Association between the oral microbiome and brain resting state connectivity in schizophrenia.

Recent microbiome-brain axis findings have shown evidence of the modulation of microbiome community as an environmental mediator in brain function and psychiatric illness. This work is focused on the role of the microbiome in understanding a rarely investigated environmental involvement in schizophrenia (SZ), especially in relation to brain circuit dysfunction. We leveraged high throughput microbial 16s rRNA sequencing and functional neuroimaging techniques to enable the delineation of microbiome-brain network links in SZ. N = 213 SZ and healthy control subjects were assessed for the oral microbiome. Among them, 139 subjects were scanned by resting-state functional magnetic resonance imaging (rsfMRI) to derive brain functional connectivity. We found a significant microbiome compositional shift in SZ beta diversity (weighted UniFrac distance, p = 6 × 10-3; Bray-Curtis distance p = 0.021). Fourteen microbial species involving pro-inflammatory and neurotransmitter signaling and H2S production, showed significant abundance alterations in SZ. Multivariate analysis revealed one pair of microbial and functional connectivity components showing a significant correlation of 0.46. Thirty five percent of microbial species and 87.8 % of brain functional network connectivity from each component also showed significant differences between SZ and healthy controls with strong performance in classifying SZ from healthy controls, with an area under curve (AUC) = 0.84 and 0.87, respectively. The results suggest a potential link between oral microbiome dysbiosis and brain functional connectivity alteration in relation to SZ, possibly through immunological and neurotransmitter signaling pathways and the hypothalamic-pituitary-adrenal axis, supporting for future work in characterizing the role of oral microbiome in mediating effects on SZ brain functional activity.

Neuroimage Analysis Methods and Artificial Intelligence Techniques for Reliable Biomarkers and Accurate Diagnosis of Schizophrenia: Achievements Made by Chinese Scholars Around the Past Decade.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is characterized by significant cognitive and behavioral disruptions. Neuroimaging techniques, particularly magnetic resonance imaging (MRI), have been widely utilized to investigate biomarkers of SZ, distinguish SZ from healthy conditions or other mental disorders, and explore biotypes within SZ or across SZ and other mental disorders, which aim to promote the accurate diagnosis of SZ. In China, research on SZ using MRI has grown considerably in recent years. STUDY DESIGN: The article reviews advanced neuroimaging and artificial intelligence (AI) methods using single-modal or multimodal MRI to reveal the mechanism of SZ and promote accurate diagnosis of SZ, with a particular emphasis on the achievements made by Chinese scholars around the past decade. STUDY RESULTS: Our article focuses on the methods for capturing subtle brain functional and structural properties from the high-dimensional MRI data, the multimodal fusion and feature selection methods for obtaining important and sparse neuroimaging features, the supervised statistical analysis and classification for distinguishing disorders, and the unsupervised clustering and semi-supervised learning methods for identifying neuroimage-based biotypes. Crucially, our article highlights the characteristics of each method and underscores the interconnections among various approaches regarding biomarker extraction and neuroimage-based diagnosis, which is beneficial not only for comprehending SZ but also for exploring other mental disorders. CONCLUSIONS: We offer a valuable review of advanced neuroimage analysis and AI methods primarily focused on SZ research by Chinese scholars, aiming to promote the diagnosis, treatment, and prevention of SZ, as well as other mental disorders, both within China and internationally.

GLUT1 promotes cell proliferation via binds and stabilizes phosphorylated EGFR in lung adenocarcinoma.

BACKGROUND: While previous studies have primarily focused on Glucose transporter type 1 (GLUT1) related glucose metabolism signaling, we aim to discover if GLUT1 promotes tumor progression through a non-metabolic pathway. METHODS: The RNA-seq and microarray data were comprehensively analyzed to evaluate the significance of GLUT1 expression in lung adenocarcinoma (LUAD). The cell proliferation, colony formation, invasion, and migration were used to test GLUT1 's oncogenic function. Co-immunoprecipitation and mass spectrum (MS) were used to uncover potential GLUT1 interacting proteins. RNA-seq, DIA-MS, western blot, and qRT-PCR to probe the change of gene and cell signaling pathways. RESULTS: We found that GLUT1 is highly expressed in LUAD, and higher expression is related to poor patient survival. GLUT1 knockdown caused a decrease in cell proliferation, colony formation, migration, invasion, and induced apoptosis in LUAD cells. Mechanistically, GLUT1 directly interacted with phosphor-epidermal growth factor receptor (p-EGFR) and prevented EGFR protein degradation via ubiquitin-mediated proteolysis. The GLUT1 inhibitor WZB117 can increase the sensitivity of LUAD cells to EGFR-tyrosine kinase inhibitors (TKIs) Gefitinib. CONCLUSIONS: GLUT1 expression is higher in LUAD and plays an oncogenic role in lung cancer progression. Combining GLUT1 inhibitors and EGFR-TKIs could be a potential therapeutic option for LUAD treatment.

