An artificial CO-releasing metalloprotein built by histidine-selective metallation.

We report the design and synthesis of an aquacarbonyl Ru(II) dication cis-[Ru(CO)2(H2O)4](2+) reagent for histidine (His)-selective metallation of interleukin (IL)-8 at site 33. The artificial, non-toxic interleukin (IL)-8-Ru(II)(CO)2 metalloprotein retained IL-8-dependent neutrophil chemotactic activity and was shown to spontaneously release CO in live cells.We thank the European Commission (Marie Curie CIG to G.J.L.B., Marie Curie IEF to O.B.), FCT Portugal (FCT Investigator to G.J.L.B.) and the EPSRC for generous funding.This is the final published version. It first appeared at http://pubs.rsc.org/en/Content/ArticleLanding/2015/CC/c4cc10204e#!divAbstract

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

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

Evaluation of urinary cysteinyl leukotrienes as biomarkers of severity and putative therapeutic targets in COVID-19 patients

Background Cysteinyl leukotrienes (CysLT) are potent inflammation-promoting mediators, but remain scarcely explored in COVID-19. We evaluated urinary CysLT (U-CysLT) relationship with disease severity and their usefulness for prognostication in hospitalized COVID-19 patients. The impact on U-CysLT of veno-venous extracorporeal membrane oxygenation (VV-ECMO) and of comorbidities such as hypertension and obesity was also assessed. Methods Blood and spot urine were collected in severe (n = 26), critically ill (n = 17) and critically ill on VV-ECMO (n = 17) patients with COVID-19 at days 1-2 (admission), 3-4, 5-8 and weekly thereafter, and in controls (n = 23) at a single time point. U-CysLT were measured by ELISA. Routine markers, prognostic scores and outcomes were also evaluated. Results U-CysLT did not differ between groups at admission, but significantly increased along hospitalization only in critical groups, being markedly higher in VV-ECMO patients, especially in hypertensives. U-CysLT values during the first week were positively associated with ICU and total hospital length of stay in critical groups and showed acceptable area under curve (AUC) for prediction of 30-day mortality (AUC: 0.734, p = 0.001) among all patients. Conclusions U-CysLT increase during hospitalization in critical COVID-19 patients, especially in hypertensives on VV-ECMO. U-CysLT association with severe outcomes suggests their usefulness for prognostication and as therapeutic targets.This work was supported by a RESEARCH 4 COVID-19 grant (project 519, reference number: 613690173) from FCT-Fundacao para a Ciencia e a Tecnologia (special support for rapid implementation projects for innovative response solutions to COVID-9 pandemic). CS-P is a recipient of a Ph.D. fellowship from FCT and MedInUP (UI/BD/150816/2020). P-PT was supported by a research contract within the scope of the RIFF-HEART project funded by FEDER via COMPETE, Portugal 2020-Operational Programme for Competitiveness and Internationalization (POCI) (POCI-01-0145-FEDER-032188) and by FCT (PTDC/MEC-CAR/32188/2017). Open access funding provided by FCT|FCCN (b-on)

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post—the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. / Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. / Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. / Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics

COVID-SGIS: A Smart Tool for Dynamic Monitoring and Temporal Forecasting of Covid-19

Background: The global burden of the new coronavirus SARS-CoV-2 is increasing at an unprecedented rate. The current spread of Covid-19 in Brazil is problematic causing a huge public health burden to its population and national health-care service. To evaluate strategies for alleviating such problems, it is necessary to forecast the number of cases and deaths in order to aid the stakeholders in the process of making decisions against the disease. We propose a novel system for real-time forecast of the cumulative cases of Covid-19 in Brazil. / Methods: We developed the novel COVID-SGIS application for the real-time surveillance, forecast and spatial visualization of Covid-19 for Brazil. This system captures routinely reported Covid-19 information from 27 federative units from the Brazil.io database. It utilizes all Covid-19 confirmed case data that have been notified through the National Notification System, from March to May 2020. Time series ARIMA models were integrated for the forecast of cumulative number of Covid-19 cases and deaths. These include 6-days forecasts as graphical outputs for each federative unit in Brazil, separately, with its corresponding 95% CI for statistical significance. In addition, a worst and best scenarios are presented. / Results: The following federative units (out of 27) were flagged by our ARIMA models showing statistically significant increasing temporal patterns of Covid-19 cases during the specified day-to-day period: Bahia, Maranhão, Piauí, Rio Grande do Norte, Amapá, Rondônia, where their day-to-day forecasts were within the 95% CI limits. Equally, the same findings were observed for Espírito Santo, Minas Gerais, Paraná, and Santa Catarina. The overall percentage error between the forecasted values and the actual values varied between 2.56 and 6.50%. For the days when the forecasts fell outside the forecast interval, the percentage errors in relation to the worst case scenario were below 5%. / Conclusion: The proposed method for dynamic forecasting may be used to guide social policies and plan direct interventions in a cost-effective, concise, and robust manner. This novel tools can play an important role for guiding the course of action against the Covid-19 pandemic for Brazil and country neighbors in South America

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

status: publishe

QUANTITATIVE REAL-TIME PCR (Q-PCR) FOR SPUTUM SMEAR DIAGNOSIS OF PULMONARY TUBERCULOSIS AMONG PEOPLE WITH HIV/AIDS

Objective: To assess quantitative real-time polymerase chain reaction (q-PCR) for the sputum smear diagnosis of pulmonary tuberculosis (PTB) in patients living with HIV/AIDS with a clinical suspicion of PTB. Method: This is a prospective study to assess the accuracy of a diagnostic test, conducted on 140 sputum specimens from 140 patients living with HIV/AIDS with a clinical suspicion of PTB, attended at two referral hospitals for people living with HIV/AIDS in the city of Recife, Pernambuco, Brazil. A Löwenstein-Jensen medium culture and 7H9 broth were used as gold standard. Results: Of the 140 sputum samples, 47 (33.6%) were positive with the gold standard. q-PCR was positive in 42 (30%) of the 140 patients. Only one (0.71%) did not correspond to the culture. The sensitivity, specificity and accuracy of the q-PCR were 87.2%, 98.9% and 95% respectively. In 39 (93%) of the 42 q-PCR positive cases, the CT (threshold cycle) was equal to or less than 37. Conclusion: q-PCR performed on sputum smears from patients living with HIV/AIDS demonstrated satisfactory sensitivity, specificity and accuracy, and may therefore be recommended as a method for diagnosing PTB