Assessing brain microstructural changes in chronic kidney disease: a diffusion imaging study using multiple models.

Objectives: To explore the effectiveness of diffusion quantitative parameters derived from advanced diffusion models in detecting brain microstructural changes in patients with chronic kidney disease (CKD). Methods: The study comprised 44 CKD patients (eGFR<59 mL/min/1.73 m2) and 35 age-and sex-matched healthy controls. All patients underwent diffusion spectrum imaging (DSI) and conventional magnetic resonance imaging. Reconstructed to obtain diffusion MRI models, including diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI) and Mean Apparent Propagator (MAP)-MRI, were processed to obtain multi-parameter maps. The Tract-Based Spatial Statistics (TBSS) analysis was utilized for detecting microstructural differences and Pearson correlation analysis assessed the relationship between renal metabolism markers and diffusion parameters in the brain regions of CKD patients. Receiver operating characteristic (ROC) curve analysis assessed the diagnostic performance of diffusion models, with AUC comparisons made using DeLong's method. Results: Significant differences were noted in DTI, NODDI, and MAP-MRI parameters between CKD patients and controls (p < 0.05). DTI indicated a decrease in Fractional Anisotropy(FA) and an increase in Mean and Radial Diffusivity (MD and RD) in CKD patients. NODDI indicated decreased Intracellular and increased Extracellular Volume Fractions (ICVF and ECVF). MAP-MRI identified extensive microstructural changes, with elevated Mean Squared Displacement (MSD) and Q-space Inverse Variance (QIV) values, and reduced Non-Gaussianity (NG), Axial Non-Gaussianity (NGAx), Radial Non-Gaussianity (NGRad), Return-to-Origin Probability (RTOP), Return-to-Axis Probability (RTAP), and Return-to-Plane Probability (RTPP). There was a moderate correlation between serum uric acid (SUA) and diffusion parameters in six brain regions (p < 0.05). ROC analysis showed the AUC values of DTI_FA ranged from 0.70 to 0.793. MAP_NGAx in the Retrolenticular part of the internal capsule R reported a high AUC value of 0.843 (p < 0.05), which was not significantly different from other diffusion parameters (p > 0.05). Conclusion: The advanced diffusion models (DTI, NODDI, and MAP-MRI) are promising for detecting brain microstructural changes in CKD patients, offering significant insights into CKD-affected brain areas.

Effects of long-term closed and socially isolating spaceflight analog environment on default mode network connectivity as indicated by fMRI.

Long-term manned spaceflight and extraterrestrial planet settlement become the focus of space powers. However, the potential influence of closed and socially isolating spaceflight on the brain function remains unclear. A 180-day controlled ecological life support system integrated experiment was conducted, establishing a spaceflight analog environment to explore the effect of long-term socially isolating living. Three crewmembers were enrolled and underwent resting-state fMRI scanning before and after the experiment. We performed both seed-based and network-based analyses to investigate the functional connectivity (FC) changes of the default mode network (DMN), considering its key role in multiple higher-order cognitive functions. Compared with normal controls, the leader of crewmembers exhibited significantly reduced within-DMN and between-DMN FC after the experiment, while two others exhibited opposite trends. Moreover, individual differences of FC changes were further supported by evidence from behavioral analyses. The findings may shed new light on the development of psychological protection for space exploration.

Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas.

The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.

Sleep deprivation changes frequency-specific functional organization of the resting human brain.

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Cognitive Load Prediction from Multimodal Physiological Signals using Multiview Learning.

Predicting cognitive load is a crucial issue in the emerging field of human-computer interaction and holds significant practical value, particularly in flight scenarios. Although previous studies have realized efficient cognitive load classification, new research is still needed to adapt the current state-of-the-art multimodal fusion methods. Here, we proposed a feature selection framework based on multiview learning to address the challenges of information redundancy and reveal the common physiological mechanisms underlying cognitive load. Specifically, the multimodal signal features (EEG, EDA, ECG, EOG, & eye movements) at three cognitive load levels were estimated during multiattribute task battery (MATB) tasks performed by 22 healthy participants and fed into a feature selection-multiview classification with cohesion and diversity (FS-MCCD) framework. The optimized feature set was extracted from the original feature set by integrating the weight of each view and the feature weights to formulate the ranking criteria. The cognitive load prediction model, evaluated using real-time classification results, achieved an average accuracy of 81.08% and an average F1-score of 80.94% for three-class classification among 22 participants. Furthermore, the weights of the physiological signal features revealed the physiological mechanisms related to cognitive load. Specifically, heightened cognitive load was linked to amplified δ and θ power in the frontal lobe, reduced α power in the parietal lobe, and an increase in pupil diameter. Thus, the proposed multimodal feature fusion framework emphasizes the effectiveness and efficiency of using these features to predict cognitive load.

