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As a result of the "Belt and Road" and "Going Global" policies, a growing number of Chinese power companies are expanding overseas, implementing global development strategies, and making investments in the overseas power industry. Several countries are undergoing an energy transition because of the rapid development of the world economy. This is being done to address the climate change issues that are a result of the overuse of fossil fuels. Brazil is also accelerating its pace of power transformation in its position as a major power generating country in Latin America. In addition to being the largest economy in South America, Brazil is also the country in which China has made the most investments. Additionally, Brazil is increasing its development efforts in wind power, photovoltaics, and other renewable energy sources in response to a large demand for renewable energy sources. Brazil's renewable energy sector offers a great deal of potential for investment based on solid foundations for cooperation between China and Brazil. The Grey Prediction Model was used for this research to forecast Brazil's renewable energy generation installed capacity, and the results show a positive trend in Brazil's renewable energy generation. There is a rapid growth in wind and photovoltaic power generation over the next 5 years, with growth rates reaching 50.39% and 182.99%, respectively, suggesting that there is potential for a broad range of development. Following this, the research applies factor analysis to assess investment risks associated with Brazil's renewable energy sector from 2000 to 2020. Based on the results of the study, Chinese power companies investing in Brazil's renewable energy sector face the greatest political risk, while other risks gradually decrease. To avoid political risks when investing, companies should place a high priority on preventing them. [ABSTRACT FROM AUTHOR]

Background: Accurate diagnosis of intrahepatic mass-forming cholangiocarcinoma (IMCC) is crucial with regard to the choice of patient management and treatment options. Purpose: To evaluate the feasibility and diagnostic performance of the LI-RADS M (LR-M) targetoid criteria on computed tomography (CT) and gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) in differentiating IMCC from hepatocellular carcinoma (HCC). Material and Methods: A total of 118 patients with IMCC and HCC were included who underwent CT and EOB-MRI examinations. Multivariate analysis was used to determine the strongest predictors differentiating IMCC from HCC. Using these predictors, a predictive model for differentiating IMCC from HCC was constructed and the performance of the model was confirmed using the receiver operating characteristic curve. Results: Multivariate analyses revealed rim-like arterial phase hyperenhancement (rim APHE) on CT and rim APHE, delayed central enhancement (DCE), and targetoid hepatobiliary phase (HBP) on MRI as independent variables significantly differentiating IMCC from HCC. The multivariate logistic regression model incorporating the three variables on EOB-MRI was constructed with an area under the curve (AUC) of 0.946, sensitivity of 87.80%, specificity of 92.21%, and accuracy of 94.60%. Per the DeLong test, the multivariate logistic regression model showed significantly higher AUC than rim APHE on CT (0.946 vs. 0.871; P = 0.008) and MRI (0.946 vs. 0.876; P = 0.003), whereas rim APHE on CT and MRI did not differ significantly (P = 0.809). Conclusion: The multivariate logistic regression model based on rim APHE, DCE, and targetoid HBP on EOB-MRI can effectively distinguish IMCC from HCC and is superior to any other targetoid appearance criterion of LI-RADS on CT and EOB-MRI. [ABSTRACT FROM AUTHOR]

