RESUMO
Background: Previous studies demonstrated that induction chemotherapy (IC) followed by de-escalated chemoradiotherapy adapted to tumor response was effective in treating childhood nasopharyngeal carcinoma (NPC), but the toxicity profile of this treatment strategy, and whether childhood patients with advanced stages can obtain enough benefits from it requires further investigation. Methods: We conducted a single-center phase II trial (NCT03020329). All participants received 3 cycles of paclitaxel liposome, cisplatin and 5-fluorouracil (TPF)-based IC. Patients who showed complete or partial response received de-escalated radiotherapy of 60 Gy with 3 cycles of concurrent cisplatin, and those who showed stable or progressive disease received standard-dose radiotherapy of 70 Gy with concurrent cisplatin. The primary endpoint was the complete response (CR) rate at the end of concurrent chemoradiotherapy (CCRT). Findings: From November 2016 to March 2021, 44 patients were recruited in the cohort. The CR rate was 80% (35/44, 95% CI, 65-90) of the whole cohort. All patients achieved CR 3 months after CCRT. By the last follow-up, the 3-year progression-free survival and overall survival were 91% (95% CI, 82-99) and 100% respectively. Dry mouth was the most common late toxicity, with an incidence of 41% (18/44), followed by skin fibrosis and hearing impairment. No patient suffered from severe late toxicity and growth retardation. Interpretation: Our results proved the efficacy and safety of TPF regimen followed by de-escalated radiotherapy with concurrent cisplatin in treating stage IVa-b childhood NPC patients. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
RESUMO
Estimating vulnerability is critical to understand human-induced influenceimpacts on the environmental system. The purpose of the current study was to integrate machine learning algorithm and Twitter data to estimate environmental vulnerability in the Brazilian Cerrado for the years 2011 and 2016. We first selected six exposure indicators and five sensitivity indicators to build an environmental vulnerability model and applied an Autoencoder algorithm to find the representation of exposure and sensitivity, respectively. Then the Displaced Ideal method was used to estimate environmental vulnerability. Finally, related historical Twitter data was mined from these two years to validate the results. The findings showed that the percent of land classified as areas of low, medium and high environmental vulnerability were 6.72%, 34.85%, and 58.44% in 2011 and 3.45%, 33.68% and 62.87% in 2016, respectively and most high environmental vulnerability areas were in the Southern Cerrado. Moreover, the Twitter data results showed that more than 85% of tweets occurred in the areas considered as high environmental vulnerability class. The work revealed that the Autoencoder algorithm can be used for environmental assessment, and the social media data has potential to effectively analyze the relationship between human activity and the environment. Although the study provided a novel perspective to estimate environmental vulnerability at a regional scale, it was necessary to develop a more comprehensive indicator system that can improve model performance in the future.
Assuntos
Mídias Sociais , Algoritmos , Brasil , Humanos , Aprendizado de MáquinaRESUMO
Agricultural expansion as a main human activity has affected pollinator's habitat, causing spatial distribution changes. Meanwhile, pollinators still provide pollination service to improve crop production. However, their spatial response is unclear because of environmental changes. This study sought to estimate spatial distribution of crop production and pollinator's richness, which can provide insights as to how they interact with the environment. We acquired environmental variables from remote sensing images and used a stacked species distribution model to predict selected bee species richness and a crop simulation model to simulate and calculate soybean production at a regional scale in the Cerrado for the period 2000-2015. Then, we analyzed their potential relationship. The results showed that higher selected bee species richness and higher soybean production occurred in the southern Cerrado. From 2000/08 to 2008/15 period, the selected bee species richness significantly decreased in the western part of the state of Bahia, the state of Goiás, and the northern region of the state of Minas Gerais; while soybean production increased in the states of Mato Grosso, Goiás, Bahia, and Tocantins. Correlation results of selected bee species richness and soybean production showed that they do not follow a linear relationship during the study period. Our findings indicate that the modeling method we proposed is robust to estimate spatial distribution of bee species richness and soybean production in the Cerrado at the regional scale and that the environment has a stronger influence on selected bee species richness than on soybean production. Moreover, climate effects and agricultural expansion are the main factors that affect their spatial distribution and interaction. Finally, our methodology provides a novel spatial perspective to analyze the relationship between pollinator and agricultural expansion corresponding with the environment, but future work is needed to collect a more comprehensive data set to improve model results.