Bioinformatics-based analysis of mechanistic differences in vascular endothelial injury ischemic stroke induced by atrial fibrillation and atherosclerosis.

Studies of the intracranial vasculature in patients with ischemic stroke caused by atherosclerosis (AS) and cardiac embolism have revealed significantly different degrees of AS, plaque, and vascular stenosis. And the endothelium has a great influence on the vasculature throughout the circulatory system, especially in the brain. This study aimed to investigate the mechanistic differences in endothelial injury between atrial fibrillation (AF)- and AS-induced ischemic stroke. All target genes of AF, AS, and the vascular endothelial cell (VC) were obtained from the GeneCards database; the differential genes of AF and AS separately associated with the VC were established by a Venn diagram. A protein-protein interaction network was created, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used to perform genomic enrichment and functional enrichment analysis. Hub genes were selected by Maximal Clique Centrality algorithm ranking and correlation linkage in the STRING database, and then, clinical serum samples were used to verify the quantitative expressions in the AF, AF stroke, AS, and AS stroke groups. Fifty-five AF-VC-related genes and ninety-three AS-VC-related genes were screened, which differed in biological function, cellular composition, and molecular function. The genes correlation between AF and vascular endothelial cells (VCs) was KRAS and PTPN11, and those correlation between AS and VCs was IL-4, IFNG, IL-17A, and CSF-2. IL-4 and CSF-2 may be relevant proteins involved in the differences in stroke mechanisms between AF and AS, and they may act by further influencing the function of their downstream cells. This study provides a preliminary theoretical basis for investigating the differences in mechanisms of endothelial injury between AF- and AS-induced ischemic stroke.

The role of microbial interactions in dental caries: Dental plaque microbiota analysis.

OBJECTIVES: Dental caries is a result of the ecological dysfunction of the polymicrobial community on the tooth surface, which evolves through microbial interactions. In this study, we conducted a thorough analysis of the dental plaque microbiome to comprehend its multi-microbial aetiology. MATERIALS AND METHOD: In this study, plaque was collected from healthy tooth surfaces, shallow carious teeth and deep carious teeth, and bacterial composition and abundance were assessed using 16S rRNA high-throughput sequencing. Random forest and LEfSe were used to profile various microorganisms at each stage. Additionally, we developed a molecular ecological network (MEN) based on random matrix theory (RMT) to examine microbial interactions for the first time. RESULTS: Our results reveal that Scardovia wiggsiae, Streptococcus mutans, and Propionibacterium acidifaciens may be associated with initial caries, and Propionibacterium acidifaciens differentiates between shallow and deep caries. As caries progressed, the alpha diversity index declined, indicating a decrease in microbial variety. The network topological indices such as centralization betweenness revealed that the caries network had become more complex, involving more microbial interactions. The shallow network revealed a high negative correlation ratio across nodes, indicating that microbes competed heavily. In contrast, the positive correlation ratio of deep network nodes was high, and microorganisms transitioned from a competitive to a synergistic state. CONCLUSIONS: This study suggests that microbial diversity and interactions are critical to caries progression and that future caries research should give greater consideration to the role of microbial interaction factors in caries progression.

Methylphenidate remediates aberrant brain network dynamics in children with attention-deficit/hyperactivity disorder: A randomized controlled trial.

