Subjective Cognitive Impairment Can Be Detected from the Decline of Complex Cognition: Findings from the Examination of Remedes 4 Alzheimer's (R4Alz) Structural Validity.

R4Alz is utilized for the early detection of minor neurocognitive disorders. It was designed to assess three main dimensions of cognitive-control abilities: working-memory capacity, attentional control, and executive functioning. OBJECTIVES: To reveal the cognitive-control dimensions that can differentiate between adults and older adults with healthy cognition, people with subjective cognitive impairment, and people diagnosed with mild cognitive impairment by examining the factorial structure of the R4Alz tool. METHODS: The study comprised 404 participants: (a) healthy adults (n = 192), (b) healthy older adults (n = 29), (c) people with SCI (n = 74), and (d) people diagnosed with MCI (n = 109). The R4Alz battery was administered to all participants, including tests that assess short-term memory storage, information processing, information updating in working memory, and selective, sustained and divided attention), task/rule-switching, inhibitory control, and cognitive flexibility. RESULTS: A two-factorial structural model was confirmed for R4Alz, with the first factor representing "fluid intelligence (FI)" and the second factor reflecting "executive functions (EF)". Both FI and EFs discriminate among all groups. CONCLUSIONS: The R4Alz battery presents sound construct validity, evaluating abilities in FI and EF. Both abilities can differentiate very early cognitive impairment (SCI) from healthy cognitive aging and MCI.

R4Alz-Revised: A Tool Able to Strongly Discriminate 'Subjective Cognitive Decline' from Healthy Cognition and 'Minor Neurocognitive Disorder'.

BACKGROUND: The diagnosis of the minor neurocognitive diseases in the clinical course of dementia before the clinical symptoms' appearance is the holy grail of neuropsychological research. The R4Alz battery is a novel and valid tool that was designed to assess cognitive control in people with minor cognitive disorders. The aim of the current study is the R4Alz battery's extension (namely R4Alz-R), enhanced by the design and administration of extra episodic memory tasks, as well as extra cognitive control tasks, towards improving the overall R4Alz discriminant validity. METHODS: The study comprised 80 people: (a) 20 Healthy adults (HC), (b) 29 people with Subjective Cognitive Decline (SCD), and (c) 31 people with Mild Cognitive Impairment (MCI). The groups differed in age and educational level. RESULTS: Updating, inhibition, attention switching, and cognitive flexibility tasks discriminated SCD from HC (p ≤ 0.003). Updating, switching, cognitive flexibility, and episodic memory tasks discriminated SCD from MCI (p ≤ 0.001). All the R4Alz-R's tasks discriminated HC from MCI (p ≤ 0.001). The R4Alz-R was free of age and educational level effects. The battery discriminated perfectly SCD from HC and HC from MCI (100% sensitivity-95% specificity and 100% sensitivity-90% specificity, respectively), whilst it discriminated excellently SCD from MCI (90.3% sensitivity-82.8% specificity). CONCLUSION: SCD seems to be stage a of neurodegeneration since it can be objectively evaluated via the R4Alz-R battery, which seems to be a useful tool for early diagnosis.

Upon Improving the Performance of Localized Healthcare Virtual Assistants.

Virtual assistants are becoming popular in a variety of domains, responsible for automating repetitive tasks or allowing users to seamlessly access useful information. With the advances in Machine Learning and Natural Language Processing, there has been an increasing interest in applying such assistants in new areas and with new capabilities. In particular, their application in e-healthcare is becoming attractive and is driven by the need to access medically-related knowledge, as well as providing first-level assistance in an efficient manner. In such types of virtual assistants, localization is of utmost importance, since the general population (especially the aging population) is not familiar with the needed "healthcare vocabulary" to communicate facts properly; and state-of-practice proves relatively poor in performance when it comes to specialized virtual assistants for less frequently spoken languages. In this context, we present a Greek ML-based virtual assistant specifically designed to address some commonly occurring tasks in the healthcare domain, such as doctor's appointments or distress (panic situations) management. We build on top of an existing open-source framework, discuss the necessary modifications needed to address the language-specific characteristics and evaluate various combinations of word embeddings and machine learning models to enhance the assistant's behaviour. Results show that we are able to build an efficient Greek-speaking virtual assistant to support e-healthcare, while the NLP pipeline proposed can be applied in other (less frequently spoken) languages, without loss of generality.