A survey of brain functional network extraction methods using fMRI data.

Functional network (FN) analyses play a pivotal role in uncovering insights into brain function and understanding the pathophysiology of various brain disorders. This paper focuses on classical and advanced methods for deriving brain FNs from functional magnetic resonance imaging (fMRI) data. We systematically review their foundational principles, advantages, shortcomings, and interrelations, encompassing both static and dynamic FN extraction approaches. In the context of static FN extraction, we present hypothesis-driven methods such as region of interest (ROI)-based approaches as well as data-driven methods including matrix decomposition, clustering, and deep learning. For dynamic FN extraction, both window-based and windowless methods are surveyed with respect to the estimation of time-varying FN and the subsequent computation of FN states. We also discuss the scope of application of the various methods and avenues for future improvements.

Impaired neurogenesis induced by fluoride via the Notch1 signaling and effects of carvacrol intervention.

The negative regulation on neurogenesis has been implicated in fluoride neurotoxicity, while the evidence is limited. To explore whether fluoride interferes with neurogenesis via the Notch1 signaling and the potential alleviation effects of carvacrol (CAR), we conducted in vivo and in vitro experiments, as well as epidemiological analyses in this study. The results showed that urinary fluoride levels and circulating Notch1 levels were associated with IQ levels in boys. NaF-treated rats had fewer neurons, lower densities of dendritic spines, depressed neurogenesis, and impaired learning and memory abilities. In vitro experiments using undifferentiated PC12 cells mimicking neurogenesis revealed that NaF suppressed differentiation and neurite outgrowth. Besides, Notch1 signaling activation was detected in vivo and in vitro. The latter was confirmed using an in vitro model supplemented with DAPT, a potent Notch1 inhibitor. Furthermore, CAR supplementation negatively regulated NICD1 and Hes1 expressions and promoted hippocampal neurogenesis, thereby improving neurological functions in NaF-treated rats. These findings indicated that the inhibition of neurogenesis in hippocampi induced by fluoride via Notch1 signaling activation may be one of the underlying mechanisms of its neurotoxicity, and that CAR significantly alleviated the neurotoxicity of NaF via the Notch1 signaling.

Mediation of mitochondrial DNA copy number and oxidative stress in fluoride-related bone mineral density alteration in Chinese farmers.

Excessive fluoride can adversely affect bone mineral density (BMD). Oxidative stress and mitochondrial dysfunction are crucial mechanisms of health damage induced by fluoride. Here, a cross-sectional survey involving 907 Chinese farmers (aged 18-60) was carried out in Tongxu County in 2017, aiming to investigate the significance of mitochondrial DNA copy number (mtDNAcn) and oxidative stress in fluoride-related BMD change. Concentrations of urinary fluoride (UF), serum oxidative stress biomarkers, including total antioxidant capacity (T-AOC), total superoxide dismutase (T-SOD), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA), as well as relative mtDNAcn in peripheral blood were determined. The multivariable linear model and mediation analysis were performed to assess associations between UF, oxidative stress, and relative mtDNAcn with BMD. Results showed that GSH-Px levels increased by 6.98 U/mL [95% confidence interval (CI) 3.41-10.56)] with each 1.0 mg/L increment of UF. After stratification, the T-AOC, relative mtDNAcn, and BMD decreased by 0.04 mmol/L (-0.08 ~ -0.01), 0.29-unit (-0.55 ~ -0.04), and 0.18-unit (-0.33 ~ -0.03) with every 1.0 mg/L elevation of UF in the excessive fluoride group (EFG, adults with UF > 1.6 mg/L), respectively. Furthermore, T-AOC and relative mtDNAcn were favorably related to the BMD in the EFG (ß = 0.82, 95%CI 0.16-1.48 for T-AOC; ß = 0.11, 95%CI 0.02-0.19 for relative mtDNAcn). Mediation analysis showed that relative mtDNAcn and T-AOC mediated 15.4% and 17.1% of the connection between excessive fluoride and reduced BMD, respectively. Findings suggested that excessive fluoride was related to lower BMD in adults, and the decrement of T-AOC and relative mtDNAcn partially mediate this relationship.

