Mid-to-Late-Life Anxiety and Sleep during Initial Phase of COVID-19: Age- and Sex-Specific Insights to Inform Future Pandemic Healthcare.

This study examined associations between COVID-19-related anxiety and sleep in middle-aged and older adults and tested whether these varied by age or sex. In June/July 2020, middle-aged/older adults aged 50+ (n = 277, 45% women, Mage = 64.68 ± 7.83) in the United States completed measures of sleep and COVID-19-related anxiety. Multiple regressions examined whether anxiety was independently associated with or interacted with age or sex in its associations with sleep health, controlling for age, education, medical conditions, sleep/pain medication use, and COVID-19 status. Greater COVID-19 anxiety was associated with worse sleep quality and daytime dysfunction. COVID-19-related anxiety interacted with age (not sex) in associations with total sleep time and sleep efficiency. Greater anxiety was associated with shorter total sleep time and lower sleep efficiency in oldest-older adults (~73 years old) and youngest-older adults (~65 years old) but not middle-aged adults (~57 years old). In mid to late life, older adults may be most vulnerable to the impact of COVID-19-related anxiety on sleep health. Social and behavioral (e.g., knowledge on age-related vulnerability to COVID-19 risk/morbidity/mortality, uncertainty, and changes to daily routines) and physiological factors (sleep disruption and age-related autonomic dysfunction) may underlie these associations. Interventions that mitigate negative pandemic-related psychological and sleep outcomes may be particularly relevant for older adults.

Basal Cell Carcinoma Diagnosis with Fusion of Deep Learning and Telangiectasia Features.

In recent years, deep learning (DL) has been used extensively and successfully to diagnose different cancers in dermoscopic images. However, most approaches lack clinical inputs supported by dermatologists that could aid in higher accuracy and explainability. To dermatologists, the presence of telangiectasia, or narrow blood vessels that typically appear serpiginous or arborizing, is a critical indicator of basal cell carcinoma (BCC). Exploiting the feature information present in telangiectasia through a combination of DL-based techniques could create a pathway for both, improving DL results as well as aiding dermatologists in BCC diagnosis. This study demonstrates a novel "fusion" technique for BCC vs non-BCC classification using ensemble learning on a combination of (a) handcrafted features from semantically segmented telangiectasia (U-Net-based) and (b) deep learning features generated from whole lesion images (EfficientNet-B5-based). This fusion method achieves a binary classification accuracy of 97.2%, with a 1.3% improvement over the corresponding DL-only model, on a holdout test set of 395 images. An increase of 3.7% in sensitivity, 1.5% in specificity, and 1.5% in precision along with an AUC of 0.99 was also achieved. Metric improvements were demonstrated in three stages: (1) the addition of handcrafted telangiectasia features to deep learning features, (2) including areas near telangiectasia (surround areas), (3) discarding the noisy lower-importance features through feature importance. Another novel approach to feature finding with weak annotations through the examination of the surrounding areas of telangiectasia is offered in this study. The experimental results show state-of-the-art accuracy and precision in the diagnosis of BCC, compared to three benchmark techniques. Further exploration of deep learning techniques for individual dermoscopy feature detection is warranted.

Hybrid Topological Data Analysis and Deep Learning for Basal Cell Carcinoma Diagnosis.

A critical clinical indicator for basal cell carcinoma (BCC) is the presence of telangiectasia (narrow, arborizing blood vessels) within the skin lesions. Many skin cancer imaging processes today exploit deep learning (DL) models for diagnosis, segmentation of features, and feature analysis. To extend automated diagnosis, recent computational intelligence research has also explored the field of Topological Data Analysis (TDA), a branch of mathematics that uses topology to extract meaningful information from highly complex data. This study combines TDA and DL with ensemble learning to create a hybrid TDA-DL BCC diagnostic model. Persistence homology (a TDA technique) is implemented to extract topological features from automatically segmented telangiectasia as well as skin lesions, and DL features are generated by fine-tuning a pre-trained EfficientNet-B5 model. The final hybrid TDA-DL model achieves state-of-the-art accuracy of 97.4% and an AUC of 0.995 on a holdout test of 395 skin lesions for BCC diagnosis. This study demonstrates that telangiectasia features improve BCC diagnosis, and TDA techniques hold the potential to improve DL performance.

Sex as a moderator of the sleep and cognition relationship in middle-aged and older adults: A preliminary investigation.

OBJECTIVES: Despite known sex differences in the prevalence of sleep disturbance and cognitive impairment, research investigating sex differences in sleep/cognition associations is limited. We examined sex as a moderator of associations between self-reported sleep and objective cognition in middle-aged/older adults. METHODS: Adults aged 50+ (32 men/31 women, Mage = 63.6 ± 7.7) completed the Pittsburgh Sleep Quality Index (PSQI) and cognitive tasks: Stroop (processing speed, inhibition), Posner (spatial attentional orienting) and Sternberg (working memory). Multiple regressions examined whether PSQI metrics (global score, sleep quality ratings, sleep duration, sleep efficiency) were independently or interactively (with sex) associated with cognition, controlling for age and education. RESULTS: Sex interacted with sleep quality ratings in its association with endogenous spatial attentional orienting (∆R2 = .10, p = .01). Worse ratings of sleep quality were associated with worse orienting in women (B = 22.73, SE = 9.53, p = .02), not men (p = .24). Sex interacted with sleep efficiency in its associations with processing speed (∆R2 = .06, p = .04). Lower sleep efficiency was associated with slower Stroop control trial performance in women (B = -15.91, SE = 7.57, p = .04), not men (p = .48). CONCLUSIONS: Preliminary findings suggest middle-aged/older women are more vulnerable to associations between poor sleep quality and low sleep efficiency on spatial attentional orienting and processing speed, respectively. Future studies in larger samples investigating sex-specific prospective sleep and cognition associations are warranted.