Chunqi Chang

Multifunctional theranostic nanoplatforms enabling precise targeting and controlled deep tissue therapy are vital. Herein, we present a self-assembled, defect-engineered, biomimetic nanoplatform (CDMBBTO), in which piezoelectric material and downconversion nanoparticles (DCNPs) are co-assembled within a polymer matrix, preventing interactions between both components, thereby allowing each to retain its intrinsic properties while collectively enabling dual second near infrared fluorescence (NIR-II FL)/magnetic resonance (MR) imaging and synergistic piezo/chemodynamic therapy (PZDT/CDT). Mn/Bi codoped piezoelectric BaTiO₃ (MBBTO) NPs exhibit a significantly enhanced piezoelectric coefficient (~5 fold higher than pristine BTO) owing to bandgap modulation induced by defect engineering. The incorporated Mn²⁺ not only catalyzes Fenton like reactions for sustained tumor suppression but also provides strong T₂ weighted MR contrast. Self-assembled NIR-II emissive DCNPs enable deep tissue optical imaging, forming an integrated theranostic system. Cloaking of U87 glioma cell membranes imparts homologous tumor targeting capability. In vivo dual modal imaging reveals efficient tumor accumulation, with peak NIR-II FL and MR signal intensities observed 6 h post-injection. Upon ultrasound activation, CDMBBTO elicits potent tumor ablation through synergistic reactive oxygen species generation and immune modulation, characterized by macrophage polarization (M2 → M1) and upregulation of proinflammatory cytokines (TNF-α and IL-6). This work establishes CDMBBTO as a powerful nanoplatform for dual modal imaging-guided, immune potentiated PZDT/CDT toward effective glioblastoma treatment.

Perceptual decision-making involves the integration and evaluation of sensory evidence to guide behavior, yet the extent to which the hippocampus contributes to decision processes across sensory contexts remains unclear. This study combined viral neuronal tracing, touchscreen-based behavioral paradigms, and in vivo multi-electrode recordings to investigate neural activity in the hippocampal CA1 region during visual decision-making (VDM) and auditory decision-making (ADM) tasks in rats. Anterograde tracing revealed that projections from the visual (V1) and auditory (Au1) cortices to CA1 were predominantly segregated, with a smaller proportion of convergent inputs, indicating partially distinct but overlapping anatomical pathways for sensory input. Behaviorally, rats exhibited significantly longer training times and slower learning curves in ADM compared to VDM, suggesting increased cognitive demands for auditory decision-making. Electrophysiological analyses of local field potentials (LFPs) showed modality- and event-specific oscillatory dynamics, with both theta (4-10 Hz) and beta (10-20 Hz) power increases occurring at different time points for VDM and ADM tasks. At the single-neuron level, a higher proportion of delay-active neurons was observed during ADM, reflecting differential temporal recruitment of CA1 neurons across auditory and visual decision contexts. Firing pattern analyses further revealed overlapping neuronal populations engaged in both tasks alongside task-preferential subpopulations. Together, these findings indicate that hippocampal CA1 is flexibly recruited across auditory and visual perceptual decision-making tasks through partially segregated inputs and task-dependent neural dynamics.

This study is the first application of personalized temporal interference (TI) (±5 mA，10 mA peak-to-peak, Δf = 20 Hz) targeting the right striatum to systematically investigate neuromodulatory effects on functional-structural plasticity within the cortico-striato-thalamo-cortical (CSTC) network. 26 healthy participants underwent TI and sham sessions, with functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) acquired before, during, and after stimulation. TI stimulation modulated the functional connectivity (FC) within the CSTC circuit, particularly showing significant reductions in FC between the right striatum and the precentral gyrus during stimulation. Furthermore, acute increases in axial diffusivity (AD) were observed in the right corticospinal tract (CST) and anterior thalamic radiation (ATR) during stimulation, with delayed elevation in fractional anisotropy (FA) in the right ATR post-stimulation. A notable negative correlation between changes in FC and AD highlights the interplay between functional and microstructural alterations. Importantly, no adverse effects were reported, indicating safety. These findings demonstrate that personalized TI stimulation effectively modulates CSTC connectivity and induces neuroplastic changes, suggesting its potential as a therapeutic strategy for its potential for neuropsychiatric disorders.

