Electrochemical Impedance Spectroscopy Based Biosensors: Mechanistic Principles, Analytical Examples and Challenges towards Commercialization for Assays of Protein Cancer Biomarkers

Impedimetric affinity biosensors are, without any doubt, among the most sensitive analytical devices available, offering low limits of detection and wide linear response ranges. There are, however, only a few papers detailing the application of impedimetric biosensors for the analysis of clinically relevant samples with due clinical performance. The fact that these devices have not found their way to any commercial or clinical use to date might be surprising, since an electrochemical assay platform based on portable potentiostats is a success story for monitoring a range of clinical parameters such as ions, haematological indicators and glucose. This review discusses the reasons behind this discrepancy and addresses the barriers to be overcome in order to achieve the point-of-care diagnostics using such devices for detection of protein oncomarkers approved by FDA. The final part of the review covers the most recent progress in the area.The financial support received from the Slovak Scientific Grant Agency VEGA 2/0137/18 and 2/0090/16 and the Slovak Research and Development Agency APVV 17-0300 and APW-15-0227 is acknowledged. The research received funding from the European Research Council (no. 311532). This publication is the result of the project implementation: Centre for materials, layers and systems for applications and chemical processes under extreme conditions - Stage I, ITMS No.: 26240120007, supported by the ERDF

A graphene-based glycan biosensor for electrochemical label-free detection of a tumor-associated antibody

The study describes development of a glycan biosensor for detection of a tumor-associated antibody. The glycan biosensor is built on an electrochemically activated/oxidized graphene screen-printed electrode (GSPE). Oxygen functionalities were subsequently applied for covalent immobilization of human serum albumin (HSA) as a natural nanoscaffold for covalent immobilization of Thomsen-nouvelle (Tn) antigen (GalNAc-O-Ser/Thr) to be fully available for affinity interaction with its analyte—a tumor-associated antibody. The step by step building process of glycan biosensor development was comprehensively characterized using a battery of techniques (scanning electron microscopy, atomic force microscopy, contact angle measurements, secondary ion mass spectrometry, surface plasmon resonance, Raman and energy-dispersive X-ray spectroscopy). Results suggest that electrochemical oxidation of graphene SPE preferentially oxidizes only the surface of graphene flakes within the graphene SPE. Optimization studies revealed the following optimal parameters: activation potential of +1.5 V vs. Ag/AgCl/3 M KCl, activation time of 60 s and concentration of HSA of 0.1 g L−1. Finally, the glycan biosensor was built up able to selectively and sensitively detect its analyte down to low aM concentration. The binding preference of the glycan biosensor was in an agreement with independent surface plasmon resonance analysis.The financial support received from the Slovak Scientific Grant Agency VEGA 2/0137/18 and 2/0090/16 from the Slovak Research and Development Agency APVV 17-0300 is acknowledged. This publication is the result of the project implementation: Centre for materials, layers and systems for applications and chemical processes under extreme conditions—Stage I, ITMS no.: 26240120007, supported by the ERDF. This publication was supported by Qatar University Collaborative Grant QUCG-CAM-19/20-2. The findings achieved herein are solely the responsibility of the authors.Scopu

Urban stormwater retention capacity of nature-based solutions at different climatic conditions

Climate change and the continuing increase in human population creates a growing need to tackle urban stormwater problems. One promising mitigation option is by using nature-based solutions (NBS) – especially sustainable urban stormwater management technologies that are key elements of NBS action. We used a synthesis approach to compile available information about urban stormwater retention capacity of the most common sustainable urban drainage systems (SUDS) in different climatic conditions. Those SUDS targeting stormwater management through water retention and removal solutions (mainly by infiltration, overland flow and evapotranspiration), were addressed in this study. Selected SUDS were green roofs, bioretention systems (i.e. rain gardens), buffer and filter strips, vegetated swales, constructed wetlands, and water-pervious pavements. We found that despite a vast amount of data available from real-life applications and research results, there is a lack of decisive information about stormwater retention and removal capacity of selected SUDS. The available data show large variability in performance across different climatic conditions. It is therefore a challenge to set conclusive widely applicable guidelines for SUDS implementation based on available water retention data. Adequate data were available only to evaluate the water retention capacity of green roofs (average 56±20%) and we provide a comprehensive review on this function. However, as with other SUDS, still the same problem of high variability in the performance (min 11% and max 99% of retention) remains. This limits our ability to determine the capacity of green roofs to support better planning and wider implementation across climate zones. The further development of SUDS to support urban stormwater retention should be informed by and developed concurrently with the adaptation strategies to cope with climate change, especially with increasing frequency of extreme precipitation events that lead to high volumes of stormwater runoff

Identification of molecular fluorophore as a component of carbon dots able to induce gelation in a fluorescent multivalent-metal-ion-free alginate hydrogel

