Severe Acute Respiratory Syndrome Coronavirus 2 detection in induced sputum of asthmatic patients using saliva sampling device

Since the beginning of the severe acute respiratory syndrome coronavirus 2 pandemic, the potential contamination of the induced sputum obtained from asthmatic patients in routine is a question of concern. The goal of this study was to assess this contamination using a saliva sample collection device. One hundred seventy-five sputum samples of asthmatic patients without fever were tested. We did not identify any positive PCR on sputum samples from asthmatic patients reporting chronic/episodic respiratory symptoms similar to what is seen in case of COVID-19. This technique was useful to evaluate the contamination of sputum samples generated during the pandemic.

Clinical experience in anti-synthetase syndrome: a monocentric retrospective analytical study.

OBJECTIVES: Anti-synthetase syndrome (ASS) is a rare autoimmune disorder combining autoantibodies and specific clinical manifestations. One of the particularities of ASS is the pleiomorphic radiological presentation seen at the initial work-up. Evaluating treatment response can also be challenging and requires specific clinical, functional, biological and radiological monitoring. For these reasons, it is fundamental to identify specific radiological and clinical features of ASS for improved diagnosis and therapeutic approaches.METHODS: We retrospectively studied all patients suffering from ASS in the CHU of Liège from 2008 to 2019. We analysed the clinical features, pulmonary function tests (PFTs), computed tomography (CT), and longitudinal evolution with regard to patient treatment.RESULTS: In the whole cohort of 30 patients, we identified 19 with anti-JO1 antibodies, 5 with anti-PL12 antibodies and 6 with anti-PL7 antibodies. The sex ratio was slightly in favour of males. Interestingly, PL-12 syndrome was more likely to be present in younger patients than those associated with other antibodies. Overall, 77% of the overall cohort exhibited specific pulmonary involvement without any significant difference with regard to the severity assessed by PFT at diagnosis. In contrast, the radiological presentation was pleomorphic for anti-JO1 syndrome, and anti-PL12 syndrome exhibited mainly ground-glass opacities (GGOs) and reticular abnormalities, while those with anti-PL7 antibodies showed reticulations and bronchiectasis. Longitudinal CT analysis mainly showed a reduction in consolidations and GGOs with specific therapies.CONCLUSION: In our single-centre retrospective study, we found different profiles for different autoantibodies according to age and radiological appearance.

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

Key pointsO_ST_ABSQuestionC_ST_ABSHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform? FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244. MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission. IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/. CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.

Development and validation of an automated radiomic CT signature for detecting COVID-19

BackgroundThe coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits. ObjectivesTo develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. MethodsIn this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance of the model. The datasets were collected from 2 different hospital sites of the CHU Liege, Belgium. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results1381 patients were included in this study. The average age was 64.4{+/-}15.8 and 63.8{+/-}14.4 years with a gender balance of 56% and 52% male in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value of the algorithm was found to be larger than 97%. ConclusionsBenchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Xurography-based microfluidic algal biosensor and dedicated portable measurement station for online monitoring of urban polluted samples.

A critical need exists to develop rapid, in situ, and real-time tools to monitor the impact of pollution discharge toxicity on aquatic ecosystems. The present paper deals with the development of a novel, simple-to-use, low-cost, portable, and user-friendly algal biosensor. In this study, a complete and autonomous portable fluorimeter was developed to assess the A-chlorophyll fluorescence of microalgae, inserted by capillarity into low-cost and disposable xurography-based microfluidic chips. Three microalgae populations were used to develop the biosensor: Chlorella vulgaris, Pseudokirchneriella subcapitata, and Chlamydomonas reinhardtii. Biosensor feasibility and sensitivity parameters, such as algal concentration and light intensity, were optimized beforehand to calibrate the biosensor sensitivity with Diuron, a pesticide known to be very toxic for microalgae. Finally, the biosensor was employed in 10 aqueous urban polluted samples (7 urban wet-weather discharges and 3 wastewater) in order to prove its reliability, reproducibility, and performance in the detection of toxic discharges in the field.

Xurography for microfluidics on a reactive solid.

In this paper, we propose a simple method to embed transparent reactive materials in a microfluidic cell, and to observe in situ the dissolution of the material. As an example, we show how to obtain the dissolution rate of a calcite window of optical quality, dissolved in water and hydrochloric acid (HCl). These fluids circulate at controlled flowrates in a channel which is obtained by xurography: double sided tape is cut out with a cutter plotter and placed between the calcite window and a non-reactive support. While the calcite window reacts in contact with the acid, its topography is measured in situ every 10 s using an interference microscope, with a pixel resolution of 4.9 µm and a vertical resolution of 50 nm. In order to avoid inlet influence on the reaction, a thin layer of photoresist is added on the calcite surface at the inlet and outlet. This layer is also used as a non reactive reference surface.