Purpose: To perform a meta-analysis evaluating the diagnostic accuracy of the Liver Imaging Reporting and Data System (LI-RADS) category ≥ 3 (LI-RADS 3-5v) for detecting hepatocellular carcinoma (HCC).Method: A systematic PubMed, Embase, and Web of Science electronic database search was performed for original diagnostic studies published through July 31, 2018. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses.Results: Eighteen studies (v2011, v2014 and v2017) involving 3386 patients were included in the meta-analysis. The pooled sensitivity and specificity of LI-RADS ≥ 3 for diagnosing HCC were 0.86 (95 % confidence interval (CI): 0.78-0.91) and 0.85 (95 % CI: 0.78-0.90), respectively. The area under the curve (AUC) was 0.92 (95 % CI: 0.89-0.94). Meta-regression analysis showed that the publication year, blinding to the reference standard and the number of readers were significant factors affecting heterogeneity. In subgroup analyses, magnetic resonance imaging (MRI) demonstrated higher sensitivity (0.82 vs. 0.73) and comparable specificity (0.79 vs. 0.78) than computed tomography (CT). For HCCs ≤30 mm, LI-RADS showed lower sensitivity of 0.72 and specificity of 0.80 compared with HCC of all sizes. LR-5 showed higher sensitivity and specificity than LR-3 (sensitivity: 0.67 vs. 0.07, P = 0.02; specificity: 0.93 vs. 0.75, p < 0.001) and higher sensitivity than LR-4 (sensitivity: 0.67 vs. 0.29, P = 0.02; specificity: 0.93 vs. 0.80, p = 0.75). LR ≥ 5 had higher specificity at the cost of decreased sensitivity than LR ≥ 3 (specificity: 0.94 vs. 0.68, p < 0.001; sensitivity: 0.66 vs. 0.74, P = 0.70) and LR ≥ 4 (specificity: 0.94 vs. 0.84, p < 0.001; sensitivity: 0.66 vs. 0.74, P = 0.77).Conclusions: LI-RADS ≥ 3 shows high diagnostic accuracy for HCCs, with a pooled sensitivity of 0.86 and specificity of 0.85. The specificity is higher for LR-5 and LR ≥ 5. However, further prospective studies on LI-RADS ≥ 3 are needed to elucidate its value for diagnosing small HCCs (≤20 mm). [ABSTRACT FROM AUTHOR]

Background: Iron status has been implicated in gastrointestinal diseases and gut microbiota, however, confounding factors may influence these associations., Objective: We performed Mendelian randomization (MR) to investigate the associations of iron status, including blood iron content, visceral iron content, and iron deficiency anemia with the incidence of 24 gastrointestinal diseases and alterations in gut microbiota., Methods: Independent genetic instruments linked with iron status were selected using a genome-wide threshold of p = 5 × 10-6 from corresponding genome-wide association studies. Genetic associations related to gastrointestinal diseases and gut microbiota were derived from the UK Biobank, the FinnGen study, and other consortia., Results: Genetically predicted higher levels of iron and ferritin were associated with a higher risk of liver cancer. Higher levels of transferrin saturation were linked to a decreased risk of celiac disease, but a higher risk of non-alcoholic fatty liver disease (NAFLD) and liver cancer. Higher spleen iron content was linked to a lower risk of pancreatic cancer. Additionally, higher levels of liver iron content were linked to a higher risk of NAFLD and liver cancer. However, certain associations lost their statistical significance upon accounting for the genetically predicted usage of cigarettes and alcohol. Then, higher levels of iron and ferritin were associated with 11 gut microbiota abundance, respectively. In a secondary analysis, higher iron levels were associated with lower diverticular disease risk and higher ferritin levels with increased liver cancer risk. Higher levels of transferrin saturation were proven to increase the risk of NAFLD, alcoholic liver disease, and liver cancer, but decrease the risk of esophageal cancer. MR analysis showed no mediating relationship among iron status, gut microbiota, and gastrointestinal diseases., Conclusion: This study provides evidence suggesting potential causal associations of iron status with gastrointestinal diseases and gut microbiota, especially liver disease., 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 Su, Peng, Gan, Wu, Ma, Zhi, Lu, Dai and Yao.)

Purpose: To establish and validate a CT-based nomogram for accurately detecting HCC in patients at high risk for the disease., Methods: A total of 223 patients were divided into training (n=161) and validation (n=62) cohorts between January of 2017 and May of 2022. Logistic analysis was performed, and clinical model and radiological model were developed separately. Finally, a nomogram was established based on clinical and radiological features. All models were evaluated using the area under the curve (AUC). DeLong's test was used to evaluate the differences among these models., Results: In the multivariate analysis, gender (p = 0.014), increased Alpha-fetoprotein (AFP) (p = 0.017), non-rim arterial phase hyperenhancement (APHE) (p = 0.011), washout (p = 0.011), and enhancing capsule (p = 0.001) were the independent differential predictors of HCC. A nomogram was formed with well-fitted calibration curves based on these five factors. The area under the curve (AUC) of the nomogram in the training and validation cohorts was 0.961(95%CI: 0.935~0.986) and 0.979 (95% CI: 0.949~1), respectively. The nomogram outperformed the clinical and the radiological models in training and validation cohorts., Conclusion: The nomogram incorporating clinical and CT features can be a simple and reliable tool for detecting HCC and achieving risk stratification in patients at high risk for HCC., 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 Liang, Wu and Wei.)