Specific label-free and real-time detection of oxidized low density lipoprotein (oxLDL) using an immunosensor with three monoclonal antibodies

Increased levels of plasma oxLDL, which is the oxidized fraction of Low Density Lipoprotein (LDL), are associated with atherosclerosis, an inflammatory disease, and the subsequent development of severe cardiovascular diseases that are today a major cause of death in modern countries. It is therefore important to find a reliable and fast assay to determine oxLDL in serum. A new immunosensor employing three monoclonal antibodies (mAbs) against oxLDL is proposed in this work as a quick and effective way to monitor oxLDL. The oxLDL was first employed to produce anti-oxLDL monoclonal antibodies by hybridoma cells that were previously obtained. The immunosensor was set-up by selfassembling cysteamine (Cyst) on a gold (Au) layer (4 mm diameter) of a disposable screen-printed electrode. Three mAbs were allowed to react with N-hydroxysuccinimide (NHS) and ethyl(dimethylaminopropyl)carbodiimide (EDAC), and subsequently incubated in the Au/Cys. Albumin from bovine serum (BSA) was immobilized further to ensure that other molecules apart from oxLDL could not bind to the electrode surface. All steps were followed by various characterization techniques such as electrochemical impedance spectroscopy (EIS) and square wave voltammetry (SWV). The analytical operation of the immunosensor was obtained by incubating the sensing layer of the device in oxLDL for 15 minutes, prior to EIS and SWV. This was done by using standard oxLDL solutions prepared in foetal calf serum, in order to simulate patient's plasma with circulating oxLDL. A sensitive response was observed from 0.5 to 18.0 mg mL 1 . The device was successfully applied to determine the oxLDL fraction in real serum, without prior dilution or necessary chemical treatment. The use of multiple monoclonal antibodies on a biosensing platform seemed to be a successful approach to produce a specific response towards a complex multi-analyte target, correlating well with the level of oxLDL within atherosclerosis disease, in a simple, fast and cheap way

Employing bacteria machinery for antibiotic detection: using DNA gyrase for ciprofloxacin detection

This work describes a new successful approach for designing biosensors that detect antibiotics. It makes use of a biomimetic strategy, by employing the biochemical target of a given antibiotic as its biorecognition element. This principle was tested herein for quinolones, which target DNA gyrase in bacteria. Ciprofloxacin (CIPRO) was tested as a representative antibiotic from the quinolone group; the sensitivity of biosensor to this group was confirmed by checking the response to another quinolone antibiotic (norfloxacin, NOR) and to a non-quinolone antibiotic (ampicillin, AMP). The biorecognition element used was DNA gyrase attached by ionic interactions to a carbon support, on a working electrode on common screen-printed electrodes (SPEs). The response against antibiotics was tested for increasing concentrations of CIPRO, NOR or AMP, and following the subsequent electrical changes by electrochemical impedance spectroscopy. The DNAgyrase biosensor showed sensitive responses for CIPRO and NOR, for concentrations down to 3.02nM and 30.2nM, respectively, with a very wide response range for CRIPRO, up to 30.2µM. Its response was also confirmed selective for quinolones, when compared to its response against AMP. Further comparison to an immunosensor of similar design (adding antibodies instead of DNA gyrase) was made, revealing favourable features for the new biomimetic biosensor with 1.52nM of limit of detection (LOD). Overall, the new approach presented herein is simple and effective for antibiotic detection, displaying a selective response against a given antibiotic group. The use of bacterial machinery as biorecognition element in biosensors may also provide a valuable tool to study the mechanism of action in bacterial cells of new drugs. This is especially important in the development of new drugs to fight bacterial resistance.The authors acknowledge funding from project PTDC/AAG-TEC/5400/2014 funded by European funds, through FEDER (European Funding or Regional Development) via COMPETE2020 – POCI (operational program for internationalization and competitively) and by national funding through the National Foundation for Science and Technology, I.P. (FCT). ARC also acknowledge funding to National Foundation for Science and Technology, I.P., through the PhD Grant, SFRH/BD/130107/2017.info:eu-repo/semantics/publishedVersio

Correction: Zika virus-derived e-diii protein displayed on immunologically optimized vlps induces neutralizing antibodies without causing enhancement of dengue virus infection. vaccines 2019, 7, 72

The authors wish to make the following correction to their paper [1]. The same image was mistakenly selected for Figures 2 and 3. The image became replaced as you see in Figure 3 below

Cross-sectional analysis of students and school workers reveals a high number of asymptomatic SARS-CoV-2 infections during school reopening in Brazilian cities

Brazil experienced one of the most prolonged periods of school closures, and reopening could have exposed students to high rates of SARS-CoV-2 infection. However, the infection status of students and school workers at the time of the reopening of schools located in Brazilian cities is unknown. Here we evaluated viral carriage by RT-PCR and seroprevalence of anti-SARS-CoV-2 antibodies (IgM and IgG) by immunochromatography in 2259 individuals (1139 students and 1120 school workers) from 28 schools in 28 Brazilian cities. We collected the samples within 30 days after public schools reopened and before the start of vaccination campaigns. Most students (n = 421) and school workers (n = 446) had active (qRT-PCR + IgM− IgG− or qRT-PCR + IgM + IgG−/+) SARS-CoV-2 infection. Regression analysis indicated a strong association between the infection status of students and school workers. Furthermore, while 45% (n = 515) of the students and 37% (n = 415) of the school workers were neither antigen nor antibody positive in laboratory tests, 16% of the participants (169 students and 193 school workers) were oligosymptomatic, including those reinfected. These individuals presented mild symptoms such as headache, sore throat, and cough. Notably, most of the individuals were asymptomatic (83.9%). These results indicate that many SARS-CoV-2 infections in Brazilian cities during school reopening were asymptomatic. Thus, our study highlights the need to promote a coordinated public health effort to guarantee a safe educational environment while avoiding exacerbating pre-existent social inequalities in Brazil, reducing social, mental, and economic losses for students, school workers, and their families

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