Comorbidade de sintomas ansiosos e depressivos em pacientes com dor crônica e o impacto sobre a qualidade de vida

BACKGROUND: Pain is an unpleasant sensory and emotional experience. Both chronic pain and depression result in substantial disability reduced HRQoL and increased health care costs and utilization. OBJECTIVES: To evaluate the strength of the association between depressive and anxiety symptoms and chronic pain, and to investigate the impact of these symptoms on health-related quality of life (HRQoL) in chronic pain individuals. METHODS: Pain was assessed by means of a Visual Analogue Scale (VAS). Depressive and anxiety symptoms were assessed by the Hospital Anxiety and Depression (HAD) scale. Quality of life was assessed by means of the SF-36. RESULTS: Four hundred patients were studied, mean age 45.6 ± 11.4 years and 82.8% female gender. According to HAD, 70% had anxiety and 60% depression symptoms. SF-36 showed mean scores < 50% for all the domains. Patients with severe pain/extreme (70.4%) had a higher frequency of anxiety than those with pain selvagem/moderada (59,5%). This was a statistically significant (p = 0.027). However, the frequency of depression did not reach statistical significance when both groups were compared p = 0.109). DISCUSSION: Depressive/anxiety symptoms and pain together have worse clinical outcomes than each condition alone.CONTEXTO: Dor é uma experiência emocional e sensorial desagradável. Tanto a dor crônica como a depressão reduzem de forma significativa a qualidade de vida, além de aumentar muito os custos dos cuidados com a saúde. OBJETIVOS: Analisar a associação entre sintomas depressivos e de ansiedade em relação à dor crônica e investigar o impacto desses sintomas na saúde e na qualidade de vida em indivíduos com dor crônica. MÉTODOS: A dor foi avaliada por meio de uma Escala Analógica Visual (VAS). Os sintomas depressivos e a ansiedade foram avaliados pela Escala Hospitalar de Ansiedade e Depressão (HAD). A qualidade de vida foi avaliada por meio do SF-36. RESULTADOS: Quatrocentos pacientes foram estudados, com idade média de 45,6 ± 11,4 anos e 82,8% são do sexo feminino. De acordo com a HAD, 70% tinham ansiedade e 60%, os sintomas de depressão. A SF-36 apresentou escores < 50% para todos os domínios. Os pacientes com dor intensa/ extrema apresentaram maior frequência (70,4%) de ansiedade do que aqueles com dor selvagem/moderada (59,5%). Essa foi uma associação estatisticamente significante (p = 0,027). No entanto, a frequência de depressão não atingiu significância estatística quando ambos os grupos foram comparados (p = 0,109). CONCLUSÃO: Os sintomas depressivos/ansiedade e dor, em conjunto, apresentaram piores desfechos clínicos de cada estado sozinho. É necessária mais investigação para determinar se o tratamento da dor ajuda os sintomas dos pacientes depressivos e se o alívio dos sintomas depressivos melhora a dor e sua morbidade

Pharmaceutical services for endemic situations in the Brazilian Amazon: organization of services and prescribing practices for Plasmodium vivax and Plasmodium falciparum non-complicated malaria in high-risk municipalities

<p>Abstract</p> <p>Background</p> <p>In spite of the fact that pharmaceutical services are an essential component of all malaria programmes, quality of these services has been little explored in the literature. This study presents the first results of the application of an evaluation model of pharmaceutical services in high-risk municipalities of the Amazon region, focusing on indicators regarding organization of services and prescribing according to national guidelines.</p> <p>Methods</p> <p>A theoretical framework of pharmaceutical services for non-complicated malaria was built based on the Rapid Evaluation Method (WHO). The framework included organization of services and prescribing, among other activities. The study was carried out in 15 primary health facilities in six high-risk municipalities of the Brazilian Amazon. Malaria individuals ≥ 15 years old were approached and data was collected using specific instruments. Data was checked by independent reviewers and fed to a data bank through double-entry. Descriptive variables were analyzed.</p> <p>Results</p> <p>A copy of the official treatment guideline was found in 80% of the facilities; 67% presented an environment for receiving and prescribing patients. Re-supply of stocks followed a different timeline; no facilities adhered to forecasting methods for stock management. No shortages or expired anti-malarials were observed, but overstock was a common finding. On 86.7% of facilities, the average of good storage practices was 48%. Time between diagnosis and treatment was zero days. Of 601 patients interviewed, 453 were diagnosed for <it>Plasmodium vivax</it>; of these, 99.3% received indications for the first-line scheme. Different therapeutic schemes were given to <it>Plasmodium falciparum </it>patients. Twenty-eight (4.6%) out of 601 were prescribed regimens not listed in the national guideline. Only 5.7% individuals received a prescription or a written instruction of any kind.</p> <p>Conclusions</p> <p>The results show that while diagnostic procedure is well established and functioning in the Brazilian malaria programme, prescribing is still an activity that is actually not performed. The absence of physicians and poor integration between malaria services and primary health services make for the lack of a prescription or written instruction for malaria patients throughout the Brazilian Amazon. This fact may lead to a great number of problems in rational use and in adherence to medication.</p

Human IgG1 Responses to Surface Localised Schistosoma mansoni Ly6 Family Members Drop following Praziquantel Treatment

The heptalaminate-covered, syncytial tegument is an important anatomical adaptation that enables schistosome parasites to maintain long-term, intravascular residence in definitive hosts. Investigation of the proteins present in this surface layer and the immune responses elicited by them during infection is crucial to our understanding of host/parasite interactions. Recent studies have revealed a number of novel tegumental surface proteins including three (SmCD59a, SmCD59b and Sm29) containing uPAR/Ly6 domains (renamed SmLy6A SmLy6B and SmLy6D in this study). While vaccination with SmLy6A (SmCD59a) and SmLy6D (Sm29) induces protective immunity in experimental models, human immunoglobulin responses to representative SmLy6 family members have yet to be thoroughly explored.Using a PSI-BLAST-based search, we present a comprehensive reanalysis of the Schistosoma mansoni Ly6 family (SmLy6A-K). Our examination extends the number of members to eleven (including three novel proteins) and provides strong evidence that the previously identified vaccine candidate Sm29 (renamed SmLy6D) is a unique double uPAR/Ly6 domain-containing representative. Presence of canonical cysteine residues, signal peptides and GPI-anchor sites strongly suggest that all SmLy6 proteins are cell surface-bound. To provide evidence that SmLy6 members are immunogenic in human populations, we report IgG1 (as well as IgG4 and IgE) responses against two surface-bound representatives (SmLy6A and SmLy6B) within a cohort of S. mansoni-infected Ugandan males before and after praziquantel treatment. While pre-treatment IgG1 prevalence for SmLy6A and SmLy6B differs amongst the studied population (7.4% and 25.3% of the cohort, respectively), these values are both higher than IgG1 prevalence (2.7%) for a sub-surface tegumental antigen, SmTAL1. Further, post-treatment IgG1 levels against surface-associated SmLy6A and SmLy6B significantly drop (p = 0.020 and p < 0.001, respectively) when compared to rising IgG1 levels against sub-surface SmTAL1.Collectively, these results expand the number of SmLy6 proteins found within S. mansoni and specifically demonstrate that surface-associated SmLy6A and SmLy6B elicit immunological responses during infection in endemic communities

ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the International Space Station

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ECOSTRESS) was launched to the International Space Station on June 29, 2018. The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as level‐3 (L3) latent heat flux (LE) data products. These data are generated from the level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are over‐represented. The 70 m high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1 km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages