SEARCH · Search PubMed

Results for “Dynamic network”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources

PubMed2025

DyNDG: Identifying Leukemia-related Genes Based on Time-series Dynamic Network by Integrating Differential Genes.

Leukemia is a malignant disease characterized by progressive accumulation with high morbidity and mortality rates, and investigating its disease genes is crucial for understanding its etiology and pathogenesis. Network propagation methods have emerged and been widely employed in disease gene prediction, but most of them focus on static biological networks, which hinders their applicability and effectiveness in the study of progressive diseases. Moreover, there is currently a lack of special algorithms for the identification of leukemia disease genes. Here, we proposed a novel Dynamic Network-based model integrating Differentially expressed Genes (DyNDG) to identify leukemia-related genes. Initially, we constructed a time-series dynamic network to model the development trajectory of leukemia. Then, we built a background-temporal multilayer network by integrating both the dynamic network and the static background network, which was initialized with differentially expressed genes at each stage. To quantify the associations between genes and leukemia, we extended a random walk process to the background-temporal multilayer network. The results demonstrate that DyNDG achieves superior accuracy compared to several state-of-the-art methods. Moreover, after excluding housekeeping genes, DyNDG yields a set of promising candidate genes associated with leukemia progression or potential biomarkers, indicating the value of dynamic network information in identifying leukemia-related genes. The implementation of DyNDG is available at both https://ngdc.cncb.ac.cn/biocode/tool/BT7617 and https://github.com/CSUBioGroup/DyNDG.

Leukemia↗

PubMed2026

Cerebellar iTBS enhances gait adaptation by modulating cortical sensorimotor network dynamics: a randomized controlled trial.

Gait adaptation enables individuals to maintain locomotor stability under persistent perturbations. Although the cerebellum is critical for sensory prediction error-based (SPE) adaptation, how cerebellar neuromodulation reshapes cortical sensorimotor networks to enhance gait adaptation remains unclear. This study investigated the behavioral effects and underlying cortical neurodynamic mechanisms of cerebellar intermittent theta-burst stimulation (iTBS) on gait adaptation. Thirty-two healthy adults received either active or sham cerebellar iTBS. Participants performed a split-belt treadmill adaptation task before and after intervention. Cortical responsiveness was evaluated using TMS-evoked EEG over primary motor cortex (M1), while resting-state EEG was analyzed to assess spectral power and directional functional connectivity. Compared to sham, cerebellar iTBS significantly enhanced gait adaptation, evidenced by a faster adaptation rate (p = 0.035) and enhanced Early Adaptation SLS (p = 0.011), without altering initial perturbation responses or post-adaptation outcomes. The iTBS increased TMS-evoked α (p = 0.031) and γ (p = 0.022) power in M1, while the α power was correlated with faster adaptation (r = 0.526, p = 0.002). Furthermore, iTBS strengthened PPC-to-M1 directed connectivity in the β (p = 0.025) and γ (p = 0.013) bands. Enhanced parieto-motor directionality were positively associated with adaptation rate (β: r = 0.515, p = 0.003; γ: r = 0.463, p = 0.009). These findings suggest that cerebellar iTBS facilitates gait adaptation by modulating cortical responsiveness and directional sensorimotor network connectivity, providing multi-level neurodynamic evidence for the cerebello-cortical modulation during gait adaptation and offering a strong physiological rationale for targeted neuromodulation in gait rehabilitation strategies.

Humans↗

PubMed2025

HiCForecast: dynamic network optical flow estimation algorithm for spatiotemporal Hi-C data forecasting.

MOTIVATION: The exploration of the 3D organization of DNA within the nucleus in relation to various stages of cellular development has led to experiments generating spatiotemporal Hi-C data. However, there is limited spatiotemporal Hi-C data for many organisms, impeding the study of 3D genome dynamics. To overcome this limitation and advance our understanding of genome organization, it is crucial to develop methods for forecasting Hi-C data at future time points from existing timeseries Hi-C data. RESULT: In this work, we designed a novel framework named HiCForecast, adopting a dynamic voxel flow algorithm to forecast future spatiotemporal Hi-C data. We evaluated how well our method generalizes forecasting data across different species and systems, ensuring performance in homogeneous, heterogeneous, and general contexts. Using both computational and biological evaluation metrics, our results show that HiCForecast outperforms the current state-of-the-art algorithm, emerging as an efficient and powerful tool for forecasting future spatiotemporal Hi-C datasets. AVAILABILITY AND IMPLEMENTATION: HiCForecast is publicly available at https://github.com/OluwadareLab/HiCForecast.

