Comparison of ibuprofen release from minitablets and capsules containing ibuprofen: β-Cyclodextrin complex

NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Pharmaceutics and Biopharmaceutics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Eur J Pharm Biopharm. 2011 May;78(1):58-66. Epub 2010 Dec 30.Mixtures containing ibuprofen (IB) complexed with b-cyclodextrin (bCD) obtained by two complexation methods [suspension/solution (with water removed by air stream, spray- and freeze-drying) and kneading technique] were processed into pharmaceutical dosage forms (minitablets and capsules). Powders (IB, bCD and IBbCD) were characterized for moisture content, densities (true and bulk), angle of repose and Carr’s index, X-ray and NMR. From physical mixtures and IBbCD complexes without other excipients were prepared 2.5-mm-diameter minitablets and capsules. Minitablets were characterized for the energy of compaction, tensile strength, friability, density and IB release (at pH 1.0 and 7.2), whereby capsules were characterized for IB release. The results from the release of IB were analyzed using different parameters, namely, the similarity factor (f2), the dissolution efficiency (DE) and the amounts released at a certain time (30, 60 and 180 min) and compared statistically (a = 0.05). The release of IB from the minitablets showed no dependency on the amount of water used in the formation of the complexes. Differences were due to the compaction force used or the presence of a shell for the capsules. The differences observed were mostly due to the characteristics of the particles (dependent on the method considered on the formation of the complexes) and neither to the dosage form nor to the complex of the IB

The influence of the preparation methods on the inclusion of model drugs in a β-cyclodextrin cavity

NOTICE: this is the author’s version of a work that was accepted for publication in European Journal of Pharmaceutics and Biopharmaceutics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Eur J Pharm Biopharm. 2009 Feb;71(2):377-386. Epub 2008 Oct 17.The work aims to prove the complexation of two model drugs (ibuprofen, IB and indomethacin, IN) by bcyclodextrin (bCD), and the effect of water in such a process, and makes a comparison of their complexation yields. Two methods were considered: kneading of a binary mixture of the drug, bCD, and inclusion of either IB or IN in aqueous solutions of bCD. In the latter method water was removed by air stream, spray-drying and freeze-drying. To prove the formation of complexes in final products, optical microscopy, UV spectroscopy, IR spectroscopy, DSC, X-ray and NMR were considered. Each powder was added to an acidic solution (pH = 2) to quantify the concentration of the drug inside bCD cavity. Other media (pH = 5 and 7) were used to prove the existence of drug not complexed in each powder, as the drugs solubility increases with the pH. It was observed that complexation occurred in all powders, and that the fraction of drug inside the bCD did not depend neither on the method of complexation nor on the processes of drying considered

Portuguese Ministers, 1851-1999: Social Background and Paths to Power

Disponível em: http://193.136.113.6/Opac/Pages/Search/Results.aspx?SearchText=UID=bb8aa8d5-c6b6-466a-81bb-fe8a67693cee&DataBase=10449_UNLFCSHThis paper provides an empirical analysis of the impact of regime changes in the composition and patterns of recruitment of the Portuguese ministerial elite throughout the last 150 years. The ‘out-of-type’, violent nature of most regime transformations accounts for the purges in and the extensive replacements of the political personnel, namely of the uppermost officeholders. In the case of Cabinet members, such discontinuities did not imply, however, radical changes in their social profile. Although there were some significant variations, a series of salient characteristics have persisted over time. The typical Portuguese minister is a male in his midforties, of middle-class origin and predominantly urban-born, highly educated and with a state servant background. The two main occupational contingents have been university professors - except for the First Republic (1910-26) - and the military, the latter having only recently been eclipsed with the consolidation of contemporary democracy. As regards career pathways, the most striking feature is the secular trend for the declining role of parliamentary experience, which the democratic regime did not clearly reverse. In this period, a technocratic background rather than political experience has been indeed the privileged credential for a significant proportion of minister

A predictive modelling tool for assessing climate, land use and hydrological change on reservoir physicochemical and biological properties

Reservoirs are fundamental for water and energy supply but vulnerable to impacts including climate change. This paper outlines the steps in the development of a model to predict how climate, land use and hydrological change could affect the physiochemical and ecological quality of reservoirs in Portugal’s Douro region. Climatic data will be downscaled for subsequent finer spatial scale models to develop scenarios and outputs. Field observations and satellite imagery analysis will create dynamic maps providing data on change in land use and vegetation cover, while Artificial Neural Networks will determine how climate, land use and vegetation cover change may influence catchment hydrology. Data from field surveys of biological indicators, greenhouse gas emissions plus additional research will be applied in the Stochastic Dynamic Methodology, a sequential modelling process based on statistical parameter estimation, developed to predict and model physiochemical and ecological changes in reservoirs. This interdisciplinary approach will provide vital modelling tools for end users essential for water resource management in Portugal and to comply with the EU Water Framework Directive

