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Studies show that executive functions and motor development are associated with each other and with learning ability. A more technological lifestyle combined with digital culture should be considered a viable alternative to stimulate children's development. Therefore, this study aimed to present a systematic mapping of the literature involving executive functions, motor development, and the use of digital games in intervention programs for elementary school children from 6 to 11 years old. Four databases were researched: PubMed, Scielo, Science Direct, and SCOPUS, including publications between 2012 and March 2021. The initial results indicated 4881 records. After the selection process, 15 manuscripts that presented the central theme of the study were selected. The main results indicated that intervention strategies are rather heterogeneous. Most of the studies demonstrated efficient results after intervention protocols, many of them were conducted in Europe, and 46% occurred in a school environment. No research was identified involving technological solutions using executive functions, motor development, and digital games in an integrated manner. Hence, this constitutes a field of future scientific research.

Advances in information and communication technologies have contributed to the increasing use of virtual learning environments as support tools in teaching and learning processes. Virtual platforms generate a large volume of educational data, and the analysis of this data allows useful information discoveries to improve learning and assist institutions in reducing disqualifications and dropouts in distance education courses. This article presents the results of a systematic mapping study aiming to identify how educational data mining, learning analytics, and collaborative groups have been applied in distance education environments. Articles were searched from 2010 to June 2020, initially resulting in 55,832 works. The selection of 51 articles for complete reading in order to answer the research questions considered a group of inclusion and exclusion criteria. Main results indicated that 53% of articles (27/51) offered intelligent services in the field of distance education, 47% (24/51) applied methods and analysis techniques in distance education environments, 21% (11/51) applied methods and analysis techniques focused on virtual learning environments logs and 5% (3/51) presented intelligent collaborative services for identification and creation of groups. This article also identified research interest clusters with highlights for the terms recommendation systems, data analysis, e-learning, educational data mining, e-learning platform and learning management system.

Distance learning has assumed a relevant role in the educational scenario. The use of Virtual Learning Environments contributes to obtaining a substantial amount of educational data. In this sense, the analyzed data generate knowledge used by institutions to assist managers and professors in strategic planning and teaching. The discovery of students' behaviors enables a wide variety of intelligent services for assisting in the learning process. This article presents a literature review in order to identify the intelligent services applied in distance learning. The research covers the period from January 2010 to May 2021. The initial search found 1316 articles, among which 51 were selected for further studies. Considering the selected articles, 33% (17/51) focus on learning systems, 35% (18/51) propose recommendation systems, 26% (13/51) approach predictive systems or models, and 6% (3/51) use assessment tools. This review allowed for the observation that the principal services offered are recommendation systems and learning systems. In these services, the analysis of student profiles stands out to identify patterns of behavior, detect low performance, and identify probabilities of dropouts from courses.

Distance Learning has enabled educational practices based on digital platforms, generating massive amounts of data. Several initiatives use this data to identify dropout contexts, mainly providing teacher support about student behavior. Approaches such as Active Methodologies are known as having good potential to involve and motivate students. This article presents a systematic mapping aiming to identify current Educational Data Mining and Learning Analytics methods. Besides, we identify Active Methodologies' application to mitigate dropout in Distance Learning. We evaluated 668 papers published from January 2015 to March 2020. The results indicate a growing application of Educational Data Mining and Learning Analytics to identify and mitigate students' abandonment in Distance Learning. However, studies with Active Methodologies to minimize dropout and enhance student permanence are scarce. Some works suggest Active Methods as a possible complement of Learning Analytics in dropout. [Note: The page range (171-204) shown on the PDF is incorrect. The correct page range is p171-203.]

The use of information communication technologies (ICT) in education brings up new possibilities of promoting learning and health experiences. In this sense, education contexts of the 21st century must consider these two areas of knowledge, especially their integration. This article presents learning practices developed with mobile devices and games in order to improve learning and well-being in children and adolescents undergoing cancer treatment in a non-formal educational setting. The methodology is based on qualitative case studies with content-based data analyses, involving informal interviews and observation methods. The study considers data from five patients who participated in the research between 2015 and 2019. The results demonstrate a positive influence of the practices with mobile technologies and games in terms of learning and in the well-being experience of patients during the treatment.

SUMMARY Lipodystrophies are characterized by complete or selective loss of adipose tissue and can be acquired or inherited. Familial partial lipodystrophy (FPLD) is a hereditary lipodystrophy commonly caused by mutations in the LMNA gene. Herein, we report two cases of FPLD associated with podocytopathies. Patient 1 was diagnosed with FPLD associated with the heterozygous p.Arg482Trp variant in LMNA and had normal glucose tolerance and hyperinsulinemia. During follow-up, she developed nephrotic-range proteinuria. Renal biopsy was consistent with minimal change disease. Patient 2 was diagnosed with FPLD associated with a de novo heterozygous p.Arg349Trp variant in LMNA. Microalbuminuria progressed to macroalbuminuria within 6 years and to nephrotic range proteinuria in the last year. He remained without diabetes and with hyperinsulinemia. Renal biopsy revealed focal segmental glomerulosclerosis not otherwise specified. This report provides further evidence of variable features of lipodystrophy associated with LMNA variants and the importance of long-term follow-up with evaluation of kidney dysfunction.

Abstract Background Systemic amyloidosis is caused by the deposition of misfolded protein aggregates in tissues, leading to progressive organ dysfunction and death. Epidemiological studies originate predominantly from high-income countries, with few data from Latin America. Due to the non-specific clinical manifestations, diagnosing amyloidosis is often challenging and patients experience a long journey and delay in diagnosis. This study aimed to assess clinical and laboratory characteristics, the diagnostic journey, and outcomes of patients with biopsy-proven systemic amyloidosis diagnosed between 2009 and 2020 at a university referral center in a middle-income Latin American country. Patients´ medical records were retrospectively reviewed. Results One hundred and forty-three patients were included. The median age at diagnosis was 60 years and 54% were male. Until the diagnosis, most of the patients (52%) were seen by at least 3 specialists, the main ones being: general practitioners (57%), nephrologists (45%), and cardiologists (38%). The most common manifestations were renal (54%) and cardiac (41%) disorders, and cachexia was seen in 36% of patients. In 72% of the cases, ≥ 2 biopsies were required until the final diagnosis. The median time from symptoms onset to diagnosis was 10.9 months, and most patients (75%) had ≥ 2 organs involved. The following subtypes were identified: AL (68%), ATTR (13%), AA (8%), AFib (4%), and inconclusive (7%). Median OS was 74.3 months in the non-AL subgroup and 18.5 months in AL. Among AL patients, those with advanced cardiac stage had the worst outcome [median OS 8.6 months versus 52.3 for stage III versus I–II, respectively (p