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[Objective] Carbon emissions from industrial production are the most important source of carbon emissions from production activities in China. The purpose of this study was to measure the carbon emission quantity and intensity of industrial production energy consumption, explore their regional differences and influencing factors, and provide corresponding policy recommendations for carbon emission reduction to help achieve carbon peak and carbon neutrality. [Methods] The regional differences, dynamic changes, and influencing factors of national and regional carbon emissions from 2005 to 2019 were analyzed using carbon emission classification, Dagum Gini coefficient, kernel density estimation, and spatial econometric models. [Results] (1) The quantity of carbon emissions from industrial energy consumption showed an increasing trend from 2005 to 2019. Carbon emission quantity was eastern region>central region>western region, then changed to eastern region>western region>central region. Carbon emission intensity showed a downward trend. The order of carbon emission intensity from high to low was always the western region, the central region, and the eastern region. (2) The overall differences of carbon emission quantity and intensity across the country were large and showed an upward trend, among which, the overall Gini coefficient of carbon emission quantity increased from 0.3793 in 2005 to 0.3861 in 2019, and the overall Gini coefficient of carbon emission intensity increased from 0.3160 in 2005 to 0.3990 in 2019. The eastern region had the largest intra-regional difference in carbon emission quantity, and the average Gini coefficient was 0.4119. The western region had the largest intra-regional difference in carbon emission intensity, and the average Gini coefficient was 0.3175. (3) Carbon emission quantity of industrial production energy consumption was mainly affected by industrial structure and foreign direct investment, and carbon emission intensity was mainly affected by foreign direct investment and scientific and technological innovation. In addition, high urbanization rate significantly increased carbon emissions in the eastern region, while significantly reduced carbon emissions in the western region, showing regional differences. [Conclusion] There were significant regional differences in carbon emissions from industrial production energy consumption, and there were obvious differences in the dynamic change characteristics of regional carbon emissions. The use of spatial econometric model can better identify the influencing factors and spatial effects of carbon emissions in the country and the three regions, which is of great significance for taking corresponding measures to reduce carbon emissions according to local conditions.

Constructing a reasonable evaluation system can help to scientifically evaluate the vulnerability of the atmospheric environment, and policy recommendations would be provided for the precise prevention and control of air pollution as well as rational use of natural resources on the basis of evaluation results. Time series global principal component analysis was used to extract principal components from the short panel data of 14 cities in Liaoning Province from 2014 to 2019, and a comprehensive assessment of vulnerability was carried out consequently. The results show that: according to the results of principal component analysis, the cities of Liaoning Province were systematically clustered into five categories. From 2014 to 2019, the atmospheric environmental vulnerability of Liaoning Province decreased volatility, and the area of severely vulnerable areas reached 78.03% in 2017. The vulnerability of the atmospheric environment in Liaoning Province in 2014, 2015, 2016 and 2018 has significant spatial autocorrelation. From 2014 to 2019, the influence of various factors on the spatial differentiation of vulnerability has changed significantly. Time series global principal component analysis can evaluate and analyze panel data of atmospheric environmental vulnerability. Targeted policy measures should be adopted to effectively control the vulnerability of the atmospheric environment in Liaoning Province. [ABSTRACT FROM AUTHOR]

The article explores Compared with the studies on the trilateral relations among China, the US and Russia, among China, the US and Europe, and among China, the US and Japan, for a long time there has been a distinct lack of studies on the trilateral relations among China, the US and Latin America1 as an independent analytic framework.

