Rapid Detection of Pseudomonas aeruginosa Biofilms via Enzymatic Liquefaction of Respiratory Samples

OriPlex: Origami-enabled multiplexed detection of respiratory pathogens

Origami biosensors leverage paper foldability to develop total analysis systems integrated in a single piece of paper. This capability can also be utilized to incorporate additional features that would be difficult to achieve with rigid substrates. In this article, we report a new design for 3D origami biosensors called OriPlex, which leverages the foldability of filter paper for the multiplexed detection of bacterial pathogens. OriPlex immunosensors detect pathogens by folding nanoparticle reservoirs containing different types of nanoprobes. This releases antibody-coated nanoparticles in a central channel where targets are captured through physical interactions. The OriPlex concept was demonstrated by detecting the respiratory pathogens Pseudomonas aeruginosa (PA) and Klebsiella pneumoniae (KP) with a limit of detection of 3.4·103 cfu mL-1 and 1.4·102 cfu mL-1, respectively, and with a turn-around time of 25 min. Remarkably, the OriPlex biosensors allowed the multiplexed detection of both pathogens spiked into real bronchial aspirate (BAS) samples at a concentration of 105 cfu mL-1 (clinical infection threshold), thus demonstrating their suitability for diagnosing lower tract respiratory infections. The results shown here pave the way for implementing OriPlex biosensors as a screening test for detecting superbugs requiring personalized antibiotics in suspected cases of nosocomial pneumonia.

Effect of nanoporous membranes thickness in electrochemical biosensing performance: application for the detection of a wound infection biomarker

Introduction: Nanoporous alumina membranes present a honeycomb-like structure characterized by two main parameters involved in their performance in electrochemical immunosening: pore diameter and pore thickness. Although this first one has been deeply studied, the effect of pore thickness in electrochemical-based nanopore immunosensors has been less taken into consideration. Methods: In this work, the influence of the thickness of nanoporous membranes in the steric blockage is studied for the first time, through the formation of an immunocomplex in their inner walls. Finally, the optimal nanoporous membranes were applied to the detection of catalase, an enzyme related with chronic wound infection and healing. Results: Nanoporous alumina membranes with a fixed pore diameter (60 nm) and variable pore thicknesses (40, 60, 100 μm) have been constructed and evaluated as immunosensing platform for protein detection. Our results show that membranes with a thickness of 40 μm provide a higher sensitivity and lower limit-of-detection (LOD) compared to thicker membranes. This performance is even improved when compared to commercial membranes (with 20 nm pore diameter and 60 μm pore thickness), when applied for human IgG as model analyte. A label-free immunosensor using a monoclonal antibody against anti-catalase was also constructed, allowing the detection of catalase in the range of 50-500 ng/mL and with a LOD of 1.5 ng/mL. The viability of the constructed sensor in real samples was also tested by spiking artificial wound infection solutions, providing recovery values of 110% and 118%. Discussion: The results obtained in this work evidence the key relevance of the nanochannel thickness in the biosensing performance. Such findings will illuminate nanoporous membrane biosensing research, considering thickness as a relevant parameter in electrochemical-based nanoporous membrane sensors.

A Critical Review on Detection of Foodborne Pathogens Using Electrochemical Biosensors

An outbreak of foodborne pathogens would cause severe consequences. Detecting and diagnosing foodborne diseases is crucial for food safety, and it is increasingly important to develop fast, sensitive, and cost-effective methods for detecting foodborne pathogens. In contrast to traditional methods, such as medium-based culture, nucleic acid amplification test, and enzyme-linked immunosorbent assay, electrochemical biosensors possess the advantages of simplicity, rapidity, high sensitivity, miniaturization, and low cost, making them ideal for developing pathogen-sensing devices. The biorecognition layer, consisting of recognition elements, such as aptamers, antibodies and bacteriophages, and other biomolecules or polymers, is the most critical component to determine the selectivity, specificity, reproducibility, and lifetime of a biosensor when detecting pathogens in a biosample. Furthermore, nanomaterials have been frequently used to improve electrochemical biosensors for sensitively detecting foodborne pathogens due to their high conductivity, surface-to-volume ratio, and electrocatalytic activity. In this review, we survey the characteristics of biorecognition elements and nanomaterials in constructing electrochemical biosensors applicable for detecting foodborne pathogens during the past five years. As well as the challenges and opportunities of electrochemical biosensors in the application of foodborne pathogen detection are discussed.

