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

A comprehensive framework for understanding security culture in organizations

Organizational security is exposed to internal and external threats, with a greater level of vulnerabilities coming from the former. Drawing upon findings from prior works as a foundation, this study aims to highlight the significant factors that influence the security culture within organizations. Phase one of the study reports upon an interview-based investigation undertaken with thirteen experienced, knowledgeable security specialists from seven organizations. The main findings confirmed the importance of the identified factors from the previous work. The focus to emerge from the interviews concludes that continuously subjecting employees to targeted training and awareness development improves security culture. Indeed, there was a clear lack of awareness and compliance regarding the implementation and clarity of security policies in organizations. Also, the inefficient training program and limit to specific employees in organizations leads to a lack of awareness and compliance

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

Cross-border movement, economic development and malaria elimination in the Kingdom of Saudi Arabia

Malaria at international borders presents particular challenges with regards to elimination. International borders share common malaria ecologies, yet neighboring countries are often at different stages of the control-to-elimination pathway. Herein, we present a case study on malaria, and its control, at the border between Saudi Arabia and Yemen. Malaria program activity reports, case data, and ancillary information have been assembled from national health information systems, archives, and other related sources. Information was analyzed as a semi-quantitative time series, between 2000 and 2017, to provide a plausibility framework to understand the possible contributions of factors related to control activities, conflict, economic development, migration, and climate. The malaria recession in the Yemeni border regions of Saudi Arabia is a likely consequence of multiple, coincidental factors, including scaled elimination activities, cross-border vector control, periods of low rainfall, and economic development. The temporal alignment of many of these factors suggests that economic development may have changed the receptivity to the extent that it mitigated against surges in vulnerability posed by imported malaria from its endemic neighbor Yemen. In many border areas of the world, malaria is likely to be sustained through a complex congruence of factors, including poverty, conflict, and migration

Cross-border movement, economic development and malaria elimination in the Kingdom of Saudi Arabia

Abstract Malaria at international borders presents particular challenges with regards to elimination. International borders share common malaria ecologies, yet neighboring countries are often at different stages of the control-to-elimination pathway. Herein, we present a case study on malaria, and its control, at the border between Saudi Arabia and Yemen. Malaria program activity reports, case data, and ancillary information have been assembled from national health information systems, archives, and other related sources. Information was analyzed as a semi-quantitative time series, between 2000 and 2017, to provide a plausibility framework to understand the possible contributions of factors related to control activities, conflict, economic development, migration, and climate. The malaria recession in the Yemeni border regions of Saudi Arabia is a likely consequence of multiple, coincidental factors, including scaled elimination activities, cross-border vector control, periods of low rainfall, and economic development. The temporal alignment of many of these factors suggests that economic development may have changed the receptivity to the extent that it mitigated against surges in vulnerability posed by imported malaria from its endemic neighbor Yemen. In many border areas of the world, malaria is likely to be sustained through a complex congruence of factors, including poverty, conflict, and migration