Prediction of axillary lymph node metastasis in early breast cancer patients with ultrasonic videos based deep learning.

Objective: To develop a deep learning (DL) model for predicting axillary lymph node (ALN) metastasis using dynamic ultrasound (US) videos in breast cancer patients. Methods: A total of 271 US videos from 271 early breast cancer patients collected from Xiang'an Hospital of Xiamen University andShantou Central Hospitabetween September 2019 and June 2021 were used as the training, validation, and internal testing set (testing set A). Additionally, an independent dataset of 49 US videos from 49 patients with breast cancer, collected from Shanghai 10th Hospital of Tongji University from July 2021 to May 2022, was used as an external testing set (testing set B). All ALN metastases were confirmed using pathological examination. Three different convolutional neural networks (CNNs) with R2 + 1D, TIN, and ResNet-3D architectures were used to build the models. The performance of the US video DL models was compared with that of US static image DL models and axillary US examination performed by ultra-sonographers. The performances of the DL models and ultra-sonographers were evaluated based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Additionally, gradient class activation mapping (Grad-CAM) technology was also used to enhance the interpretability of the models. Results: Among the three US video DL models, TIN showed the best performance, achieving an AUC of 0.914 (95% CI: 0.843-0.985) in predicting ALN metastasis in testing set A. The model achieved an accuracy of 85.25% (52/61), with a sensitivity of 76.19% (16/21) and a specificity of 90.00% (36/40). The AUC of the US video DL model was superior to that of the US static image DL model (0.856, 95% CI: 0.753-0.959, P<0.05). The Grad-CAM technology confirmed the heatmap of the model, which highlighted important subregions of the keyframe for ultra-sonographers' review. Conclusion: A feasible and improved DL model to predict ALN metastasis from breast cancer US video images was developed. The DL model in this study with reliable interpretability would provide an early diagnostic strategy for the appropriate management of axillary in the early breast cancer patients.

Association between long-term green space exposure and mortality in China: A difference-in-differences analysis of national data in 2000, 2010 and 2019.

BACKGROUND: Effects of green space on human health have been well-documented in western, high-income countries. Evidence for similar effects in China is limited. Moreover, the underlying mechanisms linking green space and mortality are yet to be established. We therefore conducted a nation-wide study to assess the association between green space and mortality in China using a difference-in-difference approach, which applied a causal framework and well controlled unmeasured confounding. In addition, we explored whether air pollution and air temperature could mediate the association. METHODS: In this analysis, we collected data on all-cause mortality and sociodemographic characteristics for each county in China from the 2000 and 2010 censuses and the 2020 Statistical Yearbook. Green space exposure was assessed using county-level normalized difference vegetation index (NDVI) and the percentage of green space (forest, grasslands, shrub land and wetland). We applied a difference-in-differences approach to evaluate the association between green space and mortality. We also performed mediation analysis (by air pollution and air temperature). RESULTS: Our sample consisted of 2726 counties in 2000 and 2010 as well as 1432 counties in 2019. In the 2000 versus 2019 comparison, a 0.1 unit increase in NDVI was associated with a 2.4 % reduction in mortality [95 % confidence interval (CI) 0.4-4.3 %], and a 10 % increase in percentage of green space was associated with a 4.7 % reduction (95 % CI 0-9.2 %) in mortality. PM2.5 and air temperature mediated 0.3 % to 12.3 % of the associations. CONCLUSIONS: Living in greener counties may be associated with lower risk of mortality in China. These findings could indicate the potential of a population-level intervention to reduce mortality in China, which has important public health implications at the county level.

Violent behavior and the network properties of psychopathological symptoms and real-life functioning in patients with schizophrenia.

Objective: To assess the interplay among psychopathological symptoms and real-life functioning, and to further detect their influence with violent behavior in patient with schizophrenia. Methods: A sample of 1,664 patients with post-violence assessments and their propensity score-matched controls without violence from a disease registration report system of community mental health service in Guangdong, China, were studied by network analysis. Ising-Model was used to estimate networks of psychopathological symptoms and real-life functioning. Then, we tested whether network properties indicated the patterns of interaction were different between cases and controls, and calculated centrality indices of each node to identify the central nodes. Sensitivity analysis was conducted to examine the difference of interaction patterns between pre-violence and post-violence assessments in violence cases. Results: Some nodes in the same domain were highly positive interrelations, while psychopathological symptoms were negatively related to real-life functioning in all networks. Many symptom-symptom connections and symptom-functioning connections were disconnected after the violence. The network density decreased from 23.53% to 12.42% without statistical significance (p = 0.338). The network structure, the global network strength, and the global clustering coefficient decreased significantly after the violence (p < 0.001, p = 0.019, and p = 0.045, respectively). Real-life functioning had a higher node strength. The strength of sleeping, lack of spontaneity and flow of conversation, and preoccupation were decreased in post-violence network of patients. Conclusion: The decreasing connectivity may indicate an increased risk of violence and early warning for detecting violence. Interventions and improving health state based on nodes with high strength might prevent violence in schizophrenia patients.

Key Social Determinants to Narrow the Gap between Health-adjusted Life Expectancy and Life Expectancy in Megacities.

Objective: Improvement in the quality of life is reflected in the narrowing of the gap between health-adjusted life expectancy (HALE) and life expectancy (LE). The effect of megacity expansion on narrowing the gap is rarely reported. This study aimed to disclose this potential relationship. Methods: Annual life tables were constructed from identified death records and population counts from multiple administrative sources in Guangzhou, China, from 2010 to 2020. Joinpoint regression was used to evaluate the temporal trend. Generalized principal component analysis and multilevel models were applied to examine the county-level association between the gap and social determinants. Results: Although LE and HALE in megacities are increasing steadily, their gap is widening. Socio-economic and health services are guaranteed to narrow this gap. Increasing personal wealth, a growing number of newborns and healthy immigrants, high urbanization, and healthy aging have helped in narrowing this gap. Conclusion: In megacities, parallel LE and HALE growth should be highly considered to narrow their gap. Multiple social determinants need to be integrated as a whole to formulate public health plans.