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The United States is currently experiencing an opioid crisis, with more than 47,000 deaths in 2017 due to opioid overdoses. Current approaches for opioid identification and quantification in body fluids include immunoassays and chromatographic methods (e.g., LC-MS, GC-MS), which require expensive instrumentation and extensive sample preparation. Our aim was to develop a portable point-of-care device that can be used for the instant detection of opioids in body fluids. Here, we reported the development of a morphine-sensitive fluorescence-based sensor chip to sensitively detect morphine in the blood using a homogeneous immunoassay without any washing steps. Morphine-sensitive illuminating peptides were identified using a high throughput one-bead one-compound (OBOC) combinatorial peptide library approach. The OBOC libraries contain a large number of random peptides with a molecular rotor dye, malachite green (MG), that are coupled to the amino group on the side chain of lysine at different positions of the peptides. The OBOC libraries were then screened for fluorescent activation under a confocal microscope, using an anti-morphine monoclonal antibody as the screening probe, in the presence and absence of free morphine. Using this novel three-step fluorescent screening assay, we were able to identify the peptide-beads that fluoresce in the presence of an anti-morphine antibody, but lost fluorescence when the free morphine was present. After the positive beads were decoded using automatic Edman microsequencing, the morphine-sensitive illuminating peptides were then synthesized in soluble form, functionalized with an azido group, and immobilized onto microfabricated PEG-array spots on a glass slide. The sensor chip was then evaluated for the detection of morphine in plasma. We demonstrated that this proof-of-concept platform can be used to develop fluorescence-based sensors against morphine. More importantly, this technology can also be applied to the discovery of other novel illuminating peptidic sensors for the detection of illicit drugs and cancer biomarkers in body fluids.

In this Research Article, a novel inkjet printing technique, micro impact printing (MI printing), is applied for the first time to combinatorial peptide microarray synthesis on amine functionalized microdisc arrays through standard Fmoc chemistry. MI printing shows great advantages in combinatorial peptide microarray synthesis compared with other printing techniques, including (1) a disposable cartridge; (2) a small spot size (80 μm) increases array density; (3) minimal loading volume (0.6 μL) and dead volume (

Traditional computational methods for pressurized water reactors are unable to handle dispersed fuel particles as the double heterogeneity and the direct volumetric homogenization can result in significant errors. In contrast, reactivity-equivalent physical transformation techniques offer high precision for addressing the double heterogeneity introduced by dispersed fuel particles. This approach converts the double heterogeneity problem into a single heterogeneity problem, which is then subsequently investigated by using the conventional pressurized water reactor computational procedure. However, it is currently empirical and takes a lot of time to obtain the right k∞. In this paper, we train the RPT model by using the existing dataset of plate-dispersed fuel and rod-dispersed fuel by a machine learning method based on a linear regression model, and we then use the new data to make predictions and derive the corresponding similarity ratios. The burnup verification, density verification, fission rate verification, and neutron energy spectrum analysis are calculated through the OpenMC program. For plate-type fuel elements, the method maintains an accuracy within 200 pcm during depletion, with deviations in the 235U density and 235U fission rate within 0.1% and neutron energy spectrum errors within 6%. For rod-type fuel elements, the method maintains an accuracy within 100 pcm during depletion, with deviations in 235U and 239Pu density within 1.5% and neutron energy spectrum errors within 1%. The numerical validation indicates that the reactivity-equivalent physical transformation method based on the linear regression model not only greatly improves the computational efficiency, but also ensures a very high accuracy to deal with double heterogeneity in nuclear reactors. [ABSTRACT FROM AUTHOR]

Traditional high-throughput drug combination screening requires automatic pipetting of drugs into high-density microtiter plates. Here, a drug-on-pillar platform is proposed for efficient combination drug screening. Using the proposed approach, combination drug screening can be carried out in a plug-and-play manner, allowing for high-throughput screening of large permutations of drug combinations at various concentrations, such that drug dispensing and cell-based screening can be temporally separated and therefore can potentially be performed at distant laboratories. The dispensing is implemented using our recently developed microfluidic pneumatic printing platform, which features a low-cost disposable cartridge that minimizes cross contamination. Moreover, our previously developed drug nanoformulation method with amphiphilic telodendrimers has been utilized to maintain drug stability in a dry form, allowing for convenient drug storage, shipping, and subsequent rehydration. Combining the features described above, we have implemented a 1260-spot drug combination array to study the effect of paired drugs against MDA-MB-231 triple negative human breast cancer cells. This study supports the feasibility of the drug-on-pillar platform for combination drug screening and has provided valuable insight into drug combination efficacy against breast cancer.

Natural genetic promoters are regulated by multiple cis and trans regulatory factors. For quantitative studies of these promoters, the concentration of only a single factor is typically varied to obtain the dose response or transfer function of the promoters with respect to the factor. Such design of experiments has limited our ability to understand quantitative, combinatorial interactions between multiple regulatory factors at promoters. This limitation is primarily due to the intractable number of experimental combinations that arise from multifactorial design of experiments. To overcome this major limitation, we integrate impact printing and cell-free systems to enable multi-dimensional studies of genetic promoters. We first present a gradient printing system which comprises parallel piezoelectric cantilever beams as a scalable actuator array to generate droplets with tunable volumes in the range of 100 pL-10 nL, which facilitates highly accurate direct dilutions in the range of 1-10 000-fold in a 1 μL drop. Next, we apply this technology to study interactions between three regulatory factors at a synthetic genetic promoter. Finally, a mathematical model of gene regulatory modules is established using the multi-parametric and multi-dimensional data. Our work creates a new frontier in the use of cell-free systems and droplet printing for multi-dimensional studies of synthetic genetic constructs.