Exploring Schizophrenia Classification Through Multimodal MRI and Deep Graph Neural Networks: Unveiling Brain Region-Specific Weight Discrepancies and Their Association With Cell-Type Specific Transcriptomic Features.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) is a prevalent mental disorder that imposes significant health burdens. Diagnostic accuracy remains challenging due to clinical subjectivity. To address this issue, we explore magnetic resonance imaging (MRI) as a tool to enhance SZ diagnosis and provide objective references and biomarkers. Using deep learning with graph convolution, we represent MRI data as graphs, aligning with brain structure, and improving feature extraction, and classification. Integration of multiple modalities is expected to enhance classification. STUDY DESIGN: Our study enrolled 683 SZ patients and 606 healthy controls from 7 hospitals, collecting structural MRI and functional MRI data. Both data types were represented as graphs, processed by 2 graph attention networks, and fused for classification. Grad-CAM with graph convolution ensured interpretability, and partial least squares analyzed gene expression in brain regions. STUDY RESULTS: Our method excelled in the classification task, achieving 83.32% accuracy, 83.41% sensitivity, and 83.20% specificity in 10-fold cross-validation, surpassing traditional methods. And our multimodal approach outperformed unimodal methods. Grad-CAM identified potential brain biomarkers consistent with gene analysis and prior research. CONCLUSIONS: Our study demonstrates the effectiveness of deep learning with graph attention networks, surpassing previous SZ diagnostic methods. Multimodal MRI's superiority over unimodal MRI confirms our initial hypothesis. Identifying potential brain biomarkers alongside gene biomarkers holds promise for advancing objective SZ diagnosis and research in SZ.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities.

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

SMART (Splitting-Merging Assisted Reliable) Independent Component Analysis for Extracting Accurate Brain Functional Networks.

Functional networks (FNs) hold significant promise in understanding brain function. Independent component analysis (ICA) has been applied in estimating FNs from functional magnetic resonance imaging (fMRI). However, determining an optimal model order for ICA remains challenging, leading to criticism about the reliability of FN estimation. Here, we propose a SMART (splitting-merging assisted reliable) ICA method that automatically extracts reliable FNs by clustering independent components (ICs) obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model orders. We extend SMART ICA to multi-subject fMRI analysis, validating its effectiveness using simulated and real fMRI data. Based on simulated data, the method accurately estimates both group-common and group-unique components and demonstrates robustness to parameters. Using two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects, the resulting reliable group-level FNs are greatly similar between the two cohorts, and interestingly the subject-specific FNs show progressive changes while age increases. Furthermore, both small-scale and large-scale brain FN templates are provided as benchmarks for future studies. Taken together, SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data, while also providing linkages between different FNs.

Enzymatic Preparation of DNA with an Expanded Genetic Alphabet Using Terminal Deoxynucleotidyl Transferase and Its Applications.

Efficient preparation of DNA oligonucleotides containing unnatural nucleobases (UBs) that can pair with their cognates to form unnatural base pairs (UBPs) is an essential prerequisite for the application of UBPs in vitro and in vivo. Traditional preparation of oligonucleotides containing unnatural nucleobases largely relies on solid-phase synthesis, which needs to use unstable nucleoside phosphoramidites and a DNA synthesizer, and is environmentally unfriendly and limited in product length. To overcome these limitations of solid-phase synthesis, we developed enzymatic methods for daily laboratory preparation of DNA oligonucleotides containing unnatural nucleobase dNaM, dTPT3, or one of the functionalized dTPT3 derivatives, which can be used for orthogonal DNA labeling or the preparation of DNAs containing UBP dNaM-dTPT3, one of the most successful UBPs to date, based on the template-independent polymerase terminal deoxynucleotidyl transferase (TdT). Here, we first provide a detailed procedure for the TdT-based preparation of DNA oligonucleotides containing 3'-nucleotides of dNaM, dTPT3, or one of dTPT3 derivatives. We then present the procedures for enzyme-linked oligonucleotide assay (ELONA) and imaging of bacterial cells using DNA oligonucleotides containing 3'-nucleotides of dTPT3 derivatives with different functional groups. The procedure for enzymatic synthesis of DNAs containing an internal UBP dNaM-dTPT3 is also described. Hopefully, these methods will greatly facilitate the application of UBPs and the construction of semi-synthetic organisms with an expanded genetic alphabet.

