Search

BackgroundBetween 2013 and 2015, the UK Biobank collected accelerometer traces from 103,712 volunteers aged between 40 and 69 years using wrist-worn triaxial accelerometers for 1 week. This data set has been used in the past to verify that individuals with chronic diseases exhibit reduced activity levels compared with healthy populations. However, the data set is likely to be noisy, as the devices were allocated to participants without a set of inclusion criteria, and the traces reflect free-living conditions. ObjectiveThis study aims to determine the extent to which accelerometer traces can be used to distinguish individuals with type 2 diabetes (T2D) from normoglycemic controls and to quantify their limitations. MethodsMachine learning classifiers were trained using different feature sets to segregate individuals with T2D from normoglycemic individuals. Multiple criteria, based on a combination of self-assessment UK Biobank variables and primary care health records linked to UK Biobank participants, were used to identify 3103 individuals with T2D in this population. The remaining nondiabetic 19,852 participants were further scored on their physical activity impairment severity based on other conditions found in their primary care data, and those deemed likely physically impaired at the time were excluded. Physical activity features were first extracted from the raw accelerometer traces data set for each participant using an algorithm that extends the previously developed Biobank Accelerometry Analysis toolkit from Oxford University. These features were complemented by a selected collection of sociodemographic and lifestyle features available from UK Biobank. ResultsWe tested 3 types of classifiers, with an area under the receiver operating characteristic curve (AUC) close to 0.86 (95% CI 0.85-0.87) for all 3 classifiers and F1 scores in the range of 0.80-0.82 for T2D-positive individuals and 0.73-0.74 for T2D-negative controls. Results obtained using nonphysically impaired controls were compared with highly physically impaired controls to test the hypothesis that nondiabetic conditions reduce classifier performance. Models built using a training set that included highly impaired controls with other conditions had worse performance (AUC 0.75-0.77; 95% CI 0.74-0.78; F1 scores in the range of 0.76-0.77 for T2D positives and 0.63-0.65 for controls). ConclusionsGranular measures of free-living physical activity can be used to successfully train machine learning models that are able to discriminate between individuals with T2D and normoglycemic controls, although with limitations because of the intrinsic noise in the data sets. From a broader clinical perspective, these findings motivate further research into the use of physical activity traces as a means of screening individuals at risk of diabetes and for early detection, in conjunction with routinely used risk scores, provided that appropriate quality control is enforced on the data collection protocol.

Potential applications of the earth-abundant, low-cost, and non-critical perovskite CaTi1-x Fe x O3-δ in electrocatalysis, photocatalysis, and oxygen-transport membranes have motivated research to tune its chemical composition and morphology. However, investigations on the decomposition mechanism(s) of CaTi1-x Fe x O3-δ under thermochemically reducing conditions are limited, and direct evidence of the nano- and atomic-level decomposition process is not available in the literature. In this work, the phase evolution of CaTi1-x Fe x O3-δ (x = 0-0.4) was investigated in a H2-containing atmosphere after heat treatments up to 600 °C. The results show that CaTi1-x Fe x O3-δ maintained a stable perovskite phase at low Fe contents while exhibiting a phase decomposition to Fe/Fe oxide nanoparticles as the Fe content increases. In CaTi0.7Fe0.3O3-δ and CaTi0.6Fe0.4O3-δ, the phase evolution to Fe/Fe oxide was greatly influenced by the temperature: Only temperatures of 300 °C and greater facilitated phase evolution. Fully coherent Fe-rich and Fe-depleted perovskite nanodomains were observed directly by atomic-resolution scanning transmission electron microscopy. Prior evidence for such nanodomain formation was not found, and it is thought to result from a near-surface Kirkendall-like phenomenon caused by Fe migration in the absence of Ca and Ti co-migration. Density functional theory simulations of Fe-doped bulk models reveal that Fe in an octahedral interstitial site is energetically more favorable than in a tetrahedral site. In addition to coherent nanodomains, agglomerated Fe/Fe oxide nanoparticles formed on the ceramic surface during decomposition, which altered the electrical transport mechanism. From temperature-dependent electrical conductivity measurements, it was found that heat treatment and phase decomposition change the transport mechanism from thermally activated p-type electronic conductivity through the perovskite to electronic conduction through the iron oxide formed by thermochemical decomposition. This understanding will be useful to those who are developing or employing this and similar earth-abundant functional perovskites for use under reducing conditions, at elevated temperatures, and when designing materials syntheses and processes.

Microfluidic droplet generation typically entails an initial stabilization period on the order of minutes, exhibiting higher variation in droplet volume until the system reaches monodisperse production. The material lost during this period can be problematic when preparing droplets from limited samples such as patient biopsies. Active droplet generation strategies such as antiphase peristaltic pumping effectively reduce stabilization time but have required off-chip control hardware that reduces system accessibility. We present a fully integrated device that employs on-chip pneumatic logic to control phase-optimized peristaltic pumping. Droplet generation stabilizes in about a second, with only one or two non-uniform droplets produced initially.

