Rapid Colorimetric Detection of Pseudomonas aeruginosa in Clinical Isolates Using a Magnetic Nanoparticle Biosensor

A rapid, sensitive, and specific colorimetric biosensor based on the use of magnetic nanoparticles (MNPs) was designed for the detection of Pseudomonas aeruginosa in clinical samples. The biosensing platform was based on the measurement of P. aeruginosa proteolytic activity using a specific protease substrate. At the N-terminus, this substrate was covalently bound to MNPs and was linked to a gold sensor surface via cystine at the C-terminus of the substrates. The golden sensor appears black to naked eyes because of the coverage of the MNPs. However, upon proteolysis, the cleaved peptide-MNP moieties will be attracted by an external magnet, revealing the golden color of the sensor surface, which can be observed by the naked eye. In vitro, the biosensor was able to detect specifically and quantitatively the presence of P. aeruginosa with a detection limit of 102 cfu/mL in less than 1 min. The colorimetric biosensor was used to test its ability to detect in situ P. aeruginosa in clinical isolates from patients. This biochip is anticipated to be useful as a rapid point-of-care device for the diagnosis of P. aeruginosa-related infections

Artificial Intelligence-Based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems

Unmanned Aerial Vehicles (UAVs), or drones, provided with camera sensors enable improved situational awareness of several emergency responses and disaster management applications, as they can function from remote and complex accessing regions. The UAVs can be utilized for several application areas which can hold sensitive data, which necessitates secure processing using image encryption approaches. At the same time, UAVs can be embedded in the latest technologies and deep learning (DL) models for disaster monitoring areas such as floods, collapsed buildings, or fires for faster mitigation of its impacts on the environment and human population. This study develops an Artificial Intelligence-based Secure Communication and Classification for Drone-Enabled Emergency Monitoring Systems (AISCC-DE2MS). The proposed AISCC-DE2MS technique majorly employs encryption and classification models for emergency disaster monitoring situations. The AISCC-DE2MS model follows a two-stage process: encryption and image classification. At the initial stage, the AISCC-DE2MS model employs an artificial gorilla troops optimizer (AGTO) algorithm with an ECC-Based ElGamal Encryption technique to accomplish security. For emergency situation classification, the AISCC-DE2MS model encompasses a densely connected network (DenseNet) feature extraction, penguin search optimization (PESO) based hyperparameter tuning, and long short-term memory (LSTM)-based classification. The design of the AGTO-based optimal key generation and PESO-based hyperparameter tuning demonstrate the novelty of our work. The simulation analysis of the AISCC-DE2MS model is tested using the AIDER dataset and the results demonstrate the improved performance of the AISCC-DE2MS model in terms of different measures

Rapid colorimetric detection of Pseudomonas aeruginosa in clinical isolates using a magnetic nanoparticle biosensor

A rapid, sensitive, and specific colorimetric biosensor based on the use of magnetic nanoparticles (MNPs) was designed for the detection of Pseudomonas aeruginosa in clinical samples. The biosensing platform was based on the measurement of P. aeruginosa proteolytic activity using a specific protease substrate. At the N-terminus, this substrate was covalently bound to MNPs and was linked to a gold sensor surface via cystine at the C-terminus of the substrates. The golden sensor appears black to naked eyes because of the coverage of the MNPs. However, upon proteolysis, the cleaved peptide-MNP moieties will be attracted by an external magnet, revealing the golden color of the sensor surface, which can be observed by the naked eye. In vitro, the biosensor was able to detect specifically and quantitatively the presence of P. aeruginosa with a detection limit of 102 cfu/mL in less than 1 min. The colorimetric biosensor was used to test its ability to detect in situ P. aeruginosa in clinical isolates from patients. This biochip is anticipated to be useful as a rapid point-of-care device for the diagnosis of P. aeruginosa-related infections

On site visual detection of Porphyromonas gingivalis related periodontitis by using a magnetic-nanobead based assay for gingipains protease biomarkers

Porphyromonas gingivalis (P. gingivalis) is a pathogen causing periodontitis. A rapid assay is described for the diagnosis of periodontal infections related to P. gingivalis. The method is making use of gingipains, a group of P. gingivalis specific proteases as a detection biomarker. Magnetic-nanobeads were labeled with gingipain-specific peptide substrates and immobilized on a gold biosensing platform via gold-thiol linkage. As a result of this, the color of the gold layer turns black. Upon cleavage of the immobilized substrates by gingipains, the magnetic-nanobeads-peptide fragments were attracted by a magnet so that the golden surface color becomes visible again. This assay is highly sensitive and specific. It is capable of detecting as little as 49 CFU·mL−1 of P. gingivalis within 30 s. Examination of periodontitis patients and healthy control saliva samples showed the potential of the assay. The simplicity and rapidity of the assay makes it an effective point-of-care device. [Figure not available: see fulltext.]

Simple and rapid peptide nanoprobe biosensor for the detection of: Legionellaceae

© 2021 The Royal Society of Chemistry.This study demonstrates the development of a sensitive, specific, and quantitative peptide-based nanoprobe prototype assay for the detection of Legionellaceae in a simple way and in a short time. In this work, proteases present in the culture supernatants of Legionella spp. were used as a biomarker. Fluorogenic peptide substrates, specific to Legionella strains culture supernatant proteases, were identified. Peptidases produced a significant increase in the fluorescence intensity following the cleavage of the dipeptide fluorogenic substrates. The specific substrates were identified and coupled with carboxyl-terminated nano-magnetic particles (NMPs). On the other hand, the C-terminal was conjugated with the cysteine residue to covalently integrate with a gold sensing platform via the Au-S linkage. Four different sensors were fabricated from the four specific substrates, which were treated with the protesase of six different species of Legionella. In the presence of specific protease, the peptide sequence is digested and the magnetic nanobeads moved out of the gold surface, resulting in the apparence of gold color. One of the nanoprobes sensitivity detects as low as 60 CFU mL-1 of Legionella anisa, Legionella micdadei, and Fluoribacter dumoffii. The cross-reactivity of the sensors was tested using other closely associated bacterial species and no significant cross-reactivity of the sensors was found. It is envisaged that this assay could be useful for screening purposes or might be supportive for the fast and easy detection of Legionella protease activity for water monitoring purposes