A paper biosensor for overcoming matrix effects interfering with the detection of sputum pyocyanin with competitive immunoassays

Detecting sputum pyocyanin (PYO) with a competitive immunoassay is a promising approach for diagnosing Pseudomonas aeruginosa respiratory infections. However, it is not possible to perform a negative control to evaluate matrix-effects in competitive immunoassays, and the highly complex sputum matrix often interferes with target detection. Here, we show that these issues are alleviated by performing competitive immunoassays with a paper biosensor. The biosensing platform consists of a paper reservoir, which contains antibody-coated gold nanoparticles, and a substrate containing a competing recognition element, which is a piece of paper modified with an albumin-antigen conjugate. Detection of PYO with a limit of detection of 4.7·10-3 µM and a dynamic range between 4.7·10-1 µM and 47.6 µM is accomplished by adding the sample to the substrate with the competing element and pressing the reservoir against it for 5 min. When tested with patient samples, the biosensor was able to qualitatively differentiate spiked from non-spiked samples, whereas ELISA did not show a clear cut-off between them. Furthermore, the relative standard deviation was lower when determining sputum with the paper-based biosensor. These features, along with a mild liquefaction step that circumvents the use of harsh chemicals or instruments, make our biosensor a good candidate for diagnosing Pseudomonas infections at the bedside through the detection of sputum PYO.

Facemask analyses for the non-invasive detection of chronic and acute P. aeruginosa lung infections using nanoparticle-based immunoassays

Pseudomonas aeruginosa (P. aeruginosa) is a pathogen that persistently colonizes the respiratory tract of patients with chronic lung diseases. The risk of acquiring a chronic P. aeruginosa infection can be minimized by rapidly detecting the pathogen in the patient's airways and promptly administrating adequate antibiotics. However, the rapid detection of P. aeruginosa in the lungs involves the analysis of sputum, which is a highly complex matrix that is not always available. Here, we propose an alternative diagnosis based on analyzing breath aerosols. In this approach, nanoparticle immunosensors identify bacteria adhered to the polypropylene layer of a surgical facemask that was previously worn by the patient. A polypropylene processing protocol was optimized to ensure the efficient capture and analysis of the target pathogen. The proposed analytical platform has a theoretical limit of detection of 105 CFU mL-1 in aerosolized mock samples, and a dynamic range between 105 and 108 CFU mL-1. When tested with facemasks worn by patients, the biosensors were able to detect chronic and acute P. aeruginosa lung infections, and to differentiate them from respiratory infections caused by other pathogens. The results shown here pave the way to diagnose Pseudomonas infections at the bedside, as well as to identify the progress from chronic to acute infection.

Understanding bacterial biofilms: From definition to treatment strategies

Bacterial biofilms are complex microbial communities encased in extracellular polymeric substances. Their formation is a multi-step process. Biofilms are a significant problem in treating bacterial infections and are one of the main reasons for the persistence of infections. They can exhibit increased resistance to classical antibiotics and cause disease through device-related and non-device (tissue) -associated infections, posing a severe threat to global health issues. Therefore, early detection and search for new and alternative treatments are essential for treating and suppressing biofilm-associated infections. In this paper, we systematically reviewed the formation of bacterial biofilms, associated infections, detection methods, and potential treatment strategies, aiming to provide researchers with the latest progress in the detection and treatment of bacterial biofilms.

Low-speed centrifugation based isolation and Personal Glucose Meter assisted synchronous quantification of Pseudomonas aeruginosa in nursing home-acquired pneumonia

Home-acquired pneumonia is a common infection and the incidence has increased in recent years as the population ages. Pseudomonas aeruginosa (P. aeruginosa) is common in nursing home residents and in hospital infections, providing a huge threat to human health. Therefore, it is essential to develop a novel and portable P. aeruginosa analysis method for the early diagnosis of home-acquired pneumonia. Few of the established approaches enable isolation and synchronous quantification of P. aeruginosa. Herein, we propose a novel platform by integrating aptamer recognition-based aggregation of target bacteria and personal glucose meters (PGMs) based readout of results. Based on this, the method enables low-speed centrifugation (4193 g) based isolation and personal glucose meter assisted synchronous quantification of P. aeruginosa. In addition, the chain displacement process is included for signal amplification that endows the method with a wide detection range of six orders of magnitudes and a low limit of detection of 36 cfu/mL. Besides the excellent sensitivity, the approach also shows a good selectivity to P. aeruginosa detection, making it a promising tool to report P. aeruginosa based home-acquired pneumonia and guiding the early-nursing of P. aeruginosa infections at the emergency department.