Methylphenidate is a widely used first-line treatment for attention deficit/hyperactivity disorder (ADHD), but the underlying circuit mechanisms are poorly understood. Here we investigate whether a single dose of osmotic release oral system methylphenidate can remediate attention deficits and aberrancies in functional circuit dynamics in cognitive control networks, which have been implicated in ADHD. In a randomized placebo-controlled double-blind crossover design, 27 children with ADHD were scanned twice with resting-state functional MRI and sustained attention was examined using a continuous performance task under methylphenidate and placebo conditions; 49 matched typically-developing (TD) children were scanned once for comparison. Dynamic time-varying cross-network interactions between the salience (SN), frontoparietal (FPN), and default mode (DMN) networks were examined in children with ADHD under both administration conditions and compared with TD children. Methylphenidate improved sustained attention on a continuous performance task in children with ADHD, when compared to the placebo condition. Children with ADHD under placebo showed aberrancies in dynamic time-varying cross-network interactions between the SN, FPN and DMN, which were remediated by methylphenidate. Multivariate classification analysis confirmed that methylphenidate remediates aberrant dynamic brain network interactions. Furthermore, dynamic time-varying network interactions under placebo conditions predicted individual differences in methylphenidate-induced improvements in sustained attention in children with ADHD. These findings suggest that a single dose of methylphenidate can remediate deficits in sustained attention and aberrant brain circuit dynamics in cognitive control circuits in children with ADHD. Findings identify a novel brain circuit mechanism underlying a first-line pharmacological treatment for ADHD, and may inform clinically useful biomarkers for evaluating treatment outcomes.

Individualized video recommendation modulates functional connectivity between large scale networks.

With the emergence of AI-powered recommender systems and their extensive use in the video streaming service, questions and concerns also arise. Why can recommended video content continuously capture users' attention? What is the impact of long-term exposure to personalized video content on one's behaviors and brain functions? To address these questions, we designed an fMRI experiment presenting participants with personally recommended videos and generally recommended ones. To examine how large-scale networks were modulated by personalized video content, graph theory analysis was applied to investigate the interaction between seven networks, including the ventral and dorsal attention networks (VAN, DAN), frontal-parietal network (FPN), salience network (SN), and three subnetworks of default mode network (dorsal medial prefrontal (dMPFC), Core, and medial temporal lobe (MTL)). Our results showed that viewing nonpersonalized video content mainly enhanced the connectivity in the DAN-FPN-Core pathway, whereas viewing personalized ones increased not only the connectivity in this pathway but also the DAN-VAN-dMPFC pathway. In addition, both personalized and nonpersonalized short videos decreased the couplings between SN and VAN as well as between two DMN subsystems, Core and MTL. Collectively, these findings uncovered distinct patterns of network interactions in response to short videos and provided insights into potential neural mechanisms by which human behaviors are biased by personally recommended content.

Multiomics Integration in Skin Diseases with Alterations in Notch Signaling Pathway: PlatOMICs Phase 1 Deployment.

The high volume of information produced in the age of omics was and still is an important step to understanding several pathological processes, providing the enlightenment of complex molecular networks and the identification of molecular targets associated with many diseases. Despite these remarkable scientific advances, the majority of the results are disconnected and divergent, making their use limited. Skin diseases with alterations in the Notch signaling pathway were extensively studied during the omics era. In the GWAS Catalog, considering only studies on genomics association (GWAS), several works were deposited, some of which with divergent results. In addition, there are thousands of scientific articles available about these skin diseases. In our study, we focused our attention on skin diseases characterized by the impairment of Notch signaling, this pathway being of pivotal importance in the context of epithelial disorders. We considered the pathologies of five human skin diseases, Hidradenitis Suppurativa, Dowling Degos Disease, Adams-Oliver Syndrome, Psoriasis, and Atopic Dermatitis, in which the molecular alterations in the Notch signaling pathway have been reported. To this end, we started developing a new multiomics platform, PlatOMICs, to integrate and re-analyze omics information, searching for the molecular interactions involved in the pathogenesis of skin diseases with alterations in the Notch signaling pathway.

Neural activity in the periaqueductal gray and other specific subcortical structures is enhanced when a selective serotonin reuptake inhibitor selectively prevents seizure-induced sudden death in the DBA/1 mouse model of sudden unexpected death in epilepsy.