The Effectiveness of a Community Nurse-Led Support Program for Dementia Caregivers in Chinese Communities: The Chongqing Ageing and Dementia Study.

Background: As the primary caregivers for people with dementia in China, family caregivers face a significant care burden that can negatively impact their mental and physical health. It is vital to investigate ways to support these caregivers. Objective: To assess the effectiveness of a program led by community nurses to support caregivers of individuals with dementia. Methods: A total of 30 caregivers received nurse-led support in addition to usual care, while 28 caregivers received only usual care. The primary outcome was caregivers' sense of competency in providing dementia care, which was measured using the Short Sense of Competence Questionnaire (SSCQ). Secondary outcomes included caregivers' ability to perform daily activities, behavioral and psychological symptoms of dementia (BPSD) using a neuropsychiatric inventory questionnaire, and quality of life using the short form health survey (SF-36). The trial was registered at the Chinese Clinical Trial Registry (ChiCTR 2300071484). Results: Compared to the control group, the intervention group had significantly higher SSCQ scores and a lower caregiver distress index over time. Physical and mental health-related quality of life also improved significantly among caregivers in the intervention group. However, there was no significant difference between the two groups in terms of activities of daily living and BPSD. Conclusions: The community nurse-led support program significantly improved caregivers' competency in providing dementia care and quality of life and reduced distress. These findings have important implications for dementia care policies, resources, and workforce development in China, including strengthening community dementia care services through collaboration with specialists in hospitals.

Recent advances of cellular stimulation with triboelectric nanogenerators.

Triboelectric nanogenerators (TENGs) are new energy collection devices that have the characteristics of high efficiency, low cost, miniaturization capability, and convenient manufacture. TENGs mainly utilize the triboelectric effect to obtain mechanical energy from organisms or the environment, and this mechanical energy is then converted into and output as electrical energy. Bioelectricity is a phenomenon that widely exists in various cellular processes, including cell proliferation, senescence, apoptosis, as well as adjacent cells' communication and coordination. Therefore, based on these features, TENGs can be applied in organisms to collect energy and output electrical stimulation to act on cells, changing their activities and thereby playing a role in regulating cellular function and interfering with cellular fate, which can further develop into new methods of health care and disease intervention. In this review, we first introduce the working principle of TENGs and their working modes, and then summarize the current research status of cellular function regulation and fate determination stimulated by TENGs, and also analyze their application prospects for changing various processes of cell activity. Finally, we discuss the opportunities and challenges of TENGs in the fields of life science and biomedical engineering, and propose a variety of possibilities for their potential development direction.

Hippocampus Parcellation via Discriminative Embedded Clustering of fMRI Functional Connectivity.

Dividing a pre-defined brain region into several heterogenous subregions is crucial for understanding its functional segregation and integration. Due to the high dimensionality of brain functional features, clustering is often postponed until dimensionality reduction in traditional parcellation frameworks occurs. However, under such stepwise parcellation, it is very easy to fall into the dilemma of local optimum since dimensionality reduction could not take into account the requirement of clustering. In this study, we developed a new parcellation framework based on the discriminative embedded clustering (DEC), combining subspace learning and clustering in a common procedure with alternative minimization adopted to approach global optimum. We tested the proposed framework in functional connectivity-based parcellation of the hippocampus. The hippocampus was parcellated into three spatial coherent subregions along the anteroventral-posterodorsal axis; the three subregions exhibited distinct functional connectivity changes in taxi drivers relative to non-driver controls. Moreover, compared with traditional stepwise methods, the proposed DEC-based framework demonstrated higher parcellation consistency across different scans within individuals. The study proposed a new brain parcellation framework with joint dimensionality reduction and clustering; the findings might shed new light on the functional plasticity of hippocampal subregions related to long-term navigation experience.

