptom and demographic data, and hence may provide an alternative approach to test-triaging that can be optimised according to prevailing conditions., (© 2021. The Author(s).)

The search for an association between disease incidence and possible risk factors using surveillance data needs to account for possible spatial and temporal correlations in underlying risk. This can be especially difficult if there are missing values for some important covariates. We present a case study to show how this problem can be overcome in a Bayesian analysis framework by adding to the usual spatio-temporal model a component for modelling the missing data.

A Bayesian latent class model was used to estimate the sensitivity and specificity of an immunoglobulin G1 serum enzyme-linked immunosorbent assay (Paralisa) and individual fecal culture to detect young deer infected with Mycobacterium avium subsp. paratuberculosis. Paired fecal and serum samples were collected, between July 2009 and April 2010, from 20 individual yearling (12-24-month-old) deer in each of 20 South Island and 18 North Island herds in New Zealand and subjected to culture and Paralisa, respectively. Two fecal samples and 16 serum samples from 356 North Island deer, and 55 fecal and 37 serum samples from 401 South Island deer, were positive. The estimate of individual fecal culture sensitivity was 77% (95% credible interval [CI] = 61-92%) with specificity of 99% (95% CI = 98-99.7%). The Paralisa sensitivity estimate was 19% (95% CI = 10-30%), with specificity of 94% (95% CI = 93-96%). All estimates were robust to variation of priors and assumptions tested in a sensitivity analysis. The data informs the use of the tests in determining infection status at the individual and herd level.

New Zealand has a high rate of reported campylobacteriosis compared with other developed countries. One possible reason is that local strains have greater heat tolerance and thus are better able to survive undercooking; this hypothesis is supported by the remarkably high D-values reported for Campylobacter jejuni in The Netherlands. The objective of this study was to investigate the thermal inactivation of isolates from New Zealand in broth, using strains that are commonly found in human cases and food samples in New Zealand. Typed Campylobacter strains were heated to a predetermined temperature using a submerged-coil heating apparatus. The first-order kinetic model has been used extensively in the calculation of the thermal inactivation parameters, D and z; however, nonlinear survival curves have been reported, and a number of models have been proposed to describe the patterns observed. Therefore, this study compared the conventional first-order model with eight nonlinear models for survival curves. Kinetic parameters were estimated using both one- and two-step regression techniques. In general, nonlinear models fit the individual inactivation data sets better than the log-linear model. However, the log-linear and the (nonlinear) Weibull models were the only models that could be successfully fitted to all data sets. For seven relevant New Zealand C. jejuni strains, at temperatures from 51.5 to 60°C, D- and z-values were obtained, ranging from 1.5 to 228 s and 4 to 5.2°C, respectively. These values are in broad agreement with published international data and do not indicate that the studied New Zealand C. jejuni strains are more heat resistant than other strains, in contrast with some reports from The Netherlands.

To quantify the sensitivity and specificity of a serum enzyme-linked immunosorbent assay (ELISA) and fecal culture (FC) tests and to estimate the prevalence of Johne's disease (JD) in New Zealand dairy herds using Bayesian methods, 4 New Zealand dairy herds were tested simultaneously by ELISA and FC 5 times over 3 lactations. Test results were dichotomized. A Bayesian regression model was developed that considered test sensitivity as a function of the covariates parity, lactation stage, and prevalence of JD, which is expected to vary between herds. It was applied to a cross-sectional subset of the data and the full, repeated measures data set. Results were compared with frequentist pseudo gold standard results of the full data. Using the regression model, sensitivity of the ELISA was higher in older animals, but the sensitivity of the FC test showed no trend across age groups. Both FC and ELISA sensitivity were lower in late lactation. Estimated prevalence was lower and FC sensitivity higher when analyzing the complete data. The regression model enabled a more accurate diagnosis of JD to be made because it incorporated cow-specific information in the diagnosis, such as age and lactation stage. The model also enabled the incorporation of previous test results for an individual when diagnosing disease. The trends in results from the regression model support the current understanding of the disease process. The advantage of repeated testing of individuals in the assessment of test performance is discussed in the current study.

The New Zealand short-tailed bat Mystacina tuberculata evolved in the absence of terrestrial mammals and initially with few potential predators. Unusual among bats, it is well adapted for the capture of prey on the ground. Bats from Fiordland, New Zealand had relatively low wing loadings and aspect ratios adapted for flight in cluttered habitats. We predicted that M. tuberculata would locate prey in air (uncluttered space) by echolocation. Echolocation call sequences associated with prey capture (terminal buzzes) were heard in the field, and bats detected and localized prey suspended on fishing line by echolocation in a flight cage. The bats emitted brief, multiharmonic echolocation calls at low duty cycle during search phase, and 64% of calls contained most energy in the fundamental harmonic. Approach- and terminal-phase calls were also broadband and multiharmonic. We predicted that bats would not use echolocation to locate prey hidden on the ground in leaf litter (cluttered space). Bats seemed unable to locate hidden prey precisely from the air and instead hunted for such prey while crawling. Echolocation calls were emitted at a low repetition rate on the ground, suggesting that here echolocation was used for orientation and not for prey detection. We experimentally removed cues available to the bats and showed that bats located mealworms in leaf litter by listening for prey-generated noises and possibly by olfaction.

A retrospective study of the numbers and range of musculoskeletal conditions presenting to the accident and emergency (A and E) department of a general hospital over two one-week periods, in February and May 1980 is described. Over a third of the patients had musculoskeletal disorders most commonly affecting the hand (19%), ankle (13%), foot (9%) and spine (8%). One in three cases involved fracture. As judged by a physiotherapist, only 20% of cases which should have been referred for physiotherapy were actually referred. The frequency and range of musculoskeletal conditions encountered in general practice, and the undergraduate curriculum for the musculoskeletal system at the Auckland Medical School is reviewed. Postgraduate education in the musculoskeletal system is essential where emphasis should be given to conditions of the hand and the spine.