Organoid/brain slice immobilization for microelectrode arrays (MEAs) and organoid-on-chip platforms have traditionally depended on hydrogels, harp-style grids, or microfluidic confinement, each with its own set of pros and cons with respect to stability, standardization, and impact on electrophysiology. Hydrogels (e.g., Polyethylene glycol or PEG, extracellular matrix like Matrigel) are widely used to immobilize 3D neural tissues on MEAs. These are known to swell, drift, and alter mechanical microenvironments, which in turn modulate network firing, synchrony, and bursting behavior. Mechanical retention via harp slice grids or similar harp devices is a long-standing practice in acute brain slice and organoid electrophysiology. These devices are typically standardized, fragile, and poorly matched to diverse well and tissue geometries. ​Microfluidic organoid chips and specialized 3D MEAs (e.g., e-Flower, organoid-on-chip platforms) have recently emerged to enable hydrogel-free trapping/encapsulation of organoids for imaging and recordings, but they often require bespoke chip designs and overly complex flow control setups. There is a lack of geometry-agnostic devices for mechanically immobilizing diverse organoids on commercial MEAs that feature consistent stability, uniform and/or tailored contact, and with minimal perturbation of electrophysiological readouts.

Quantum‑correlated photon imaging experiments first used pairs of entangled photons so that an image was recovered only from correlations between the two detection paths rather than from either beam alone. Similar correlation and entanglement ideas have been attempted for higher energies and to positron‑annihilation photons, motivating quantum‑based Positron Emission Tomography (PET) concepts in which the additional quantum information carried by annihilation photon pairs could enhance image quality or add new types of contrast beyond conventional PET. In parallel, quantum‑inspired transmission imaging has been proposed as an alternative to Computed Tomography (CT), which today relies on a well‑characterized but fundamentally stochastic X‑ray source, and is limited by Poisson photon statistics, dose requirements, and capped contrast for soft‑tissue. Traditional X‑ray and CT imaging are governed by Poisson statistics, where independent, random photon arrivals make the variance equal to the mean, and has fundamentally bound SNR for a given dose. Research on quantum‑correlated transmission schemes has looked at image formation with higher‑order correlations between photons (rather than simple independent counting) such that performance is no longer capped by standard Poisson statistics, which can in principle lead to superior SNR and sharper anatomical detail at a given dose. To date, quantum‑based X‑ray implementations of this idea have largely relied on spontaneous parametric down‑conversion (SPDC) to generate entangled or correlated photon pairs, but SPDC at X‑ray‑level energies has extremely low conversion efficiency and pair rates—often only a few pairs per second—rendering such medical or biological imaging impractical. Quantum correlation of Annihilation Photon Imaging (QAPI) brings the correlation concepts into a PET‑like regime by using positron annihilation as a bright source of 511 keV gamma‑ray pairs while assuming a transmission‑imaging role similar to CT. QAPI is designed to exploit the strengths of both worlds: unlike CT, it can count the incident annihilation photons via the idler channel and operate in a high‑transmission regime that permits binomial transmission statistics. The PET‑like 511 keV photons introduce challenges that do not exist for CT, including low interaction probability in tissue and detectors, reduced single‑photon detection efficiency, and the need for precise coincidence timing between the signal and idler counts. For any high‑energy, photon-based imaging, including emerging quantum schemes, there is a fundamental tension between dose (especially for biological tissues that are highly susceptible to damage, cell death, or mutation when exposed to ionizing radiation) and the photon statistics needed for adequate SNR. Moreover, the dose‑normalized performance for quantum approaches is still not well established.

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A novel class of bioinspired photo-initiators enabling visible light-driven polymer gelation that improves cell-biomaterial compatibility across thicker tissues.

Researchers at the University of California, Davis have developed a computer-implemented method for accurately classifying congenital heart defects in newborns using pulse oximetry and machine learning.

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A mechanically adaptive hydrogel that changes color in response to force exerted by living cells, enabling force sensing through optical signals.

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A novel 3D bioprintable hydrogel platform enables precise spatial delivery of biochemical gradients to engineer in vitro tissue models with area-specific identities.

A multimodal, wireless wearable device enabling continuous and detailed stress assessment and subclassification.

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CAPTaINs provide a novel, selective, and stable method for selective degradation of protein targets.