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Depressive symptoms and cognitive impairment often coexisted in the elderly. This study investigates the effect of late-life depressive symptoms on risk of mild cognitive impairment (MCI).
A total of 14,231 dementia- and MCI free participants aged 60+ from the Survey of Health, Ageing, and Retirement in Europe were followed-up for 10 years to detect incident MCI. MCI was defined as 1.5 standard deviation (SD) below the mean of the standardized global cognition score. Depressive symptoms were assessed by a 12-item Europe-depression scale (EURO-D). Severity of depressive symptoms was grouped as: no/minimal (score 0–3), moderate (score 4–5), and severe (score 6–12). Significant depressive symptoms (SDSs) were defined as EURO-D score ≥ 4.
During an average of 8.2 (SD = 2.4)-year follow-up, 1,352 (9.50%) incident MCI cases were identified. SDSs were related to higher MCI risk (hazard ratio [HR] = 1.26, 95% confidence intervals [CI]: 1.10–1.44) in total population, individuals aged 70+ (HR = 1.35, 95% CI: 1.14–1.61) and women (HR = 1.28, 95% CI: 1.08–1.51) in Cox proportional hazard model adjusting for confounders. In addition, there was a dose–response association between the severity of depressive symptoms and MCI incidence in total population, people aged ≥70 years and women (p-trend <0.001).
Significant depressive symptoms were associated with higher incidence of MCI in a dose–response fashion, especially among people aged 70+ years and women. Treating depressive symptoms targeting older population and women may be effective in preventing MCI.
This study examined the relationships between social capital, perceived neighborhood environment, and depressive symptoms among older adults living in rural China, and the moderating effect of self-rated health (SRH) in these relationships.
A quota sampling method was applied to recruit 447 participants aged 60 years and older in rural communities in Jilin province, China in 2019.
Depressive symptoms were measured by the Center for Epidemiologic Studies Depression Scale. Structural equation modeling was used to build latent constructs of social capital and test the proposed model. Multiple group analysis was used to test the moderation effects.
Cognitive social capital and structural social capital were both associated with depressive symptoms controlling for participants’ demographics, socioeconomic status, and health status. After adding perceived environment variables in the model, the relationship between cognitive social capital and depressive symptoms became nonsignificant, while structural social capital remained became a significant factor (β = −.168, p < .01). Satisfaction with health care was significantly associated with depressive symptoms among those with poor SRH (β = −.272, p < .01), whereas satisfaction with security and transportation were strongly associated with depressive symptoms among those with good SRH (security: β = −.148, p < .01; transportation: β = −.174, p < .01).
Study findings highlighted the importance of social capital and neighborhood environment as potential protective factors of depressive symptoms in later life. Policy and intervention implications were also discussed.
In clinical environments, orthopedic implants are associated with a risk of infection during implantation. However, the growth paths of bacteria on metal, which is nontransparent, are difficult to observe. In this study, we visualized the DH5-alpha Escherichia coli bacterial growth path on the surface of magnesium by using scanning electron microscope (SEM) images and constructed a convolutional neural network-based artificial intelligence (AI) system to identify metal surfaces, bacteria, and its generated products to grade the growth stage of the bacteria implanted on the magnesium. The detection result of the E. coli growth stage by the AI system was close to that manually marked by experts, and it may greatly accelerate the investigation of the bacterial growth process in various types of metallic material.