Índice de ajuste de comorbidade para a 10a revisão da classificação internacional de doenças

OBJECTIVE: To develop a Charlson-like comorbidity index based on clinical conditions and weights of the original Charlson comorbidity index. METHODS: Clinical conditions and weights were adapted from the International Classification of Diseases, 10th revision and applied to a single hospital admission diagnosis. The study included 3,733 patients over 18 years of age who were admitted to a public general hospital in the city of Rio de Janeiro, southeast Brazil, between Jan 2001 and Jan 2003. The index distribution was analyzed by gender, type of admission, blood transfusion, intensive care unit admission, age and length of hospital stay. Two logistic regression models were developed to predict in-hospital mortality including: a) the aforementioned variables and the risk-adjustment index (full model); and b) the risk-adjustment index and patient's age (reduced model). RESULTS: Of all patients analyzed, 22.3% had risk scores >;1, and their mortality rate was 4.5% (66.0% of them had scores >;1). Except for gender and type of admission, all variables were retained in the logistic regression. The models including the developed risk index had an area under the receiver operating characteristic curve of 0.86 (full model), and 0.76 (reduced model). Each unit increase in the risk score was associated with nearly 50% increase in the odds of in-hospital death. CONCLUSIONS: The risk index developed was able to effectively discriminate the odds of in-hospital death which can be useful when limited information is available from hospital databases.OBJETIVO: Desarrollar un índice de co-morbilidad a partir de las condiciones clínicas y de los pesos de índice original de co-morbilidad de Charlson. MÉTODOS: Las condiciones clínicas y pesos del índice de Charlson fueron adaptados según la Clasificación Internacional de Enfermedades - 10a Revisión, y aplicados al diagnóstico principal de internación hospitalaria. Fueron estudiados 3.733 pacientes arriba de 18 años hospitalizados en el hospital general público del municipio de Rio de Janeiro (sudeste de Brasil), de 2001-2003. La distribución del índice fue de acuerdo con el género, tipo de admisión, presencia de transfusión de sangre, admisión a la unidad de terapia intensiva, edad y tiempo de internación. Dos modelos de regresión logística fueron desarrollados con el objetivo de prevenir la mortalidad hospitalaria: a) con las variables arriba y el índice de co-morbilidad (modelo completo); y b) conteniendo solo el índice y la edad de los pacientes (modelo reducido). RESULTADOS: Dentro del total de pacientes analizados, 22,3% tuvieron puntajes >;1 para el índice y su taza de mortalidad fue 4,5% (66,0% de los cuales con puntajes >;1). A excepción del género y del tipo de admisión todas las variables fueron retenidas en la regresión. Los modelos tuvieron una área bajo la curva característica ROC igual a 0,86 (modelo completo) y 0,76 (modelo reducido). Cada aumento de una unidad en los puntajes del índice fue asociado con un aumento de casi 50% en la probabilidad de mortalidad hospitalaria. CONCLUSIONES: El índice desarrollado puede discriminar probabilidades de mortalidad con una eficacia aceptable, el que puede ser útil al ser usado con bancos de datos hospitalarios con información limitada.OBJETIVO: Desenvolver um índice de co-morbidade a partir das condições clínicas e dos pesos do índice de co-morbidade de Charlson. MÉTODOS: As condições clínicas e pesos do índice de Charlson foram adaptados segundo a Classificação Internacional de Doenças 10a Revisão, e aplicados ao diagnóstico principal de internação hospitalar. Foram estudados 3.733 pacientes acima de 18 anos hospitalizados em hospital geral público do município do Rio de Janeiro, RJ, 2001-2003. A distribuição do índice foi de acordo com o gênero, tipo da admissão, presença de transfusão de sangue, admissão à unidade de terapia intensiva, idade e tempo de internação. Dois modelos de regressão logística foram desenvolvidos com o objetivo de prever a mortalidade hospitalar desses pacientes: a) com as variáveis acima e o índice de co-morbidade (modelo completo); e b) contendo apenas o índice e a idade dos pacientes (modelo reduzido). RESULTADOS: Dentre o total de pacientes analisados, 22,3% possuíam escores >;1 para o índice e sua taxa de mortalidade foi 4,5% (66,0% dos quais com escores >;1). Exceto gênero e do tipo de admissão, todas as variáveis foram retidas na regressão. Os modelos tiveram uma área sob a curva característica ROC igual a 0,86 (modelo completo) e 0,76 (modelo reduzido). Cada aumento de uma unidade nos escores do índice foi associado com um aumento de quase 50% na probabilidade de mortalidade hospitalar. CONCLUSÕES: O índice desenvolvido pôde discriminar probabilidades de mortalidade com uma eficácia aceitável, o que pode ser útil ao lidar-se com bancos de dados hospitalares com informação limitada

Analysis of the effect of ultrasound on Hymenaea courbaril L. seeds / Análise do efeito do ultrassom em sementes de Hymenaea courbaril L.

Hymenaea courbaril L. popularly known as Jatobá is a native species of the Amazon. We selected 100 seeds for the morphometric analysis, for the water content of the seeds, four samples of 5 g each were used, for the germination test, four groups of seeds were evaluated: control group without ultrasound application and three other groups that were submitted to ultrasound application for 2 minutes (U2), 3 minutes (U3) and 4 minutes (U4) at a frequency of 3 MHz and an intensity of 2 W/cm² of exposure. Each group divided into four repetitions of 25 seeds each, were transferred to an incubator (B.O.D.) with a photoperiod of 12 h of light per day and 100% relative humidity, each group being repeated twice and tested at two temperatures (30 °C and 35 °C). The seed has 9.36% fresh mass, 23.62 mm, 14.1 mm, 65.59 mm in length, width, and perimeter respectively and 79.03% purity. The ultrasound waves applied for 3 minutes favored seed germination of 86.12% (30 ºC) and 83.04% (35 ºC) and mean germination time of  21.75 days (30°C) and 21.81 days (35°C) for two and three minutes respectively.  Therefore, the ultrasound technique is considered useful and promising tool for breaking tegumentary dormancy in Jatobá seeds. 

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

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research