Direct analysis of complex reaction mixtures: formose reaction

Complex reaction mixtures, like those postulated on early Earth, present an analytical challenge because of the number of components, their similarity, and vastly different concentrations. Interpreting the reaction networks is typically based on simplified or partial data, limiting our insight. We present a new approach based on online monitoring of reaction mixtures formed by the formose reaction by ion-mobility-separation mass-spectrometry. Monitoring the reaction mixtures led to large data sets that we analyzed by non-negative matrix factorization, thereby identifying ion-signal groups capturing the time evolution of the network. The groups comprised ≈300 major ion signals corresponding to sugar-calcium complexes formed during the formose reaction. Multivariate analysis of the kinetic profiles of these complexes provided an overview of the interconnected kinetic processes in the solution, highlighting different pathways for sugar growth and the effects of different initiators on the initial kinetics. Reconstructing the network's topology further, we revealed so far unnoticed fast retro-aldol reaction of ketoses, which significantly affects the initial reaction dynamics. We also detected the onset of sugar-backbone branching for C6 sugars and cyclization reactions starting for C5 sugars. This top-down analytical approach opens a new way to analyze complex dynamic mixtures online with unprecedented coverage and time resolutio

Iterative design of training data to control intricate enzymatic reaction networks

Abstract Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce different product ratios while maintaining a minimum level of overall conversion efficiency. Our workflow scales with the complexity of the system and enables the optimization of previously unobtainable network outputs

Optical percutaneous needle biopsy of the liver: a pilot animal and clinical study

This paper presents the results of the experiments which were performed using the optical biopsy system specially developed for in vivo tissue classification during the percutaneous needle biopsy (PNB) of the liver. The proposed system includes an optical probe of small diameter acceptable for use in the PNB of the liver. The results of the feasibility studies and actual tests on laboratory mice with inoculated hepatocellular carcinoma and in clinical conditions on patients with liver tumors are presented and discussed. Monte Carlo simulations were carried out to assess the diagnostic volume and to trace the sensing depth. Fluorescence and diffuse reflectance spectroscopy measurements were used to monitor metabolic and morphological changes in tissues. The tissue oxygen saturation was evaluated using a recently developed approach to neural network fitting of diffuse reflectance spectra. The Support Vector Machine Classification was applied to identify intact liver and tumor tissues. Analysis of the obtained results shows the high sensitivity and specificity of the proposed multimodal method. This approach allows to obtain information before the tissue sample is taken, which makes it possible to significantly reduce the number of false-negative biopsies