Light-sheet fluorescence microscopy (LSFM) has been present in cell biology laboratories for quite some time, mainly as custom-made systems, with imaging applications ranging from single cells (in the micrometer scale) to small organisms (in the millimeter scale). Such microscopes distinguish themselves for having very low phototoxicity levels and high spatial and temporal resolution, properties that make them ideal for a large range of applications. These include the study of cellular dynamics, in particular cellular motion which is essential to processes such as tumor metastasis and tissue development. Experimental setups make extensive use of microdevices (bioMEMS) that provide better control over the substrate environment than traditional cell culture experiments. For example, to mimic in vivo conditions, experiment biochemical dynamics, and trap, move or count cells. Microdevices provide a higher degree of empirical complexity but, so far, most have been designed to be imaged through wide-field or confocal microscopes. Nonetheless, the properties of LSFM render it ideal for 3D characterization of active cells. When working with microdevices, confocal microscopy is more widespread than LSFM even though it suffers from higher phototoxicity and slower acquisition speeds. It is sometimes possible to illuminate with a light-sheet microdevices designed for confocal microscopes. However, these bioMEMS must be redesigned to exploit the full potential of LSFM and image more frequently on a wider scale phenomena such as motion, traction, differentiation, and diffusion of molecules. The use of microdevices for LSFM has extended beyond cell tracking studies into experiments regarding cytometry, spheroid cultures and lab-on-a-chip automation. Due to light-sheet microscopy being in its early stages, a setup of these characteristics demands some degree of optical expertise; and designing three-dimensional microdevices requires facilities, ingenuity, and experience in microfabrication. In this paper, we explore different approaches where light-sheet microscopy can achieve single-cell and subcellular resolution within microdevices, and provide a few pointers on how these experiments may be improved.
Our aim is to establish a framework where reinforcement learning (RL) of optimizing interventions retrospectively allows us a regulatory compliant pathway to prospective clinical testing of the learned policies in a clinical deployment. We focus on infections in intensive care units which are one of the major causes of death and difficult to treat because of the complex and opaque patient dynamics, and the clinically debated, highly-divergent set of intervention policies required by each individual patient, yet intensive care units are naturally data rich. In our work, we build on RL approaches in healthcare ("AI Clinicians"), and learn off-policy continuous dosing policy of pharmaceuticals for sepsis treatment using historical intensive care data under partially observable MDPs (POMDPs). POMPDs capture uncertainty in patient state better by taking in all historical information, yielding an efficient representation, which we investigate through ablations. We compensate for the lack of exploration in our retrospective data by evaluating each encountered state with a best-first tree search. We mitigate state distributional shift by optimizing our policy in the vicinity of the clinicians' compound policy. Crucially, we evaluate our model recommendations using not only conventional policy evaluations but a novel framework that incorporates human experts: a model-agnostic pre-clinical evaluation method to estimate the accuracy and uncertainty of clinician's decisions versus our system recommendations when confronted with the same individual patient history ("shadow mode").
Three-dimensional imaging of live processes at a cellular level is a challenging task. It requires high-speed acquisition capabilities, low phototoxicity, and low mechanical disturbances. Three-dimensional imaging in microfluidic devices poses additional challenges as a deep penetration of the light source is required, along with a stationary setting, so the flows are not perturbed. Different types of fluorescence microscopy techniques have been used to address these limitations; particularly, confocal microscopy and light sheet fluorescence microscopy (LSFM). This manuscript proposes a novel architecture of a type of LSFM, single-plane illumination microscopy (SPIM). This custom-made microscope includes two mirror galvanometers to scan the sample vertically and reduce shadowing artifacts while avoiding unnecessary movement. In addition, two electro-tunable lenses fine-tune the focus position and reduce the scattering caused by the microfluidic devices. The microscope has been fully set up and characterized, achieving a resolution of 1.50 μm in the x-y plane and 7.93 μm in the z-direction. The proposed architecture has risen to the challenges posed when imaging microfluidic devices and live processes, as it can successfully acquire 3D volumetric images together with time-lapse recordings, and it is thus a suitable microscopic technique for live tracking miniaturized tissue and disease models.
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