Brazilian Flora 2020: Leveraging the power of a collaborative scientific network

International audienceThe shortage of reliable primary taxonomic data limits the description of biological taxa and the understanding of biodiversity patterns and processes, complicating biogeographical, ecological, and evolutionary studies. This deficit creates a significant taxonomic impediment to biodiversity research and conservation planning. The taxonomic impediment and the biodiversity crisis are widely recognized, highlighting the urgent need for reliable taxonomic data. Over the past decade, numerous countries worldwide have devoted considerable effort to Target 1 of the Global Strategy for Plant Conservation (GSPC), which called for the preparation of a working list of all known plant species by 2010 and an online world Flora by 2020. Brazil is a megadiverse country, home to more of the world's known plant species than any other country. Despite that, Flora Brasiliensis, concluded in 1906, was the last comprehensive treatment of the Brazilian flora. The lack of accurate estimates of the number of species of algae, fungi, and plants occurring in Brazil contributes to the prevailing taxonomic impediment and delays progress towards the GSPC targets. Over the past 12 years, a legion of taxonomists motivated to meet Target 1 of the GSPC, worked together to gather and integrate knowledge on the algal, plant, and fungal diversity of Brazil. Overall, a team of about 980 taxonomists joined efforts in a highly collaborative project that used cybertaxonomy to prepare an updated Flora of Brazil, showing the power of scientific collaboration to reach ambitious goals. This paper presents an overview of the Brazilian Flora 2020 and provides taxonomic and spatial updates on the algae, fungi, and plants found in one of the world's most biodiverse countries. We further identify collection gaps and summarize future goals that extend beyond 2020. Our results show that Brazil is home to 46,975 native species of algae, fungi, and plants, of which 19,669 are endemic to the country. The data compiled to date suggests that the Atlantic Rainforest might be the most diverse Brazilian domain for all plant groups except gymnosperms, which are most diverse in the Amazon. However, scientific knowledge of Brazilian diversity is still unequally distributed, with the Atlantic Rainforest and the Cerrado being the most intensively sampled and studied biomes in the country. In times of “scientific reductionism”, with botanical and mycological sciences suffering pervasive depreciation in recent decades, the first online Flora of Brazil 2020 significantly enhanced the quality and quantity of taxonomic data available for algae, fungi, and plants from Brazil. This project also made all the information freely available online, providing a firm foundation for future research and for the management, conservation, and sustainable use of the Brazilian funga and flora

At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods

By September 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are often, however, developed with small unrepresentative samples and with methodological limitations. It is highly important to develop predictive tools for pulmonary embolism (PE) in COVID-19 patients as one of the most severe preventable complications of COVID-19. Early recognition can help provide life-saving targeted anti-coagulation therapy right at admission. Using a dataset of more than 800,000 COVID-19 patients from an international cohort, we propose a cost-sensitive gradient-boosted machine learning model that predicts occurrence of PE and death at admission. Logistic regression, Cox proportional hazards models, and Shapley values were used to identify key predictors for PE and death. Our prediction model had a test AUROC of 75.9% and 74.2%, and sensitivities of 67.5% and 72.7% for PE and all-cause mortality respectively on a highly diverse and held-out test set. The PE prediction model was also evaluated on patients in UK and Spain separately with test results of 74.5% AUROC, 63.5% sensitivity and 78.9% AUROC, 95.7% sensitivity. Age, sex, region of admission, comorbidities (chronic cardiac and pulmonary disease, dementia, diabetes, hypertension, cancer, obesity, smoking), and symptoms (any, confusion, chest pain, fatigue, headache, fever, muscle or joint pain, shortness of breath) were the most important clinical predictors at admission. Age, overall presence of symptoms, shortness of breath, and hypertension were found to be key predictors for PE using our extreme gradient boosted model. This analysis based on the, until now, largest global dataset for this set of problems can inform hospital prioritisation policy and guide long term clinical research and decision-making for COVID-19 patients globally. Our machine learning model developed from an international cohort can serve to better regulate hospital risk prioritisation of at-risk patients. © The Author(s) 2024