Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.
ContextObesity has been reported as a potential risk factor for the aggressiveness of papillary thyroid cancer (PTC), but the data gathered so far are conflicting.ObjectiveThe aim of our study was to evaluate the relationship between body mass index (BMI) and aggressiveness of PTC at the diagnosis and clinical outcome.MethodsA total of 337 patients who underwent radioactive iodine (RAI) therapy between March 2017 and May 2020 were recruited. Patients were divided into four groups: underweight (BMI<18.5 kg/m2), normal weight (18.5-24.9 kg/m2), overweight (25-29.9 kg/m2), and obese (BMI≥ 30 kg/m2). Treatment and follow-up were defined according to criteria used in the 2015 ATA guidelines.ResultsThis study included 337 patients with PTC (71.5% women, median age 45.21 ± 13.04 years). The mean BMI was 24.2 ± 3.1 kg/m2. Obese groups had a higher age than the other groups (P = 0.001). Moreover, obese patients had larger tumor sizes and higher T stage, compared to overweight, normal weight, and underweight patients (P = 0.007). After a median follow-up of 32 months, 279 patients (82.7%) had achieved an excellent response (ER) to therapy. The overall ER rates were compared between groups, and they did not differ significantly.ConclusionsWe demonstrated that BMI may have an additive effect on the aggressiveness of PTC, but did not have an effect on the response to therapy after high-dose RAI therapy.
Purpose
To explore the factors that influence the short-term clinical outcome after the first
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I treatment of papillary thyroid micro carcinoma (PTMC).
Patients and Methods
From October 2015 to June 2018, patients who were diagnosed with PTMC with lymph node metastasis were analyzed retrospectively, excluding patients with incomplete clinical data, distant metastasis, positive TGAb, TSH<30 mIU/L. The baseline data of sex, age, time from last surgery to first
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I treatment, tumor pathology information, and biochemical information were collected before admission. All patients included had radioactive iodine (RAI) with 3.70 GBq. The treatment response of patients was evaluated 6–8 months after discharge. By means of univariate and multivariate analysis, including excellent response (ER) and non-excellent response (NER) groups of clinical data, we assessed the impact of
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I on patients’ outcome. A nomogram model was established based on the above independent risk factors.
Results
A total of 206 patients (59 males and 147 females, mean age 43.4 ± 10.6 years) were included in the study. The median follow-up time was 169.4 ± 10.5 days, including 139 patients in ER group (67.4%) and 67 patients in NER group (32.5%). Four factors including combining Hashimoto’s thyroiditis, pre-ablative Tg levels, UIE levels, and lateral lymph node numbers were statistically different between ER group and NER group with significance at
P
< 0.05. Further multivariate analysis showed that Hashimoto’s thyroiditis and Ps-Tg levels could be used as independent factors. The model verification showed that the C-index of the modeling set was 0.822, indicating that the nomogram model had a good predicted accuracy.
Conclusion
Our data suggest that coexisting Hashimoto’s thyroiditis and elevated Ps-Tg levels are predictive factors for short-term outcome of thyroid micro papillary carcinoma after
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I treatment. Also, the nomogram model had a good predicted accuracy.
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