408 Cases of Genital Ambiguity Followed by Single Multidisciplinary Team during 23 Years: Etiologic Diagnosis and Sex of Rearing

Objective. To evaluate diagnosis, age of referral, karyotype, and sex of rearing of cases with disorders of sex development (DSD) with ambiguous genitalia. Methods. Retrospective study during 23 years at outpatient clinic of a referral center. Results. There were 408 cases; 250 (61.3%) were 46,XY and 124 (30.4%) 46,XX and 34 (8.3%) had sex chromosomes abnormalities. 189 (46.3%) had 46,XY testicular DSD, 105 (25.7%) 46,XX ovarian DSD, 95 (23.3%) disorders of gonadal development (DGD), and 19 (4.7%) complex malformations. The main etiology of 46,XX ovarian DSD was salt-wasting 21-hydroxylase deficiency. In 46,XX and 46,XY groups, other malformations were observed. In the DGD group, 46,XY partial gonadal dysgenesis, mixed gonadal dysgenesis, and ovotesticular DSD were more frequent. Low birth weight was observed in 42 cases of idiopathic 46,XY testicular DSD. The average age at diagnosis was 31.7 months. The final sex of rearing was male in 238 cases and female in 170. Only 6.6% (27 cases) needed sex reassignment. Conclusions. In this large DSD sample with ambiguous genitalia, the 46,XY karyotype was the most frequent; in turn, congenital adrenal hyperplasia was the most frequent etiology. Malformations associated with DSD were common in all groups and low birth weight was associated with idiopathic 46,XY testicular DSD

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Development and validation of a nomogram to predict kidney survival at baseline in patients with C3 glomerulopathy

10 p.-4 fig.-2 tab. 1 graph. abst.Background: C3 glomerulopathy is a rare and heterogeneous complement-driven disease. It is often challenging to accurately predict in clinical practice the individual kidney prognosis at baseline. We herein sought to develop and validate a prognostic nomogram to predict long-term kidney survival.Methods: We conducted a retrospective, multicenter observational cohort study in 35 nephrology departments belonging to the Spanish Group for the Study of Glomerular Diseases. The dataset was randomly divided into a training group (n = 87) and a validation group (n = 28). The least absolute shrinkage and selection operator (LASSO) regression was used to screen the main predictors of kidney outcome and to build the nomogram. The accuracy of the nomogram was assessed by discrimination and risk calibration in the training and validation sets.Results: The study group comprised 115 patients, of whom 46 (40%) reached kidney failure in a median follow-up of 49 months (range 24–112). No significant differences were observed in baseline estimated glomerular filtration rate (eGFR), proteinuria or total chronicity score of kidney biopsies, between patients in the training versus those in the validation set. The selected variables by LASSO were eGFR, proteinuria and total chronicity score. Based on a Cox model, a nomogram was developed for the prediction of kidney survival at 1, 2, 5 and 10 years from diagnosis. The C-index of the nomogram was 0.860 (95% confidence interval 0.834–0.887) and calibration plots showed optimal agreement between predicted and observed outcomes.Conclusions: We constructed and validated a practical nomogram with good discrimination and calibration to predict the risk of kidney failure in C3 glomerulopathy patients at 1, 2, 5 and 10 years.Work on this study was supported by the Instituto de Salud Carlos III / Fondo Europeo de Desarrollo Regional (ISCIII/FEDER; grants PI16/01685 and PI19/1624) and Red de Investigación Renal (RD12/0021/0029; to M.P.) and the Autonomous Region of Madrid (S2017/BMD-3673; to M.P.). S.R.d.C. is supported by the Ministerio de Economia y Competitividad (grant PID2019-104912RB-I00) and the Autonomous Region of Madrid (grant S2017/BMD-3673).Peer reviewe

Educomunicação, Transformação Social e Desenvolvimento Sustentável

Esta publicação apresenta os principais trabalhos dos GTs do II Congresso Internacional de Comunicação e Educação nos temas Transformação social, com os artigos que abordam principalmente Educomunicação e/ou Mídia-Educação, no contexto de políticas de diversidade, inclusão e equidade; e, em Desenvolvimento Sustentável os artigos que abordam os avanços da relação comunicação/educação no contexto da educação ambiental e desenvolvimento sustentável

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost