Adropin regulates macrophage phenotype via PPARγ signalling: A preliminary study of adropin and Crohn's disease.

Macrophage polarization is increasingly recognized as a vital pathogenetic factor in Crohn's disease (CD). Adropin is a secreted protein implicated in energy homeostasis, chiefly linked to glucose and lipid metabolism. However, the significance of adropin in CD is not clear. The objective of this study was to detect the expression of adropin in CD patients and investigate the effect of adropin on macrophage polarization induced by lipopolysaccharide (LPS) and its potential mechanism. Our study showed that serum adropin levels were markedly lower in patients with CD in active (CDA) than patients with CD in remission (CDR) and control groups (p < 0.01), however, there was no significant difference between in remission CD and healthy controls (p > 0.05). The colon mucous adropin levels in CDA were distinctly higher than CDR and controls (p < 0.01), while a significant difference between in remission CD and in healthy controls was not observed (p > 0.05). Exploration of the specific mechanism of action indicated that adropin promoted LPS-induced RAW264.7 macrophage polarization to M2 phenotype by modulating the expression and nuclear translocation of peroxisome proliferator receptor gamma (PPARγ), which may help weaken the intestinal inflammatory response. PPARγ inhibitor GW9662 reversed adropin-induced M2 macrophage polarization. Knockdown of GPR19, an adropin receptor, abrogated the M2 macrophage polarization caused by PPARγ. These findings suggest that adropin in colonic mucosa is a protective response in patients with active Crohn's disease.

TIPE2 aggravates experimental colitis and disrupts intestinal epithelial barrier integrity by activating JAK2/STAT3/SOCS3 signal pathway.

Ulcerative colitis (UC) is a chronic relapsing and progressive inflammatory disease of the colon. TIPE2 is a negative regulator of innate and adaptive immunity that maintains immune homeostasis. We found that TIPE2 was highly expressed in mucosa of mice with colitis. However, the role of TIPE2 in colitis remains unclear. We induced colitis in mice with dextran sulfate sodium (DSS) and treated them with TIPE2, and investigated the inflammatory activity of the colon in vivo by cytokines detection and histopathological analyses. We also measured inflammatory alteration and tight junctions induced by DSS in vitro. The results demonstrated that administration of TIPE2 promoted the severity of colitis in mice and human colon epithelial cells. Furthermore, TIPE2 aggravated intestinal epithelial barrier dysfunction by decreasing the expression of the tight junction proteins Occludin, Claudin-1 and ZO-1. In addition, TIPE2 exacerbated intestinal inflammatory response by inhibiting the expression of SOCS3, remarkably activating JAK2/STAT3 signaling pathway, and increasing the translocation of phosphorylated STAT3 into the nucleus. Silencing of TIPE2 attenuated the DSS-induced activation of JAK2/STAT3, thereby rescuing epithelial inflammatory injury and restoring barrier dysfunction. These results indicate that TIPE2 augments experimental colitis and disrupted the integrity of the intestinal epithelial barrier by activating the JAK2/STAT3/SOCS3 signaling pathway.

MASER: Enhancing EEG Spatial Resolution With State Space Modeling.

Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER, a novel super-resolution approach for EEG recording. In MASER, we design the eMamba block for extracting EEG features based on the principles of state space models (SSMs). We further stack eMamba blocks to form a low-resolution feature extractor and a high-resolution signal predictor, which enhances the feature representation. During the training of MASER, we fully consider the characteristics of multidimensional biological series signals, incorporating a smoothness constraint loss to achieve more consistent high-resolution reconstructions. MASER pioneers EEG-oriented state space modeling, effectively capturing the temporal dynamics and latent states, thereby revealing complex neural interactions over time. Extensive experiments show that the proposed MASER outperforms the state-of-the-art methods in super-resolution quality on two public EEG datasets, with normalized mean square error reduced by 16.25% and Pearson correlation improved by 1.13%. Moreover, a case study of motor imagery recognition highlights the advantages conferred by high-resolution EEG signals. With a 4x increase in spatial resolution by MASER, the recognition accuracy improves by 5.74%, implying a significant performance elevation in brain-computer interface (BCI) command mapping. By enhancing the spatial resolution of EEG signals, MASER makes EEG-based applications more accessible, reducing cost and setup time while maintaining high performance across various domains such as gaming, education, and healthcare.

M2DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.

Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is essential for effective treatment and intervention. Thanks to the rapid development of brain imaging technology and machine learning algorithms, diagnostic classification of psychiatric diseases can be achieved based on brain images. However, due to divergences in scanning machines or parameters, the generalization capability of diagnostic classification models has always been an issue. We propose Meta-learning with Meta batch normalization and Distance Constraint (M2DC) for training diagnostic classification models. The framework can simulate the train-test domain shift situation and promote intra-class cohesion, as well as inter-class separation, which can lead to clearer classification margins and more generalizable models. To better encode dynamic brain graphs, we propose a concatenated spatiotemporal attention graph isomorphism network (CSTAGIN) as the backbone. The network is trained for the diagnostic classification of major depressive disorder (MDD) based on multi-site brain graphs. Extensive experiments on brain images from over 3261 subjects show that models trained by M2DC achieve the best performance on cross-site diagnostic classification tasks compared to various contemporary domain generalization methods and SOTA studies. The proposed M2DC is by far the first framework for multi-source closed-set domain generalizable training of diagnostic classification models for MDD and the trained models can be applied to reliable auxiliary diagnosis on novel data.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder.

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Identification of FASN Gene Polymorphisms, Expression and Their Relationship with Body Size Traits in Guizhou White Goat (Capra hircus) with Different Genders.

To investigate the nucleotide variation sites (SNPs) and expression differences of the fatty acid synthase gene (FASN) in Guizhou white goats, the relationship between the variation and body size traits was investigated. In this study, DNA was extracted from the blood of 100 samples of white goats from different regions in Guizhou province, China, and the variation sites were screened using pooled sequencing by mixing DNA samples, and 242 blood samples with body size traits were used for association analysis. The allele frequency, genotype frequency, homozygosity, heterozygosity and effective gene number were calculated by using PopGene 32.0 software, the population polymorphism information content was calculated by using PIC software (Version 0.6), and the state of genetic balance of the genes was analyzed by using the chi-square test. The mRNA of FASN gene expression levels in male and female goats were investigated by using real-time fluorescence quantitative PCR (RT-qPCR). The general linear mixed model of MINTAB software (Version 16.0) was used to analyze the association between FASN gene nucleotide mutation sites and body size traits. The results showed that there was one nucleotide mutation site g.141 C/T in the target fragment of FASN gene amplification, and revealed two alleles, C and T, and three genotypes CC, CT and TT. The genotype frequencies for CC, CT and TT were 0.4308, 0.4205 and 0.1487, respectively. The allele frequencies for C and T were 0.6410 and 0.3590, respectively. The genetic homozygosity (Ho) was higher than the heterozygosity (He). The χ2 test showed that the mutation site was in the Hardy-Weinberg equilibrium state (p > 0.05). The RT-qPCR results showed that the FASN gene had different expression levels in the longissimus dorsi muscle of male and female goats, and its expression was significantly higher in male goats than in female goats. The association analysis results showed that the mutation of the FASN gene had different effects on body size traits of male and female goats, and the presence of the populations of the T allele and the TT genotype recorded higher body size traits (body weight, heart girth and wither height) in female populations. Therefore, the site of the FASN gene can be used as a candidate marker for the early selection of growth traits in Guizhou white goats.

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.

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.

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.

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.

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.

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.