Toward the Rational Design of Galactosylated Glycoclusters That Target Pseudomonas aeruginosa Lectin A (LecA): Influence of Linker Arms That Lead to Low-Nanomolar Multivalent Ligands.

Anti-infectious strategies against pathogen infections can be achieved through antiadhesive strategies by using multivalent ligands of bacterial virulence factors. LecA and LecB are lectins of Pseudomonas aeruginosa implicated in biofilm formation. A series of 27 LecA-targeting glycoclusters have been synthesized. Nine aromatic galactose aglycons were investigated with three different linker arms that connect the central mannopyranoside core. A low-nanomolar (Kd =19 nm, microarray) ligand with a tyrosine-based linker arm could be identified in a structure-activity relationship study. Molecular modeling of the glycoclusters bound to the lectin tetramer was also used to rationalize the binding properties observed.

Gold coated optical fibers as three-dimensional electrodes for microfluidic enzymatic biofuel cells: Toward geometrically enhanced performance.

For the first time, we report on the preliminary evaluation of gold coated optical fibers (GCOFs) as three-dimensional (3D) electrodes for a membraneless glucose/O2 enzymatic biofuel cell. Two off-the-shelf 125 µm diameter GCOFs were integrated into a 3D microfluidic chip fabricated via rapid prototyping. Using soluble enzymes and a 10 mM glucose solution flowing at an average velocity of 16 mm s(-1) along 3 mm long GCOFs, the maximum power density reached 30.0 ± 0.1 µW cm(-2) at a current density of 160.6 ± 0.3 µA cm(-2). Bundles composed of multiple GCOFs could further enhance these first results while serving as substrates for enzyme immobilization.

All-organic electrostrictive polymer composites with low driving electrical voltages for micro-fluidic pump applications.

This paper focuses on the improvement of a relaxor ferroelectric terpolymer, i.e., poly (vinylidene fluoride-trifluoroethylene-chlorofluoroethylene) [P(VDF-TrFE-CFE)], filled with a bis(2-ethylhexyl) phthalate (DEHP). The developed material gave rise to a significantly increased longitudinal electrostrictive strain, as well as an increased mechanical energy density under a relatively low electric field. These features were attributed to the considerably enhanced dielectric permittivity and a decreased Young modulus as a result of the introduction of only small DEHP plasticizer molecules. In addition, the plasticizer-filled terpolymer only exhibited a slight decrease of the dielectric breakdown strength, which was a great advantage with respect to the traditional polymer-based electrostrictive composites. More importantly, the approach proposed herein is promising for the future development and scale-up of new high-performance electrostrictive dielectrics under low applied electrical fields through modification simply by blending with a low-cost plasticizer. An experimental demonstration based on a flexible micro-fluidic application is described at the end of this paper, confirming the attractive characteristics of the proposed materials as well as the feasibility of integrating them as micro-actuators in small-scale devices.

Electrokinetic biomolecule preconcentration using xurography-based micro-nano-micro fluidic devices.

In this paper we introduce a low cost rapid prototyping framework for designing Micro-Nano-Micro (MNM) fluidic preconcentration device based on ion concentration polarization (ICP) phenomenon. Xurography-based microchannels are separated by a strip of ion perm-selective Nafion membrane which plays the role of nanofluidic potential barrier for the negatively charged molecules. As a result, by using this rapid and inexpensive fabrication technique, it is possible to get preconcentration plugs as high as 5000 fold with an original symmetric electroosmotic flow (EOF) condition. Due to its simplicity and performance, this device could be implemented in various bioanalysis systems.

Electrochemical boron-doped diamond film microcells micromachined with femtosecond laser: application to the determination of water framework directive metals.

Planar electrochemical microcells were micromachined in a microcrystalline boron-doped diamond (BDD) thin layer using a femtosecond laser. The electrochemical performances of the new laser-machined BDD microcell were assessed by differential pulse anodic stripping voltammetry (DPASV) determinations, at the nanomolar level, of the four heavy metal ions of the European Water Framework Directive (WFD): Cd(II), Ni(II), Pb(II), Hg(II). The results are compared with those of previously published BDD electrodes. The calculated detection limits are 0.4, 6.8, 5.5, and 2.3 nM, and the linearities go up to 35, 97, 48, and 5 nM for, respectively, Cd(II), Ni(II) Pb(II), and Hg(II). The detection limits meet with the environmental quality standard of the WFD for three of the four metals. It was shown that the four heavy metals could be detected simultaneously in the concentration ratio usually measured in sewage or runoff waters.

Combining microfluidics and electrochemical detection.

This paper describes two configurations that integrate electrochemical detection into microfluidic devices. The first configuration is a low-cost approach based on the use of PCB technology. This device was applied to electrochemiluminescence detection. The second configuration was used to carry out amperometric quantification of electroactive species using a serial dilution microfluidic system.

Amperometric quantification based on serial dilution microfluidic systems.