Algorithms↗

PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗

PubMed2026

Network oscillatory dynamics accompany cerebral bioenergetic defence in hypoxia.

A network physiology framework investigated how coordinated interactions among multiple organ systems collectively support the preservation of cerebral bioenergetic function and better distinguish adaptive from maladaptive responses to hypoxia. Twelve healthy males were passively exposed to 6 h of normoxia (21% O2) and hypoxia (12% O2) in a randomised, single-blind, crossover design. Venous blood was assayed for oxidative-nitrosative stress (OXNOS, spectroscopy/chemiluminescence) and neurovascular unit (hs-ELISA) biomarkers. Global cerebral delivery of O2 and glucose were determined by duplex ultrasound. Clinical acute mountain sickness (AMS+) was diagnosed in five participants. Cerebral substrate delivery was well maintained in both hypoxia and AMS+ (p < 0.05 vs normoxia and AMS-) despite marked arterial hypoxemia. Bioenergetic defence coincided with pronounced elevations in the spectral amplitude and phase synchronisation of very low-frequency oscillations (VLFOs, 0.03-0.06 Hz), which were evident across multiple organ systems and most prominent within the cerebral network. Systemic VLFOs were further exaggerated and more functionally connected in AMS+ in the absence of exaggerated systemic OXNOS or structural damage/destabilisation of the neurovascular unit (both p < 0.05 vs normoxia and AMS-). Collectively, these findings suggest that AMS, while characterised by debilitating symptomatology, may reflect a neuroprotective adaptive as opposed to pathologically maladaptive phenotype.

Humans↗

PubMed2026

Dual-tasking reveals severity-dependent reorganization of cortical beta energy landscapes in Parkinson's disease.

Dual-task impairment is a hallmark of Parkinson's disease (PD), yet the large-scale neural mechanisms underlying postural-motor interference remain poorly understood. In particular, it is unclear how cortical network dynamics reorganize across disease severity when postural control competes with concurrent task demands. This study investigated EEG-derived beta-band cortical energy landscapes in healthy older adults, early-stage PD, and mid-stage PD during single- and dual-task conditions. Dual-task behavioral cost increased with disease severity for concurrent manual performance (p < 0.001), whereas a quadratic pattern was observed for postural performance. Energy landscape analysis revealed severity-dependent reconfiguration of cortical beta dynamics. Dual-task-related landscape changes in effective network flexibility (ΔNeff), landscape geometry (ΔEvar and ΔGmag), and dominant low-energy attractor organization (ΔLow mass and ΔLow area) showed significant monotonic trends (p < 0.05), reflecting progressive constrained cortical network dynamics with advancing PD severity. In addition, dual-task-related landscape alterations were associated with clinical severity, as indexed by Hoehn and Yahr stage (|r| = 0.353-0.423, p = 0.016-0.048), and showed associations with motor impairment, as measured by MDS-UPDRS part III scores (|r| = 0.333-0.455, p = 0.009-0.063). These findings demonstrate that dual-task demands induce severity-dependent reconfiguration of cortical beta energy landscapes in PD. Energy landscape geometry may capture systems-level neural constraints associated with dual-task susceptibility in PD, providing a physiologically grounded framework to characterize disease-related functional vulnerability.

Humans↗

PubMed2026

Integrative multi-omics and single-cell analysis identifies EGFR pathway activation and metabolic reprogramming as potential synthetic lethal vulnerabilities in resistance to the FGFR inhibitor AZD4547.