Adaptive Feature Selection With Augmented Attributes.

In many dynamic environment applications, with the evolution of data collection ways, the data attributes are incremental and the samples are stored with accumulated feature spaces gradually. For instance, in the neuroimaging-based diagnosis of neuropsychiatric disorders, with emerging of diverse testing ways, we get more brain image features over time. The accumulation of different types of features will unavoidably bring difficulties in manipulating the high-dimensional data. It is challenging to design an algorithm to select valuable features in this feature incremental scenario. To address this important but rarely studied problem, we propose a novel Adaptive Feature Selection method (AFS). It enables the reusability of the feature selection model trained on previous features and adapts it to fit the feature selection requirements on all features automatically. Besides, an ideal l0-norm sparse constraint for feature selection is imposed with a proposed effective solving strategy. We present the theoretical analyses about the generalization bound and convergence behavior. After tackling this problem in a one-shot case, we extend it to the multi-shot scenario. Plenty of experimental results demonstrate the effectiveness of reusing previous features and the superior of l0-norm constraint in various aspects, together with its effectiveness in discriminating schizophrenic patients from healthy controls.

SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation.

Insufficient annotated data and minor lung lesions pose big challenges for computed tomography (CT)-aided automatic COVID-19 diagnosis at an early outbreak stage. To address this issue, we propose a Semi-Supervised Tri-Branch Network (SS-TBN). First, we develop a joint TBN model for dual-task application scenarios of image segmentation and classification such as CT-based COVID-19 diagnosis, in which pixel-level lesion segmentation and slice-level infection classification branches are simultaneously trained via lesion attention, and individual-level diagnosis branch aggregates slice-level outputs for COVID-19 screening. Second, we propose a novel hybrid semi-supervised learning method to make full use of unlabeled data, combining a new double-threshold pseudo labeling method specifically designed to the joint model and a new inter-slice consistency regularization method specifically tailored to CT images. Besides two publicly available external datasets, we collect internal and our own external datasets including 210,395 images (1,420 cases versus 498 controls) from ten hospitals. Experimental results show that the proposed method achieves state-of-the-art performance in COVID-19 classification with limited annotated data even if lesions are subtle, and that segmentation results promote interpretability for diagnosis, suggesting the potential of the SS-TBN in early screening in insufficient labeled data situations at the early stage of a pandemic outbreak like COVID-19.

Parcellating the human brain using resting-state dynamic functional connectivity.

Brain cartography has expanded substantially over the past decade. In this regard, resting-state functional connectivity (FC) plays a key role in identifying the locations of putative functional borders. However, scant attention has been paid to the dynamic nature of functional interactions in the human brain. Indeed, FC is typically assumed to be stationary across time, which may obscure potential or subtle functional boundaries, particularly in regions with high flexibility and adaptability. In this study, we developed a dynamic FC (dFC)-based parcellation framework, established a new functional human brain atlas termed D-BFA (DFC-based Brain Functional Atlas), and verified its neurophysiological plausibility by stereo-EEG data. As the first dFC-based whole-brain atlas, the proposed D-BFA delineates finer functional boundaries that cannot be captured by static FC, and is further supported by good correspondence with cytoarchitectonic areas and task activation maps. Moreover, the D-BFA reveals the spatial distribution of dynamic variability across the brain and generates more homogenous parcels compared with most alternative parcellations. Our results demonstrate the superiority and practicability of dFC in brain parcellation, providing a new template to exploit brain topographic organization from a dynamic perspective. The D-BFA will be publicly available for download at https://github.com/sliderplm/D-BFA-618.

Gradient Matching Federated Domain Adaptation for Brain Image Classification.

