Comprehending the potential of metallic, lipid, and polymer-based nanocarriers for treatment and management of depression

Association between sleep duration, depression and breast cancer in the United States: a national health and nutrition examination survey analysis 2009-2018

OBJECTIVE

Breast cancer is the most common cancer in women, threatening both physical and mental health. The epidemiological evidence for association between sleep duration, depression and breast cancer is inconsistent. The aim of this study was to determine the association between them and build machine-learning algorithms to predict breast cancer.

METHODS

A total of 1,789 participants from the National Health and Nutrition Examination Survey (NHANES) were included in the study, and 263 breast cancer patients were identified. Sleep duration was collected using a standardized questionnaire, and the Nine-item Patient Health Questionnaire (PHQ-9) was used to assess depression. Logistic regression yielded multivariable-adjusted breast cancer odds ratios (OR) and 95% confidence intervals (CI) for sleep duration and depression. Then, six machine learning algorithms, including AdaBoost, random forest, Boost tree, artificial neural network, limit gradient enhancement and support vector machine, were used to predict the development of breast cancer and find out the best algorithm.

RESULTS

Body mass index (BMI), race and smoking were statistically different between breast cancer and non-breast cancer groups. Participants with depression were associated with breast cancer (OR = 1.99, 95%CI: 1.55-3.51). Compared with 7-9h of sleep, the ORs for <7 and >9 h of sleep were 1.25 (95% CI: 0.85-1.37) and 1.05 (95% CI: 0.95-1.15), respectively. The AdaBoost model outperformed other machine learning algorithms and predicted well for breast cancer, with an area under curve (AUC) of 0.84 (95%CI: 0.81-0.87).

CONCLUSIONS

No significant association was observed between sleep duration and breast cancer, and participants with depression were associated with an increased risk for breast cancer. This finding provides new clues into the relationship between breast cancer and depression and sleep duration, and provides potential evidence for subsequent studies of pathological mechanisms.

Temporal trends and age-period-cohort analysis of depression in U.S. adults from 2013 to 2022

BACKGROUND

In the United States, the lifetime prevalence of depression in the US population is 20.6 %. We aimed to understand the temporal trends in the prevalence of depression among adults in the United States during the period 2013-2022 as well as the effects of age, period, and cohort effects on the prevalence of depression.

METHODS

Data from 3,139,488 participants in the U.S. Behavioral Risk Factor Surveillance System (BRFSS) from 2013 to 2022 were used in this study. The joinpoint regression model was used to calculate annual percentage change (APC) and average annual percentage change (AAPC) to learn about the time trends in the prevalence of depression. Age-period-cohort models were used to estimate the effects of age, period, and birth cohort effects on the prevalence of depression.

RESULTS

The prevalence of depression among adults in the United States showed an overall increasing trend from 2013 to 2022. The rate of increase was greater in males than females, with AAPC values of 1.44 % (95 % CI: 0.32-2.18), and 1.23 % (95 % CI: 0.32-2.25), respectively. Regarding the age effect, the risk of depression among adults in the United States generally showed an increasing and then decreasing trend with age. The risk of developing the condition reached its maximum at 50-54 years (RR = 1.28, 95 % CI = 1.26-1.30). Regarding the period effect, the risk of depression among US adults was higher during 2018-2022 than during 2013-2017. The overall cohort effect for depression prevalence was a higher risk for those born later, with a maximum RR of 1.51 (95 % CI: 1.47-1.54).

CONCLUSION

The prevalence of adult depression in the United States is showing an increasing trend. Middle-aged people and those born later in life deserve more attention as high-risk groups. It is recommended that the condition burden of depression be reduced with the promotion of healthy lifestyles, the promotion of interpersonal communication, as well as enhanced mental health education and mental health literacy.

How Coparenting Is Linked to Depression among Chinese Young Girls and Boys: Evidence from a Network Analysis

This study aimed to explore the relationship between parental coparenting and depression among Chinese young adolescents and potential gender differences via network analysis. Thus, 793 fourth-grade students (girls: 281 (35.40%), Mage = 9.99 years, SD = 0.59 years) were recruited from three primary schools in Northern China. The young adolescents rated their depression and perceived paternal and maternal coparenting. Network analysis was used to detect the central nodes and bridge mechanisms among coparenting and depressive components. The results indicated that paternal and maternal consistency as well as maternal conflict were the most central components in the coparenting-depression network. Paternal consistency, maternal conflict and paternal disparagement in coparenting, as well as somatic complaints and positive affect in adolescents' depression, exhibited high bridge strengths, suggesting those constructs served as vital bridges to connect the two subnetworks. Moreover, paternal consistency showed a higher bridge strength in the boys' network than the girls' one, whereas the edge linking adolescents' positive affect to paternal disparagement and integrity was stronger in the girls' network. This study contributes to the understanding of associations between parental coparenting and young adolescents' depression and offered insights into targeted interventions for early adolescent depression by enhancing parental coparenting.

Current research trends of nanomedicines

Owing to the inherent shortcomings of traditional therapeutic drugs in terms of inadequate therapeutic efficacy and toxicity in clinical treatment, nanomedicine designs have received widespread attention with significantly improved efficacy and reduced non-target side effects. Nanomedicines hold tremendous theranostic potential for treating, monitoring, diagnosing, and controlling various diseases and are attracting an unfathomable amount of input of research resources. Against the backdrop of an exponentially growing number of publications, it is imperative to help the audience get a panorama image of the research activities in the field of nanomedicines. Herein, this review elaborates on the development trends of nanomedicines, emerging nanocarriers, in vivo fate and safety of nanomedicines, and their extensive applications. Moreover, the potential challenges and the obstacles hindering the clinical translation of nanomedicines are also discussed. The elaboration on various aspects of the research trends of nanomedicines may help enlighten the readers and set the route for future endeavors.