Characterising Australian memory clinics: current practice and service needs informing national service guidelines

Background: Memory clinics (MCs) play a key role in accurate and timely diagnoses and treatment of dementia and mild cognitive impairment. However, within Australia, there are little data available on current practices in MCs, which hinder international comparisons for best practice, harmonisation efforts and national coordination. Here, we aimed to characterise current service profiles of Australian MCs. Methods: The ‘Australian Dementia Network Survey of Expert Opinion on Best Practice and the Current Clinical Landscape’ was conducted between August-September 2020 as part of a larger-scale Delphi process deployed to develop national MC guidelines. In this study, we report on the subset of questions pertaining to current practice including wait-times and post-diagnostic care. Results: Responses were received from 100 health professionals representing 60 separate clinics (45 public, 11 private, and 4 university/research clinics). The majority of participants were from clinics in metropolitan areas (79%) and in general were from high socioeconomic areas. While wait-times varied, only 28.3% of clinics were able to offer an appointment within 1-2 weeks for urgent referrals, with significantly more private clinics (58.3%) compared to public clinics (19.5%) being able to do so. Wait-times were less than 8 weeks for 34.5% of non-urgent referrals. Only 20.0 and 30.9% of clinics provided cognitive interventions or post-diagnostic support respectively, with 7.3% offering home-based reablement programs, and only 12.7% offering access to group-based education. Metropolitan clinics utilised neuropsychological assessments for a broader range of cases and were more likely to offer clinical trials and access to research opportunities. Conclusions: In comparison to similar countries with comprehensive government-funded public healthcare systems (i.e., United Kingdom, Ireland and Canada), wait-times for Australian MCs are long, and post-diagnostic support or evidence-based strategies targeting cognition are not common practice. The timely and important results of this study highlight a need for Australian MCs to adopt a more holistic service of multidisciplinary assessment and post-diagnostic support, as well as the need for the number of Australian MCs to be increased to match the rising number of dementia cases

Mainstream short-cut N removal modelling: current status and perspectives

This work gives an overview of the state-of-the-art in modelling of short-cut processes for nitrogen removal in mainstream wastewater treatment and presents future perspectives for directing research efforts in line with the needs of practice. The modelling status for deammonification (i.e., anammox-based) and nitrite-shunt processes is presented with its challenges and limitations. The importance of mathematical models for considering N2O emissions in the design and operation of short-cut nitrogen removal processes is considered as well. Modelling goals and potential benefits are presented and the needs for new and more advanced approaches are identified. Overall, this contribution presents how existing and future mathematical models can accelerate successful full-scale mainstream short-cut nitrogen removal applications

Sampling Rate Prediction of Biosensors in Wireless Body Area Networks using Deep-Learning Methods

In this paper, we propose a scheme which aims at determining and forecasting sampling rate of active biosensors in Wireless Body Area Networks (WBANs). In this regard, from the first round until a certain round, the sampling rate of biosensors would be determined. Accordingly, we introduce our modified Fisher test, develop Spline interpolation method, introduce three main parameters namely information of patient's activity, patient's risk and pivot biosensor's value. Then, by employing these parameters plus introduced statistical and mathematical based strategies, the sampling rate of the active biosensors in the next round would be determined at the end of each entire round. After reaching a pre-denoted round the sampling rate of biosensors would be predicted through forecasting methods. In this regard, we develop two machine learning based techniques namely Adaptive Neuro Fuzzy Inference System (ANFIS) and Long Short Term Memory (LSTM) and compare them with four famous similar techniques. In addition to using forecasted sampling frequencies of the biosensors for controlling their energy expenditure, these forecasted values would also be used to forecast patient's status in the future. This is the first work in this domain that uses current information of the patient to determine adaptive sampling frequency and then employs the time series of determined sampling frequencies to forecast the patient's status and biosensors energy expenditure in the future. For estimating our schemes, we simulated them in MATLAB R2018b software and compared the results with a number of similar schemes. Based on the simulation results, the proposed schemes are capable to reduce data traffic by 81, decrease energy consumption of the network by 73 while having the capability of predicting sampling rate of biosensors with 97 accuracy. © 2020 Elsevier B.V

Tackling Dementia Together via The Australian Dementia Network (ADNeT): A Summary of Initiatives, Progress and Plans

In 2018, the Australian Dementia Network (ADNeT) was established to bring together Australia’s leading dementia researchers, people with living experience and clinicians to transform research and clinical care in the field. To address dementia diagnosis, treatment, and care, ADNeT has established three core initiatives: the Clinical Quality Registry (CQR), Memory Clinics, and Screening for Trials. Collectively, the initiatives have developed an integrated clinical and research community, driving practice excellence in this field, leading to novel innovations in diagnostics, clinical care, professional development, quality and harmonization of healthcare, clinical trials, and translation of research into practice. Australia now has a national Registry for Mild Cognitive Impairment and dementia with 55 participating clinical sites, an extensive map of memory clinic services, national Memory and Cognition Clinic Guidelines and specialized screening for trials sites in five states. This paper provides an overview of ADNeT’s achievements to date and future directions. With the increase in dementia cases expected over coming decades, and with recent advances in plasma biomarkers and amyloid lowering therapies, the nationally coordinated initiatives and partnerships ADNeT has established are critical for increased national prevention efforts, co-ordinated implementation of emerging treatments for Alzheimer’s disease, innovation of early and accurate diagnosis, driving continuous improvements in clinical care and patient outcome and access to post-diagnostic support and clinical trials. For a heterogenous disorder such as dementia, which is now the second leading cause of death in Australia following cardiovascular disease, the case for adequate investment into research and development has grown even more compelling

Gene Expression and Protein Adaptations in Mammalian Hibernation

An understanding of the protein adaptations that support mammalian hibernation is coming from several different approaches. New studies in my lab are (a) using cDNA library screening to identify genes that are up-regulated in hibernation, (b) analyzing the role of reversible protein phosphorylation in the control of membrane ion pumps in torpor, (c) assessing temperature-dependent properties of protein kinases that alter their function in euthermic vs hibernating states, and (d) characterizing fatty acid binding proteins of hibernating vs nonhibernating species to identify properties that support intracellular fatty acid transport at low body temperatures