Federated learning has shown its unique advantages in many different tasks, including brain image analysis. It provides a new way to train deep learning models while protecting the privacy of medical image data from multiple sites. However, previous studies suggest that domain shift across different sites may influence the performance of federated models. As a solution, we propose a gradient matching federated domain adaptation (GM-FedDA) method for brain image classification, aiming to reduce domain discrepancy with the assistance of a public image dataset and train robust local federated models for target sites. It mainly includes two stages: 1) pretraining stage; we propose a one-common-source adversarial domain adaptation (OCS-ADA) strategy, i.e., adopting ADA with gradient matching loss to pretrain encoders for reducing domain shift at each target site (private data) with the assistance of a common source domain (public data) and 2) fine-tuning stage; we develop a gradient matching federated (GM-Fed) fine-tuning method for updating local federated models pretrained with the OCS-ADA strategy, i.e., pushing the optimization direction of a local federated model toward its specific local minimum by minimizing gradient matching loss between sites. Using fully connected networks as local models, we validate our method with the diagnostic classification tasks of schizophrenia and major depressive disorder based on multisite resting-state functional MRI (fMRI), respectively. Results show that the proposed GM-FedDA method outperforms other commonly used methods, suggesting the potential of our method in brain imaging analysis and other fields, which need to utilize multisite data while preserving data privacy.

Functional orderly topography of brain networks associated with gene expression heterogeneity.

The human cerebral cortex is vastly expanded relative to nonhuman primates and rodents, leading to a functional orderly topography of brain networks. Here, we show that functional topography may be associated with gene expression heterogeneity. The neocortex exhibits greater heterogeneity in gene expression, with a lower expression of housekeeping genes, a longer mean path length, fewer clusters, and a lower degree of ordering in networks than archicortical and subcortical areas in human, rhesus macaque, and mouse brains. In particular, the cerebellar cortex displays greater heterogeneity in gene expression than cerebellar deep nuclei in the human brain, but not in the mouse brain, corresponding to the emergence of novel functions in the human cerebellar cortex. Moreover, the cortical areas with greater heterogeneity, primarily located in the multimodal association cortex, tend to express genes with higher evolutionary rates and exhibit a higher degree of functional connectivity measured by resting-state fMRI, implying that such a spatial distribution of gene expression may be shaped by evolution and is favourable for the specialization of higher cognitive functions. Together, the cross-species imaging and genetic findings may provide convergent evidence to support the association between the orderly topography of brain function networks and gene expression.

Altered cerebellar-motor loop in benign adult familial myoclonic epilepsy type 1: The structural basis of cortical tremor.

OBJECTIVE: Cortical tremor/myoclonus is the hallmark feature of benign adult familial myoclonic epilepsy (BAFME), the mechanism of which remains elusive. A hypothesis is that a defective control in the preexisting cerebellar-motor loop drives cortical tremor. Meanwhile, the basal ganglia system might also participate in BAFME. This study aimed to discover the structural basis of cortical tremor/myoclonus in BAFME. METHODS: Nineteen patients with BAFME type 1 (BAFME1) and 30 matched healthy controls underwent T1-weighted and diffusion tensor imaging scans. FreeSurfer and spatially unbiased infratentorial template (SUIT) toolboxes were utilized to assess the motor cortex and the cerebellum. Probabilistic tractography was generated for two fibers to test the hypothesis: the dentato-thalamo-(M1) (primary motor cortex) and globus pallidus internus (GPi)-thalamic projections. Average fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) of each tract were extracted. RESULTS: Cerebellar atrophy and dentate nucleus alteration were observed in the patients. In addition, patients with BAFME1 exhibited reduced AD and FA in the left and right dentato-thalamo-M1 nondecussating fibers, respectively false discovery rate (FDR) correction q < .05. Cerebellar projections showed negative correlations with somatosensory-evoked potential P25-N33 amplitude and were independent of disease duration and medication. BAFME1 patients also had increased FA and decreased MD in the left GPi-thalamic projection. Higher FA and lower RD in the right GPi-thalamic projection were also observed (FDR q < .05). SIGNIFICANCE: The present findings support the hypothesis that the cerebello-thalamo-M1 loop might be the structural basis of cortical tremor in BAFME1. The basal ganglia system also participates in BAFME1 and probably serves a regulatory role.

Enhancing anti-tumor effect of ultrasensitive bimetallic RuCu nanoparticles as radiosensitizers with dual enzyme-like activities.