OBJECTIVE: Sudden unexpected death in epilepsy (SUDEP) is a critical issue in epilepsy, and DBA/1 mice are a useful animal model of this devastating epilepsy sequela. The serotonin hypothesis for SUDEP proposes that modifying serotonergic function significantly alters susceptibility to seizure-induced respiratory arrest (S-IRA). Agents that enhance serotonergic function, including a selective serotonin reuptake inhibitor, fluoxetine, selectively prevent S-IRA in DBA/1 mice. This study examined fluoxetine-induced changes in brain activity using manganese-enhanced magnetic resonance imaging (MEMRI) to reveal sites in the DBA/1 mouse brain where fluoxetine acts to prevent S-IRA. METHODS: DBA/1 mice were subjected to audiogenic seizures (Sz) after saline or fluoxetine (45 mg/kg, intraperitoneal) administration. Control DBA/1 mice received fluoxetine or saline, but Sz were not evoked. A previous MEMRI study established the regions of interest (ROIs) for Sz in the DBA/1 mouse brain, and the present study examined MEMRI differences in the ROIs of these mouse groups. RESULTS: The neural activity in several ROIs was significantly increased in fluoxetine-treated DBA/1 mice that exhibited Sz but not S-IRA when compared to the saline-treated mice that exhibited both Sz and respiratory arrest. These structures included the periaqueductal gray (PAG), amygdala, reticular formation (sensorimotor-limbic network), Kölliker-Fuse nucleus, facial-parafacial group (respiratory network), and pontine raphe. Of these ROIs, only the PAG showed significantly decreased neural activity with saline pretreatment when seizure-induced respiratory arrest occurred as compared to saline treatment without seizure. SIGNIFICANCE: The PAG is known to play an important compensatory role for respiratory distress caused by numerous exigent situations in normal animals. The pattern of fluoxetine-induced activity changes in the present study suggests that PAG may be the most critical target for fluoxetine's action to prevent seizure-induced sudden death. These findings have potential clinical importance, because there is evidence of anomalous serotonergic function and PAG imaging abnormalities in human SUDEP.

Resting-state Functional Connectivity is an Age-dependent Predictor of Motor Learning Abilities.

This magnetoencephalography study investigates how ageing modulates the relationship between pre-learning resting-state functional connectivity (rsFC) and subsequent learning. Neuromagnetic resting-state activity was recorded 5 min before motor sequence learning in 14 young (19-30 years) and 14 old (66-70 years) participants. We used a seed-based beta-band power envelope correlation approach to estimate rsFC maps, with the seed located in the right primary sensorimotor cortex. In each age group, the relation between individual rsFC and learning performance was investigated using Pearson's correlation analyses. Our results show that rsFC is predictive of subsequent motor sequence learning but involves different cross-network interactions in the two age groups. In young adults, decreased coupling between the sensorimotor network and the cortico-striato-cerebellar network is associated with better motor learning, whereas a similar relation is found in old adults between the sensorimotor, the dorsal-attentional and the DMNs. Additionally, age-related correlational differences were found in the dorsolateral prefrontal cortex, known to subtend attentional and controlled processes. These findings suggest that motor skill learning depends-in an age-dependent manner-on subtle interactions between resting-state networks subtending motor activity on the one hand, and controlled and attentional processes on the other hand.

Synteny analysis of genes and distribution of loci controlling oil content and fatty acid profile based on QTL alignment map in Brassica napus.

BACKGROUND: Deciphering the genetic architecture of a species is a good way to understand its evolutionary history, but also to tailor its profile for breeding elite cultivars with desirable traits. Aligning QTLs from diverse population in one map and utilizing it for comparison, but also as a basis for multiple analyses assure a stronger evidence to understand the genetic system related to a given phenotype. RESULTS: In this study, 439 genes involved in fatty acid (FA) and triacylglycerol (TAG) biosyntheses were identified in Brassica napus. B. napus genome showed mixed gene loss and insertion compared to B. rapa and B. oleracea, and C genome had more inserted genes. Identified QTLs for oil (OC-QTLs) and fatty acids (FA-QTLs) from nine reported populations were projected on the physical map of the reference genome "Darmor-bzh" to generate a map. Thus, 335 FA-QTLs and OC-QTLs could be highlighted and 82 QTLs were overlapping. Chromosome C3 contained 22 overlapping QTLs with all trait studied except for C18:3. In total, 218 candidate genes which were potentially involved in FA and TAG were identified in 162 QTLs confidence intervals and some of them might affect many traits. Also, 76 among these candidate genes were found inside 57 overlapping QTLs, and candidate genes for oil content were in majority (61/76 genes). Then, sixteen genes were found in overlapping QTLs involving three populations, and the remaining 60 genes were found in overlapping QTLs of two populations. Interaction network and pathway analysis of these candidate genes indicated ten genes that might have strong influence over the other genes that control fatty acids and oil formation. CONCLUSION: The present results provided new information for genetic basis of FA and TAG formation in B. napus. A map including QTLs from numerous populations was built, which could serve as reference to study the genome profile of B. napus, and new potential genes emerged which might affect seed oil. New useful tracks were showed for the selection of population or/and selection of interesting genes for breeding improvement purpose.

Age differences in the neural correlates of distraction regulation: A network interaction approach.

Older adults show decrements in the ability to ignore or suppress distraction relative to younger adults. However, age differences in the neural correlates of distraction control and the role of large-scale network interaction in regulating distractors are scarcely examined. In the current study, we investigated age differences in how the anticorrelation between an externally oriented dorsal attention network (DAN) and an internally focused default mode network (DMN) is related to inhibiting distractors presented during a 1-back working memory task. For both young and older adults, the extent of DAN-DMN anticorrelation predicted reduced distractibility. Activation in a common set of frontal and insular control regions during the task was, however, associated with opposite patterns of network interaction and distractibility in the age groups. For older adults, recruitment of these regions was associated with greater DAN-DMN anticorrelation and less distractibility (better performance). For younger adults, it was associated with decreased DAN-DMN anticorrelation and more distractibility (worse performance). Our findings demonstrate the age-dependent relationship between DAN-DMN interaction patterns and engagement of control regions during an externally oriented distraction control task. This suggests that engagement of those regions may play a compensatory role for older adults but may be indicative of less efficient neural control mechanisms in younger adults.

Drivers shaping the diversity and biogeography of total and active bacterial communities in the South China Sea.

To test the hypothesis that different drivers shape the diversity and biogeography of the total and active bacterial community, we examined the bacterial community composition along two transects, one from the inner Pearl River estuary to the open waters of the South China Sea (SCS) and the other from the Luzon Strait to the SCS basin, using 454 pyrosequencing of the 16S rRNA and 16S rRNA gene (V1-3 regions) and thereby characterizing the active and total bacterial community, respectively. The diversity and biogeographic patterns differed substantially between the active and total bacterial communities. Although the composition of both the total and active bacterial community was strongly correlated with environmental factors and weakly correlated with geographic distance, the active bacterial community displayed higher environmental sensitivity than the total community and particularly a greater distance effect largely caused by the active assemblage from deep waters. The 16S rRNA vs. rDNA relationships indicated that the active bacteria were low in relative abundance in the SCS. This might be due to a high competition between active bacterial taxa as indicated by our community network models. Based on these analyses, we speculate that high competition could cause some dispersal limitation of the active bacterial community resulting in a distinct distance-decay relationship. Altogether, our results indicated that the biogeographic distribution of bacteria in the SCS is the result of both environmental control and distance decay.

Identification of Potential Breast Cancer Stem Cell Biomarkers in the Secretome Using a Network Interaction Approach Analysis.