Pseudomonas aeruginosa detection based on droplets incubation using an integrated microfluidic chip, laser spectroscopy, and machine learning

Pseudomonas aeruginosa is an opportunist pathogen responsible for causing several infections in the human body, especially in patients with weak immune systems. The proposed approach reports a novel pathogens detection system based on cultivating microdroplets and acquiring the scattered light signals from the incubated droplets using a microfluidic device. Initially, the microdroplets were generated and incubated to cultivate bacteria inside the microdroplets. The second part of the microfluidic chip is the detection module, embedded with three optical fibers to connect laser light and photosensors. The incubated droplets were reinjected in the detection module and passed through the laser light. The surrounding photosensors were arranged symmetrically at 45° to the flowing channel for acquiring the scattered light signal. The noise was removed from the acquired data, and time-domain waveform features were evaluated. The acquired features were trained using machine learning classifiers to classify P. aeruginosa. The k-nearest neighbors (KNN) showed superior classification performance with 95.6 % accuracy among other classifiers, including logistic regression (LR), support vector machines (SVM), and naïve Bayes (NB). The proposed research was performed to validate the method for pathogens detection with a concentration of 10⁵ CFU/mL. The total duration of 6 h is required to test the sample, including five hours for droplets incubation and one hour for sample preparation and detection using light scattering module. The results indicate that acquiring the light scattering patterns from incubated droplets can detect P. aeruginosa using machine learning classification. The proposed system is anticipated to be helpful as a rapid device for diagnosing pathogenic infections.

Antimicrobial efficacy of self-locomotive manganese oxide nanozyme-doped diatom microbubbler on orthodontic brackets in vitro

BACKGROUND

Orthodontic brackets provide a favorable environment for Streptococcus mutans biofilm formation, increasing the risk of white spots and dental caries. Manganese oxide (MnO₂) nanozyme-doped diatom microbubbler (DM) is a recently developed material for biofilm removal. DM can generate oxygen by catalase-mimicking activity in Hydrogen peroxide (H₂O₂) solution and move with ejecting oxygen microbubbles to produce a mechanical self-cleansing effect. This study aimed to evaluate the feasibility of DM as a novel bracket cleaner.

METHODS

DM was prepared according to the protocol and analyzed using a scanning electron microscope (SEM). We treated S. mutans biofilms grown over bracket with phosphate-buffered saline (PBS group), 0.12% chlorhexidine (CHX group), 3% H₂O₂ (H₂O₂ group), and co-treatment with 3 mg/mL of DM and 3% H₂O₂ (DM group). The biofilm removal effect was analyzed using crystal violet assay, and the results were observed using SEM. The viability of S. mutans in remaining biofilms was evaluated using confocal laser scanning microscopy (CLSM). Finally, we examined the effect of all materials on mature multispecies biofilms formed on debonded brackets.

RESULTS

Crystal violet assay results revealed that the CHX group removed more biofilms than the control group, and the DM group removed biofilms more effectively than the CHX group (p < 0.0001). SEM and CLSM images showed that CHX killed S. mutans but failed to remove most biofilms on brackets. However, DM effectively removed biofilms and mature multispecies biofilms on debonded brackets (p < 0.0001).

CONCLUSIONS

Co-treatment with DM and H₂O₂ is effective in removing biofilms on orthodontic brackets compared to conventional antibacterial agents.

Improved cytometric analysis of untouched lung leukocytes by enzymatic liquefaction of sputum samples

Background

Phenotyping sputum-resident leukocytes and evaluating their functional status are essential analyses for exploring the cellular basis of pathological processes in the lungs, and flow cytometry is widely recognized as the gold-standard technique to address them. However, sputum-resident leukocytes are found in respiratory samples which need to be liquefied prior to cytometric analysis. Traditional liquefying procedures involve the use of a reducing agent such as dithiothreitol (DTT) in temperature-controlled conditions, which does not homogenize respiratory samples efficiently and impairs cell viability and functionality.