Data collected from multiple sensors contain rich fault information for bearing fault diagnosis. However, it is difficult to leverage these joint information due to the following reasons. First, signals from different channels often have asynchronous sampling lengths, which can greatly hinder subsequent processing. Second, unpaired frequency transformations can lead to significant algorithmic degradation. Third, handling multichannel data simultaneously can be quite time-consuming. To solve above three challenges, in this article, we newly propose a multichannel sparse Bayesian learning-based approach for bearing fault diagnosis. The key innovations include the following: first, extend the existing sparse frequency learning model into multichannel form, accommodating variable-length signals across different channels; second, identify the fault frequencies from the signal envelopes without any frequency transformations by leveraging the common sparsity of the fault frequencies-of-interest in the frequency domain; lastly, construct a real-valued data model with a special unitary matrix, and integrate the generalized approximate message passing method into the Bayesian framework to enhance the fault detection performance while reducing computational complexity. Since the message passing has the property of elementwise operation and can provide exact marginal posteriors, the proposed method can achieve the state-of-the-art performance with low complexity. Both simulation and real datasets can demonstrate it.

Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps < 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = −3.674, P < 0.001 for cognitive shifting; Z = 2.594, P < 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children’s brain development are also addressed.

Integrated sensing and communication (ISAC) can be integrated into the beyond 5G (B5G) and 6G communication systems to achieve high-resolution radar sensing with wireless communication signals. Joint target detection and channel estimation are essential for various emerging applications in massive MIMO ISAC systems. Inspired by the idea that partial radar targets could also be the communication scatterers, in this paper, we consider the joint target detection and multi-user downlink channel estimation problem for the massive MIMO ISAC systems, where the potential common sparsity among the sensing and multi-user communication channels is exploited to enhance the ISAC detection/estimation performance. In order to decrease communication overhead and computational complexity, we initially introduce a distributed scheme for joint target detection and multi-user downlink channel estimation. Subsequently, we propose a novel sparse Bayesian learning (SBL) framework that concurrently characterizes common and individual sparsity of signals-of-interest. Moreover, a fast hybrid message passing algorithm with distributed processing is devised for joint target detection and channel estimation. Compared with the existing methods, the proposed method has lower communication overhead and computational complexity, and can be applied to the more challenging multi-user downlink channel estimation task. Simulation results are provided to verify the efficiency of the proposed method.

Introduction: Speaker diarization is an essential preprocessing step for diagnosing cognitive impairments from speech-based Montreal cognitive assessments (MoCA).Methods: This paper proposes three enhancements to the conventional speaker diarization methods for such assessments. The enhancements tackle the challenges of diarizing MoCA recordings on two fronts. First, multi-scale channel interdependence speaker embedding is used as the front-end speaker representation for overcoming the acoustic mismatch caused by far-field microphones. Specifically, a squeeze-and-excitation (SE) unit and channel-dependent attention are added to Res2Net blocks for multi-scale feature aggregation. Second, a sequence comparison approach with a holistic view of the whole conversation is applied to measure the similarity of short speech segments in the conversation, which results in a speaker-turn aware scoring matrix for the subsequent clustering step. Third, to further enhance the diarization performance, we propose incorporating a pairwise similarity measure so that the speaker-turn aware scoring matrix contains both local and global information across the segments.Results: Evaluations on an interactive MoCA dataset show that the proposed enhancements lead to a diarization system that outperforms the conventional x-vector/PLDA systems under language-, age-, and microphone-mismatch scenarios.Discussion: The results also show that the proposed enhancements can help hypothesize the speaker-turn timestamps, making the diarization method amendable to datasets without timestamp information.

Cancer drug loading strategies with liposome-based nanomedicine for crossing the BBB and targeting the delivery of brain tumors. In vivo and in vitro models with imaging and therapeutic modalities.

General linear modeling (GLM) has been widely employed to estimate the hemodynamic changes observed by functional near infrared spectroscopy (fNIRS) technology, which are found to be nonlinear rather than linear, however. Therefore, GLM might not be appropriate for modeling the hemodynamic changes evoked by cognitive processing in developmental neurocognitive studies. There is an urgent need to identify a better statistical model to fit into the nonlinear fNIRS data. This study addressed this need by developing a quadratic equation model to reanalyze the existing fNIRS data (N = 38, M_age = 5.0 years, SD = 0.69 years, 17 girls) collected from the mixed-order design Dimensional Change Card Sort (DCCS) task and verified the model with a new set of data with the Habit-DisHabit design. First, comparing the quadratic and cubic modeling results of the mixed-order design data indicated that the proposed quadratic equation was better than GLM and cubic regression to model the oxygenated hemoglobin (HbO) changes in this task. Second, applying this quadratic model with the Habit-DisHabit design data verified its suitability and indicated that the new design was more effective in identifying the neural correlates of cognitive shifting than the mixed-order design. These findings jointly indicate that Habit-DisHabit Design with a quadratic equation might better model the hemodynamic changes in preschoolers during the DCCS task.