UCSD Performance-Based Skills Assessment (UPSA): validation of a Brazilian version in patients with schizophrenia

AbstractThe UCSD Performance-based Skills Assessment (UPSA) is a measure of Functional Capacity and assesses skills involved in community tasks. It has good psychometrics properties, and is currently recommended as a co-primary assessment of cognition in the MATRICS Project. To our knowledge so far, there are no studies in western developing countries concerning Functional Capacity in Schizophrenia. The aims of this study were to translate, culturally adapt and validate the UPSA to assess Functional Capacity in community-dwelling patients with Schizophrenia living in Brazil. Eighty-two subjects (52 patients, 30 controls) were evaluated using: the Brazilian version of the UPSA (UPSA-1-BR), PANSS, Personal and Social Performance (PSP) and Global Assessment of Functioning (GAF). In the reliability test, UPSA-1-BR showed good Internal Consistency (Cronbach’s alpha of 0.88) and strong correlation between test and retest (4-month gap; r = 0.91; p < 0.01). Spearman’s rho values showed a moderate correlation between UPSA-1-BR and both PSP (0.50; p < 0.01) and GAF (0.46; p < 0.01) scores. UPSA-1-BR is capable of differentiating people with and without Schizophrenia. Patients scored lower than controls (58.9 versus 79.1), with an AUC of 0.79 (95%IC: 0.69–0.89). Sensitivity and specificity values of 0.71 and 0.70, respectively, were found in the cut-off point of 73.5, for separation of patients and controls, with predictive values of 80% (positive) and 58% (negative). UPSA-B-BR was also evaluated. UPSA-1-BR and its brief version presented adequate psychometric properties and proved to be valid and reliable instruments in the assessment of Functional Capacity in subjects with Schizophrenia

Analysis of the crack growth behavior in a double cantilever beam adhesive fracture test using digital image processing techniques

Digital image processing (DIP) techniques offer interesting possibilities in various fields of science.Automated analyses may significantly reduce the necessary manpower for certain cumbersometasks. The analysis of large series of images may be done in less time, since automatedimage processing techniques are able to work efficiently and with constant quality 24h per day.In this work, a series of images obtained by a high-speed camera is analyzed in order to determinethe crack growth behavior during a double cantilever beam (DCB) test [1]. The presentwork represents a contribution to the effort of automatizing the crack growth measurement,comparing various different techniques which may later be optimized for a specific task.Detecting cracks automatically from test images obtained by a digital camera is a difficult task,since the quality of crack images depends on the test conditions. The roughness of the specimensurface, luminance condition, and the camera itself may influence the detection quality.The specimens tested in this work where painted with white colour since this was found to leadto the best contrast for crack detection. High accuracy may only be expected if a sufficientlyhigh resolution is acquired by the camera and if the available lens setup is optimized for thespecific task.The DCB test is performed in order to obtain the experimental compliance-crack length curveof a polymeric adhesive. Accurate and reliable crack length measurement is indispensable forthe generation of the previously mentioned compliance-crack length curves. It should be notedthat due to the lenses used, unlike shown by Ryu [2], the distance to the specimen is higher than800 mm. This distance has to be reduced by the use of a different lens setup in order to get abetter accuracy of the results. Nevertheless a comparison between different DIP methods is possible.Four different algorithms were developed using The MathWorks MatLab, Massachusetts[3] in order to automatically measure the crack length and a comparison of the obtained resultsis made.Algorithm A is based on thresholding [4] each image of the sequence in order to detect thewhite painted region around the crack. In algorithm B, the image sequence is processed by afilter which reinforces horizontal lines such as the crack, and then isolated pixels are removedfrom the images using morphological cleaning [4]. In algorithm C, the first of two consecutiveimages is subtracted from the second one in order to detect the crack as a difference betweenboth images. Algorithm D is based on the optical flow concept developed by Horn [5]. Thebasic idea is to determine the velocity of each pixel in the image when this changes its positionfrom one image to the next in the analyzed sequence, and relate this information to the growing crack

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

Patologias atuais: a compulsão e a sociedade dos excessos: Current pathologies: compulsion and the society of excesses

O artigo em tela tem por objetivo analisar os aspectos biopsicossociais da conduta compulsiva de consumo. Propõe-se a apresentar os elementos psicológicos contidos nesse comportamento, além de verificar quais são os resultados decorrentes dessa compulsão. O consumo compulsivo, também chamado de oniomania, é um transtorno causado pela ansiedade despertada pela necessidade de comprar e saciada, somente, quando é materializada a aquisição daquilo que se deseja comprar. O estudo em questão pode ser classificado como sendo de cunho bibliográfico, a partir da análise de documentos publicados em forma de artigos científicos e livros em formato digital

