Removal of polycyclic aromatic hydrocarbons by biosorbents

Introduction: - Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental carcinogens. They are formed during the incomplete combustion of organic matter. Humans are exposed to PAHs by various sources, including occupational environments, cigarette smoke, vehicle exhaust, and dietary sources as grilled and flame-broiled food. - In vivo studies in animals proved that PAHs are associated to cancer, and epidemiologic studies with exposed workers, especially in coke ovens and aluminium smelters, have shown clear excess of lung cancer and highly suggestive excesses of bladder cancer. - These compounds can enter in drinking water sources by precipitation and runoff on the earth’s surface. - Portuguese legislation for water for human consumption (DL 306/2007) proposes the determination of five PAHs; limits of the maximum concentration are 0.10 µg/L for total BghiP, BbF, BkF, IcdP, and 0.010 µg/L for BaP.This work was financial suported by the COMPETE program, under the Watercork project (nº. 2009/552).N/

Autoantibodies against type I IFNs in patients with life-threatening COVID-19

Interindividual clinical variability in the course of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is vast. We report that at least 101 of 987 patients with life-threatening coronavirus disease 2019 (COVID-19) pneumonia had neutralizing immunoglobulin G (IgG) autoantibodies (auto-Abs) against interferon-w (IFN-w) (13 patients), against the 13 types of IFN-a (36), or against both (52) at the onset of critical disease; a few also had auto-Abs against the other three type I IFNs. The auto-Abs neutralize the ability of the corresponding type I IFNs to block SARS-CoV-2 infection in vitro. These auto-Abs were not found in 663 individuals with asymptomatic or mild SARS-CoV-2 infection and were present in only 4 of 1227 healthy individuals. Patients with auto-Abs were aged 25 to 87 years and 95 of the 101 were men. A B cell autoimmune phenocopy of inborn errors of type I IFN immunity accounts for life-threatening COVID-19 pneumonia in at least 2.6% of women and 12.5% of men

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

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

Chemically activated high grade nanoporous carbons from low density renewable biomass (Agave sisalana) for the removal of pharmaceuticals

Hypothesis: Enlarging the range of viable nanoporous carbon precursors, namely by the acid treatment of low density biomass residues, can overcome issues related with the availability and quality of raw materials that have potential impact on cost and quality grade of the final product. Experiments: Nanoporous carbons were prepared following a two-step process: HSO digestion/polycondensation of biomass waste (Agave sisalana, sisal) at temperature below 100 °C and atmospheric pressure to obtain acid-chars that were further chemically activated with KOH or KCO. Selected synthesized nanoporous carbons were tested for the removal of pharmaceutical compounds – ibuprofen and iopamidol – in aqueous solutions. Findings: The structure and density of the acid-chars are highly dependent on the concentration of HSO used in the digestion and polycondensation steps. An adequate choice of the acid-char synthesis conditions, activating agent and contact method allowed to feature nanoporous carbons with specific surface areas ranging from 600 to 2300 m g and apparent densities reaching 600 kg m. The adsorption capacity of a sample obtained by KOH-activation for the removal of micropollutants from water was twice higher than the value attained by a golden activated carbon (Cabot-Norit) commercialized for this specific purpose.This work was developed under the financial support of projects UID/MULTI/00612/2013 (CQB) and UID/ QUI/50006/2013 - POCI/01/0145/FERDER/007265 (REQUIMTE) from FCT/MEC through national funds and co-financed by FEDER, under the Partnership Agreement PT2020. ASM thanks FCT for a Post-doctoral grant SFRH/BPD/86693/2012

Removal of an analgesic using activated carbons prepared from urban and industrial residues

International audienc

Surface heterogeneity effects of activated carbons on the kinetics of paracetamol removal from aqueous solution

International audienc

Dual role of copper on the reactivity of activated carbons from coal and lignocellulosic precursors

International audienc

Detection rate of unknown primary tumour by using somatostatin receptor PET/CT in patients with metastatic neuroendocrine tumours: a meta-analysis.

The high diagnostic performance of somatostatin receptor positron emission tomography with computed tomography (PET/CT) in neuroendocrine tumours (NETs) was demonstrated by several articles. However, only some studies evaluated the detection rate (DR) of this imaging method in patients with metastatic NETs and unknown primary tumours (CUP-NETs). Therefore, we aimed to perform a meta-analysis to add evidence-based data in this setting. A comprehensive computer literature search of studies listed in PubMed/MEDLINE, EMBASE, and Cochrane library databases through December 2018 and regarding the use of somatostatin receptor PET/CT in patients with CUP-NETs was carried out. Pooled DR of CUP-NETs by using somatostatin receptor PET/CT was calculated. A pooled analysis evaluating the percentage of change of management by using somatostatin receptor PET/CT in these patients was also performed. Twelve studies on the use of somatostatin receptor PET/CT in detecting CUP-NETs in 383 metastatic patients were included. The meta-analysis of all these studies provided the following DR on a per patient-based analysis: 56% (95% confidence interval (95% CI): 48-63%). Moderate heterogeneity among the selected studies was found (I <sup>2</sup> = 50%), whereas a significant publication bias was excluded by Egger's test (p = 0.45). The most common primary tumour sites were the bowel and the pancreas. A change of management by using somatostatin receptor PET/CT was demonstrated in 20% (95% CI: 10-33%) of patients with CUP-NET. Somatostatin receptor PET/CT is very useful in detecting CUP-NETs in patients with metastatic disease. More studies on the change of management by using this imaging method in this setting are needed