MICOS Complex Loss Governs Age-Associated Murine Mitochondrial Architecture and Metabolism in the Liver, While Sam50 Dictates Diet Changes.

The liver, the largest internal organ and a metabolic hub, undergoes significant declines due to aging, affecting mitochondrial function and increasing the risk of systemic liver diseases. How the mitochondrial three-dimensional (3D) structure changes in the liver across aging, and the biological mechanisms regulating such changes confers remain unclear. In this study, we employed Serial Block Face-Scanning Electron Microscopy (SBF-SEM) to achieve high-resolution 3D reconstructions of murine liver mitochondria to observe diverse phenotypes and structural alterations that occur with age, marked by a reduction in size and complexity. We also show concomitant metabolomic and lipidomic changes in aged samples. Aged human samples reflected altered disease risk. To find potential regulators of this change, we examined the Mitochondrial Contact Site and Cristae Organizing System (MICOS) complex, which plays a crucial role in maintaining mitochondrial architecture. We observe that the MICOS complex is lost during aging, but not Sam50. Sam50 is a component of the sorting and assembly machinery (SAM) complex that acts in tandem with the MICOS complex to modulate cristae morphology. In murine models subjected to a high-fat diet, there is a marked depletion of the mitochondrial protein SAM50. This reduction in Sam50 expression may heighten the susceptibility to liver disease, as our human biobank studies corroborate that Sam50 plays a genetically regulated role in the predisposition to multiple liver diseases. We further show that changes in mitochondrial calcium dysregulation and oxidative stress accompany the disruption of the MICOS complex. Together, we establish that a decrease in mitochondrial complexity and dysregulated metabolism occur with murine liver aging. While these changes are partially be regulated by age-related loss of the MICOS complex, the confluence of a murine high-fat diet can also cause loss of Sam50, which contributes to liver diseases. In summary, our study reveals potential regulators that affect age-related changes in mitochondrial structure and metabolism, which can be targeted in future therapeutic techniques.

Simple cyclic chalcone dye with multiple optical functions: Piezochromism and lysosomes staining.

Both artificially synthesized and naturally occurring cyclic chalcones have been widely studied for their excellent biological activities. However, research on its photophysical properties is still limited. In the present study, we designed and synthesized a small molecule fluorescent dye based on the ICT effect, using dimethylamino as the electron-donating group and carbonyl as the electron withdrawing group, and investigated its photophysical properties in depth. Although YB is a simple small molecule, it exhibits significant piezochromic properties. The fluorescence of YB can change from green to yellow through grinding. After solvent fumigation, the fluorescence reverts to green. Furthermore, YB was used successfully in the lysosomal targeting. This study expands the research on the photophysical properties of cyclic chalcone and give richness to application of cyclic chalcone compounds.

Multi-Parametric Molecular Imaging of the Brain Using Optimized Multi-TE Subspace MRSI.

OBJECTIVE: To develop a novel multi-TE MR spectroscopic imaging (MRSI) approach to enable label-free, simultaneous, high-resolution mapping of several molecules and their biophysical parameters in the brain. METHODS: The proposed method uniquely integrated an augmented molecular-component-specific subspace model for multi-TE 1H-MRSI signals, an estimation-theoretic experiment optimization (nonuniform TE selection) for molecule separation and parameter estimation, a physics-driven subspace learning strategy for spatiospectral reconstruction and molecular quantification, and a new accelerated multi-TE MRSI acquisition for generating high-resolution data in clinically relevant times. Numerical studies, phantom and in vivo experiments were conducted to validate the optimized experiment design and demonstrate the imaging capability offered by the proposed method. RESULTS: The proposed TE optimization improved estimation of metabolites, neurotransmitters and their T2's over conventional TE choices, e.g., reducing variances of neurotransmitter concentration by  âˆ¼  40% and metabolite T2 by  âˆ¼  60%. Simultaneous metabolite and neurotransmitter mapping of the brain can be achieved at a nominal resolution of 3.4 × 3.4 × 6.4 mm 3. High-resolution, 3D metabolite T2 mapping was made possible for the first time. The translational potential of the proposed method was demonstrated by mapping biochemical abnormality in a post-traumatic epilepsy (PTE) patient. CONCLUSION: The feasibility for high-resolution mapping of metabolites/neurotransmitters and metabolite T2's within clinically relevant time was demonstrated. We expect our method to offer richer information for revealing and understanding metabolic alterations in neurological diseases. SIGNIFICANCE: A novel multi-TE MRSI approach was presented that enhanced the technological capability of multi-parametric molecular imaging of the brain. The proposed method presents new technology development and application opportunities for providing richer molecular level information to uncover and comprehend metabolic changes relevant in various neurological applications.

