Exploring the relationship between melanopsin gene variants, sleep, and markers of brain health.

Introduction: Melanopsin is a photopigment with roles in mediating sleep and circadian-related processes, which are often disrupted in Alzheimer's disease (AD). Melanopsin also impacts cognition and synaptogenesis. This study investigated the associations between melanopsin genetic variants, sleep, and markers of brain health.

The HOTspots Digital Surveillance: Conceptualisation to Clinical Deployment.

The HOTspots digital surveillance platform (HOTspots) is a critical technology of the HOTspots Surveillance and Response Program. It provides timely point-of-care access to pathology and demographic data from previously underserved regions. Co-designed with clinicians, epidemiologists, and health policy makers, the platform provides the evidence-base to empower efficient clinical management of patients with antimicrobial resistant (AMR) infections and supports national disease surveillance efforts in Australia.

Alzheimer's disease biomarker utilization at first referral enhances differential diagnostic precision with simultaneous exclusion of Creutzfeldt-Jakob disease.

Most suspected Creutzfeldt-Jakob disease (CJD) cases are eventually diagnosed with other disorders. We assessed the utility of investigating Alzheimer's disease (AD) biomarkers and neurofilament light (NfL) in patients when CJD is suspected. The study cohort consisted of cerebrospinal fluid (CSF) samples referred for CJD biomarker screening wherein amyloid beta 1-42 (Aβ1-42), phosphorylated tau 181 (p-tau181), and total tau (t-tau) could be assessed via Elecsys immunoassays ( = 419) and NfL via enzyme-linked immunosorbent assay (ELISA;  = 161).

Repurposing antibiotic resistance surveillance data to support treatment of recurrent infections in a remote setting.

In northern Australia, a region with limited access to healthcare and a substantial population living remotely, antibiotic resistance adds to the complexity of treating infections. Focussing on Escherichia coli urinary tract infections (UTIs) and Staphylococcus aureus skin & soft tissue infections (SSTIs) captured by a northern Australian antibiotic resistance surveillance system, we used logistic regression to investigate predictors of a subsequent resistant isolate during the same infection episode. We also investigated predictors of recurrent infection.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan.

A qualitative exploration of the non-financial costs of cancer care for Aboriginal and Torres Strait Islander Australians.

Objective: Knowledge is growing about cancer care and financial costs for Aboriginal and Torres Strait Islander people. However, much remains unknown about the true costs of cancer care, encompassing financial, emotional, and spiritual aspects. We aimed to explore and explain how non-financial costs affect the health-seeking behaviours of these clients.

Can non-typeable carriage surveillance data infer antimicrobial resistance associated with otitis media?

Importance: In remote communities of the Northern Territory, Australia, children experience high rates of otitis media (OM), commonly caused by non-typeable (NTHi). Few data exist on antibiotic susceptibility of NTHi from OM.

Objective: To determine whether population-level nasopharyngeal NTHi antibiotic susceptibility data could inform antibiotic treatment for OM.

Cerebrospinal fluid levels of fatty acid-binding protein 3 are associated with likelihood of amyloidopathy in cognitively healthy individuals.

Introduction: Fatty acid-binding protein 3 (FABP3) is a biomarker of neuronal membrane disruption, associated with lipid dyshomeostasis-a notable Alzheimer's disease (AD) pathophysiological change. We assessed the association of cerebrospinal fluid (CSF) FABP3 levels with brain amyloidosis and the likelihood/risk of developing amyloidopathy in cognitively healthy individuals.

Methods: FABP3 levels were measured in CSF samples of cognitively healthy participants, > 60 years of age ( = 142), from the Australian Imaging, Biomarkers & Lifestyle Flagship Study of Ageing (AIBL).

Amyloid and tau PET-positive cognitively unimpaired individuals are at high risk for future cognitive decline.

A major unanswered question in the dementia field is whether cognitively unimpaired individuals who harbor both Alzheimer's disease neuropathological hallmarks (that is, amyloid-β plaques and tau neurofibrillary tangles) can preserve their cognition over time or are destined to decline. In this large multicenter amyloid and tau positron emission tomography (PET) study (n = 1,325), we examined the risk for future progression to mild cognitive impairment and the rate of cognitive decline over time among cognitively unimpaired individuals who were amyloid PET-positive (A) and tau PET-positive (T) in the medial temporal lobe (AT) and/or in the temporal neocortex (AT) and compared them with AT and AT groups. Cox proportional-hazards models showed a substantially increased risk for progression to mild cognitive impairment in the AT (hazard ratio (HR) = 19.

Disease burden, associated mortality and economic impact of antimicrobial resistant infections in Australia.

Background: The growing spread of antimicrobial resistance (AMR) is accepted as a threat to humans, animals and the environment. This threat is considered to be both country specific and global, with bacteria resistant to antibiotic treatment geographically dispersed. Despite this, we have very few Australian estimates available that use national surveillance data supplemented with measures of risk, to generate reliable and actionable measures of AMR impact.

Contribution of socio-economic factors in the spread of antimicrobial resistant infections in Australian primary healthcare clinics.

Objectives: To effectively contain antimicrobial-resistant (AMR) infections, we must better understand the social determinates of health that contribute to transmission and spread of infections.

Methods: We used clinical data from patients attending primary healthcare clinics across three jurisdictions of Australia (2007-2019). Escherichia coli (E.

The Increased Length of Hospital Stay and Mortality Associated With Community-Associated Infections in Australia.

Background: An increasing proportion of antibiotic-resistant infections are community acquired. However, the burden of community-associated infections (CAIs) and the resulting impact due to resistance have not been well described.

Methods: We conducted a multisite, retrospective case-cohort study of all acute care hospital admissions across 134 hospitals in Australia.

Plasma p217+tau versus NAV4694 amyloid and MK6240 tau PET across the Alzheimer's continuum.

Introduction: We evaluated a new Simoa plasma assay for phosphorylated tau (P-tau) at aa217 enhanced by additional p-tau sites (p217+tau).

Methods: Plasma p217+tau levels were compared to F-NAV4694 amyloid beta (Aβ) positron emission tomography (PET) and F-MK6240 tau PET in 174 cognitively impaired (CI) and 223 cognitively unimpaired (CU) participants.

Results: Compared to Aβ- CU, the plasma levels of p217+tau increased 2-fold in Aβ+ CU and 3.

The antimicrobial resistance travel tool, an interactive evidence-based educational tool to limit antimicrobial resistance spread.

Background: International travel has been recognized as a risk factor contributing to the spread of antimicrobial resistance (AMR). However, tools focused on AMR in the context of international travel and designed to guide decision-making are limited. We aimed at developing an evidence-based educational tool targeting both healthcare professionals (HCPs) and international travellers to help prevent the spread of AMR.

A deep learning framework identifies dimensional representations of Alzheimer's Disease from brain structure.

Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuroimaging signatures. When applied to regional volumes derived from T1-weighted MRI (two studies; 2,832 participants; 8,146 scans) including cognitively normal individuals and those with cognitive impairment and dementia, Smile-GAN identified four patterns or axes of neurodegeneration.

Deep Generative Medical Image Harmonization for Improving Cross-Site Generalization in Deep Learning Predictors.

Background: In the medical imaging domain, deep learning-based methods have yet to see widespread clinical adoption, in part due to limited generalization performance across different imaging devices and acquisition protocols. The deviation between estimated brain age and biological age is an established biomarker of brain health and such models may benefit from increased cross-site generalizability.

Purpose: To develop and evaluate a deep learning-based image harmonization method to improve cross-site generalizability of deep learning age prediction.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Introduction: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

Methods: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning.

Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan.

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old).