Search

Background: Timely and informed public health responses to infectious diseases such as COVID-19 necessitate reliable information about infection dynamics. The case ascertainment rate (CAR), the proportion of infections that are reported as cases, is typically much less than one and varies with testing practices and behaviours, making reported cases unreliable as the sole source of data. The concentration of viral RNA in wastewater samples provides an alternate measure of infection prevalence that is not affected by clinical testing, healthcare-seeking behaviour or access to care., Methods: We construct a state-space model with observed data of levels of SARS-CoV-2 in wastewater and reported case incidence and estimate the hidden states of the effective reproduction number, R, and CAR using sequential Monte Carlo methods., Results: We analyse data from 1 January 2022 to 31 March 2023 from Aotearoa New Zealand. Our model estimates that R peaks at 2.76 (95% CrI 2.20, 3.83) around 18 February 2022 and the CAR peaks around 12 March 2022. We calculate that New Zealand's second Omicron wave in July 2022 is similar in size to the first, despite fewer reported cases. We estimate that the CAR in the BA.5 Omicron wave in July 2022 is approximately 50% lower than in the BA.1/BA.2 Omicron wave in March 2022., Conclusions: Estimating R, CAR, and cumulative number of infections provides useful information for planning public health responses and understanding the state of immunity in the population. This model is a useful disease surveillance tool, improving situational awareness of infectious disease dynamics in real-time., (© 2024. The Author(s).)

Glycosylation is the most common form of post-translational modification in the brain. Aberrant glycosylation has been observed in cerebrospinal fluid and brain tissue of Alzheimer's disease (AD) cases, including dysregulation of terminal sialic acid (SA) modifications. While alterations in sialylation have been identified in AD, the localization of SA modifications on cellular or aggregate-associated glycans is largely unknown because of limited spatial resolution of commonly utilized methods. The present study aims to overcome these limitations with novel combinations of histologic techniques to characterize the sialylation landscape of O- and N-linked glycans in autopsy-confirmed AD post-mortem brain tissue. Sialylated glycans facilitate important cellular functions including cell-to-cell interaction, cell migration, cell adhesion, immune regulation, and membrane excitability. Previous studies have not investigated both N- and O-linked sialylated glycans in neurodegeneration. In this study, the location and distribution of sialylated glycans were evaluated in three brain regions (frontal cortex, hippocampus, and cerebellum) from 10 AD cases using quantitative digital pathology techniques. Notably, we found significantly greater N-sialylation of the Aβ plaque microenvironment compared with O-sialylation. Plaque-associated microglia displayed the most intense N-sialylation proximal to plaque pathology. Further analyses revealed distinct differences in the levels of N- and O-sialylation between cored and diffuse Aβ plaque morphologies. Interestingly, phosphorylated tau pathology led to a slight increase in N-sialylation and no influence of O-sialylation in these AD brains. Confirming our previous observations in mice with novel histologic approach, these findings support microglia sialylation appears to have a relationship with AD protein aggregates while providing potential targets for therapeutic strategies., (© 2024 The Authors. Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.)

Invertebrate-derived DNA (iDNA) metabarcoding from carrion flies is a powerful, non-invasive tool that has value for assessing vertebrate diversity. However, unknowns exist around the factors that influence vertebrate detections, such as spatial limits to iDNA signals or if detections are influenced by taxonomic class or estimated biomass of the vertebrates of interest. Using a bulk-collection method, we captured flies from within a zoo and along transects extending 4 km away from this location. From 920 flies, we detected 28 vertebrate species. Of the 28 detected species, we identified 9 species kept at the zoo, 8 mammals and 1 bird, but no reptiles. iDNA detections were highly geographically localized, and only a few zoo animals were detected outside the zoo setting. However, due to the low number of detections in our dataset, we found no influence of the taxonomic group or the estimated biomass of animals on their detectability. Our data suggest that iDNA detections from bulk-collected carrion flies, at least in urban settings in Australia, are predominantly determined by geographic proximity to the sampling location. This study presents an important step in understanding how iDNA techniques can be used in biodiversity monitoring., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Global biodiversity is declining at an ever-increasing rate. Yet effective policies to mitigate or reverse these declines require ecosystem condition data that are rarely available. Morphology-based bioassessment methods are difficult to scale, limited in scope, suffer prohibitive costs, require skilled taxonomists, and can be applied inconsistently between practitioners. Environmental DNA (eDNA) metabarcoding offers a powerful, reproducible and scalable solution that can survey across the tree-of-life with relatively low cost and minimal expertise for sample collection. However, there remains a need to condense the complex, multidimensional community information into simple, interpretable metrics of ecological health for environmental management purposes. We developed a riverine taxon-independent community index (TICI) that objectively assigns indicator values to amplicon sequence variants (ASVs), and significantly improves the statistical power and utility of eDNA-based bioassessments. The TICI model training step uses the Chessman iterative learning algorithm to assign health indicator scores to a large number of ASVs that are commonly encountered across a wide geographic range. New sites can then be evaluated for ecological health by averaging the indicator value of the ASVs present at the site. We trained a TICI model on an eDNA dataset from 53 well-studied riverine monitoring sites across New Zealand, each sampled with a high level of biological replication ( n  = 16). Eight short-amplicon metabarcoding assays were used to generate data from a broad taxonomic range, including bacteria, microeukaryotes, fungi, plants, and animals. Site-specific TICI scores were strongly correlated with historical stream condition scores from macroinvertebrate assessments (macroinvertebrate community index or MCI; R 2  = 0.82), and TICI variation between sample replicates was minimal (CV = 0.013). Taken together, this demonstrates the potential for taxon-independent eDNA analysis to provide a reliable, robust and low-cost assessment of ecological health that is accessible to environmental managers, decision makers, and the wider community., Competing Interests: Shaun P. Wilkinson, Amy A. Gault and Susan A. Welsh are current employees of Wilderlab NZ Ltd., a commercial eDNA processing laboratory. Megan Shaffer was employed by Wilderlab NZ Ltd. during the course of this study. Joshua P. Smith is an employee of Waikato Regional Council, Hamilton, New Zealand. Bruno O. David was employed by Waikato Regional Council, Hamilton, New Zealand, during the course of this study. Andy S. Hicks is currently employed by the Ministry for the Environment, Wellington, New Zealand, and was employed by Hawke’s Bay Regional Council, Napier, New Zealand, during the course of this study. Daniel R. Fake was employed by Hawke’s Bay Regional Council, Napier, New Zealand, during the course of this study. Alastair M Suren is employed by Bay of Plenty Regional Council, Whakatāne, New Zealand., (©2024 Wilkinson et al.)