Introduction: Post-acute COVID syndrome (PACS) is a growing concern, given its impact on mental health and quality of life. However, its effects on cerebral white matter remain poorly understood, particularly in non-hospitalized cohorts. The goals of this cross-sectional, observational study were to examine (1) whether PACS was associated with distinct alterations in white matter microstructure, compared to symptom-matched non-COVID viral infection; and (2) whether microstructural alterations correlated with indices of post-COVID emotional health. Methods: Data were collected for 54 symptomatic individuals who tested positive for COVID-19 (mean age 41 ± 12 yrs., 36 female) and 14 controls who tested negative for COVID-19 (mean age 41 ± 14 yrs., 8 female), with both groups assessed an average of 4-5 months after COVID testing. Diffusion magnetic resonance imaging data were collected, and emotional health was assessed via the NIH emotion toolbox, with summary scores indexing social satisfaction, well-being and negative affect. Results: Despite similar symptoms, the COVID-19 group had reduced mean and axial diffusivity, along with increased mean kurtosis and neurite dispersion, in deep white matter. After adjusting for social satisfaction, higher levels of negative affect in the COVID-19 group were also correlated with increased mean kurtosis and reduced free water in white matter. Discussion: These results provide preliminary evidence that indices of white matter microstructure distinguish PACS from symptomatic non-COVID infection. Moreover, white matter effects seen in PACS correlate with the severity of emotional sequelae, providing novel insights into this highly prevalent disorder. [ABSTRACT FROM AUTHOR]

This article examines the impact of magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy on caregiver burden in individuals with essential tremor (ET). The study included 18 patients with medication-refractory ET and their caregivers. The results showed that MRgFUS thalamotomy not only reduced the need for caregiver assistance with daily tasks but also decreased caregiver burden. However, there was no significant change in ET-specific caregiver burden. The study suggests that caregiver burden in ET may be more influenced by caregivers' perception of patients' distress rather than solely by tremor severity. Further research with larger sample sizes and longer follow-up durations is needed to explore these findings. [Extracted from the article]

Manufacturing thick (>10 mm) alumina (Al2O3) parts through the ceramic additive manufacturing technique of digital light processing (DLP) is difficult due to the large amount of polymer that must be removed. Nearly 35–65 vol% of a fugitive UV‐curable polymer is used in commercial ceramic slurries in order to maintain shape through the DLP process. During the thermal debind, gases form and escape quickly due to pressure build‐up, leading to defects in the form of print layer delamination. Here, we analyze the polymer decomposition through thermogravimetric analysis in order to design a less aggressive thermal debind schedule through strategic thermal holds and decreased heating rates. The new schedule led to a 83% reduction in average delaminations per sample (from 6.9 delaminations per sample to 1.2 delaminations per sample) and up to a 100% increase in wall thickness (from nominally 10–20 mm). A complex, Al2O3 turbine rotor (with maximum wall thickness of 5 mm in the center hub section) of a JetCat P400 engine was fabricated to compare the new schedule to the manufacturer provided one. The improved thermal debind schedule produced a turbine rotor with no observable delamination defects. [ABSTRACT FROM AUTHOR]

Aims: To examine the psychometric properties of the Diabetes Management Experiences Questionnaire (DME‐Q). Adapted from the validated Glucose Monitoring Experiences Questionnaire, the DME‐Q captures satisfaction with diabetes management irrespective of treatment modalities. Methods: The DME‐Q was completed by adults with type 1 diabetes as part of a randomized controlled trial comparing hybrid closed loop (HCL) to standard therapy. Most psychometric properties were examined with pre‐randomization data (n = 149); responsiveness was examined using baseline and 26‐week follow‐up data (n = 120). Results: Pre‐randomization, participants' mean age was 44 ± 12 years, 52% were women. HbA1c was 61 ± 11 mmol/mol (7.8 ± 1.0%), diabetes duration was 24 ± 12 years and 47% used an insulin pump prior to the trial. A forced three‐factor analysis revealed three expected domains, that is, 'Convenience', 'Effectiveness' and 'Intrusiveness', and a forced one‐factor solution was also satisfactory. Internal consistency reliability was strong for the three subscales (α range = 0.74–0.84) and 'Total satisfaction' (α = 0.85). Convergent validity was demonstrated with moderate correlations between DME‐Q 'Total satisfaction' and diabetes distress (PAID: rs = −0.57) and treatment satisfaction (DTSQ; rs = 0.58). Divergent validity was demonstrated with a weak correlation with prospective/retrospective memory (PRMQ: rs = −0.16 and − 0.13 respectively). Responsiveness was demonstrated, as participants randomized to HCL had higher 'Effectiveness' and 'Total satisfaction' scores than those randomized to standard therapy. Conclusions: The 22‐item DME‐Q is a brief, acceptable, reliable measure with satisfactory structural and construct validity, which is responsive to intervention. The DME‐Q is likely to be useful for evaluation of new pharmaceutical agents and technologies in research and clinical settings. [ABSTRACT FROM AUTHOR]

Background: Neurological symptoms associated with coronavirus disease 2019 (COVID‐19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post‐COVID‐19 timeframe. Purpose: To determine whether adults who experienced flu‐like symptoms due to COVID‐19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu‐like symptoms but tested negative for COVID‐19. Study Type: Prospective observational. Population: A total of 39 adults who previously self‐isolated at home due to COVID‐19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu‐like symptoms but had a negative COVID‐19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). Field Strength and Sequences: A 3.0 T; T1‐weighted magnetization‐prepared rapid gradient and echo‐planar turbo gradient‐spin echo arterial spin labeling sequences. Assessment: Arterial spin labeling was used to estimate CBF. A self‐reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID‐19 and control groups and between those with (n = 11) and without self‐reported ongoing fatigue (n = 28) within the COVID‐19 group. Statistical Tests: Between‐group and within‐group comparisons of CBF were performed in a voxel‐wise manner, controlling for age and sex, at a family‐wise error rate of 0.05. Results: Relative to controls, the COVID‐19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t‐statistic = 5.21). Within the COVID‐19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self‐reported on‐going fatigue. Data Conclusion: These cross‐sectional data revealed regional CBF decreases in the COVID‐19 group, suggesting the relevance of brain physiology in the post‐COVID‐19 timeframe. This research may help elucidate the heterogeneous symptoms of the post‐COVID‐19 condition. Evidence Level: 2. Technical Efficacy: Stage 3. [ABSTRACT FROM AUTHOR]