SNR Enhancement for Multi-TE MRSI Using Joint Low-Dimensional Model and Spatial Constraints.

We present a novel method to enhance the SNR for multi-TE MR spectroscopic imaging (MRSI) data by integrating learned nonlinear low-dimensional model and spatial constraints. A deep complex convolutional autoencoder (DCCAE) was developed to learn a nonlinear low-dimensional representation of the high-dimensional multi-TE 1H spectroscopy signals. The learned model significantly reduces the data dimension thus serving as an effective constraint for noise reduction. A reconstruction formulation was proposed to integrate the spatiospectral encoding model, the learned model, and a spatial constraint for an SNR-enhancing reconstruction from multi-TE data. The proposed method has been evaluated using both numerical simulations and in vivo brain MRSI experiments. The superior denoising performance of the proposed over alternative methods was demonstrated, both qualitatively and quantitatively. In vivo multi-TE data was used to assess the improved metabolite quantification reproducibility and accuracy achieved by the proposed method. We expect the proposed SNR-enhancing reconstruction to enable faster and/or higher-resolution multi-TE 1H-MRSI of the brain, potentially useful for various clinical applications.

High-resolution, 3D multi-TE 1 H MRSI using fast spatiospectral encoding and subspace imaging.

PURPOSE: To develop a novel method to achieve fast, high-resolution, 3D multi-TE 1 H-MRSI of the brain. METHODS: A new multi-TE MRSI acquisition strategy was developed that integrates slab selective excitation with adiabatic refocusing for better volume coverage, rapid spatiospectral encoding, sparse multi-TE sampling, and interleaved water navigators for field mapping and calibration. Special data processing strategies were developed to interpolate the sparsely sampled data, remove nuisance signals, and reconstruct multi-TE spatiospectral distributions with high SNR. Phantom and in vivo experiments have been carried out to demonstrate the capability of the proposed method. RESULTS: The proposed acquisition can produce multi-TE 1 H-MRSI data with three TEs at a nominal spatial resolution of 3.4 × 3.4 × 5.3 mm3 in around 20 min. High-SNR brain metabolite spatiospectral reconstructions can be obtained from both a metabolite phantom and in vivo experiments by the proposed method. CONCLUSION: High-resolution, 3D multi-TE 1 H-MRSI of the brain can be achieved within clinically feasible time. This capability, with further optimizations, could be translated to clinical applications and neuroscience studies where simultaneously mapping metabolites and neurotransmitters and TE-dependent molecular spectral changes are of interest.

Asymmetrically coating Pt nanoparticles on magnetic silica nanospheres for target cell capture and therapy.

A Janus cargo has been developed via the combination of magnetic mesoporous silica (MMS) with asymmetric decoration of Pt nanoparticles (PtNPs). Mesoporous morphology of MMS provides plenty of space for loading photosensitizers and targeting agents; the magnetic feature endows the as-formed nanospheres with satisfactory isolation function in removal of low abundant target cells. The excellent catalytic ability of PtNPs can effectively alleviate the hypoxia condition of tumor microenvironment via the decomposition of hydrogen peroxide (H2O2), as well as an O2-drived nanomotor for highly efficient drug release. Using CCRF-CEM as the model target cell, the Janus cargo is demonstrated to possess significantly improved performance in cell capture and photodynamic therapy. Specially, owing to the patchy Pt decoration, the loaded photosensitizers exhibit a more efficient release behavior. More importantly, asymmetric O2-emission from one side of the nanocargo acts as a driving force, which could effectively accelerate the motion ability of cargo in cell media, thus leading to an enhanced therapeutic effect compared with the traditionally symmetric nanocargo. This Janus cargo would offer a new paradigm to design highly efficient drug carrier for gaining an improved photodynamic therapy in hypoxic cancer cells.