Radiotherapy (RT), through the generation of reactive oxygen species (ROS) and DNA damage to tumor cells caused by high-energy irradiation, has been a widely applied cancer treatment strategy in clinic. However, the therapeutic effect of traditional RT is restricted by the insufficient radiation energy deposition and the side effects on normal tissues. Recently, multifunctional nano-formulations and synergistic therapy has been developed as attractive strategies for used to enhancing the efficacy and safety of RT. Herein, we show that a bimetallic nanozyme (copper-modified ruthenium nanoparticles, RuCu NPs), containing the high atomic number (Z) element Ru as a novel radiosensitizer, offers an ideal solution to RT sensitization, with ultrasensitive peroxidase (POD)-like activity and catalase (CAT)-like activity. Density functional theory (DFT) calculations also clarified the optimal POD-like catalytic ratio of RuCu NPs and further revealed the mechanism of its supper catalytic activity. Under X-ray exposure, RuCu NPs coated with poly(ethylene glycol) (PEG) exhibited simultaneously improved the ROS production and relieved tumor hypoxia in the acid tumor microenvironment (TME), and demonstrated remarkable therapeutic efficacy in the MDA-MB-231 breast cancer model. Our results provide a proof-of-concept for a RT sensitization strategy, which combine the intrinsic nature of high-Z element and the advantages of nanozymes to overcome the tricky drawbacks existed in radiotherapy, and further open a new direction of exploring novel nanozyme-based strategies for tumor catalytic therapy and synergistic radiotherapy.

Effects of Low Mental Energy from Long Periods of Work on Brain-Computer Interfaces.

Brain-computer interfaces (BCIs) provide novel hands-free interaction strategies. However, the performance of BCIs is affected by the user's mental energy to some extent. In this study, we aimed to analyze the combined effects of decreased mental energy and lack of sleep on BCI performance and how to reduce these effects. We defined the low-mental-energy (LME) condition as a combined condition of decreased mental energy and lack of sleep. We used a long period of work (>=18 h) to induce the LME condition, and then P300- and SSVEP-based BCI tasks were conducted in LME or normal conditions. Ten subjects were recruited in this study. Each subject participated in the LME- and normal-condition experiments within one week. For the P300-based BCI, we used two decoding algorithms: stepwise linear discriminant (SWLDA) and least square regression (LSR). For the SSVEP-based BCI, we used two decoding algorithms: canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA). Accuracy and information transfer rate (ITR) were used as performance metrics. The experimental results showed that for the P300-based BCI, the average accuracy was reduced by approximately 35% (with a SWLDA classifier) and approximately 40% (with a LSR classifier); the average ITR was reduced by approximately 6 bits/min (with a SWLDA classifier) and approximately 7 bits/min (with an LSR classifier). For the SSVEP-based BCI, the average accuracy was reduced by approximately 40% (with a CCA classifier) and approximately 40% (with a FBCCA classifier); the average ITR was reduced by approximately 20 bits/min (with a CCA classifier) and approximately 19 bits/min (with a FBCCA classifier). Additionally, the amplitude and signal-to-noise ratio of the evoked electroencephalogram signals were lower in the LME condition, while the degree of fatigue and the task load of each subject were higher. Further experiments suggested that increasing stimulus size, flash duration, and flash number could improve BCI performance in LME conditions to some extent. Our experiments showed that the LME condition reduced BCI performance, the effects of LME on BCI did not rely on specific BCI types and specific decoding algorithms, and optimizing BCI parameters (e.g., stimulus size) can reduce these effects.

Altered Functional Network Connectivity of Precuneus and Executive Control Networks in Type 2 Diabetes Mellitus Without Cognitive Impairment.

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls. Clinical, biochemical, and neuropsychological assessments were conducted in parallel with resting-state functional magnetic resonance imaging, and the cognitive status of the patients was assessed using the Montreal Cognitive Assessment-B (MoCA-B) score. Independent component analysis revealed a positive correlation between the salience network and the visual network and a negative connection between the left executive control network and the default mode network in patients with T2DM. The differences in dynamic brain network connectivity were observed in the precuneus, visual, and executive control networks. Internal network connectivity was primarily affected in the thalamus, inferior parietal lobe, and left precuneus. Hemoglobin A1c level, body mass index, MoCA-B score, and grooved pegboard (R) assessments indicated significant differences between the two groups (p < 0.05). Our findings show that key changes in functional connectivity in diabetes occur in the precuneus and executive control networks that evolve before patients develop cognitive deficits. In addition, the current study provides useful information about the role of the thalamus, inferior parietal lobe, and precuneus, which might be potential biomarkers for predicting the clinical progression, assessing the cognitive function, and further understanding the neuropathology of T2DM.

