An impedimetric sensor for monitoring the growth of Staphylococcus epidermidis

Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces

The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.

An Integrated Microsystem for Real-Time Detection and Threshold-Activated Treatment of Bacterial Biofilms

Bacterial biofilms are the primary cause of infections in medical implants and catheters. Delayed detection of biofilm infections contributes to the widespread use of high doses of antibiotics, leading to the emergence of antibiotic-resistant bacterial strains. Accordingly, there is an urgent need for systems that can rapidly detect and treat biofilm infections in situ. As a step toward this goal, in this work we have developed for the first time a threshold-activated feedback-based impedance sensor-treatment system for combined real-time detection and treatment of biofilms. Specifically, we demonstrate the use of impedimetric sensing to accurately monitor the growth of Escherichia coli biofilms in microfluidic flow cells by measuring the fractional relative change (FRC) in absolute impedance. Furthermore, we demonstrate the use of growth measurements as a threshold-activated trigger mechanism to initiate successful treatment of biofilms using bioelectric effect (BE), applied through the same sensing electrode array. This was made possible through a custom program that (a) monitored the growth and removal of biofilms within the microfluidic channels in real-time and (b) enabled the threshold-based activation of BE treatment. Such BE treatment resulted in a ∼74.8 % reduction in average biofilm surface coverage as compared to the untreated negative control. We believe that this smart microsystem for integrated biofilm sensing and treatment will enable future development of autonomous biosensors optimized for accurate real-time detection of the onset of biofilms and their in situ treatment, directly on the surfaces of medical implants.

Microelectrical sensors as emerging platforms for protein biomarker detection in point-of-care diagnostics

Current methods used to measure protein expression on microarrays, such as labeled fluorescent imaging, are not well suited for real-time, diagnostic measurements at the point of care. Studies have shown that microelectrical sensors utilizing silica nanowire, impedimetric, surface acoustic wave, magnetic nanoparticle and microantenna technologies have the potential to impact disease diagnosis by offering sensing characteristics that rival conventional sensing techniques. Their ability to transduce protein binding events into electrical signals may prove essential for the development of next-generation point-of-care devices for molecular diagnostics, where they could be easily integrated with microarray, microfluidic and telemetry technologies. However, common limitations associated with the microelectrical sensors, including problems with sensor fabrication and sensitivity, must first be resolved. This review describes governing technical concepts and provides examples demonstrating the use of various microelectrical sensors in the diagnosis of disease via protein biomarkers.

Challenges of electrochemical impedance spectroscopy in protein biosensing

Electrochemical impedance spectroscopy (EIS) measurement, performed in the presence of a redox agent, is a convenient method to measure molecular interactions of electrochemically inactive compounds taking place on the electrode surface. High sensitivity of the method, being highly advantageous, can be also associated with nonspecific impedance changes that could be easily mistaken for specific interactions. Therefore, it is necessary to be aware of all possible causes and perform parallel control experiments to rule them out. We present the results obtained during the early stages of aptamer-based sensor development, utilizing a model system of human alpha thrombin interacting with a thiolated DNA aptamer, immobilized on gold electrodes. EIS measurements took place in the presence of iron ferrocyanides. In addition to known method limitations, that is, inability to discriminate between specific and nonspecific binding (both causing impedance increase), we have found other factors leading to nonspecific impedance changes, such as: (i) initial electrode contamination; (ii) repetitive measurements; (iii) additional cyclic voltammetry (CV) or differential pulse voltammetry (DPV) measurements; and (iv) additional incubations in the buffer between measurements, which have never been discussed before. We suggest ways to overcome the method limitations.