We introduce a simple approach to fabricate fluorescent multivalent metal ion-free alginate hydrogels, which can be produced using carbon dots accessible from natural sources (citric acid and L-cysteine). Molecular fluorophore 5-oxo-2,3-dihydro-5H-[1,3]-thiazolo[3,2-a] pyridine-3,7-dicarboxylic acid (TPDCA), which is formed during the synthesis of carbon dots, is identified as a key segment for the crosslinking of hydrogels. The crosslinking happens through dynamic complexation of carboxylic acid groups of TPDCA and alginate cages along with sodium ions. The TPDCA derived hydrogels are investigated regarding to their thermal, rheological and optical properties, and found to exhibit characteristic fluorescence of this aggregated molecular fluorophore. Moreover, gradient hydrogels with tunable mechanical and optical properties and controlled release are obtained upon immersion of the hydrogel reactors in solutions of divalent metal ions (Ca2+, Cu2+, and Ni2+) with a higher affinity to alginate. © 2019, The Author(s).Qatar National Research Fund (Qatar foundation) [8-878-1-172]; Qatar University grant [QUCG-CAM-19/20-2]; Germany/Hong Kong Joint Research Scheme - Research Grants Council of Hong Kong; Germany Academic Exchange Service of Germany [G-CityU103/16]; VEGA Scientific Grant AgencyVedecka grantova agentura MSVVaS SR a SAV (VEGA) [2/0158/17

Assessment of predicted enzymatic activity of α‐N‐acetylglucosaminidase variants of unknown significance for CAGI 2016

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α‐N‐acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population‐scale analysis of disease epidemiology and rare variant association analysis

DNA isolation protocol effects on nuclear DNA analysis by microarrays, droplet digital PCR, and whole genome sequencing, and on mitochondrial DNA copy number estimation.

Potential bias introduced during DNA isolation is inadequately explored, although it could have significant impact on downstream analysis. To investigate this in human brain, we isolated DNA from cerebellum and frontal cortex using spin columns under different conditions, and salting-out. We first analysed DNA using array CGH, which revealed a striking wave pattern suggesting primarily GC-rich cerebellar losses, even against matched frontal cortex DNA, with a similar pattern on a SNP array. The aCGH changes varied with the isolation protocol. Droplet digital PCR of two genes also showed protocol-dependent losses. Whole genome sequencing showed GC-dependent variation in coverage with spin column isolation from cerebellum. We also extracted and sequenced DNA from substantia nigra using salting-out and phenol / chloroform. The mtDNA copy number, assessed by reads mapping to the mitochondrial genome, was higher in substantia nigra when using phenol / chloroform. We thus provide evidence for significant method-dependent bias in DNA isolation from human brain, as reported in rat tissues. This may contribute to array "waves", and could affect copy number determination, particularly if mosaicism is being sought, and sequencing coverage. Variations in isolation protocol may also affect apparent mtDNA abundance

Gap-filling eddy covariance methane fluxes:Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET)

In vitro fertilization does not increase the incidence of de novo copy number alterations in fetal and placental lineages

Although chromosomal instability (CIN) is a common phenomenon in cleavage-stage embryogenesis following in vitro fertilization (IVF)1,2,3, its rate in naturally conceived human embryos is unknown. CIN leads to mosaic embryos that contain a combination of genetically normal and abnormal cells, and is significantly higher in in vitro-produced preimplantation embryos as compared to in vivo-conceived preimplantation embryos4. Even though embryos with CIN-derived complex aneuploidies may arrest between the cleavage and blastocyst stages of embryogenesis5,6, a high number of embryos containing abnormal cells can pass this strong selection barrier7,8. However, neither the prevalence nor extent of CIN during prenatal development and at birth, following IVF treatment, is well understood. Here we profiled the genomic landscape of fetal and placental tissues postpartum from both IVF and naturally conceived children, to investigate the prevalence and persistence of large genetic aberrations that probably arose from IVF-related CIN. We demonstrate that CIN is not preserved at later stages of prenatal development, and that de novo numerical aberrations or large structural DNA imbalances occur at similar rates in IVF and naturally conceived live-born neonates. Our findings affirm that human IVF treatment has no detrimental effect on the chromosomal constitution of fetal and placental lineages

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

Nanoscale-controlled architecture for the development of ultrasensitive lectin biosensors applicable in glycomics

In this mini-review the most advanced patterning protocols and transducing schemes for the development of ultrasensitive label-free and label-based lectin biosensors for the glycoprofiling of disease markers and some cancerous cells are described. The performance of such lectin biosensors with interfacial properties tuned to the nanoscale are critically compared to the most sensitive immunoassay format of analysis, and challenges for the future are discussed. In addition the key elements for further development of these devices are considered, with a view to improvement of their robustness and practical applicability.APVV 0282-11 and VEGA 2/0162/14 provided by the Slovak grant agencies. European Research Council (ERC Grant Agreement No. 311532) and from the European Union Seventh Framework Program (FP/2007�2013 with Grant Agreement No. 317420). NPRP grant 6-381-1-078 from the Qatar National Research Fund (a member of the Qatar Foundation).Scopu