A new positron emission tomography (PET) tracer, composed of 18F labeled maltohexaose (MH18F), can image bacteria in vivo with a sensitivity and specificity that is orders of magnitude better than fluorodeoxyglucose (18FDG). MH18F can detect early stage infections composed of as few as 105 E.coli colony forming units (CFUs), and can identify drug resistance in bacteria in vivo. MH18F has the potential to improve the diagnosis of bacterial infections given its unique combination of high specificity and sensitivity for bacteria.

The study of glycobiology has been seriously hampered due to lack of an ideal assay tool. This work proposes a robust carbohydrate monolayer platform to solve the problems of active site inaccessibility and lectin denaturation associated with protein arrays reported for detection of cell surface carbohydrates and develops a convenient method for monitoring cell surface carbohydrate sites of interest, with high sensitivity, acceptable rapidity, low cost, and excellent extensibility. It utilizes the competitive binding of solid-surface-confined and cell-surface-residing carbohydrates to quantum dot labeled carbohydrate recognition protein and subsequent voltammetric quantification of the metal signature. The mannan monolayer strategy exhibited sensitive response to K562 cells and possessed potential specificity due to the specific interaction between lectin and corresponding carbohydrate. By comparing the competitive binding of K562 cells with mannan in solutions, the average Con A binding capacity of a single K562 cell could be estimated to correspond to 6.9 pg or 2.3 x 10(10) mannose moieties. This strategy integrates the advantages of surface assembly, nanotechnology, bioconjugate techniques, and electrochemical detection and can be expanded for profiling cell surface carbohydrates and high-throughput multiple detection by simultaneously using more pairs of lectin and carbohydrate owing to the multiple coding capability of QDs, which provides an important protocol for the quantitative evaluation of cell surface carbohydrate sites.

Therapeutics based on transcription factors have the potential to revolutionize medicine but have had limited clinical success due to delivery problems1–4. The delivery of transcription factors is challenging because it requires developing a delivery vehicle that can complex transcription factors, target cells, and stimulate endosomal disruption, with minimal toxicity5,6. In this report we present a novel multifunctional oligonucleotide, termed DARTs (DNA Assembled Recombinant Transcription factors), which can deliver transcription factors with high efficiency in vivo. DARTs are composed of an oligonucleotide that contains a transcription factor binding sequence and hydrophobic membrane disruptive chains that are masked by acid cleavable galactose residues. DARTs have a unique molecular architecture, which allows them to bind transcription factors, trigger endocytosis in hepatocytes, and stimulate endosomal disruption. The DARTs target hepatocytes as a result of the galactose residues and can disrupt endosomes efficiently with minimal toxicity, because unmasking of their hydrophobic domains selectively occurs in the acidic environment of the endosome. We show here that DARTs can deliver the transcription factor Nuclear erythroid 2-related factor 2 (Nrf2) to the liver, catalyze the transcription of Nrf2 downstream genes, and rescue mice from acetaminophen induced liver injury.

Flow cytometry holds promise to accelerate antibiotic susceptibility determinations; however, without robust multidimensional statistical analysis, general discrimination criteria have remained elusive. In this study, a new statistical method, probability binning signature quadratic form (PB-sQF), was developed and applied to analyze flow cytometric data of bacterial responses to antibiotic exposure. Both sensitive lab strains (Escherichia coli and Pseudomonas aeruginosa) and a multidrug resistant, clinically isolated strain (E. coli) were incubated with the bacteria-targeted dye, maltohexaose-conjugated IR786, and each of many bactericidal or bacteriostatic antibiotics to identify changes induced around corresponding minimum inhibition concentrations (MIC). The antibiotic-induced damages were monitored by flow cytometry after 1-h incubation through forward scatter, side scatter, and fluorescence channels. The 3-dimensional differences between the flow cytometric data of the no-antibiotic treated bacteria and the antibiotic-treated bacteria were characterized by PB-sQF into a 1-dimensional linear distance. A 99% confidence level was established by statistical bootstrapping for each antibiotic-bacteria pair. For the susceptible E. coli strain, statistically significant increments from this 99% confidence level were observed from 1/16x MIC to 1x MIC for all the antibiotics. The same increments were recorded for P. aeruginosa, which has been reported to cause difficulty in flow-based viability tests. For the multidrug resistant E. coli, significant distances from control samples were observed only when an effective antibiotic treatment was utilized. Our results suggest that a rapid and robust antimicrobial susceptibility test (AST) can be constructed by statistically characterizing the differences between sample and control flow cytometric populations, even in a label-free scheme with scattered light alone. These distances vs paired controls coupled with rigorous statistical confidence limits offer a new path toward investigating initial biological responses, screening for drugs, and shortening time to result in antimicrobial sensitivity testing.