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Introduction: Similar to chronic pain conditions, individuals with endometriosis can be affected by central sensitization syndrome (CSS), which is characterized by a loss of analgesia and central amplification of pain. Transcranial direct current stimulation (tDCS) has shown potential as an effective intervention to improve pain generated by other chronic pain conditions impacted by CSS, such as fibromyalgia and chronic pelvic issues. This study aims to evaluate the effectiveness of tDCS on pain, fatigue, and quality of life among patients affected by endometriosis. Methods: This is a single-center, parallel, double-blinded, randomized, controlled clinical trial protocol study. We aim to recruit 40 participants affected by endometriosis (active group, n = 20; sham group, n = 20). Anodal tDCS will be delivered at an intensity of 2mA, applied over the primary motor cortex for 20 minutes per day for 10 consecutive days. There will be four assessment times: 1 week before beginning the intervention; on the 10th day following the last tDCS session; and 1 and 2 months after the last tDCS session. Pain evaluated by the algometry will be the primary outcome. Pain intensity, quality of life, fatigue, and global perception of change will be the secondary outcomes. We will calculate the effects of the active versus sham stimulation on primary and secondary outcomes by using generalized estimated equations or mixed model analysis. The effect size calculation will represent the effect measure. We expect that only the active group show reductions in pain, fatigue, and quality of life. The results of this trial will produce an important first step in providing evidence on the effectiveness of neuromodulation for the management of pain and will provide data to support new studies on tDCS. Registration: Brazilian Clinical Trials Registry (RBR-4q69573). [ABSTRACT FROM AUTHOR]

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km2 between 1970 and 2020. This represents a range of 60.8%–61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2024;20:1140–1155. © 2023 SETAC Key Points: The study's main contribution lies in the hindcasting of past land use and land cover scenarios, using a tool primarily used for forecasting future scenarios.The algorithm's performance is significantly influenced by the heterogeneity of the area of interest studied.We estimate that Aristida spp. fields have lost more than 60% of their original cover in the past 50 years. [ABSTRACT FROM AUTHOR]

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Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)