The practical impact of analytical probes that transduce in the near-infrared (nIR) has been dampened by the lack of cost-effective and portable nIR fluorimeters. Herein, we demonstrate straightforward designs for an inexpensive microplate reader and a portable fluorimeter. These instruments require minimally complex machining and fabrication and operate with an open-source programming language (Python). Complete wiring diagrams, assembly diagrams, and scripts are provided. To demonstrate the utility of these two instruments, we performed high-throughput and field-side measurements of soil samples to evaluate the effect of soil management strategies on extracellular proteolytic, cellulolytic, and lignin-modifying activities. This was accomplished with fluorescent enzyme probes that utilized uniquely sensitive transducers exclusive to the nIR spectrum, single-walled carbon nanotubes. We also used the portable fluorimeter to evaluate spatial variations of proteolytic activity within individual field plots, while minimizing the effects of soil storage and handling. These demonstrations indicate the utility of these fluorimeters for translating analytical probes that operate in the nIR beyond the laboratory and into actual use.
Membrane-active molecules are of great importance to drug delivery and antimicrobials applications. While the ability to prototype new membrane-active molecules has improved greatly with the advent of automated chemistries and rapid biomolecule expression techniques, testing methods are still limited by throughput, cost, and modularity. Existing methods suffer from feasibility constraints of working with pathogenic, living cells and by intrinsic limitations of model systems. Herein, we demonstrate an abiotic sensor that uses semiconducting single-walled carbon nanotubes (SWNT) as near infrared fluorescent transducers to report membrane interactions. This sensor is comprised of SWNT aqueously suspended in a phospholipid monolayer; these SWNT probes are very sensitive to solvent access (changes in permittivity) and thus report morphological changes to the membrane by modulation of fluorescent signal where binding and disruption are reported as brightening and attenuation, respectively. This mechanism is first demonstrated with chemical and physical membrane-disruptive agents including ethanol, sodium dodecyl sulfate, and application of electrical pulses. Known cell-penetrating and antimicrobial peptides are then used to demonstrate how the dynamic response of these sensors can be deconvoluted to evaluate different, parallel mechanisms of interaction. Lastly, SWNT functionalized in several different bacterial lipopolysaccharides (P aeruginosa, K pneumoniae, and E coli) are used to evaluate a panel of known membrane-disrupting antimicrobials to demonstrate that drug selectivity can be assessed by suspension of SWNT with different membrane materials.
The artificial pancreas (AP) is an electro-mechanical device to control glucose (G) levels in the blood for people with diabetes using mathematical modeling and control system technology. There are many variables not measured and modeled by these devices that affect G levels. This work evaluates the effectiveness of two control systems for the case where critical inputs are unmeasured. This work compares and evaluates two predictive feedback control (FBC) algorithms in two unmeasured input studies. In the first study, the process is a dynamic transfer function model with one measured input variable and one unmeasured input variable. The process for the second study is a diabetes simulator with insulin feed rate (IFR) measured and carbohydrate consumption (CC) unmeasured. The feedback predictive control (FBPC) approach achieved much better control performance than model predictive control (MPC) in both studies. In the first study, MPC was shown to get worse as the process lag increases but FBPC was unaffected by process lag. In the diabetes simulation study, for five surrogate type 1 diabetes subjects, the standard deviation of G about its mean (standard deviation) (i.e., the set point) was 133% larger for MPC relative to FBPC. For FBPC, its standard deviation was less than 10% larger for unmeasured CC versus measured CC. Thus, FBPC appears to be a more effective AP control algorithm than MPC for unmeasured disturbances and may not perform much worse in practice when CC is measured versus when it is unmeasured since CC can be very inaccurate in real situations.
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