Graphene Multiplexed Sensor for Point-of-Need Viral Wastewater-Based Epidemiology.

Wastewater-based epidemiology (WBE) can help mitigate the spread of respiratory infections through the early detection of viruses, pathogens, and other biomarkers in human waste. The need for sample collection, shipping, and testing facilities drives up the cost of WBE and hinders its use for rapid detection and isolation in environments with small populations and in low-resource settings. Given the ubiquitousness and regular outbreaks of respiratory syncytial virus, SARS-CoV-2, and various influenza strains, there is a rising need for a low-cost and easy-to-use biosensing platform to detect these viruses locally before outbreaks can occur and monitor their progression. To this end, we have developed an easy-to-use, cost-effective, multiplexed platform able to detect viral loads in wastewater with several orders of magnitude lower limit of detection than that of mass spectrometry. This is enabled by wafer-scale production and aptamers preattached with linker molecules, producing 44 chips at once. Each chip can simultaneously detect four target analytes using 20 transistors segregated into four sets of five for each analyte to allow for immediate statistical analysis. We show our platform's ability to rapidly detect three virus proteins (SARS-CoV-2, RSV, and Influenza A) and a population normalization molecule (caffeine) in wastewater. Going forward, turning these devices into hand-held systems would enable wastewater epidemiology in low-resource settings and be instrumental for rapid, local outbreak prevention.

Automated analysis of cell-matrix adhesions in 2D and 3D environments.

Cell-matrix adhesions are of great interest because of their contribution to numerous biological processes, including cell migration, differentiation, proliferation, survival, tissue morphogenesis, wound healing, and tumorigenesis. Adhesions are dynamic structures that are classically defined on two-dimensional (2D) substrates, though the need to analyze adhesions in more physiologic three-dimensional (3D) environments is being increasingly recognized. However, progress has been greatly hampered by the lack of available tools to analyze adhesions in 3D environments. To address this need, we have developed a platform for the automated analysis, segmentation, and tracking of adhesions (PAASTA) based on an open source MATLAB framework, CellAnimation. PAASTA enables the rapid analysis of adhesion dynamics and many other adhesion characteristics, such as lifetime, size, and location, in 3D environments and on traditional 2D substrates. We manually validate PAASTA and utilize it to quantify rate constants for adhesion assembly and disassembly as well as adhesion lifetime and size in 3D matrices. PAASTA will be a valuable tool for characterizing adhesions and for deciphering the molecular mechanisms that regulate adhesion dynamics in 3D environments.

Ascorbate reacts with singlet oxygen to produce hydrogen peroxide.

Singlet oxygen is a highly reactive electrophilic species that reacts rapidly with electron-rich moieties, such as the double bonds of lipids, thiols, and ascorbate (AscH-). The reaction of ascorbate with singlet oxygen is rapid (k = 3 x 10(8) M(-1) s(-1)). Here we have investigated the stoichiometry of this reaction. Using electrodes to make simultaneous, real-time measurements of oxygen and hydrogen peroxide concentrations, we have investigated the products of this reaction. We have demonstrated that hydrogen peroxide is a product of this reaction. The stoichiometry for the reactants of the reaction (1 1O2 + 1AscH--->1H2O2 + 1dehydroascorbic) is 1:1. The formation of H2O2 results in a very different oxidant that has a longer lifetime and much greater diffusion distance. Thus, locally produced singlet oxygen with a half-life of 1 ns to 1 micros in a biological setting is changed to an oxidant that has a much longer lifetime and thus can diffuse to distant targets to initiate biological oxidations.

Nitric oxide as a cellular antioxidant: a little goes a long way.

Nitric oxide (NO*) is an effective chain-breaking antioxidant in free radical-mediated lipid oxidation (LPO). It reacts rapidly with peroxyl radicals as a sacrificial chain-terminating antioxidant. The goal of this work was to determine the minimum threshold concentration of NO* required to inhibit Fe2+ -induced cellular lipid peroxidation. Using oxygen consumption as a measure of LPO, we simultaneously measured nitric oxide and oxygen concentrations with NO* and O2 electrodes. Ferrous iron and dioxygen were used to initiate LPO in docosahexaenoic acid-enriched HL-60 and U937 cells. Bolus addition of NO* (1.5 microM) inhibited LPO when the NO* concentration was greater than 50 nM. Similarly, using (Z)-1-[N-(3-ammoniopropyl)-N-(n-propyl)amino]diazen-1-ium-1,2-diolate as a NO* donor we found that an average steady-state NO* concentration of at least 72 +/- 9 nM was required to blunt LPO. As long as the concentration of NO* was above 13 +/- 8 nM the inhibition was sustained. Once the concentration of NO* fell below this value, the rate of lipid oxidation accelerated as measured by the rate of oxygen consumption. Our model suggests that a continuous production of NO* that would yield a steady-state concentration of only 10-20 nM is capable of inhibiting Fe2+ -induced LPO.

Monte Carlo simulations of a nozzle for the treatment of ocular tumours with high-energy proton beams.

By the end of 2002, 33 398 patients worldwide had been treated with proton radiotherapy, 10 829 for eye diseases. The dose prediction algorithms used today for ocular proton therapy treatment planning rely on parameterizations of measured proton dose distributions, i.e., broad-beam and pencil-beam techniques, whose predictive capabilities are inherently limited by severe approximations and simplifications in modelling the radiation transport physics. In contrast, the Monte Carlo radiation transport technique can, in principle, provide accurate predictions of the proton treatment beams by taking into account all the physical processes involved, including coulombic energy loss, energy straggling, multiple Coulomb scattering, elastic and nonelastic nuclear interactions, and the transport of secondary particles. It has not been shown, however, whether it is possible to commission a proton treatment planning system by using data exclusively from Monte Carlo simulations of the treatment apparatus and a phantom. In this work, we made benchmark comparisons between Monte Carlo predictions and measurements of an ocular proton treatment beamline. The maximum differences between absorbed dose profiles from simulations and measurements were 6% and 0.6 mm, while typical differences were less than 2% and 0.2 mm. The computation time for the entire virtual commissioning process is less than one day. The study revealed that, after a significant development effort, a Monte Carlo model of a proton therapy apparatus is sufficiently accurate and fast for commissioning a treatment planning system.