BACKGROUND: Although fibroblast growth factor receptor (FGFR) inhibitors (FGFRi) have demonstrated clinical promise, the inevitable emergence of acquired resistance remains a critical bottleneck, severely compromising their long-term clinical efficacy. The pan-cancer molecular landscape and heterogeneous mechanisms driving this resistance, ranging from genetic alterations to dynamic network rewiring, remain poorly understood. METHODS: We integrated large-scale pharmacogenomic profiling of the FGFR inhibitor AZD4547 from the GDSC2 and PRISM databases with single-cell RNA sequencing to dissect the multi-omics landscape of FGFRi resistance across 312 cell lines from 8 cancer types. This multi-omics framework was further extended by machine learning modeling and systematic synthetic lethality screening to uncover actionable therapeutic targets. In vitro viability assays and western blot analysis were subsequently conducted to experimentally evaluate the predicted FGFR-EGFR synthetic lethality. RESULTS: Our dual-database analysis unveiled a multi-dimensional atlas of FGFRi resistance. We identified cancer-specific genomic drivers, such as ELF4 amplification in glioblastoma, alongside key transcriptomic markers including UCP2 and FSCN1, highlighting a shift towards metabolic reprogramming and epithelial-mesenchymal transition (EMT). Single-cell analysis unveiled that resistance is linked to the heterogeneous enrichment of baseline subpopulations characterized by distinct metaprograms, including cell-cycle dysregulation. Furthermore, a random forest model built on a LASSO-derived transcriptomic signature was constructed, demonstrating promising predictive capability for AZD4547 sensitivity (mean test-set AUC = 0.73, 95% CI [0.63, 0.80]); the signature generalized well to erdafitinib but showed limited transferability to some other FGFR inhibitors (e.g. pemigatinib, BGJ398). Most notably, our synthetic lethal screening revealed a convergent reliance on compensatory RTK signaling (specifically EGFR pathway enrichment) and downstream MAPK/PI3K cascades in resistant phenotypes, providing converging computational evidence for EGFR pathway activation as an adaptive bypass mechanism. This predicted synthetic lethality was experimentally supported in two FGFR-dependent cell line models (RT112 and CCLP1), in which combined FGFR-EGFR inhibition produced marked synergistic antiproliferative effects. CONCLUSIONS: This study establishes a comprehensive multi-omics atlas of resistance to the FGFR inhibitor AZD4547, delineating convergent mechanisms of metabolic reprogramming and EGFR-mediated bypass signaling. Our findings characterize the resistance as a dynamic network rewiring and nominate rational combination strategies to overcome this therapeutic bottleneck. While FGFR-EGFR co-inhibition is experimentally supported, metabolic co-targeting remains a computationally derived, hypothesis-generating strategy.

Benzamides↗

PubMed2026

CACNA1C Genetic Variants Differentially Affect Neuronal Networks Through Divergent Pathways.

BACKGROUND: CACNA1C encodes the pore-forming subunit of the L-type calcium channel Cav1.2. Common variants in CACNA1C are associated with psychiatric disorders, whereas rare single nucleotide variants cause CACNA1C-related disorder, a multisystem disorder with symptoms that include autism spectrum disorder (ASD), intellectual disability, and seizures. However, the cellular mechanisms linking CACNA1C dysfunction to neurodevelopmental phenotypes remain poorly understood. METHODS: We generated isogenic CACNA1C loss-of-function induced pluripotent stem cell lines and reprogrammed a line from an individual carrying a novel predicted gain-of-function variant (p.Ala1521Pro) in CACNA1C. Neuronal activity was assessed using multielectrode arrays, pharmacological manipulation, and gene expression analysis. Early developmental phenotypes were examined using quantitative reverse transcriptase polymerase chain reaction, immunocytochemistry, and RNA sequencing. RESULTS: Neurons carrying CACNA1C variants displayed opposing alterations in network dynamics, depending on variant type. Pharmacological and molecular assays indicated that these network differences were associated with dysregulated GABAergic (gamma-aminobutyric acidergic) signaling. Early developmental analysis revealed that loss of CACNA1C altered rosette morphology, CREB (cAMP response element binding protein) phosphorylation, and transcriptional programs related to axonogenesis and synaptic signaling, indicating effects on neuronal differentiation. The patient line exhibited opposing effects on rosette morphology and CREB signaling, reflecting variant-specific effects. CONCLUSIONS: These findings demonstrate that Cav1.2 regulates excitatory-inhibitory balance, network organization, and aspects of neurodevelopment. Divergent effects of CACNA1C variants highlight how altered Cav1.2 signaling contributes to variable neurodevelopmental phenotypes, including ASD and epilepsy, and establish a framework for defining CACNA1C variant effects in human neurons.