Objective: To understand the cognition for insomnia and preference for acupuncture in breast cancer survivors based on the in-depth interview., Methods: Thirty breast cancer survivors with insomnia symptoms were collected for in-depth interview. The interview questions included three aspects, i.e. sleep expectation, cognition for insomnia (discomfort caused by insomnia, and underlying inducing factors of insomnia) and the preference for acupuncture (treatment methods used in the past, the reasons for not choosing acupuncture, and the tendency of acupuncture treatment). Using Colaizzi content analysis method, the data was analyzed., Results: Regarding sleep expectation, most breast cancer survivors with insomnia symptoms were able to maintain normal activity in daytime. Insomnia symptoms often led to fatigue, and the inducing factors of insomnia referred to the treatment with endocrine therapy, anticipatory anxiety and inadequate sleep hygiene. All of the patients had received pharmacotherapy. The use proportion of non-pharmacological therapies was relatively low, and acupuncture was not chosen due to "not familiar with" and "fear of pain". Concerning to the preference for acupuncture, patients preferred the therapeutic methods of acupuncture with mild pain sensation and gentle stimulation; and the treatment should be more acceptable if delivered 2 or 3 times a week., Conclusion: Breast cancer survivors have the expectations for sleep, and are willing to receive the treatment with medication for their sleep disorders. Because of lack of the knowledge for acupuncture effect on insomnia and fear of strong needling sensation, a part of patients are unwilling to be treated with acupuncture therapy, but they are expected to receive the treatment with acupuncture while feeling more comfortable.

Objectives: To evaluate the outcomes of adjuvant radiotherapy in patients with esophageal cancer (EC) who underwent esophagectomy following neoadjuvant chemoradiotherapy (NCRT)., Methods: The data of EC patients who received adjuvant therapy after NCRT between 2004 to 2019 was retrieved from the SEER database. The patients were split into the adjuvant radiotherapy with or without chemotherapy (RT±CT) and the adjuvant chemotherapy (CT) groups. The process of propensity score matching (PSM) was employed., Results: Following PSM, 157 patients in total were recruited in each treatment group. There were no significant variations in either overall survival (OS) or cancer-specific survival (CSS) between the RT±CT and CT groups (median OS: 28 months versus. 51 months, p =0.063; median CSS: 31 months versus. 52 months, p =0.16). Within the CT group, patients with ypI/II or cI/II tumor stage, positive lymph node ratio (LNR) ≤0.1, and tumor size ≥50 mm ( p <0.05) had higher OS compared to the RT±CT groups. Among patients with cT3-4 tumors in N-stage downstaging group, the OS and CSS were significantly greater for those underwent RT±CT as opposed to the CT group (5-year OS:56.6% versus 19.4%, p =0.042; 5-year CSS:67.9% versus. 19.4%, p =0.023). Multivariate Cox regression analysis identified the tumor histology grade as an independent prognostic factor of OS and CSS., Conclusion: Radiotherapy-based adjuvant therapy does not significantly improve the prognosis of EC patients after NCRT, although it may provide a survival benefit for patients with cT3-4 tumors in N-stage downstaging., (Copyright: © Saudi Medical Journal.)

Rectal cancer (RC) is a globally prevalent malignant tumor, presenting significant challenges in its management and treatment. Currently, magnetic resonance imaging (MRI) offers superior soft tissue contrast and radiation-free effects for RC patients, making it the most widely used and effective detection method. In early screening, radiologists rely on patients' medical radiology characteristics and their extensive clinical experience for diagnosis. However, diagnostic accuracy may be hindered by factors such as limited expertise, visual fatigue, and image clarity issues, resulting in misdiagnosis or missed diagnosis. Moreover, the distribution of surrounding organs in RC is extensive with some organs having similar shapes to the tumor but unclear boundaries; these complexities greatly impede doctors' ability to diagnose RC accurately. With recent advancements in artificial intelligence, machine learning techniques like deep learning (DL) have demonstrated immense potential and broad prospects in medical image analysis. The emergence of this approach has significantly enhanced research capabilities in medical image classification, detection, and segmentation fields with particular emphasis on medical image segmentation. This review aims to discuss the developmental process of DL segmentation algorithms along with their application progress in lesion segmentation from MRI images of RC to provide theoretical guidance and support for further advancements in this field., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang, Yang, Yang, Wang, Ye, Chen, Fu and Xu.)