TiO2 Nano-test tubes as a solid visual platform for sensitive Pb2+ ion detection based on a fluorescence resonance energy transfer (FRET) process.

A cost-effective, facile, and sensitive fluorescence sensing strategy for Pb2+ ion detection has been developed based on the fluorescence resonance energy transfer (FRET) between carbon quantum dots (CQDs) and Au nanoparticles (NPs). Glutathione (GSH)-synthesized CQDs acted as both the fluorescence donor and the sorbent to extract Pb2+ ions from the solution via Pb-GSH complexes. Pb2+ ions on CQDs reacted with -SH groups on AuNPs to generate sandwich-type Au-PdS-CQDs, leading to a dramatic decrease in the fluorescence of the CQDs. To expand the potential applications of this strategy, we constructed a sensing strategy using self-organized TiO2 nanotube arrays (TiNTs). The high aspect ratio and transparency for light emitted from the CQDs enabled the TiNTs to serve as a sensitive solid visual platform for the highly selective detection of Pb2+ ions with a detection limit as low as 4.1 × 10-8 mg mL-1. More importantly, the long observation length combined with a small volume enabled a sample acquisition volume of only 2.1 × 10-3 µL, which is smaller than the traditional fluorescence analysis in solution and on commercially available test paper, thus endowing this visual platform with the potential for use in single-cell diagnostics.

Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations.

Short-echo-time (TE) proton magnetic resonance spectroscopic imaging (MRSI) allows for simultaneously mapping a number of molecules in the brain, and has been recognized as an important tool for studying in vivo biochemistry in various neuroscience and disease applications. However, separation of the metabolite and macromolecule (MM) signals present in the short-TE data with significant spectral overlaps remains a major technical challenge. This work introduces a new approach to solve this problem by integrating imaging physics and representation learning. Specifically, a mixed unsupervised and supervised learning-based strategy was developed to learn the metabolite and MM-specific low-dimensional representations using deep autoencoders. A constrained reconstruction formulation is proposed to integrate the MRSI spatiospectral encoding model and the learned representations as effective constraints for signal separation. An efficient algorithm was developed to solve the resulting optimization problem with provable convergence. Simulation and experimental results have been obtained to demonstrate the component-specific representation power of the learned models and the capability of the proposed method in separating metabolite and MM signals for practical short-TE [Formula: see text]-MRSI data.

Introducing graphitic carbon nitride nanosheets as supersandwich-type assembly on porous electrode for ultrasensitive electrochemiluminescence immunosensing.

Biomarkers in blood or tissue provide essential information for clinical screening and early disease diagnosis. However, increasing the sensitivity of detecting biomarkers remains a major challenge in a wide variety of electrochemical immunoassays. Herein, we present an electrochemiluminescence (ECL) immunosensing strategy with 1: Nn amplification ratio (target-to-signal probe) for biomarkers detection on a porous gold electrode. The high porosity of the electrode surface provides enough bonding sites for capturing the target biomolecules and thus many DNA labels can be introduced. On the basis of this concept, a great number of graphitic carbon nitride (g-C3N4) nanosheets are employed to create a supersandwich-type assembly on a porous electrode via the DNA hybridization process. Furthermore, compared with the traditional sandwich immunoassay (the ratio of target-to-signal probe is 1 : 1), the supersandwich construction can introduce a large number of signal probes, thus resulting in a highly improved sensitivity. The proposed ECL immunosensor exhibits an excellent performance in a concentration range from 0.01 fg mL-1 to 1 µg mL-1 with an ultralow detection limit of 0.001 fg mL-1 (S/N = 3) and excellent selectivity. This sensing strategy could be developed into a real-time assay for the disease-related molecular targets, with many practical applications in biotechnology and life science.

Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models.

