26 research outputs found
Associations between wearable device-measured sleep variability and cognition among older adults
Healthy sleep habits are protective against adverse health outcomes, but it
is unclear how strongly sleep intraindividual variability is associated with
cognitive function among older adults. In this study we aimed to examine how
accelerometer-derived intraindividual variability in sleep duration,
efficiency, onset timing, and offset timing is associated with cognition using
cross-sectional data from the 2011-2014 waves of the National Health and
Nutrition Examination Survey (NHANES). Cognition was assessed by creating a
composite measure derived by summing z-scores from the Digit Symbol
Substitution Test (DSST), Consortium to Establish a Registry for Alzheimers
Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). A
final cohort of 2508 older adults aged 60+ with at least three days of
accelerometer wear time who completed all three cognitive tests were included
in this study. After centering all sleep intraindividual variability metrics
and adjusting for demographic factors, the presence of diabetes, depressive
symptoms, and measures of functional independence, we found that increased
intraindividual variability in sleep onset timing was associated with worse
cognition (Beta, -0.12; 95% CI, -0.19 to -0.05), as was increased
intraindividual variability in sleep efficiency (Beta, -0.12; 95% CI, -0.20 to
-0.05), and increased intraindividual variability in sleep duration (Beta,
-0.10; 95% CI, -0.17 to -0.03). This study suggests that sleep guidance aimed
at preserving cognition among older adults could be revised to include a focus
on sleep consistency regarding onset timing, quality, and duration.Comment: Typo corrected in the abstrac
Using wearable device-based machine learning models to autonomously identify older adults with poor cognition
Conducting cognitive tests is time-consuming for patients and clinicians.
Wearable device-based prediction models allow for continuous health monitoring
under normal living conditions and could offer an alternative to identifying
older adults with cognitive impairments for early interventions. In this study,
we first derived novel wearable-based features related to circadian rhythms,
ambient light exposure, physical activity levels, sleep, and signal processing.
Then, we quantified the ability of wearable-based machine-learning models to
predict poor cognition based on outcomes from the Digit Symbol Substitution
Test (DSST), the Consortium to Establish a Registry for Alzheimers Disease
Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found
that the wearable-based models had significantly higher AUCs when predicting
all three cognitive outcomes compared to benchmark models containing age, sex,
education, marital status, household income, diabetic status, depression
symptoms, and functional independence scores. In addition to uncovering
previously unidentified wearable-based features that are predictive of poor
cognition such as the standard deviation of the midpoints of each persons most
active 10-hour periods and least active 5-hour periods, our paper provides
proof-of-concept that wearable-based machine learning models can be used to
autonomously screen older adults for possible cognitive impairments. Such
models offer cost-effective alternatives to conducting initial screenings
manually in clinical settings
Coordinate-based Neural Network for Fourier Phase Retrieval
Fourier phase retrieval is essential for high-definition imaging of nanoscale
structures across diverse fields, notably coherent diffraction imaging. This
study presents the Single impliCit neurAl Network (SCAN), a tool built upon
coordinate neural networks meticulously designed for enhanced phase retrieval
performance. Remedying the drawbacks of conventional iterative methods which
are easiliy trapped into local minimum solutions and sensitive to noise, SCAN
adeptly connects object coordinates to their amplitude and phase within a
unified network in an unsupervised manner. While many existing methods
primarily use Fourier magnitude in their loss function, our approach
incorporates both the predicted magnitude and phase, enhancing retrieval
accuracy. Comprehensive tests validate SCAN's superiority over traditional and
other deep learning models regarding accuracy and noise robustness. We also
demonstrate that SCAN excels in the ptychography setting
Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus
Systemic lupus erythematosus (SLE; OMIM 152700) is a genetically complex autoimmune disease. Genome-wide association studies (GWASs) have identified more than 50 loci as robustly associated with the disease in single ancestries, but genome-wide transancestral studies have not been conducted. We combined three GWAS data sets from Chinese (1,659 cases and 3,398 controls) and European (4,036 cases and 6,959 controls) populations. A meta-analysis of these studies showed that over half of the published SLE genetic associations are present in both populations. A replication study in Chinese (3,043 cases and 5,074 controls) and European (2,643 cases and 9,032 controls) subjects found ten previously unreported SLE loci. Our study provides further evidence that the majority of genetic risk polymorphisms for SLE are contained within the same regions across both populations. Furthermore, a comparison of risk allele frequencies and genetic risk scores suggested that the increased prevalence of SLE in non-Europeans (including Asians) has a genetic basis
Associations between wearable device-measured sleep variability and cognition among older adults
Importance: Healthy sleep habits are protective against adverse health outcomes, but it is unclear how strongly sleep intraindividual variability is associated with cognitive function among older adults. Objective: To examine how intraindividual variability in sleep duration, efficiency, onset timing, and offset timing is associated with cognition among older adults in the United States. Design: Cross-sectional Setting: 2011-2014 waves of the National Health and Nutrition Examination Survey (NHANES) Participants: Older adults aged 60+ with valid accelerometer and cognitive test data Exposures: Accelerometer-derived variability in sleep duration, efficiency, onset timing, and offset timing. Average metrics were also considered for comparison purposes. Main Outcome and Measures: A composite cognitive measure derived by summing z-scores from the Digit Symbol Substitution Test (DSST), Consortium to Establish a Registry for Alzheimer’s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Results: A final cohort of 2508 older adults aged 60+ with at least three days of accelerometer wear time who completed all three cognitive tests in the NHANES 2011-2014 waves were included in this study. After adjusting for demographic factors, the presence of diabetes, depressive symptoms, and measures of functional independence, we found that increased intraindividual variability in sleep onset timing was associated with worse cognition (β, -0.12; 95% CI, -0.19 to -0.05), as was increased intraindividual variability in sleep efficiency (β, -0.12; 95% CI, -0.20 to -0.05), and increased intraindividual variability in sleep duration (β, -0.10; 95% CI, -0.17 to -0.03). Conclusion and Relevance: This study found that greater intraindividual variability in sleep duration, efficiency, and onset timing were significantly associated with worse cognition among older adults. Sleep variability metrics can be useful targets for interventions seeking to decrease the risk of cognitive impairments.</p