BACKGROUND: Breast cancer stem cells (BCSCs) play a role in the high rates of resistance, recurrence, and metastasis. The precise biomarkers of BCSCs can assist effectively in identifying cancer, assessing prognosis, diagnosing, and monitoring therapy. The aim of this study was to give a complete analysis for predicting specific biomarkers of BCSCs. METHODS: We aggregated profile datasets in this work to shed light on the underlying critical genes and pathways of BCSCs. We obtained two expression profiling by array datasets (GSE7513 and GSE7515) from the Gene Expression Omnibus (GEO) database to identify biomarkers in BCSCs. Enrichr was used to do functional analysis, including gene ontology (GO) and reactome pathway. Furthermore, the protein-protein interaction (PPI) of these differential expression genes (DEGs) was visualized using Cytoscape with the search tool for the retrieval of interacting genes (STRING). The hub genes in the PPI network were chosen for further investigation. RESULTS: We identified 65 up-regulated and 190 down- regulated DEGs and the GO enrichment analysis revealed that these DEGs were enriched in biological process related to tumorigenesis and stemness, including alter the extracellular matrix's physicochemical properties, cytoskeletal reorganisation, adhesion, motility, migration, growth, and survival. The Reactome analysis indicated that these DEGs were also involved in modulating function of ECM, regulation cancer metabolism and angiogenesis, tumor growth, proliferation, and metastasis. CONCLUSION: Our bioinformatic study revealed that FYN, INADL, OCLN, F11R, and TOP2A were potential biomarker panel of BCSCs from secretome.

Aspongopus chinensis Dallas induces pro-apoptotic and cell cycle arresting effects in hepatocellular carcinoma cells by modulating miRNA and mRNA expression.

Aspongopus chinensis Dallas is a traditional Chinese medicinal insect with several anticancer properties can inhibit cancer cell growth, by inhibiting cell division, autophagy and cell cycle. However, the precise therapeutics effects and mechanisms of this insect on liver cancer are still unknown. This study examined the inhibitory influence of A. chinensis on the proliferation of hepatocellular carcinoma (HCC) cells and explore the underlying mechanism using high-throughput sequencing. The results showed that A. chinensis substantially reduced the viability of Hep G2 cells. A total of 33 miRNAs were found to be upregulated, while 43 miRNAs were downregulated. Additionally, 754 mRNAs were upregulated and 863 mRNAs were downregulated. Significant enrichment of differentially expressed genes was observed in signaling pathways related to tumor cell growth, cell cycle regulation, and apoptosis. Differentially expressed miRNAs exhibited a targeting relationship with various target genes, including ARC, HSPA6, C11orf86, and others. Hence, cell cycle and apoptosis were identified by flow cytometry. These findings indicate that A. chinensis impeded cell cycle advancement, halted the cell cycle in the G0/G1 and S stages, and stimulated apoptosis. Finally, mouse experiments confirmed that A. chinensis significantly inhibits tumor growth in vivo. Therefore, our findings indicate that A. chinensis has a notable suppressive impact on the proliferation of HCC cells. The potential mechanism of action could involve the regulation of mRNA expression via miRNA, ultimately leading to cell cycle arrest and apoptosis. The results offer a scientific foundation for the advancement and application of A. chinensis in the management of HCC.

Network interactions with functional roles and evolutionary relationships for BURP domain-containing proteins in chickpea and model species.

The functional significance and evolutionary relationships of BURP domain-containing genes unique to plants is of interest. Network analysis reveals different associations of BURP proteins with other proteins and functional terms, throwing light on their involvement in various biological processes and pathways. The gene expression data reveals that BURP genes are affected by salinity stress, reflecting diverse expression patterns in roots and shoots.

Disrupted Dynamic Interactions Between Large-Scale Brain Networks in Cocaine Users Are Associated With Dependence Severity.

BACKGROUND: Substance use disorder is conceptualized as a neuropsychiatric disease with multifaceted phenotypic manifestations including disrupted interactions between brain networks. While the current understanding of brain network interactions is mostly based on static functional connectivity, accumulating evidence suggests that temporal dynamics of these network interactions may better reflect brain function and disease-related dysfunction. We thus investigated brain dynamics in cocaine use disorder and assessed their relationship with cocaine dependence severity. METHODS: Using a time frame analytical approach on resting-state functional magnetic resonance imaging data of 54 cocaine users and 54 age- and sex-matched healthy control participants, we identified temporally recurring brain network configuration patterns, termed brain states. With Menon's triple network model as a guide, we characterized these state dynamics by quantifying their occurrence rate and transition probability. Group differences in the state dynamics and their association with cocaine dependence were assessed. RESULTS: Three recurrent brain states with spatial patterns resembling the default mode, salience, and executive control networks were identified. Compared with healthy control subjects, cocaine users showed a higher default mode state occurrence rate and higher probability of transitioning from the salience state to the default mode state, with the former being attributed to the latter. A composite state transition probability negatively correlated with cocaine dependence severity. CONCLUSIONS: Our results provide novel evidence supporting the triple network model. While confirming hyperactivity of default mode network in cocaine users, our findings indicate the failure of salience network in toggling between default mode and executive control networks in cocaine use disorder.

