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Background We aimed to investigate the associations of long‐term exposure to ambient formaldehyde with hypertension and angina pectoris symptoms in Chinese adults. Methods and Results Participants' information was obtained from the WHO SAGE (World Health Organization Study on Global Aging and Adult Health) study. The Cox proportional hazards regression model was applied to estimate the associations of formaldehyde with hypertension and angina pectoris symptoms. Mediating effect analysis was used to investigate the mediating effect of hypertension between formaldehyde exposure and angina pectoris symptoms. Long‐term exposure to formaldehyde was positively associated with the risk of angina pectoris symptoms (hazard ratio [HR], 1.66 [95% CI, 1.29–2.13], per interquartile range [IQR], 3.33, 1015 molecules/cm2) and hypertension (HR, 1.17 [95% CI, 1.02–1.34], per IQR, 3.34, 1015 molecules/cm2). The associations between formaldehyde and angina pectoris symptoms were greater in participants aged ≥65 years (HR, 1.90 [95% CI, 1.29–2.80]) and in rural areas (HR, 2.71 [95% CI, 1.54–4.77]), whereas the associations of formaldehyde with hypertension were stronger in men (HR, 1.27 [95% CI, 1.02–1.58]), rural areas (HR, 1.22 [95% CI, 0.94–1.59]), and in ever smokers (HR, 1.33 [95% CI, 1.02–1.72]). The mediation effect analysis indicated that 18.44% (95% CI, 2.17–37.65) of the association between formaldehyde exposure and angina pectoris symptoms was mediated by hypertension. Conclusions Long‐term exposure to ambient formaldehyde was positively associated with hypertension and angina pectoris symptoms. The effects of formaldehyde may be modified by age, sex, urbanicity, and smoking status. Hypertension might play a mediating effect in formaldehyde‐induced angina pectoris symptoms.

Abstract Precise assembly of biomolecules into functional aggregate structures represents a key characteristic of nature living cells, and is critical for the cellular processes. The construction of artificial aggregates in living cells by responding to intracellular specific stimuli has been applied in elucidating molecular mechanisms of naturally cellular processes and interfering with cellular processes, which is potential and significant in biomedicine. DNA (deoxyribonucleic acid) features sequence programmability, precise assembly and versatility, and therefore is regarded as the potential candidate for constructing versatile functional aggregates in living cells. In this review, we summarize our recent efforts of employing DNA to in situ construct versatile aggregates in living cells via responding intracellular triggers, and the subsequent bio‐interference of the DNA aggregates. Finally, we discuss the remaining challenges and opportunities in the field, and envision that rational design and construction of versatile DNA aggregates in living system would be a promising solution for precision and personalized therapeutics.

Abstract The accurate and robust automatic segmentation of cardiac structures in magnetic resonance imaging (MRI) is significant in calculating cardiac clinical functional indices, and diagnosing heart diseases. Most U‐Net based methods use pooling, transposed convolution, and skip connection operations to integrate the multiscale features for improved segmentation in cardiac MRI. However, this architecture lacks adequate semantic connection between the channel and spatial information, and robustness in segmenting objects with significant shape variations. In this paper, a new multiscale feature attentive U‐Net for cardiac MRI structural segmentation method is proposed. An attention mechanism is adopted after concatenating the multi‐level features to aggregate different scale features and determine on which features to focus. Cascade and parallel dilated convolution is also employed in the decoder blocks and skip connection is employed to enhance the ability of sensing receptive fields for multiscale context information. Furthermore, deep supervision approach with a loss function that combines the dice and cross‐entropy losses to reduce overfitting and ensure better prediction is introduced. The proposed method was evaluated on three public cardiac datasets. The experimental results indicate that the method achieved competitive segmentation performance with the three datasets, which verifies the robustness and generalisability of the proposed network. In comparison with conventional U‐Net methods, the model leverages attention mechanism and dilated convolution block, which increases the semantic connection between the channel and the spatial information, and improves the robustness of the right ventricle segmentation performance. From the view of the Dice scores and segmentation results, the multiscale feature attentive U‐Net method is one of effective methods in segmenting cardiac MRI structures.