Methods

Here we propose an enzymatic method that rapidly liquefies samples by means of generating O₂ bubbles with endogenous catalase. Sputum specimens from patients with suspected pulmonary infection were treated with DTT, the enzymatic method or PBS. We used turbidimetry to compare the liquefaction degree and cell counts were determined using a hemocytometer. Finally, we conducted a comparative flow cytometry study for evaluating frequencies of sputum-resident neutrophils, eosinophils and lymphocytes and their activation status after liquefaction.

Results

Enzymatically treated samples were better liquefied than those treated with DTT or PBS, which resulted in a more accurate cytometric analysis. Frequencies of all cell subsets analyzed within liquefied samples were comparable between liquefaction methods. However, the gentle cell handling rendered by the enzymatic method improves cell viability and retains in vivo functional characteristics of sputum-resident leukocytes (with regard to HLA-DR, CD63 and CD11b expression).

Conclusion

In conclusion, the proposed enzymatic liquefaction method improves the cytometric analysis of respiratory samples and leaves the cells widely untouched for properly addressing functional analysis of lung leukocytes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12575-022-00181-z.

Inflammation-Responsive Nanovalves of Polymer-Conjugated Dextran on a Hole Array of Silicon Substrate for Controlled Antibiotic Release

Poly(methacrylic acid) (PMAA) brushes were tethered on a silicon surface possessing a 500-nm hole array via atom transfer radical polymerization after the modification of the halogen group. Dextran-biotin (DB) was sequentially immobilized on the PMAA chains to obtain a P(MAA-DB) brush surrounding the hole edges on the silicon surface. After loading antibiotics inside the holes, biphenyl-4,4′-diboronic acid (BDA) was used to cross-link the P(MAA-DB) chains through the formation of boronate esters to cap the hole and block the release of the antibiotics. The boronate esters were disassociated with reactive oxygen species (ROS) to open the holes and release the antibiotics, thus indicating a reversible association. The total amount of drug inside the chip was approximately 52.4 μg cm⁻², which could be released at a rate of approximately 1.6 μg h⁻¹ cm⁻² at a ROS concentration of 10 nM. The P(MAA-DB) brush-modified chip was biocompatible without significant toxicity toward L929 cells during the antibiotic release. The inflammation-triggered antibiotic release system based on a subcutaneous implant chip not only exhibits excellent efficacy against bacteria but also excellent biocompatibility, recyclability, and sensitivity, which can be easily extended to other drug delivery systems for numerous biomedical applications without phagocytosis- and metabolism-related issues.

Rapid detection of Pseudomonas aeruginosa based on lab-on-a-chip platform using immunomagnetic separation, light scattering, and machine learning

The rapid detection of the pathogenic bacteria in patient samples is crucial to expedient patient care. The proposed approach reports the development of a novel lab-on-a-chip device for the rapid detection of P. aeruginosa based on immunomagnetic separation, optical scattering, and machine learning. The immunomagnetic particles with a diameter of 5 μm were synthesized for isolating P. aeruginosa from the test sample. A microfluidic chip was fabricated, and three optical fibers were embedded for connecting a laser light and two photodetectors. The laser light was pointed towards the channel to pass light through the sample. A pair of photodetectors via optical fibers were arranged symmetrically at 45° to the channel. The photodetectors acquired scattered light from the flowing sample and converted the light to an electrical signal. The sample containing immunomagnetic beads linked with bacteria was injected into the microfluidic chip. The optimized conditions for performing the experiments were characterized for real-time detection of P. aeruginosa. The data acquisition system recorded the real-time light scattering from the test sample. After removing noise from the output waveform, five different time-domain statistical features were extracted from each waveform: standard mean, standard variance, skewness, kurtosis, and coefficient of variation. The pathogens classification was performed by training the discrimination model using extracted features based on machine learning algorithms. The support vector machines (SVM) with a sigmoid function kernel showed superior classification performance with 97.9% accuracy among other classifiers, including k-nearest neighbors (KNN), logistic regression (LR), and naïve Bayes (NB). The method can detect P. aeruginosa specifically and quantitatively with a limit of detection of 10² CFU/mL. The device can classify P. aeruginosa within 10 min with a total assay time of 25 min. The device was used to test its ability to detect pathogen from the serum and sputum specimens spiked with 10⁵ CFU/mL concentration of P. aeruginosa. The results indicate that light scattering combined with machine learning can be used to detect P. aeruginosa. The proposed technique is anticipated to be helpful as a rapid device for diagnosing P. aeruginosa related infections.