More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method.

The integration of neuroimaging with artificial intelligence is crucial for advancing the diagnosis of mental disorders. However, challenges arise from incomplete matching between diagnostic labels and neuroimaging. Here, we propose a label-noise filtering-based dimensional prediction (LAMP) method to identify reliable biomarkers and achieve accurate prediction for mental disorders. Our method proposes to utilize a label-noise filtering model to automatically filter out unclear cases from a neuroimaging perspective, and then the typical subjects whose diagnostic labels align with neuroimaging measures are used to construct a dimensional prediction model to score independent subjects. Using fMRI data of schizophrenia patients and healthy controls (n = 1,245), our method yields consistent scores to independent subjects, leading to more distinguishable relabeled groups with an enhanced classification accuracy of 31.89%. Additionally, it enables the exploration of stable abnormalities in schizophrenia. In summary, our LAMP method facilitates the identification of reliable biomarkers and accurate diagnosis of mental disorders using neuroimages.

Correlations between bone metabolism biomarkers and fluoride exposure in adults and children.

Increased exposure to fluoride, which notably affects bone metabolism, is a global concern. However, the correlations and sensitivity of bone metabolism to fluoride remain controversial. In this cross-sectional study, 549 children (aged 7-12 years) and 504 adults (≥ 18 years old) were recruited in the high-fluoride areas of the Henan Province. Urinary fluoride (UF) level was determined using a fluoride electrode. Fasting venous blood serum was collected to measure bone metabolism biomarkers. The selected bone metabolism biomarkers for children included bone alkaline phosphatase (BALP), serum alkaline phosphatase (ALP), osteocalcin (OCN), calcitonin (CT), parathyroid hormone (PTH), phosphorus (P5+), and calcium (Ca2+). For adults, the biomarkers included ALP, CT, PTH, ß-CrossLaps (ß-CTX), P5+, and Ca2+. The correlations between UF and bone metabolism biomarkers were analyzed using binary logistic regression, a trend test, a generalized additive model, and threshold effect analysis. Regression analysis indicated a significant correlation between serum OCN, PTH, and UF levels in children aged 7-9 years. Serum OCN, PTH, and BALP contents were significantly correlated with UF in boys (P < 0.05). Furthermore, the interaction between age and UF affected serum P5+ and PTH (P < 0.05). The generalized additive model revealed nonlinear dose-response relationships between P5+, BALP, and UF contents in children (P < 0.05). Serum OCN level was linearly correlated with the UF concentration (P < 0.05). Similarly, a significant correlation was observed between ß-CTX and UF levels in adults. In addition, significant correlations were observed between UF-age and serum Ca2+, ß-CTX, and PTH contents. There was a non-linear correlation between serum Ca2+, P5+, and ß- CTX and UF levels (P < 0.05). Overall, serum OCN, BALP, and P5+ levels can serve as sensitive bone metabolism biomarkers in children, while ß-CTX, P5+, and Ca2+ can be considered fluoride-sensitive bone metabolism biomarkers in adults.

Proteomics Sequencing Reveals the Role of TGF-ß Signaling Pathway in the Peripheral Blood of Offspring Rats Exposed to Fluoride.