Magnetic resonance spectroscopic imaging (MRSI) is a powerful molecular imaging modality but has very limited speed, resolution, and SNR tradeoffs. Construction of a low-dimensional model to effectively reduce the dimensionality of the imaging problem has recently shown great promise in improving these tradeoffs. This paper presents a new approach to model and reconstruct the spectroscopic signals by learning a nonlinear low-dimensional representation of the general MR spectra. Specifically, we trained a deep neural network to capture the low-dimensional manifold, where the high-dimensional spectroscopic signals reside. A regularization formulation is proposed to effectively integrate the learned model and physics-based data acquisition model for MRSI reconstruction with the capability to incorporate additional spatiospectral constraints. An efficient numerical algorithm was developed to solve the associated optimization problem involving back-propagating the trained network. Simulation and experimental results were obtained to demonstrate the representation power of the learned model and the ability of the proposed formulation in producing SNR-enhancing reconstruction from the practical MRSI data.

A portable dual-mode sensor based on a TiO2 nanotube membrane for the evaluation of telomerase activity.

A portable dual-mode sensing platform based on a self-standing TiO2 nanotube membrane is developed for simultaneously performing both qualitative analysis by the naked eye and quantitative analysis by ionic current. This dual-mode diagnosis strategy exhibits a high performance in telomerase detection in urine specimens from patients with bladder cancer.

Protein Shell-Encapsulated Pt Clusters as Continuous O2-Supplied Biocoats for Photodynamic Therapy in Hypoxic Cancer Cells.

As a highly oxygen-dependent process, the effect of photodynamic therapy is often obstructed by the premature leakage of photosensitizers and the lack of oxygen in hypoxic cancer cells. To overcome these limitations, this study designs bovine serum albumin protein (BSA)-encapsulated Pt nanoclusters (PtBSA) as O2-supplied biocoats and further incorporates them with mesoporous silica nanospheres to develop intelligent nanoaggregates for achieving improved therapeutic outcomes against hypoxic tumors. The large number of amino groups on BSA can provide sufficient functional groups to anchor tumor targeting agents and thus enhance the selective cellular uptake efficiency. Owing to the outstanding biocompatibility features of BSA and the state-of-the-art catalytic activity of Pt nanoclusters, the nanocomposites have lower dark cytotoxicity, and O2 continuously evolves via the decomposition of H2O2 in a tumor microenvironment. Both in vivo and in vitro experiments indicate that the resulting nanocomposites can effectively relieve hypoxic conditions, specifically induce necrotic cell apoptosis, and remarkably hinder tumor growth. Our results illuminate the great potential of BSA-encapsulated Pt nanoclusters as versatile biocoats in designing intelligent nanocarriers for hypoxic-resistant photodynamic therapy.

Nitrogen-doped carbon nanospheres derived from cocoon silk as metal-free electrocatalyst for glucose sensing.

Nitrogen-doped carbon materials have attracted tremendous attention because of their high activity in electrocatalysis. In the present work, cocoon silk -- a biomass material is used to prepare porous carbon fibers due to its abundant nitrogen content. The as-prepared carbon microfibers have been activated and disintegrated into carbon nanospheres (CNS) with a diameter of 20--60 nm by a simple nitric acid refluxing process. Considering their excellent electrocatalytic activity towards the reduction of oxygen, the CNS modified electrodes are further applied in the construction of glucose amperometric biosensor using glucose oxidase as a model. The proposed biosensor exhibits fast response, high sensitivity, good stability and selectivity for glucose detection with a wide linear range from 79.7 to 2038.9 µM, and a detection limit of 39.1 µM. The performance is comparable to leading literature results indicating a great potential for electrochemical sensing application.

Carbon cladded TiO2 nanotubes: fabrication and use in 3D-RuO2 based supercapacitors.

In this work we introduce a facile procedure that allows a highly conformal coating of self-organized TiO2 nanotubes (NTs) with a graphite-like thin carbon layer. This provides a platform to enhance the functionality of TiO2 nanotubes for a wide range of applications. Here we show that such modified nanotubes can serve as a 3D scaffold for an ideal decoration with RuO2 nanoparticles. Used as 3D pseudocapacitor electrode, capacitance values of up to 80 times higher than plain TiO2 NTs, and a very high yield of utilization of RuO2 (872 F g(-1)) and excellent long-term cycling stability can be reached.