Background It is reported that appropriately 50% of early breast cancer patients with 1–2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1–2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1–2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs. Methods We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. Results The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682–0.771) in the primary cohort and 0.722 (95% CI: 0.653–0.792) in the validation cohort. The addition of CA 15–3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732–0.815) vs. 0.727 (0.682–0.771), P < 0.001) and validation cohort (AUC: (0.777 (0.713–0.840) vs. 0.722 (0.653–0.792), P < 0.001). Decision curve analysis demonstrated that the nomograms were clinically useful. Conclusion The nomograms based on pathological features can be used to identify axillary non-SLN metastases in breast cancer patients with 1–2 positive SLN. In addition, the combination of STMs and pathological features can identify patients with patients with axillary non-SLN metastases more accurately than pathological characteristics alone.

The Yangtze River Delta region contributes nearly 16% of the national carbon emissions and is the key area for carbon emission reduction in China. Accurately grasping the spatial evolution characteristics of carbon emissions and the interaction between counties and regions is of great practical significance for precise and collaborative carbon reduction. This study firstly explores the spatial layout and dynamic evolution characteristics of county carbon emissions in the Yangtze River Delta region from 2000 to 2018 by using spatial statistical analysis, secondly identifies the influencing factors of county carbon emissions (CAR) in the Yangtze River Delta region from dynamic and static dimensions respectively by using static and dynamic Spatial Dubin Model, and finally judges the spatial spillover effects of each factor. We find that county carbon emissions are more complex and more diverse in non-synchronous state compared to provinces and cities. The high carbon areas in the Yangtze River Delta region are concentrated in Shanghai and its neighboring cities, as well as industrial counties under the jurisdiction of other sub-core cities, which are continuously clustered towards the center. We have made some theoretical discussions on the results of the spillover effects of various factors on carbon emissions, and concluded that economic of scale (ECO) and industrial structure (IND) have a “polarization effect”, population size (POP) is consistent with the Malthusian view, technological advance (TEC) has a “cumulative effect”, and environmental quality (ENV) The “pollution paradise effect” is mitigated. Finally, we believe that the main unit of precise carbon reduction can take the form of “city-county” combination, and the government should implement differentiated and coordinated carbon reduction policies.

Background It is reported that appropriately 50% of early breast cancer patients with 1–2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1–2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1–2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs. Methods We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. Results The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682–0.771) in the primary cohort and 0.722 (95% CI: 0.653–0.792) in the validation cohort. The addition of CA 15 − 3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732–0.815) vs. 0.727 (0.682–0.771), P < 0.001) and validation cohort (AUC: (0.777 (0.713–0.840) vs. 0.722 (0.653–0.792), P < 0.001). Decision curve analysis demonstrated that the nomograms were clinically useful. Conclusion The nomograms based on pathological features can be used to identify axillary non-SLN metastases in breast cancer patients with 1–2 positive SLN. In addition, the combination of STMs and pathological features can identify patients with patients with axillary non-SLN metastases more accurately than pathological characteristics alone.