CACNA1C↗

PubMed2024

Multi-organ gene expression analysis and network modeling reveal regulatory control cascades during the development of hypertension in female spontaneously hypertensive rat.

Hypertension is a multifactorial disease with stage-specific gene expression changes occurring in multiple organs over time. The temporal sequence and the extent of gene regulatory network changes occurring across organs during the development of hypertension remain unresolved. In this study, female spontaneously hypertensive (SHR) and normotensive Wistar Kyoto (WKY) rats were used to analyze expression patterns of 96 genes spanning inflammatory, metabolic, sympathetic, fibrotic, and renin-angiotensin (RAS) pathways in five organs, at five time points from the onset to established hypertension. We analyzed this multi-dimensional dataset containing ~15,000 data points and developed a data-driven dynamic network model that accounts for gene regulatory influences within and across visceral organs and multiple brainstem autonomic control regions. We integrated the data from female SHR and WKY with published multiorgan gene expression data from male SHR and WKY. In female SHR, catecholaminergic processes in the adrenal gland showed the earliest gene expression changes prior to inflammation-related gene expression changes in the kidney and liver. Hypertension pathogenesis in male SHR instead manifested early as catecholaminergic gene expression changes in brainstem and kidney, followed by an upregulation of inflammation-related genes in liver. RAS-related gene expression from the kidney-liver-lung axis was downregulated and intra-adrenal RAS was upregulated in female SHR, whereas the opposite pattern of gene regulation was observed in male SHR. We identified disease-specific and sex-specific differences in regulatory interactions within and across organs. The inferred multi-organ network model suggests a diminished influence of central autonomic neural circuits over multi-organ gene expression changes in female SHR. Our results point to the gene regulatory influence of the adrenal gland on spleen in female SHR, as compared to brainstem influence on kidney in male SHR. Our integrated molecular profiling and network modeling identified a stage-specific, sex-dependent, multi-organ cascade of gene regulation during the development of hypertension.

Animals↗

PubMed2026

Examining early-phase symptom trajectories in interpersonal psychotherapy versus antidepressant medication for adults with depression: A dynamic time warp network analysis.

BACKGROUND: Depression is characterized by substantial symptom heterogeneity, which is often concealed when examining total severity scores. Analyzing symptom-level change can improve our understanding of treatment effects and recovery processes. This study, therefore, examined dynamic symptom networks during early-phase interpersonal psychotherapy (IPT) and selective serotonin reuptake inhibitor (SSRI) antidepressant treatment, assessing patterns of symptom change across as well as differences between treatments. METHODS: Using weekly item-level Hamilton Depression Rating Scale (HAM-D) data from a randomized clinical trial comparing IPT and SSRIs for adults with depression, this preregistered study examined symptom trajectories in the first six weeks of treatment with Dynamic Time Warping (DTW). RESULTS: Depressive symptom trajectories and DTW-based symptom networks were largely similar for IPT and SSRI. In both conditions, changes in somatic symptoms of anxiety and middle insomnia tended to precede improvements in depressed mood. CONCLUSIONS: Early symptom change may occur outside the core affective domain, underscoring the importance of monitoring symptoms broadly. Symptom-level patterns may reflect patients' stage of recovery and provide clinically relevant information beyond total severity scores. The absence of differences in improvement patterns between IPT and SSRI suggest few indications for treatment selection based on baseline symptom profiles. Future research should replicate and extend these findings to subsequent treatment phases using more frequent assessments and a broader range of interventions.

Humans↗

PubMed2026

The DLX/Notch axis is necessary for spatiotemporal regulation of neural cell fate.