Large-Scale Thalamocortical Triple Network Dysconnectivities in Patients With First-Episode Psychosis and Individuals at Risk for Psychosis.

BACKGROUND AND HYPOTHESIS: Aberrant thalamocortical connectivity and large-scale network interactions among the default mode network (DMN), salience network (SN), and executive control network (ECN) (ie, triple networks) have been regarded as critical in schizophrenia pathophysiology. Despite the importance of network properties and the role of the thalamus as an integrative hub, large-scale thalamocortical triple network functional connectivities (FCs) in different stages of the psychotic disorder have not yet been reported. STUDY DESIGN: Thirty-nine first-episode psychosis (FEP) patients, 75 individuals at clinical high risk (CHR) for psychosis, 46 unaffected relatives (URs) of schizophrenia patients with high genetic loading, and 110 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI). Modular community detection was used to identify cortical and thalamic resting-state networks, and thalamocortical network interactions were compared across the groups. STUDY RESULTS: Thalamic triple networks included higher-order thalamic nuclei. Thalamic SN-cortical ECN FC was greater in the FEP group than in the CHR, UR, and HC groups. Thalamic DMN-cortical DMN and thalamic SN-cortical DMN FCs were greater in FEP and CHR participants. Thalamic ECN-cortical DMN and thalamic ECN-cortical SN FCs were greater in FEP patients and URs. CONCLUSIONS: These results highlight critical modulatory functions of thalamic triple networks and the shared and distinct patterns of thalamocortical triple network dysconnectivities across different stages of psychotic disorders. The current study findings suggest that large-scale thalamocortical triple network dysconnectivities may be used as an integrative biomarker for extending our understanding of the psychosis pathophysiology and for targeting network-based neuromodulation therapeutics.

Connect the dots: sketching out microbiome interactions through networking approaches.

Microbiome networking analysis has emerged as a powerful tool for studying the complex interactions among microorganisms in various ecological niches, including the human body and several environments. This analysis has been used extensively in both human and environmental studies, revealing key taxa and functional units peculiar to the ecosystem considered. In particular, it has been mainly used to investigate the effects of environmental stressors, such as pollution, climate change or therapies, on host-associated microbial communities and ecosystem function. In this review, we discuss the latest advances in microbiome networking analysis, including methods for constructing and analyzing microbiome networks, and provide a case study on how to use these tools. These analyses typically involve constructing a network that represents interactions among microbial taxa or functional units, such as genes or metabolic pathways. Such networks can be based on a variety of data sources, including 16S rRNA sequencing, metagenomic sequencing, and metabolomics data. Once constructed, these networks can be analyzed to identify key nodes or modules important for the stability and function of the microbiome. By providing insights into essential ecological features of microbial communities, microbiome networking analysis has the potential to transform our understanding of the microbial world and its impact on human health and the environment.

The Social Connectome - Moving Toward Complexity in the Study of Brain Networks and Their Interactions in Social Cognitive and Affective Neuroscience.

