Autofluorescence endoscopy is a promising functional imaging technique to improve screening of pre-cancerous or early cancer lesions in the gastrointestinal (GI) tract. Tissue autofluorescence signal is weak compared to white light reflectance imaging. Conventional forward-viewing endoscopes are inefficient in the collection of light from objects of interest along on the GI luminal wall. A key component of a complete autofluorescence endoscope is the light collection module. In this paper, we report the design, optimization, prototype development, and testing of an endoscope objective that is capable of acquiring simultaneous forward and radial views. The radial-view optical design was optimized for a balance between image quality and light collection. Modulation transfer function (MTF), entrance pupil radius, manufacturability, and field-of-view were parameters used in the lens optimization. In comparison with the typical forward-viewing endoscopes, our nonsequential ray trace simulations suggest the proposed radial-view design is more practical in the light collection. To validate the proposed simulation methods, a 3:1 scaled-up prototype was fabricated. Contrast measurements were taken with the prototype, and then compared with the simulated MTF.
High-throughput metabolomics can be used to optimize cell growth for enhanced production or for monitoring cell health in bioreactors. It has applications in cell and gene therapies, vaccines, biologics, and bioprocessing. NMR metabolomics is a method that allows for fast and reliable experimentation, requires only minimal sample preparation, and can be set up to take online measurements of cell media for bioreactor monitoring. This type of application requires a fully automated metabolite quantification method that can be linked with high-throughput measurements. In this review, we discuss the quantifier requirements in this type of application, the existing methods for NMR metabolomics quantification, and the performance of three existing quantifiers in the context of NMR metabolomics for bioreactor monitoring.
Bioreactors are useful tools for bioprocessing and production of biologics, gene therapies and vaccines. Streaming data-driven process control systems can be valuable in lowering the cost of production or discovering novel reaction pathways. Nuclear Magnetic resonance (NMR) is an inexpensive spectroscopy technique that has characteristics that make it appropriate for on-line, high-throughput measurement of metabolic changes in a bioreactor vessel. Future quantitative NMR (qNMR) advancements for processing this type of streaming data could grant a unique possibility for in-situ bioprocessing applications. One significant challenge for 1D 1H qNMR is that the spectrum of a compound can deviate from its spectrum in a reference setting, especially across the various spectrometer frequency and concentration profile of metabolite mixture in the biofluid sample. A robust predictive or constraint model on the generative mechanism of the measured NMR signal can help guide future qNMR developments. We present an approximated 1D 1H NMR signal model that shows promise in fitting chemical shifts and other interpretable parameters for small mixtures of compounds. Our model use reference chemistry parameters of compounds to derive patterns between its nuclei via spin Hamiltonian simulations and hierarchical convex clustering on a spin angular momentum feature between the nuclei, which are quantum subsystems. These patterns are used to construct a surrogate model of the compound mixture with a lower degrees-of-freedom. Our approach does not require any phase or baseline correction techniques to pre-process the data, making it a generative model that fully accounts for the relative phase information, which is usually attenuated in a heuristic manner and ignored in conventional NMR data processing. We demonstrate the potential of this new methodology by fitting against real-world NMR reference compound experiments.
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