Neuronal-glial cell fate switch during forebrain development is highly regulated. DLX transcription factors are necessary for promoting GABAergic interneuron differentiation and migration but the mechanisms for concomitant repression of glial fate in neural progenitors remain elusive. Here, the DLX2 regulatory network dynamic in the developing ventral telencephalon is characterised using a multi-omic approach at single-cell resolution, including single-cell whole genome spatial transcriptomics. We identify a secondary proliferative zone in the ventral subventricular zone and spatiotemporal-context dependent Notch pathway repression by DLX2 in maintaining progenitor populations and facilitating neural differentiation. We find that DLX2 controls cell fate determination by directly repressing Notch signalling genes as well as glial fate-promoting transcription factors, thereby inhibiting early adoption of oligodendroglial differentiation during neurogenesis. Here, we show that temporal cell fate switch is mediated by DLX2 via a multilayer gene regulatory network, redefining current understanding of neuronal-glial cell specification mechanisms in the developing telencephalon.

Animals↗

PubMed2025

Transcriptome-wide analysis reveals sequence selection to avoid mRNA aggregation in E. coli.

The stability of RNA base pairing and its limited four-letter code create an intrinsic potential for promiscuous RNA-RNA interactions. In vitro, such interactions drive RNA to self-assemble into aggregates. This raises a fundamental unanswered question: within a confined cellular volume at physiological mRNA abundances, how much aggregation would arise from sequence-encoded chemistry alone? Here, we establish this baseline with large-scale kinetic simulations of the E. coli transcriptome. Our simulations reveal that sequence-encoded base-pairing energetics is sufficient to generate a dynamic network of large aggregates, organized by long, multivalent mRNA hubs. Strikingly, evolutionary analysis shows that native E. coli sequences exhibit clear signatures of selection to counteract this propensity: they fold more stably, minimize unstructured regions, and form weaker intermolecular contacts than dinucleotide-preserving controls. These findings demonstrate that maintaining transcriptome solubility has been a significant, previously unrecognized constraint shaping genome evolution, and provide a new lens to interpret cellular RNA management.

Biological Sciences (Biophysics and Computational ↗

PubMed2026

Tahoe-100M: Mapping drug-induced molecular phenotypes at single-cell resolution.

We present Tahoe-100M, a giga-scale single-cell perturbation atlas comprising 100 million transcriptomes from 50 diverse cancer cell lines treated with 1,100 drug-dose conditions. This parallel profiling of thousands of perturbations at single-cell resolution with minimal batch effects is enabled by the Mosaic platform, which multiplexes genetically distinct cell models into balanced "cell villages." Beyond cataloging transcriptomic shifts, Tahoe-100M systematically quantifies cellular phenotypes, including proliferation, cytotoxicity, lineage-specific vulnerabilities, and cell-cycle changes. It captures population-level transcriptomic heterogeneity, characterizing whether drug responses drive cells toward divergent fates or convergent states. Pathway-based signatures define drug-induced expression programs, classify mechanisms of action, reveal off-target activities, and expose adaptive stress responses associated with resistance. By unifying cellular and molecular readouts, this broadly applicable perturbation atlas advances our ability to model gene regulation, drug response, and network dynamics. Its public release enables the training of AI frameworks to advance predictive models of cell behavior.

Humans↗

PubMed2026

Brain-wide spontaneous neural avalanches: Definition, functional dynamics and cognitive relevance.

Although spontaneous activity is ubiquitous across multiple spatiotemporal scales, its functional organization and cognitive relevance remain poorly understood. Following the classic neuronal avalanche framework, a spontaneous avalanche is defined as consecutively active frames separated by inactive time bins. Hence, multiple distinct avalanches may be considered as one avalanche, thereby ignoring their spatial and temporal distinguishability. Furthermore, group-level power-law fitting of such neural avalanches is often performed to evaluate brain criticality (referring to a system perched between order and disorder) due to the limited recording length of macroscale neuroimaging (such as functional magnetic resonance imaging), and the functional representation of brain-wide neural avalanches is largely unexplored. To address these issues, we proposed large-scale neural avalanches as a single, spatially consecutive cascade pattern and further investigated their functional dynamics, network propagation, and association with task-evoked activity. Compared with the conventional inactive-bin definition, our current approach is more favorable to power-law fitting of avalanche size and duration distributions at the individual level. We also demonstrated that participants whose brain activities were close to the critical point tend to have higher cognitive abilities. Notably, the ratio of neural avalanches that evolved from primary sensory to association networks negatively correlated with cognitive abilities. Moreover, the geometric distance between low-dimensional representations of task-evoked activity and spontaneous avalanches was associated with behavioral performance. This study not only provides a promising avenue for measuring avalanche criticality based on human whole-brain neuroimaging, but also suggests that spontaneous neural avalanches and their low-dimensional representations contribute to human cognitive abilities.

