Improved green and red GRAB sensors for monitoring dopaminergic activity in vivo.

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a large network of dopaminergic projections. To dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent G-protein-coupled receptor activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity and signal-to-noise ratio with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

Magnetic/Acoustic Dual-Controlled Microrobot Overcoming Oto-Biological Barrier for On-Demand Multidrug Delivery against Hearing Loss.

Multidrug combination therapy in the inner ear faces diverse challenges due to the distinct physicochemical properties of drugs and the difficulties of overcoming the oto-biologic barrier. Although nanomedicine platforms offer potential solutions to multidrug delivery, the access of drugs to the inner ear remains limited. Micro/nanomachines, capable of delivering cargo actively, are promising tools for overcoming bio-barriers. Herein, a novel microrobot-based strategy to penetrate the round window membrane (RWM) is presented and multidrug in on-demand manner is delivered. The tube-type microrobot (TTMR) is constructed using the template-assisted layer-by-layer (LbL) assembly of chitosan/ferroferric oxide/silicon dioxide (CS/Fe3O4/SiO2) and loaded with anti-ototoxic drugs (curcumin, CUR and tanshinone IIA, TSA) and perfluorohexane (PFH). Fe3O4 provides magnetic actuation, while PFH ensures acoustic propulsion. Upon ultrasound stimulation, the vaporization of PFH enables a microshotgun-like behavior, propelling the drugs through barriers and driving them into the inner ear. Notably, the proportion of drugs entering the inner ear can be precisely controlled by varying the feeding ratios. Furthermore, in vivo studies demonstrate that the drug-loaded microrobot exhibits superior protective effects and excellent biosafety toward cisplatin (CDDP)-induced hearing loss. Overall, the microrobot-based strategy provides a promising direction for on-demand multidrug delivery for ear diseases.

Improved dual-color GRAB sensors for monitoring dopaminergic activity in vivo.

Dopamine (DA) plays multiple roles in a wide range of physiological and pathological processes via a vast network of dopaminergic projections. To fully dissect the spatiotemporal dynamics of DA release in both dense and sparsely innervated brain regions, we developed a series of green and red fluorescent GPCR activation-based DA (GRABDA) sensors using a variety of DA receptor subtypes. These sensors have high sensitivity, selectivity, and signal-to-noise properties with subsecond response kinetics and the ability to detect a wide range of DA concentrations. We then used these sensors in freely moving mice to measure both optogenetically evoked and behaviorally relevant DA release while measuring neurochemical signaling in the nucleus accumbens, amygdala, and cortex. Using these sensors, we also detected spatially resolved heterogeneous cortical DA release in mice performing various behaviors. These next-generation GRABDA sensors provide a robust set of tools for imaging dopaminergic activity under a variety of physiological and pathological conditions.

Optimal linear estimation under unknown nonlinear transform.

Linear regression studies the problem of estimating a model parameter ß* ∈ℝ p , from n observations [Formula: see text] from linear model yi = 〈xi , ß*〉 + ε i . We consider a significant generalization in which the relationship between 〈xi , ß*〉 and yi is noisy, quantized to a single bit, potentially nonlinear, noninvertible, as well as unknown. This model is known as the single-index model in statistics, and, among other things, it represents a significant generalization of one-bit compressed sensing. We propose a novel spectral-based estimation procedure and show that we can recover ß* in settings (i.e., classes of link function f) where previous algorithms fail. In general, our algorithm requires only very mild restrictions on the (unknown) functional relationship between yi and 〈xi , ß*〉. We also consider the high dimensional setting where ß* is sparse, and introduce a two-stage nonconvex framework that addresses estimation challenges in high dimensional regimes where p ≫ n. For a broad class of link functions between 〈xi , ß*〉 and yi , we establish minimax lower bounds that demonstrate the optimality of our estimators in both the classical and high dimensional regimes.