Purification of plasmid DNA vectors by aqueous two-phase extraction and hydrophobic interaction chromatography

The current study explores the possibility of using a polyethyleneglycol(PEG)/ammonium sulphate aqueous two-phase system (ATPS) as an early step in a process for the purification of a model 6.1 kbp plasmid DNA (pDNA) vector. Neutralised alkaline lysates were fed directly to ATPS. Conditions were selected to direct pDNA towards the salt-rich bottom phase, so that this stream could be subsequently processed by hydrophobic interaction chromatography (HIC). Screening of the best conditions for ATPS extraction was performed using three PEG molecular weights (300, 400, 600) and varying the tie-line length, phase volume ratio and lysate load. For a 20 % (w/w) lysate load, the best results were obtained with PEG 600 using the shortest tie-line (38.16 % w/w). By further manipulating the system composition along this tie-line in order to obtain a top/bottom phase volume ratio of 9.3 (35 % w/w PEG 600, 6% w/w NH4)2SO4), it was possible to recover 100 % of pDNA in the bottom phase with a 3-fold increase in concentration. Further increase in the lysate load up to 40 %(w/w) with this system resulted in a 8-fold increase in pDNA concentration, but with a yield loss of 15 %. The ATPS extraction was integrated with HIC and the overall process compared with a previously defined process that uses sequential precipitations with isopropanol and ammonium sulphate prior to HIC. Although the final yield is lower in the ATPS-based process the purity grade of the final pDNA product is higher. This shows that it is possible to substitute the time-consuming two-step precipitation procedure by a simple ATPS extraction.Portuguese Ministry of Science and Technology - POCTI/BIO/47245/2002

Characterisation of Innate Fungal Recognition in the Lung

The innate recognition of fungi by leukocytes is mediated by pattern recognition receptors (PRR), such as Dectin-1, and is thought to occur at the cell surface triggering intracellular signalling cascades which lead to the induction of protective host responses. In the lung, this recognition is aided by surfactant which also serves to maintain the balance between inflammation and pulmonary function, although the underlying mechanisms are unknown. Here we have explored pulmonary innate recognition of a variety of fungal particles, including zymosan, Candida albicans and Aspergillus fumigatus, and demonstrate that opsonisation with surfactant components can limit inflammation by reducing host-cell fungal interactions. However, we found that this opsonisation does not contribute directly to innate fungal recognition and that this process is mediated through non-opsonic PRRs, including Dectin-1. Moreover, we found that pulmonary inflammatory responses to resting Aspergillus conidia were initiated by these PRRs in acidified phagolysosomes, following the uptake of fungal particles by leukocytes. Our data therefore provides crucial new insights into the mechanisms by which surfactant can maintain pulmonary function in the face of microbial challenge, and defines the phagolysosome as a novel intracellular compartment involved in the innate sensing of extracellular pathogens in the lung

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

Gender differences in inflammatory bowel disease: Explaining body image dissatisfaction

The aim of this study was to examine the role of body image problems in the context of inflammatory bowel disease and to explore gender differences in these associations. A sample of inflammatory bowel disease patients (60 males and 140 females) was collected. Findings from a multi-group analysis show that inflammatory bowel disease symptomatology may impact on body image in both male and female patients through the effect of body-image-related cognitive fusion. Body image difficulties in the context of inflammatory bowel disease should not be a neglected dimension in research aiming at understanding the psychosocial effects of inflammatory bowel disease and by health professionals working with these patients

Exploring the Impact of Covid-19-Related Perceptions on Psychological Distress and Quality of Life in an International Gastrointestinal Cohort Over Time Guided by the Common Sense Model

The aim of this longitudinal study was to examine changes in COVID-19 and illness-related perceptions, gastrointestinal symptoms, coping, catastrophising, psychological distress, and QoL during the COVID-19 pandemic. A total of 831 adults with a gastrointestinal condition completed an online questionnaire at baseline (May-October 2020). Of those, 270 (32.5%) participants (85.2% female, mean age = 47.3 years) provided follow-up data (March-May 2021). Repeated-measures multiple analysis of variance and a cross-lagged panel model were used to test the study hypotheses. Gastrointestinal symptoms and COVID-19 perceptions at follow-up were strongly predicted by their baseline values, while illness perceptions were predicted by baseline gastrointestinal symptoms. Cross-lagged relationships indicated a reciprocal relationship between gastrointestinal symptoms and psychological distress. Moreover, gastrointestinal symptoms had substantial predictive utility, strongly predicting future gastrointestinal symptoms, and to a lesser extent, more negative illness perceptions, greater psychological distress, and greater use of adaptive coping strategies across time