Humans↗

PubMed2026

Sanguinarine as a multi-target therapeutic candidate for laryngeal cancer: insights from network pharmacology, molecular dynamics and in vitro validation.

OBJECTIVE: To identify the core targets and elucidate the potential molecular mechanisms of sanguinarine (SA) against laryngeal squamous cell carcinoma (LSCC), and to validate its antitumor effects in vitro. METHODS: Potential targets of SA were predicted using SwissTargetPrediction, TargetNet, and SuperPred and intersected with LSCC-related targets obtained from the GeneCards, OMIM, and DISEASES databases. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. A protein-protein interaction (PPI) network was constructed using the STRING database (combined score > 0.900), and topological parameters including degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and local average connectivity (LAC) were calculated in Cytoscape to identify core genes based on median thresholds. Molecular docking and 100-ns molecular dynamics (MD) simulations were conducted for epidermal growth factor receptor (EGFR), Phosphatidylinositide-3-kinase catalytic subunit alpha (PIK3CA), phosphatidylinositol-4,5-biphosphate 3-kinase catalytic subunit β (PIK3CB), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit delta (PIK3CD), and Non-Receptor Tyrosine Kinase (SRC). The effects of SA on LSCC were evaluated using CCK-8, colony formation, Transwell migration, and wound-healing assays in TU177 cells and TU212. RESULTS: A total of 213 common targets were identified, which were significantly enriched in PI3K-Akt signaling, EGFR tyrosine kinase inhibitor resistance, and adhesion- and migration-related pathways. The PPI network comprised 259 nodes and 259 edges, from which five core genes-PIK3CA, PIK3CB, PIK3CD, EGFR, and SRC-were identified. Molecular docking revealed strong binding affinities between SA and the PI3K family proteins (- 9.79 to - 10.96 kcal/mol), as well as EGFR (- 8.58 kcal/mol) and SRC (- 6.77 kcal/mol). MD simulations indicated greater stability of SA complexes with EGFR and PI3K family members compared with SRC. In vitro assays demonstrated that SA significantly inhibited TU177 cell and TU212 cell proliferation, colony formation, and migration. CONCLUSION: SA may exert anti-laryngeal cancer effects through synergistic multi-target inhibition centered on the EGFR/SRC/PI3K signaling axis, highlighting its potential as a promising therapeutic candidate for LSCC.

Humans↗

PubMed2025

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.

Post-traumatic stress (PTS) encompasses a range of psychological responses following trauma, which may lead to more severe outcomes such as post-traumatic stress disorder (PTSD). Identifying early neuroimaging biomarkers that link brain function to PTS outcomes is critical for understanding PTSD risk. This longitudinal study examines the association between brain dynamic functional network connectivity (dFNC) and current/future PTS symptom severity, and the impact of sex on this relationship. By analyzing 275 participants' dFNC data obtained ~2 weeks after trauma exposure, we noted that brain dynamics of an inter-network brain state link negatively with current (r=-0.197, p corrected = 0.0079) and future (r=-0.176, p corrected = 0.0176) PTS symptom severity. Also, dynamics of an intra-network brain state correlated with future symptom intensity (r = 0.205, p corrected = 0.0079). We additionally observed that the association between the network dynamics of the inter-network and intra-network brain state with symptom severity is more pronounced in female group. Our findings highlight a potential link between brain network dynamics in the aftermath of trauma with current and future PTSD outcomes, with a stronger effect in female group, underscoring the importance of sex differences.