Over the past 150 years of neuroscientific research, the field has undergone a tremendous evolution. Starting out with lesion-based inference of brain function, functional neuroimaging, introduced in the late 1980s, and increasingly fine-grained and sophisticated methods and analyses now allow us to study the live neural correlates of complex behaviors in individuals and multiple agents simultaneously. Classically, brain-behavior coupling has been studied as an association of a specific area in the brain and a certain behavioral outcome. This has been a crucial first step in understanding brain organization. Social cognitive processes, as well as their neural correlates, have typically been regarded and studied as isolated functions and blobs of neural activation. However, as our understanding of the social brain as an inherently dynamic organ grows, research in the field of social neuroscience is slowly undergoing the necessary evolution from studying individual elements to how these elements interact and their embedding within the overall brain architecture. In this article, we review recent studies that investigate the neural representation of social cognition as interacting, complex, and flexible networks. We discuss studies that identify individual brain networks associated with social affect and cognition, interaction of these networks, and their relevance for disorders of social affect and cognition. This perspective on social cognitive neuroscience can highlight how a more fine-grained understanding of complex network (re-)configurations could improve our understanding of social cognitive deficits in mental disorders such as autism spectrum disorder and schizophrenia, thereby providing new impulses for methods of interventions.

Proteomic Markers in the Muscles and Brain of Pigs Recovered from Hemorrhagic Stroke.

(1) Background: Stroke is the leading cause of serious long-term disability. Walking dysfunction and paresis of the upper extremities occurs in more than 80% of people who have had a stroke. (2) Methods: We studied post-genomic markers in biosamples of muscle and brain tissue from animals that underwent intracerebral hematoma and recovered after 42 days. Our purpose was to understand the biological mechanisms associated with recovery from hemorrhagic stroke. We analyzed the peptides formed after trypsinolysis of samples by HPLC-MS, and the results were processed by bioinformatics methods, including the establishment of biochemical relationships (gene to gene) using topological omics databases such as Reactome and KEGG. (3) Results: In the pig brain, unique compounds were identified which are expressed during the recovery period after traumatic injury. These are molecular factors of activated microglia, and they contribute to the functional recovery of neurons and reduce instances of hematoma, edema, and oxidative stress. Complexes of the main binding factors of the neurotrophins involved in the differentiation and survival of nerve cells were found in muscles. (4) Conclusions: A network of gene interactions has been constructed for proteins involved in the regulation of synaptic transmission, in particular presynaptic vesicular and endocytic processes. The presence of transmitters and transporters associated with stimulation of NMDA receptors at neuromuscular junctions shows the relationship between upper motor neurons and neuromuscular junctions.

Bioinformatics analysis of gene expression profile and functional analysis in periodontitis and Parkinson's disease.

Periodontitis is a chronic inflammatory disease inextricably linked to both the innate and acquired immune systems of the body. Parkinson's disease (PD) is a neurodegenerative disease caused by immune system dysfunction. Although recent studies suggest that a clinical relationship exists between PD and periodontitis, the pathogenesis of this relationship is unclear. Therefore, in the present study, we obtained datasets of periodontitis and PD from the Gene Expression Omnibus (GEO) database and extracted 785 differentially expressed genes (DEGs), including 15 common upregulated genes and four common downregulated genes. We performed enrichment analyses of these DEGs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses. We found that the genes were mainly enriched in keratinocyte differentiation, neuronal cell bodies, and structural constituents of epidermis terms, and pathways such as immune response and synaptic pathways. In addition, we screened matching hub genes by constructing a protein-protein interaction (PPI) network map and a Molecular Complex Detection (MCODE) map using the Cytoscape software. The hub genes were then subjected to GO enrichment analysis, which revealed that the dopamine biosynthetic process, dopaminergic synapse and dopamine-binding terms, and dopaminergic synapse and serotonergic synapse pathways were primarily where they were expressed. Finally, we selected four of these genes for validation in the periodontitis and PD datasets, and we confirmed that these hub genes were highly sensitive and specific for diagnosing and monitoring PD and periodontitis. In conclusion, the above experimental results indicate that periodontitis is a high-risk factor for PD, and the association between these two conditions is mainly manifested in immune and dopamine-related pathways. Hub genes, such as the CDSN, TH, DDC, and SLC6A3 genes, may serve as potential biomarkers for diagnosing or detecting PD.

A Triple-Network Dynamic Connection Study in Alzheimer's Disease.

Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.