This paper describes a microfluidic device fabricated in poly(dimethylsiloxane) that was employed to perform amperometric quantifications using on-chip calibration curves and on-chip standard addition methods. This device integrated a network of Au electrodes within a microfluidic structure designed for automatic preparation of a series of solutions containing an electroactive molecule at a concentration linearly decreasing. This device was first characterized by fluorescence microscopy and then evaluated with a model electroactive molecule such as Fe(CN(6))(4-). Operating a quantification in this microfluidic parallel approach rather than in batch mode allows a reduced analysis time to be achieved. Moreover, the microfluidic approach is compatible with the on-chip calibration of sensors simultaneously to the analysis, therefore preventing problems due to sensor response deviation with time. When using the on-chip calibration and on-chip standard addition method, we reached concentration estimation better than 5%. We also demonstrated that compared to the calibration curve approach, the standard addition mode is less complex to operate. Indeed, in this case, it is not necessary to take into account flow rate discrepancies as in the calibration approach.

In-capillary derivatization and capillary electrophoresis separation of amino acid neurotransmitters from brain microdialysis samples.

A new in-capillary derivatization method with naphtalene-2,3-dicarboxyaldehyde (NDA)/CN(-) has been developed for capillary electrophoresis with laser-induced fluorescence detection of brain microdialysate amino acids. Samples are sandwiched between two plugs of reagent mixture at the capillary inlet and subsequently separated. Highest derivatization yields are obtained by using a reagent to sample plug length ratio equal to 4, performing a first electrophoretic mixing followed by a zero potential amplification step before applying the separation voltage and using a NaCN to NDA concentration ratio equal to 1. This new single-step methodology allows the analysis of amino acid neurotransmitters in rat brain microdialysis samples.

A new immunosensor for breast cancer cell detection using antibody-coated long alkylsilane self-assembled monolayers in a parallel plate flow chamber.

We designed a new efficient and reliable immunosensor and demonstrated its analytic performance to capture breast cancer MCF7 and T47D cells, under laminar flow, onto antibody-coated long alkylsilane self-assembled monolayers (SAMs) in a parallel plate flow chamber. The surface floor of the laminar flow chamber was grafted with an amino-terminated long alkyl chain spacer, 21-aminohenicosyl trichlorosilane (AHTS) followed by tethering a specific monoclonal antibody directed against the human epithelial cell adhesion molecule (EpCAM) antigen, which is overexpressed in primary breast cancer. Properties of the AHTS- and antibody-grafted surface floor were compared to that of surface floors coated with the short alkyl spacers 3-glycidoxy-propyl trimethoxysilane (GPTS) or 3-aminopropyl triethoxysilane (APTES) and antibodies. A theoretical model was constructed according to the geometry of the flow chamber in order to calculate the trajectories that would use cell flows. Cell capture experiments demonstrated that cell immobilization was optimized throughout the whole flow chamber. High cell capture was yielded on antibody-tethered long alkyl AHTS surface. This new procedure offers multiple advantages: a versatile tool readily applied to a panel of purified antibodies, an enrichment of cell immobilization using repetitive cell flow, and a stable capturing surface suitable for long term storage and handling.

Streaming current measurements in zirconia-coated capillaries.

The electroosmotic flow created in zirconia-modified capillaries has been previously investigated. In this paper, we compared the electroosmotic data set with streaming current measurements and we related all these data through zeta-potential. Streaming current measurements give an excellent indication on the direction and the value of the electroosmotic mobility of an electrolyte/capillary system for a large set of experimental conditions: 2 < pH < 12, 0 < ACN < 80 %, 10(-4) M < [SO(2- )4 ] < 4 x 10(-2) M. A good correlation between zeta-potential from streaming current measurements and zeta-potential from electroosmotic mobility measurements was observed (r2 = 0.95). However, the values obtained from streaming current were always slightly lower than the one calculated from electroosmotic mobility (slope = 0.86, sigma = 0.06). In zirconia-coated capillaries the zeta-potential can be tuned from -50 to +100 mV depending on the composition of the electrolyte.

Zeta potential determination by streaming current modelization and measurement in electrophoretic microfluidic systems.

Electrophoresis in capillary and microfluidic systems, used in analytical chemistry to separate charged species, are quite sensitive to surface phenomena in terms of separation performances. In order to improve theses performances, new surface functionalization techniques are required. There is a need for methods to provide fast and accurate quantification about surface charges at liquid/solid interfaces. We present a fast, simple, and low-cost technique for the measurement of the zeta-potential, via the modelization and the measurement of streaming currents. Due to the small channel cross section in microfluidic devices, the streaming current modelization is easier than the streaming potential measurement. The modelization combines microfluidic simulations based on the Navier-Stokes equation and charge repartition simulations based on the Poisson-Boltzmann equation. This method has been validated with square and circular cross section shape fused-silica capillaries and can be easily transposed to any lab-on-chip microsystems.