Developing Digital Tools for Remote Clinical Research:How to Evaluate the Validity and Practicality of Active Assessments in Field Settings

The ability of remote research tools to collect granular, high-frequency data on symptoms and digital biomarkers is an important strength because it circumvents many limitations of traditional clinical trials and improves the ability to capture clinically relevant data. This approach allows researchers to capture more robust baselines and derive novel phenotypes for improved precision in diagnosis and accuracy in outcomes. The process for developing these tools however is complex because data need to be collected at a frequency that is meaningful but not burdensome for the participant or patient. Furthermore, traditional techniques, which rely on fixed conditions to validate assessments, may be inappropriate for validating tools that are designed to capture data under flexible conditions. This paper discusses the process for determining whether a digital assessment is suitable for remote research and offers suggestions on how to validate these novel tools

Emotional/Psychiatric Symptom Change and Amygdala Volume After Anterior Temporal Lobectomy

Introduction Patients who undergo anterior temporal lobectomy (ATL) to treat temporal lobe epilepsy (TLE) often experience worsened or de novo psychiatric symptoms. There is evidence to suggest that the pathophysiology of epilepsy and mood disorders are linked both functionally or structurally in the brain.1,2 While several studies have examined the role that changes in hippocampal volume may play in predicting post-surgical depression, the role of the amygdala in such prediction has been overlooked, despite extensive literature demonstrating its contribution to emotion processing and expression.3,4 The goal of this project was to determine if change in amygdala volume is a predictor of depression and/or anxiety in TLE patients who undergo ATL, with specific attention given to side of surgery. Methods Data was collected from 32 patients who underwent ATLs (19 right, 13 left, matched samples). Pre- and post-surgery Personality Assessment Inventory (PAI) data were collected on 14 ATL patients. The following PAI subscales were utilized in this analysis: Anxiety: PAIANX; Anxiety Related Disorder: PAIARD; Depression: PAIDEP). Volumetric analysis was performed on pre- and post-surgical T1 MRIs using Freesurfer’s longitudinal processing function. Left and right amygdala volumes, change scores, and amygdala asymmetry ratios were calculated taking into account whole brain volume. 55% of the patients were seizurefree after 1 year (RTLE= 8, LTLE= 9); 29% received an Engel Class score of 2 or 3 (RTLE= 7, LTLE= 2

Sum rule for the backward spin polarizability of the nucleon from a backward dispersion relation

A new sum rule for $\gamma_\pi$, the backward spin polarizability of the nucleon, is derived from a backward-angle dispersion relation. Taking into account single- and multi-pion photoproduction in the s-channel up to the energy 1.5 GeV and resonances in the t-channel with mass below 1.5 GeV, it is found for the proton and neutron that $[\gamma_\pi]_p$ = -39.5 +/- 2.4 and $[\gamma_\pi]_n$ = 52.5 +/- 2.4, respectively, in units of 10^{-4} fm^4.Comment: 10 pages, 1 figure, revtex. Submitted to Phys. Lett.

Decay constants, semi-leptonic and non-leptonic decays in a Bethe-Salpeter Model

We evaluate the decay constants for the B and $D$ mesons and the form factors for the semileptonic decays of the B meson to $D$ and $D^*$ mesons in a Bethe-Salpeter model. From data we extract $V_{cb}=0.039 \pm 0.002$ from ${\bar B} \to D^* l {\bar{\nu}}$ and $V_{cb}=0.037 \pm 0.004$ from ${\bar B} \to D l {\bar{\nu}}$ decays. The form factors are then used to obtain non-leptonic decay partial widths for $B\to D \pi (K)$ and $B \to D D (D_s)$ in the factorization approximation.Comment: 15 Pages, 3 Postscript figures (available also from [email protected]

Effects of Vacuum Annealing on the Conduction Characteristics of ZnO Nanosheets

This paper is open acess and available in full at http://www.nanoscalereslett.com/content/10/1/368 .ZnO nanosheets are a relatively new form of nanostructure and have demonstrated potential as gas-sensing devices and dye sensitised solar cells. For integration into other devices, and when used as gas sensors, the nanosheets are often heated. Here we study the effect of vacuum annealing on the electrical transport properties of ZnO nanosheets in order to understand the role of heating in device fabrication. A low cost, mass production method has been used for synthesis and characterisation is achieved using scanning electron microscopy (SEM), photoluminescence (PL), auger electron spectroscopy (AES) and nanoscale two-point probe. Before annealing, the measured nanosheet resistance displayed a non-linear increase with probe separation, attributed to surface contamination. Annealing to 300 °C removed this contamination giving a resistance drop, linear probe spacing dependence, increased grain size and a reduction in the number of n-type defects. Further annealing to 500 °C caused the n-type defect concentration to reduce further with a corresponding increase in nanosheet resistance not compensated by any further sintering. At 700 °C, the nanosheets partially disintegrated and the resistance increased and became less linear with probe separation. These effects need to be taken into account when using ZnO nanosheets in devices that require an annealing stage during fabrication or heating during use

Rethinking the patient: using Burden of Treatment Theory to understand the changing dynamics of illness

