fac-Aqua­(2-carboxy­ethyl-κ2 C,O)trichlorido­tin(IV)–1,4,7,10,13-penta­oxacyclo­penta­deca­ne–water (1/1/2)

In the title compound, [Sn(C3H5O2)Cl3(H2O)]·C10H20O5·2H2O, the SnIV atom is octa­hedrally coordinated within a fac-CO2Cl3 donor set, arising from the C,O-bidentate carboxy­ethyl ligand, a water mol­ecule and three chloride ligands. In the crystal, supra­molecular chains linked by O—H⋯O hydrogen bonds propagate along the c axis These chains are connected into layers in the ac plane via C—H⋯O inter­actions

Hereditary angioedema: quality of life in Brazilian patients

OBJECTIVE: Hereditary angioedema is a serious medical condition caused by a rare autosomal dominant genetic disorder and it is associated with deficient production or dysfunction of the C1 esterase inhibitor. In most cases, affected patients experience unexpected and recurrent crises of subcutaneous, gastrointestinal and laryngeal edema. The unpredictability, intensity and other factors associated with the disease impact the quality of life of hereditary angioedema patients. We evaluated the quality of life in Brazilian hereditary angioedema patients. METHODS: Patients older than 15 years with any severity of hereditary angioedema and laboratory confirmation of C1 inhibitor deficiency were included. Two questionnaires were used: a clinical questionnaire and the SF-36 (a generic questionnaire). This protocol was approved by the Ethics Committee of Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo. RESULTS: The SF-36 showed that 90.4% (mean) of all the patients had a score below 70 and 9.6% had scores equal to or higher than 70. The scores of the eight dimensions ranged from 51.03 to 75.95; vitality and social aspects were more affected than other arenas. The internal consistency of the evaluation was demonstrated by a Cronbach's alpha value above 0.7 in seven of the eight domains. CONCLUSIONS: In this study, Brazilian patients demonstrated an impaired quality of life, as measured by the SF-36. The most affected domains were those related to vitality and social characteristics. The generic SF-36 questionnaire was relevant to the evaluation of quality of life; however, there is a need for more specific instruments for better evaluation

Bioinformatics-Based Identification of Expanded Repeats: A Non-reference Intronic Pentamer Expansion in RFC1 Causes CANVAS

Genomic technologies such as next-generation sequencing (NGS) are revolutionizing molecular diagnostics and clinical medicine. However, these approaches have proven inefficient at identifying pathogenic repeat expansions. Here, we apply a collection of bioinformatics tools that can be utilized to identify either known or novel expanded repeat sequences in NGS data. We performed genetic studies of a cohort of 35 individuals from 22 families with a clinical diagnosis of cerebellar ataxia with neuropathy and bilateral vestibular areflexia syndrome (CANVAS). Analysis of whole-genome sequence (WGS) data with five independent algorithms identified a recessively inherited intronic repeat expansion [(AAGGG)exp] in the gene encoding Replication Factor C1 (RFC1). This motif, not reported in the reference sequence, localized to an Alu element and replaced the reference (AAAAG)11 short tandem repeat. Genetic analyses confirmed the pathogenic expansion in 18 of 22 CANVAS-affected families and identified a core ancestral haplotype, estimated to have arisen in Europe more than twenty-five thousand years ago. WGS of the four RFC1-negative CANVAS-affected families identified plausible variants in three, with genomic re-diagnosis of SCA3, spastic ataxia of the Charlevoix-Saguenay type, and SCA45. This study identified the genetic basis of CANVAS and demonstrated that these improved bioinformatics tools increase the diagnostic utility of WGS to determine the genetic basis of a heterogeneous group of clinically overlapping neurogenetic disorders

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

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