Novel Biorecognition Elements against Pathogens in the Design of State-of-the-Art Diagnostics

Infectious agents, especially bacteria and viruses, account for a vast number of hospitalisations and mortality worldwide. Providing effective and timely diagnostics for the multiplicity of infectious diseases is challenging. Conventional diagnostic solutions, although technologically advanced, are highly complex and often inaccessible in resource-limited settings. An alternative strategy involves convenient rapid diagnostics which can be easily administered at the point-of-care (POC) and at low cost without sacrificing reliability. Biosensors and other rapid POC diagnostic tools which require biorecognition elements to precisely identify the causative pathogen are being developed. The effectiveness of these devices is highly dependent on their biorecognition capabilities. Naturally occurring biorecognition elements include antibodies, bacteriophages and enzymes. Recently, modified molecules such as DNAzymes, peptide nucleic acids and molecules which suffer a selective screening like aptamers and peptides are gaining interest for their biorecognition capabilities and other advantages over purely natural ones, such as robustness and lower production costs. Antimicrobials with a broad-spectrum activity against pathogens, such as antibiotics, are also used in dual diagnostic and therapeutic strategies. Other successful pathogen identification strategies use chemical ligands, molecularly imprinted polymers and Clustered Regularly Interspaced Short Palindromic Repeats-associated nuclease. Herein, the latest developments regarding biorecognition elements and strategies to use them in the design of new biosensors for pathogens detection are reviewed.

Optimized detection of lung IL-6 via enzymatic liquefaction of low respiratory tract samples: application for managing ventilated patients

Lung IL-6 is a promising biomarker for predicting respiratory failure during pulmonary infections. This biomarker is found in respiratory samples which need to be liquefied prior to analysis. Traditional liquefying methods use reducing agents such as dithiothreitol (DTT). However, DTT impairs immunodetection and does not liquefy highly viscous samples. We propose an enzymatic method that liquefies samples by means of generating O₂ bubbles with endogenous catalase. Low respiratory tract specimens from 48 mechanically ventilated patients (38 with SARS-CoV-2 infection) were treated with DTT or with the enzymatic method. We used turbidimetry to compare the liquefaction degree and IL-6 was quantified with ELISA. Finally, we used AUC-ROC, time-to-event and principal component analysis to evaluate the association between respiratory compromise or local inflammation and IL-6 determined with both methods. Enzymatically treated samples were better liquefied than those reduced by DTT, which resulted in higher ELISA signals. Lung IL-6 levels obtained with the enzymatic procedure were negatively correlated with the oxygenation index (PaO₂/FiO₂) and the time of mechanical ventilation. The proposed enzymatic liquefaction method improves the sensitivity for lung IL-6 detection in respiratory samples, which increases its predictive power as a biomarker for evaluating respiratory compliance.

Hand-Powered Microfluidics for Parallel Droplet Digital Loop-Mediated Isothermal Amplification Assays

Droplet digital loop-mediated isothermal amplification (ddLAMP) is an important assay for pathogen detection due to its high accuracy, specificity, and ability to quantify nucleic acids. However, performing ddLAMP requires expensive instrumentation and the need for highly trained personnel with expertise in microfluidics. To make ddLAMP more accessible, a ddLAMP assay is developed, featuring significantly decreased operational difficulty and instrumentation requirements. The proposed assay consists of three simplified steps: (1) droplet generation step, in which a LAMP mixture can be emulsified just by manually pulling a syringe connected to a microfluidic device. In this step, for the first time, we verify that highly monodispersed droplets can be generated with unstable flow rates or pressures, allowing untrained personnel to operate the microfluidic device and perform ddLAMP assay; (2) heating step, in which the droplets are isothermally heated in a water bath, which can be found in most laboratories; and (3) result analysis step, in which the ddLAMP result can be determined using only a fluorescence microscopy and an open-source analyzing software. Throughout the process, no droplet microfluidic expertise or equipment is required. More importantly, the proposed system enables multiple samples to be processed simultaneously with a detection limit of 10 copies/μL. The test is simple and intuitive to operate in most laboratories for multi-sample detection, significantly enhancing the accessibility and detection throughput of the ddLAMP technique.