The impact of environmental regulation on renewable energy development in China from the perspective of spatial effects.

From a spatial perspective, this paper constructs a spatial Durbin model based on the adjacent weight matrix to analyze the impact of environmental regulation on the development of renewable energy in 30 provinces (autonomous regions and municipalities directly under the Central Government) in China from 2007 to 2020. The results show that (1) both environmental regulation and renewable energy development have positive spatial autocorrelation and form relatively similar spatial agglomeration areas, specifically showing the characteristics of "high-high" agglomeration in resource-rich areas and "low-low" agglomeration in resource-scarce areas. (2) Environmental regulation significantly promotes the development of local renewable energy and has a significant spatial spillover effect on the development of renewable energy in neighboring provinces. (3) The moderating effect results show that renewable energy installed capacity and electricity demand play an inverse moderating role between environmental regulation and renewable energy development. (4) From the perspective of different regions, the impact of environmental regulation on the development of renewable energy is heterogeneous. Based on the research conclusions, this paper finally provides corresponding policy suggestions from three aspects: regional cooperation, technical support, and differentiation.

Development and validation of nomograms to intraoperatively predict metastatic patterns in regional lymph nodes in patients diagnosed with esophageal cancer.

BACKGROUND: An accurate intraoperative prediction of lymph node metastatic risk can help surgeons in choosing precise surgical procedures. We aimed to develop and validate nomograms to intraoperatively predict patterns of regional lymph node (LN) metastasis in patients with esophageal cancer. METHODS: The prediction model was developed in a training cohort consisting of 487 patients diagnosed with esophageal cancer who underwent esophagectomy with complete LN dissection from January 2016 to December 2016. Univariate and multivariable logistic regression were used to identify independent risk factors that were incorporated into a prediction model and used to construct a nomogram. Contrast-enhanced computed tomography reported LN status and was an important comparative factor of clinical usefulness in a validation cohort. Nomogram performance was assessed in terms of calibration, discrimination, and clinical usefulness. An independent validation cohort comprised 206 consecutive patients from January 2017 to December 2017. RESULTS: Univariate analysis and multivariable logistic regression revealed three independent predictors of metastatic regional LNs, three independent predictors of continuous regional LNs, and two independent predictors of skipping regional LNs. Independent predictors were used to build three individualized prediction nomograms. The models showed good calibration and discrimination, with area under the curve (AUC) values of 0.737, 0.738, and 0.707. Application of the nomogram in the validation cohort yielded good calibration and discrimination, with AUC values of 0.728, 0.668, and 0.657. Decision curve analysis demonstrated that the three nomograms were clinically useful in the validation cohort. CONCLUSION: This study presents three nomograms that incorporate clinicopathologic factors, which can be used to facilitate the intraoperative prediction of metastatic regional LN patterns in patients with esophageal cancer.

Clinicopathologic predictors of metastasis of different regional lymph nodes in patients intraoperatively diagnosed with stage-I non-small cell lung cancer.

BACKGROUND: Selection of the best lymph node for dissection is a controversial topic in clinical stage-I non-small cell lung cancer (NSCLC). Here, we sought to identify the clinicopathologic predictors of regional lymph node metastasis in patients intraoperatively diagnosed with stage-I NSCLC. METHODS: A retrospective review of 595 patients intraoperatively diagnosed as stage I non-small-cell lung cancer who underwent lobectomy with complete lymph node dissection was performed. Univariate and multivariable logistic regression analysis was performed to determine the independent predictors of regional lymph node metastasis. RESULTS: Univariate logistic regression and multivariable analysis revealed three independent predictors of the presence of metastatic hilar lymph nodes, five independent predictors for lobe specific mediastinal lymph nodes, two independent predictors for lobe nonspecific mediastinal lymph nodes and two independent predictors for skipping mediastinal lymph nodes. CONCLUSIONS: A complete mediastinal lymph node dissection may be considered for patients suspected of nerve invasion and albumin (> 43.1 g/L) or nerve and vascular invasions. Lobe-specific lymph node dissection should probably be performed for patients suspected of pulmonary membrane invasion, vascular invasion, CEA (> 2.21 ng/mL), and tumor (> 1.6 cm) in the right lower lobe or mixed lobes. Hilar lymph node dissection should probably be performed for patients suspected of having bronchial mucosa and cartilage invasion, vascular invasion, and CEA (> 2.21 ng/mL).