Identifying the most predictive risk factors for future cognitive impairment among elderly Chinese
Importance: In China, the societal burden of cognitive impairments continues to increase as the country ages, but our knowledge remains limited regarding how to accurately predict future cognitive impairment at the individual level for preventative interventions. Identifying the most predictive risk factors and socioeconomic groups where predictions are less accurate would provide a foundation for developing targeted prediction models that can identify elderly at high risks of future cognitive impairments. Objectives: To quantify how well demographics, instrumental activities of daily living, activities of daily living, cognitive tests, social factors, psychological factors, diet, exercise and sleep, chronic diseases, and three recently published prediction models predict future cognitive impairments in the general Chinese population and among male, female, rural, urban, educated, and uneducated elderly. Design: The Chinese Longitudinal Healthy Longevity Survey (CLHLS) is a prospective cohort study of elderly Chinese from 23 provinces. Individual information from the 2011 CLHLS survey was used to predict if participants would become cognitively impaired by follow-up in 2014. Setting: Population-based. Participants: 4047 CLHLS participants 60 years of age or older without cognitive impairments at baseline were included. Main Outcome: Cognitive impairment was identified through the Chinese language version of the Mini Mental State Examination (MMSE). Predictive ability was quantified using the AUC, sensitivity, and specificity across 20 repeats of 10-fold cross validation where the target variable was an indicator of cognitive impairment 3 years from the baseline survey. Results: A total of 337 (8.3%) elderly Chinese became cognitively impaired by the follow up survey. The risk factor groups with the most predictive ability in the general population were demographics (AUC, 0.78, 95% CI, 0.77-0.78), cognitive tests (AUC, 0.72, 95% CI, 0.72-0.73), and instrumental activities of daily living (AUC, 0.71, 95% CI, 0.70-0.71). Demographics, cognitive tests, instrumental activities of daily living, and all three re-created prediction models had significantly higher AUCs when making predictions among women compared to men and among the uneducated compared to the educated. Dietary factors, which have yet to be included in prediction models in China, had more predictive power (AUC, 0.59, 95% CI, 0.58-0.60) than activities of daily living (AUC, 0.57, 95% CI, 0.56-0.57), psychological factors (AUC, 0.58, 95% CI, 0.57-0.59), and chronic diseases (AUC, 0.53, 95% CI, 0.52-0.53). Conclusion and relevance: This study suggests that demographics, cognitive tests, and instrumental activities of daily living are the most useful risk factors for predicting future cognitive impairment among elderly Chinese. However, the most useful risk factors and existing models have lower predictive power among male, urban, and educated elderly Chinese. More efforts are needed to ensure that equally accurate risk assessments can be conducted across different socioeconomic groups in China.</p
Using wearable device-based machine learning models to autonomously identify older adults with poor cognition
Conducting cognitive tests is time-consuming for patients and clinicians. Wearable device-based prediction models allow for continuous health monitoring under normal living conditions and could offer an alternative to identifying older adults with cognitive impairments for early interventions. In this study, we first derived novel wearable-based features related to circadian rhythms, ambient light exposure, physical activity levels, sleep, and signal processing. Then, we quantified the ability of wearable-based machine-learning models to predict poor cognition based on outcomes from the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer’s Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). We found that the wearable-based models had significantly higher AUCs when predicting all three cognitive outcomes compared to benchmark models containing age, sex, education, marital status, household income, diabetic status, depression symptoms, and functional independence scores. In addition to uncovering previously unidentified wearable-based features that are predictive of poor cognition such as the standard deviation of the midpoints of each person’s most active 10-hour periods and least active 5-hour periods, our paper provides proof-of-concept that wearable-based machine learning models can be used to autonomously screen older adults for possible cognitive impairments. Such models offer cost-effective alternatives to conducting initial screenings manually in clinical settings.</p
LncRNA ADAMTS9-AS1 knockdown restricts cell proliferation and EMT in non-small cell lung cancer
A recent bioinformatics analysis identified
long non‐coding RNA antisense 1 ADAMTS9-AS1 as an
independent prognostic marker in several tumors,
including prostate cancer and bladder cancer.
Nevertheless, the prognostic value and functional role of
ADAMTS9-AS1 in non-small cell lung cancer (NSCLC)
remain elusive. Here, we first found that the expression
of ADAMTS9-AS1 was significantly upregulated in
NSCLC tissues compared with adjacent normal tissues
using quantitative real time PCR analysis. Clinically, we
observed that ADAMTS9-AS1 expression was
associated with TNM stage, lymph node metastasis and
poor prognosis in NSCLC patients. By performing lossof-function assay in A549 and 95D cells, our in vitro
experiments further showed that knockdown of
ADAMTS9-AS1 remarkedly suppressed cell
proliferation, caused cell cycle G0/G1 arrest and
apoptosis, and inhibited cell migration and invasion in
NSCLC cells using CCK-8, colony formation, flow
cytometry and transwell assays. Moreover, we found that
ADAMTS9-AS1 knockdown downregulated the
expression of CDK4, N-cadherin, Vimentin, but
upregulated the expression of Bad and E-cadherin. In
summary, our results revealed that ADAMTS9-AS1 may
serve as a potential therapeutic target for the treatment of
patients with NSCL