Macrophage-Targeted Sonodynamic/Photothermal Synergistic Therapy for Preventing Atherosclerotic Plaque Progression Using CuS/TiO2 Heterostructured Nanosheets.

Sonodynamic therapy (SDT) and photothermal therapy (PTT) are two effective strategies for the treatment of atherosclerotic plaques. However, the low yield of reactive oxygen species (ROS) of conventional organic sonosensitizers and the low biosafety of hyperthermia limit the therapeutic efficacy of SDT and PTT. Herein, we report copper sulfide/titanium oxide heterostructure nanosheets modified with hyaluronic acid (HA) and PEG (HA-HNSs) for low-intensity sonodynamic and mild-photothermal synergistic therapy for early atherosclerotic plaques. CuS/TiO2 heterostructure nanosheets (HNSs) show high electron-hole separation efficiency and superior sonodynamic performance, because it has high surface energy crystal facets as well as a narrow band. Moreover, HNSs exhibit intense absorbance in the NIR-II region, which endows the nanosheets with excellent photothermal performance. With a further modification of HA, HA-HNSs can selectively target intraplaque proinflammatory macrophages through CD44-HA interaction. Because SDT reduces the expression of heat shock protein 90 and PTT facilitates the sonocatalytic process, the combination of SDT and PTT based on HA-HNSs could synergistically induce proinflammatory macrophage apoptosis. More importantly, the synergistic therapy prevents the progression of early atherosclerotic plaque by removing lesional macrophages and mitigating inflammation. Taken together, this work provides a macrophage-targeting sonodynamic/photothermal synergistic therapy, which is an effective translational clinical intervention for early atherosclerotic plaques.

Clinical Validation of BCI-Controlled Wheelchairs in Subjects With Severe Spinal Cord Injury.

Brain-controlled wheelchairs are one of the most promising applications that can help people gain mobility after their normal interaction pathways have been compromised by neuromuscular diseases. The feasibility of using brain signals to control wheelchairs has been well demonstrated by healthy people in previous studies. However, most potential users of brain-controlled wheelchairs are people suffering from severe physical disabilities or who are in a "locked-in" state. To further validate the clinical practicability of our previously proposed P300-based brain-controlled wheelchair, in this study, 10 subjects with severe spinal cord injuries participated in three experiments and completed ten predefined tasks in each experiment. The average accuracy and information transfer rate (ITR) were 94.8% and 4.2 bits/min, respectively. Moreover, we evaluated the physiological and cognitive burdens experienced by these individuals before and after the experiments. There were no significant changes in vital signs during the experiment, indicating minimal physiological and cognitive burden. The patients' average systolic blood pressure before and after the experiment was 113±13.7 mmHg and 114±11.9 mmHg, respectively (P = 0.122). The patients' average heart rates before and after the experiment were 79±8.4/min and 79±8.2/min, respectively (P = 0.147). The average task load, measured by the National Aeronautics and Space Administration task load index, ranged from 10.0 to 25.5. The results suggest that the proposed P300-based brain-controlled wheelchair is safe and reliable; additionally, it does not significantly increase the patient's physical and mental task burden, demonstrating its potential value in clinical applications. Our study promotes the development of a more practical brain-controlled wheelchair system.

Recent advances in photorelease complexes for therapeutic applications.

Photorelease complexes represent a class of agents for which UV-visible light triggers the expulsion of a specfic molecule that is intrinsically part of the inner coordination sphere or held in close proximity to the metal centre. The reaction does not occur in the ground-state complex and requires a photon, but an additional agent may be present that facilitates the release process. In this context, the perspective article covers recent papers from the past five years (2017-2021) on metal-based complexes containing ligands that are expelled under light activation. In addition, the examples primarily focus on ligands with potential biological activity and have specfic therapeutic applications. Some examples include NO, CO, Cl-, peptides, pharmacophores and redox-active compounds.