The underlying mechanism of fluorosis has not been fully elucidated. The purpose of this study was to explore the mechanism of fluorosis induced by sodium fluoride (NaF) using proteomics. Six offspring rats exposed to fluoride without dental fluorosis were defined as group A, 8 offspring rats without fluoride exposure were defined as control group B, and 6 offspring rats exposed to fluoride with dental fluorosis were defined as group C. Total proteins from the peripheral blood were extracted and then separated using liquid chromatography-tandem mass spectrometry. The identified criteria for differentially expressed proteins were fold change > 1.2 or < 0.83 and P < 0.05. Gene Ontology function annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the oeCloud tool. The 177 upregulated and 22 downregulated proteins were identified in the A + C vs. B group. KEGG pathway enrichment analysis revealed that transforming growth factor-ß (TGF-ß) signaling pathway significantly enriched. PPI network constructed using Cytoscape confirmed RhoA may play a crucial role. The KEGG results of genes associated with fluoride and genes associated with both fluoride and inflammation in the GeneCards database also showed that TGF-ß signaling pathway was significantly enriched. The immunofluorescence in HPA database showed that the main expression sites of RhoA are plasma membrane and cytosol, while the main expression site of Fbn1 is the Golgi apparatus. In conclusion, long-term NaF intake may cause inflammatory response in the peripheral blood of rats by upregulating TGF-ß signaling pathway, in which RhoA may play a key role.

How and when awe improves meaning in life: The role of authentic-self pursuit and trait authenticity.

Awe is theoretically proposed as a meaning-making emotion. However, empirical evidence has shown that awe has mixed effects on meaning in life. The explanations for such complicated results have been limited. To fill this gap, in this research, we aimed to clarify how and when awe contributes to meaning in life. In six studies (N = 1,115), we examined the indirect effect of awe on meaning in life through authentic-self pursuit as well as trait authenticity's moderating effect on this indirect effect. We consistently found a positive indirect effect of awe on meaning in life via authentic-self pursuit (Studies 1-3 and Study 5), which arised beyond happiness and self-smallness (Studies 2a, 2b, and 3) and also held for awe brought on by a threatening experience (Study 3). Moreover, we found that manipulating authentic-self pursuit improved meaning in life (Study 4). Importantly, the main effect of awe on meaning in life and indirect effect of awe on meaning in life through authentic-self pursuit were significant for those with low to average rather than high trait authenticity (Study 5). These findings facilitate the understanding of awe as a meaning-making emotion. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effect of zinc intake on association between fluoride exposure and abnormal sex steroid hormones among US pubertal males: NHANES, 2013-2016.

Excessive fluoride exposure can disturb the balance of sex hormones. Zinc is essential for sex hormone synthesis and spermatogenesis. But it is not clear how zinc affects the relationship of fluoride exposure with abnormal sex steroid hormones. Here, a total of 1008 pubertal males from the National Health and Nutrition Examination Survey (NHANES) in two cycles (2013-2014, 2015-2016) were enrolled. The concentrations of water fluoride and plasma fluoride and the levels of serum testosterone, estradiol, and sex hormone binding globulin (SHBG) were measured. Two 24-h dietary recall interviews were conducted to assess the dietary zinc intake. The relationships of fluoride exposure and zinc intake with sex hormones were examined using linear regression and logistic regression models, while the generalized additive model was used to evaluate their non-linear relationship. Our findings revealed that for every two-fold increase in plasma fluoride concentration, testosterone levels decreased by 7.27% (95% CI - 11.49%, - 2.86%) and estradiol levels decreased by 8.73% (95% CI - 13.61%, - 3.57%). There was also significant non-linear association observed between zinc intake and SHBG levels. Being in the first tertile of plasma fluoride had a 60% lower risk of high SHBG (OR = 0.40, 95% CI 0.18, 0.89) compared with being in the second tertile. When compared to the first tertile, being in the second tertile of zinc intake was associated with a 63% (OR = 0.37, 95% CI 0.14, 0.98) lower risk of high SHBG. Furthermore, we observed an interactive effect between the plasma fluoride and zinc intake on estradiol and SHBG, as well as the risk of high SHBG (P-interaction < 0.10). These findings suggest that fluoride exposure and zinc intake can affect sex steroid hormone levels and the risk of high SHBG. Notably, zinc intake may alleviate the increased risk of high SHBG and the abnormal changes of estradiol and SHBG caused by higher fluoride exposure.

A Novel Method for Multi-subject fMRI Data Analysis: Independent Component Analysis with Clustering Embedded (ICA-CE).