Software reusability dataset based on static analysis metrics and reuse rate information.

The widely adopted component-based development paradigm considers the reuse of proper software components as a primary criterion for successful software development. As a result, various research efforts are directed towards evaluating the extent to which a software component is reusable. Prior efforts follow expert-based approaches, however the continuously increasing open-source software initiative allows the introduction of data-driven alternatives. In this context we have generated a dataset that harnesses information residing in online code hosting facilities and introduces the actual reuse rate of software components as a measure of their reusability. To do so, we have analyzed the most popular projects included in the maven registry and have computed a large number of static analysis metrics at both class and package levels using SourceMeter tool [2] that quantify six major source code properties: complexity, cohesion, coupling, inheritance, documentation and size. For these projects we additionally computed their reuse rate using our self-developed code search engine, AGORA [5]. The generated dataset contains analysis information regarding more than 24,000 classes and 2000 packages, and can, thus, be used as the information basis towards the design and development of data-driven reusability evaluation methodologies. The dataset is related to the research article entitled "Measuring the Reusability of Software Components using Static Analysis Metrics and Reuse Rate Information" [1].

REMEDES for Alzheimer-R4Alz Battery: Design and Development of a New Tool of Cognitive Control Assessment for the Diagnosis of Minor and Major Neurocognitive Disorders.

BACKGROUND: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are acknowledged stages of the clinical spectrum of Alzheimer's disease (AD), and cognitive control seems to be among the first neuropsychological predictors of cognitive decline. Existing tests are usually affected by educational level, linguistic abilities, cultural differences, and social status, constituting them error-prone when differentiating between the aforementioned stages. Creating robust neuropsychological tests is therefore prominent. OBJECTIVE: The design of a novel psychometric battery for the cognitive control and attention assessment, free of demographic effects, capable to discriminate cognitively healthy aging, SCD, MCI, and mild Dementia (mD). METHODS: The battery initial hypothesis was tuned using iterations of administration on random sampling healthy older adults and people with SCD, MCI, and mD, from the area of Thessaloniki, Greece. This resulted in the first release of the REflexes MEasurement DEviceS for Alzheimer battery (REMEDES for Alzheimer-R4Alz). RESULTS: The first release lasts for almost an hour. The battery was design to assess working memory (WM) including WM storage, processing, and updating, enriched by episodic buffer recruitment. It was also designed to assess attention control abilities comprising selective, sustained, and divided attention subtasks. Finally, it comprises an inhibitory control, a task/rule switching or set-shifting, and a cognitive flexibility subtask as a combination of inhibition and task/rule switching abilities. CONCLUSION: The R4Alz battery is an easy to use psychometric battery with increasing difficulty levels and assumingly ecological validity, being entertaining for older adults, potentially free of demographic effects, and promising as a more accurate and early diagnosis tool of neurodegeneration.

The hackathon model to spur innovation around global mHealth.

The challenge of providing quality healthcare to underserved populations in low- and middle-income countries (LMICs) has attracted increasing attention from information and communication technology (ICT) professionals interested in providing societal impact through their work. Sana is an organisation hosted at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology that was established out of this interest. Over the past several years, Sana has developed a model of organising mobile health bootcamp and hackathon events in LMICs with the goal of encouraging increased collaboration between ICT and medical professionals and leveraging the growing prevalence of cellphones to provide health solutions in resource limited settings. Most recently, these events have been based in Colombia, Uganda, Greece and Mexico. The lessons learned from these events can provide a framework for others working to create sustainable health solutions in the developing world.