<b>Background</b> In this article we outline Burden of Treatment Theory, a new model of the relationship between sick people, their social networks, and healthcare services. Health services face the challenge of growing populations with long-term and life-limiting conditions, they have responded to this by delegating to sick people and their networks routine work aimed at managing symptoms, and at retarding - and sometimes preventing - disease progression. This is the new proactive work of patient-hood for which patients are increasingly accountable: founded on ideas about self-care, self-empowerment, and self-actualization, and on new technologies and treatment modalities which can be shifted from the clinic into the community. These place new demands on sick people, which they may experience as burdens of treatment.<p></p> <b>Discussion</b> As the burdens accumulate some patients are overwhelmed, and the consequences are likely to be poor healthcare outcomes for individual patients, increasing strain on caregivers, and rising demand and costs of healthcare services. In the face of these challenges we need to better understand the resources that patients draw upon as they respond to the demands of both burdens of illness and burdens of treatment, and the ways that resources interact with healthcare utilization.<p></p> <b>Summary</b> Burden of Treatment Theory is oriented to understanding how capacity for action interacts with the work that stems from healthcare. Burden of Treatment Theory is a structural model that focuses on the work that patients and their networks do. It thus helps us understand variations in healthcare utilization and adherence in different healthcare settings and clinical contexts

Association of Accelerometry-Measured Physical Activity and Cardiovascular Events in Mobility-Limited Older Adults: The LIFE (Lifestyle Interventions and Independence for Elders) Study.

BACKGROUND:Data are sparse regarding the value of physical activity (PA) surveillance among older adults-particularly among those with mobility limitations. The objective of this study was to examine longitudinal associations between objectively measured daily PA and the incidence of cardiovascular events among older adults in the LIFE (Lifestyle Interventions and Independence for Elders) study. METHODS AND RESULTS:Cardiovascular events were adjudicated based on medical records review, and cardiovascular risk factors were controlled for in the analysis. Home-based activity data were collected by hip-worn accelerometers at baseline and at 6, 12, and 24 months postrandomization to either a physical activity or health education intervention. LIFE study participants (n=1590; age 78.9±5.2 [SD] years; 67.2% women) at baseline had an 11% lower incidence of experiencing a subsequent cardiovascular event per 500 steps taken per day based on activity data (hazard ratio, 0.89; 95% confidence interval, 0.84-0.96; P=0.001). At baseline, every 30 minutes spent performing activities ≥500 counts per minute (hazard ratio, 0.75; confidence interval, 0.65-0.89 [P=0.001]) were also associated with a lower incidence of cardiovascular events. Throughout follow-up (6, 12, and 24 months), both the number of steps per day (per 500 steps; hazard ratio, 0.90, confidence interval, 0.85-0.96 [P=0.001]) and duration of activity ≥500 counts per minute (per 30 minutes; hazard ratio, 0.76; confidence interval, 0.63-0.90 [P=0.002]) were significantly associated with lower cardiovascular event rates. CONCLUSIONS:Objective measurements of physical activity via accelerometry were associated with cardiovascular events among older adults with limited mobility (summary score >10 on the Short Physical Performance Battery) both using baseline and longitudinal data. CLINICAL TRIAL REGISTRATION:URL: http://www.clinicaltrials.gov. Unique identifier: NCT01072500

Inferring learning from big data:The importance of a transdisciplinary and multidimensional approach

The use of big data in higher education has evolved rapidly with a focus on the practical application of new tools and methods for supporting learning. In this paper, we depart from the core emphasis on application and delve into a mostly neglected aspect of the big data conversation in higher education. Drawing on developments in cognate disciplines, we analyse the inherent difficulties in inferring the complex phenomenon that is learning from big datasets. This forms the basis of a discussion about the possibilities for systematic collaboration across different paradigms and disciplinary backgrounds in interpreting big data for enhancing learning. The aim of this paper is to provide the foundation for a research agenda, where differing conceptualisations of learning become a strength in interpreting patterns in big datasets, rather than a point of contention

Achieving Optimal Growth through Product Feedback Inhibition in Metabolism

Recent evidence suggests that the metabolism of some organisms, such as Escherichia coli, is remarkably efficient, producing close to the maximum amount of biomass per unit of nutrient consumed. This observation raises the question of what regulatory mechanisms enable such efficiency. Here, we propose that simple product-feedback inhibition by itself is capable of leading to such optimality. We analyze several representative metabolic modules—starting from a linear pathway and advancing to a bidirectional pathway and metabolic cycle, and finally to integration of two different nutrient inputs. In each case, our mathematical analysis shows that product-feedback inhibition is not only homeostatic but also, with appropriate feedback connections, can minimize futile cycling and optimize fluxes. However, the effectiveness of simple product-feedback inhibition comes at the cost of high levels of some metabolite pools, potentially associated with toxicity and osmotic imbalance. These large metabolite pool sizes can be restricted if feedback inhibition is ultrasensitive. Indeed, the multi-layer regulation of metabolism by control of enzyme expression, enzyme covalent modification, and allostery is expected to result in such ultrasensitive feedbacks. To experimentally test whether the qualitative predictions from our analysis of feedback inhibition apply to metabolic modules beyond linear pathways, we examine the case of nitrogen assimilation in E. coli, which involves both nutrient integration and a metabolic cycle. We find that the feedback regulation scheme suggested by our mathematical analysis closely aligns with the actual regulation of the network and is sufficient to explain much of the dynamical behavior of relevant metabolite pool sizes in nutrient-switching experiments