The analysis of multi-subject functional magnetic resonance imaging (fMRI) data and the extraction of accurate brain functional networks (FNs) are of great importance. However, traditional independent component analysis (ICA) methods perform analysis on multi-subject fMRI data under the condition of known or assumed classes of subjects, which may decrease its ability to extract accurate individual brain FNs. Although a previous method named clusterwise ICA (C-ICA) clusters subjects and obtains shared FNs in group-level for each class, its clustering performance on complex data is not ideal. To address the issues, we propose a novel method called independent component analysis with clustering embedded (ICA-CE) that can achieve both the estimation of individual FNs and the clustering of subjects in an unsupervised or semi-supervised manner. Using the simulated data with different properties, ICA-CE achieved better clustering performance than group ICA followed by K-means and C-ICA, and the mean accuracy of extracted individual FNs obtained by ICA-CE was greater than 90%. Using the task-related fMRI data from Human Connectome Project (HCP), our method also achieved higher clustering accuracy, while extracting task-related class-specific FNs. In summary, ICA-CE is effective in estimating accurate brain FNs while achieving the clustering of multiple subjects.Clinical Relevance- Our method is promising in estimating accurate brain functional networks for patients with brain disorders and outputting related class label for each subject.

How Does Aging Affect Whole-brain Functional Network Connectivity? Evidence from An ICA Method.

Many studies have shown that changes in the functional connectivity are diverse along with aging. However, few studies have addressed how aging affects connectivity among large-scale brain networks, and it is challenging to examine gradual aging trajectories from middle adulthood to old age. In this work, based on large-sample fMRI data from 6300 subjects aged between 49 to 73 years, we apply an independent component analysis-based method called NeuroMark to extract brain functional networks and their connectivity (i.e., functional network connectivity (FNC)), and then propose a two-level statistical analysis method to explore robust aging-related changes in functional network connectivity. We found that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network and cerebellum. Our results emphasize the diversity of brain aging and provide new evidence for non-pathological aging of the whole brain.Clinical Relevance-This provides new evidence for non-pathological aging of functional network connectivity in the whole brain.

Corrigendum: Application of single-cell sequencing to the research of tumor microenvironment.

[This corrects the article DOI: 10.3389/fimmu.2023.1285540.].

Application of single-cell sequencing to the research of tumor microenvironment.

Single-cell sequencing is a technique for detecting and analyzing genomes, transcriptomes, and epigenomes at the single-cell level, which can detect cellular heterogeneity lost in conventional sequencing hybrid samples, and it has revolutionized our understanding of the genetic heterogeneity and complexity of tumor progression. Moreover, the tumor microenvironment (TME) plays a crucial role in the formation, development and response to treatment of tumors. The application of single-cell sequencing has ushered in a new age for the TME analysis, revealing not only the blueprint of the pan-cancer immune microenvironment, but also the heterogeneity and differentiation routes of immune cells, as well as predicting tumor prognosis. Thus, the combination of single-cell sequencing and the TME analysis provides a unique opportunity to unravel the molecular mechanisms underlying tumor development and progression. In this review, we summarize the recent advances in single-cell sequencing and the TME analysis, highlighting their potential applications in cancer research and clinical translation.

Iron and copper: critical executioners of ferroptosis, cuproptosis and other forms of cell death.

Regulated cell death (RCD) is a regulable cell death that involves well-organized signaling cascades and molecular mechanisms. RCD is implicated in fundamental processes such as organ production and tissue remodeling, removing superfluous structures or cells, and regulating cell numbers. Previous studies have not been able to reveal the complete mechanisms, and novel methods of RCD are constantly being proposed. Two metal ions, iron (Fe) and copper (Cu) are essential factors leading to RCDs that not only induce ferroptosis and cuproptosis, respectively but also lead to cell impairment and eventually diverse cell death. This review summarizes the direct and indirect mechanisms by which Fe and Cu impede cell growth and the various forms of RCD mediated by these two metals. Moreover, we aimed to delineate the interrelationships between these RCDs with the distinct pathways of ferroptosis and cuproptosis, shedding light on the complex and intricate mechanisms that govern cellular survival and death. Finally, the prospects outlined in this review suggest a novel approach for investigating cell death, which may involve integrating current therapeutic strategies and offer a promising solution to overcome drug resistance in certain diseases. Video Abstract.