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

Evolutionary characterization of lung adenocarcinoma morphology in TRACERx

Lung adenocarcinomas (LUADs) display a broad histological spectrum from low-grade lepidic tumors through to mid-grade acinar and papillary and high-grade solid, cribriform and micropapillary tumors. How morphology reflects tumor evolution and disease progression is poorly understood. Whole-exome sequencing data generated from 805 primary tumor regions and 121 paired metastatic samples across 248 LUADs from the TRACERx 421 cohort, together with RNA-sequencing data from 463 primary tumor regions, were integrated with detailed whole-tumor and regional histopathological analysis. Tumors with predominantly high-grade patterns showed increased chromosomal complexity, with higher burden of loss of heterozygosity and subclonal somatic copy number alterations. Individual regions in predominantly high-grade pattern tumors exhibited higher proliferation and lower clonal diversity, potentially reflecting large recent subclonal expansions. Co-occurrence of truncal loss of chromosomes 3p and 3q was enriched in predominantly low-/mid-grade tumors, while purely undifferentiated solid-pattern tumors had a higher frequency of truncal arm or focal 3q gains and SMARCA4 gene alterations compared with mixed-pattern tumors with a solid component, suggesting distinct evolutionary trajectories. Clonal evolution analysis revealed that tumors tend to evolve toward higher-grade patterns. The presence of micropapillary pattern and ‘tumor spread through air spaces’ were associated with intrathoracic recurrence, in contrast to the presence of solid/cribriform patterns, necrosis and preoperative circulating tumor DNA detection, which were associated with extra-thoracic recurrence. These data provide insights into the relationship between LUAD morphology, the underlying evolutionary genomic landscape, and clinical and anatomical relapse risk

The artificial intelligence-based model ANORAK improves histopathological grading of lung adenocarcinoma

The introduction of the International Association for the Study of Lung Cancer grading system has furthered interest in histopathological grading for risk stratification in lung adenocarcinoma. Complex morphology and high intratumoral heterogeneity present challenges to pathologists, prompting the development of artificial intelligence (AI) methods. Here we developed ANORAK (pyrAmid pooliNg crOss stReam Attention networK), encoding multiresolution inputs with an attention mechanism, to delineate growth patterns from hematoxylin and eosin-stained slides. In 1,372 lung adenocarcinomas across four independent cohorts, AI-based grading was prognostic of disease-free survival, and further assisted pathologists by consistently improving prognostication in stage I tumors. Tumors with discrepant patterns between AI and pathologists had notably higher intratumoral heterogeneity. Furthermore, ANORAK facilitates the morphological and spatial assessment of the acinar pattern, capturing acinus variations with pattern transition. Collectively, our AI method enabled the precision quantification and morphology investigation of growth patterns, reflecting intratumoral histological transitions in lung adenocarcinoma

The evolution of lung cancer and impact of subclonal selection in TRACERx

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource

The evolution of non-small cell lung cancer metastases in TRACERx

Metastatic disease is responsible for the majority of cancer-related deaths1. We report the longitudinal evolutionary analysis of 126 non-small cell lung cancer (NSCLC) tumours from 421 prospectively recruited patients in TRACERx who developed metastatic disease, compared with a control cohort of 144 non-metastatic tumours. In 25% of cases, metastases diverged early, before the last clonal sweep in the primary tumour, and early divergence was enriched for patients who were smokers at the time of initial diagnosis. Simulations suggested that early metastatic divergence more frequently occurred at smaller tumour diameters (less than 8 mm). Single-region primary tumour sampling resulted in 83% of late divergence cases being misclassified as early, highlighting the importance of extensive primary tumour sampling. Polyclonal dissemination, which was associated with extrathoracic disease recurrence, was found in 32% of cases. Primary lymph node disease contributed to metastatic relapse in less than 20% of cases, representing a hallmark of metastatic potential rather than a route to subsequent recurrences/disease progression. Metastasis-seeding subclones exhibited subclonal expansions within primary tumours, probably reflecting positive selection. Our findings highlight the importance of selection in metastatic clone evolution within untreated primary tumours, the distinction between monoclonal versus polyclonal seeding in dictating site of recurrence, the limitations of current radiological screening approaches for early diverging tumours and the need to develop strategies to target metastasis-seeding subclones before relapse