Bacterial Membrane Mimetics: From Biosensing to Disease Prevention and Treatment

Membrane-Disruptive Effects of Fatty Acid and Monoglyceride Mitigants on E. coli Bacteria-Derived Tethered Lipid Bilayers

We report electrochemical impedance spectroscopy measurements to characterize the membrane-disruptive properties of medium-chain fatty acid and monoglyceride mitigants interacting with tethered bilayer lipid membrane (tBLM) platforms composed of E. coli bacterial lipid extracts. The tested mitigants included capric acid (CA) and monocaprin (MC) with 10-carbon long hydrocarbon chains, and lauric acid (LA) and glycerol monolaurate (GML) with 12-carbon long hydrocarbon chains. All four mitigants disrupted E. coli tBLM platforms above their respective critical micelle concentration (CMC) values; however, there were marked differences in the extent of membrane disruption. In general, CA and MC caused larger changes in ionic permeability and structural damage, whereas the membrane-disruptive effects of LA and GML were appreciably smaller. Importantly, the distinct magnitudes of permeability changes agreed well with the known antibacterial activity levels of the different mitigants against E. coli, whereby CA and MC are inhibitory and LA and GML are non-inhibitory. Mechanistic insights obtained from the EIS data help to rationalize why CA and MC are more effective than LA and GML at disrupting E. coli membranes, and these measurement capabilities support the potential of utilizing bacterial lipid-derived tethered lipid bilayers for predictive assessment of antibacterial drug candidates and mitigants.

The convergence of traditional and digital biomarkers through AI-assisted biosensing: A new era in translational diagnostics?

Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of "point-of-care" (POC) diagnostics is finally showcased.