Journal Article↗

PubMed2026

Integrative computational analysis combining network pharmacology, regulatory network modeling, and molecular dynamics reveals the mechanisms of Quanshen compound in ITP.

UNLABELLED: Immune thrombocytopenia (ITP) is a hemorrhagic disorder caused by immune dysfunction. Quanshen Compound (QSC) is an in-house preparation developed by the Uyghur Hospital in Hotan Prefecture. This study primarily investigates and validates the potential pharmacological basis and mechanism of action of QSC in modulating immune thrombopoiesis. Based on the multi-database screening of the QSC and the related targets of ITP, the intersection was obtained to construct a protein-protein interaction (PPI) network and screen the core targets; the intersection targets were analyzed for gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using R packages; a component-target-pathway network was constructed to screen the key active components and their mechanisms of action. At the same time, the TF-mRNA-miRNA regulatory network of the core targets was constructed, and chromosome localization and subcellular localization analysis were performed; further, the binding stability of key components and core targets was verified through molecular docking and molecular dynamics simulation. A total of 227 potential target sites were screened out, among which TNF, IL6, AKT1, TP53 and IL1B were the core targets. The enrichment results indicated that these intersecting target sites mainly participated in inflammatory responses, immune regulation and hemostasis-related biological processes, and were significantly enriched in the PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation and PD-1/PD-L1 signaling pathway. The core target TF-mRNA-miRNA regulatory network contained 184 nodes and 200 edges, suggesting that the core targets were subject to multi-level regulation. Molecular docking results showed that the main active components had good binding activity with the core targets, and molecular dynamics simulation further verified the stability of the complex. QSC may improve ITP through a multi-component, multi-target, and multi-pathway synergistic mechanism involving key targets such as TNF, IL6, AKT1, TP53, and IL1B, as well as the PI3K-Akt signaling pathway. These findings provide new insights into the potential therapeutic mechanisms of QSC against ITP and warrant further experimental validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00718-0.

Immune thrombocytopenia↗

PubMed2026

Targeting EGFR in cancer using Terminalia arjuna: An integrated In Silico, molecular dynamics, experimental validation, and network pharmacology study.

The Epidermal Growth Factor Receptor (EGFR) plays a pivotal role in 20-60% of cancer cases, including glioblastoma, lung adenocarcinoma, and head and neck squamous cell carcinoma, as reported in The Cancer Genome Atlas (TCGA) dataset. The present study employed an integrated in silico and experimental workflow to evaluate EGFR-targeted compounds from Terminalia arjuna. Drug-likeness and ADMET screening were performed, followed by molecular docking and 1000 ns molecular dynamics simulations. In vitro validation was conducted using cancer cell-based assays and network pharmacology to explore the molecular mechanisms associated with the identified compound. Screening shortlisted eight compounds from T. arjuna. Molecular docking identified Arjunaside C (-8.2 kcal/mol), Arjunapthanoloside (-7.7 kcal/mol), and Beta-sitosterol (-7.4 kcal/mol) as potential EGFR inhibitors compared to Erlotinib (-6.6 kcal/mol). Arjunapthanoloside formed more H-bonds and exhibited most stable interactions with EGFR. MD simulations at 1000 ns revealed lower RMSD, RMSF, SASA, and Rg values for the Arjunapthanoloside-EGFR complex, indicating enhanced stability. Direct binding validation was limited by the unavailability of purified Arjunapthanoloside; therefore, Arjuna extract was evaluated, which demonstrated potent cytotoxicity with an IC₅₀ of 9 µg/mL in H357 oral cancer cells. Flow cytometry confirmed apoptosis-mediated cell death by increased early- and late-apoptotic cell populations. Network pharmacology analysis further identified additional targets (MMP3, MMP7, MMP9, and HRAS) that are directly involved in various cancers. Overall, the findings provide new insights into the therapeutic potential of Arjunapthanoloside as a stable compound that interacts with EGFR from T. arjuna, highlighting its significance in EGFR-targeted anticancer research.

ErbB Receptors↗