Search PubMedSearch

SEARCH · Search PubMed

Results for “Central executive 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.

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

451 records · Page 3Linked to original sources

Genome-Wide Characterization of β-Glucosidase (TaBGLU) Genes in Bread Wheat and Their Expression Under Drought, Cold, and Combined Stress.

Glycoside hydrolase 1 (GH1) β-glucosidases were known to activate hormone conjugates and defense metabolites, yet their genomic organization and stress-response dynamics in wheat remained incompletely defined. We therefore performed an integrated characterization of TaBGLUs spanning phylogeny, gene structure and conserved motifs, subcellular localization, promoter cis-elements, Gene Ontology enrichment, protein-protein interaction networks, and targeted expression profiling. Wheat TaBGLUs partitioned into well-supported clades that shared canonical GH1 catalytic residues and a largely conserved motif scaffold. Subcellular localization predictions indicated predominant nuclear and chloroplast targeting, with a smaller cohort directed to secretory or endomembrane compartments. Promoters were enriched for light-responsive, hormone-related (ABA, JA/SA, auxin, GA) and stress-associated (MYB/WRKY, heat, low temperature) cis-elements, and functional annotations were consistent with roles in carbohydrate and cell-wall metabolism, hormone homeostasis, and defense. Network analysis revealed a densely connected TaBGLU submodule embedded within broader carbohydrate and defense interaction networks, suggesting coordinated or cooperative functions. Expression profiling under cold, drought, and combined drought and cold demonstrated broad stress inducibility, with early activation detected by 6 h, cold-responsive maxima typically at 12 h, drought-responsive peaks predominating at 24 h, and combined stress eliciting both earlier and more sustained expression maxima between 12-24 h. Representative strongly responsive genes included TaBGLU20, TaBGLU44, TaBGLU6, and TaBGLU23, which showed pronounced late induction under combined stress, TaBGLU30, which exhibited an earlier combined-stress peak, and TaBGLU12, which displayed a marked late drought-specific response. Taken together, this integrated genomic, regulatory, and expression atlas refined the wheat BGLU repertoire relative to previous gene model inventories, highlighted candidate TaBGLUs with central network positions and strong stress inducibility, and provided concrete entry points for functional validation and breeding for improved stress resilience.

Triticum

Control of foreign DNA: emerging roles of xenogeneic silencers.

Bacteria continuously acquire foreign DNA through horizontal gene transfer, yet its successful integration depends on regulatory mechanisms that balance genome protection with evolutionary innovation. Xenogeneic silencers are central to this process: they preferentially bind AT-rich DNA, a common feature of many horizontally acquired genetic elements, and repress its transcription. Recent studies, however, reveal a much broader regulatory repertoire. Beyond transcriptional repression, these proteins contribute to chromosome organization by forming higher-order nucleoprotein complexes and phase-separated condensates that shape bacterial nucleoid architecture. Furthermore, they play roles in regulating bacteriophage infection cycles, including mechanisms by which phages hijack host silencing activities for their own benefit. Their extensive regulatory reach, spanning virulence genes, biofilm formation, specialized metabolite production, and mobile genetic elements (MGEs), underscores their central role in connecting environmental signals, including fluctuations in the second messenger c-di-GMP, with gene expression, and genome organization. The diversification of xenogeneic silencers across bacterial chromosomes, plasmids, phages, and other MGEs highlights their evolutionary significance. Together, these recent findings position xenogeneic silencers as dynamic regulatory modules that shape the fate of foreign DNA across the horizontal gene transfer network.

Gene Transfer, Horizontal

Deciphering CD8+ T cell exhaustion in human cancers through single-cell and spatial transcriptomics.

Exhausted CD8+ T cells (Tex) within the tumor microenvironment (TME) represents a critical barrier limiting anti-tumor immune responses. Tex cells are characterized by upregulated inhibitory immune checkpoint receptors, reduced cytotoxicity, and functional heterogeneity. Their genomic features and regulatory networks remain poorly defined, and only a minority of patients respond to immune checkpoint blockade (ICB) therapy. Single-cell RNA sequencing (scRNA-seq), through high-resolution transcriptomic profiling, has revealed diverse Tex subpopulations, identified subpopulation-specific marker genes and regulatory pathways. Spatial transcriptomics has further mapped the spatial distribution of Tex and their interaction networks with immune cells, tumor cells, and stromal cells, elucidating the impact of spatial heterogeneity on Tex functionality. Current studies indicate that the exhausted state of Tex is dynamic and modifiable, with functional differences among subpopulations closely associated with tumor progression and therapeutic response. However, the genomic characteristics, epigenetic regulation, and spatial interaction mechanisms of Tex require further exploration. This review summarizes recent advances in high-resolution omics technologies for precisely dissecting Tex heterogeneity, functional features, and interactions with other cells. It emphasizes the central value of optimizing Tex-targeted tumor immunotherapy strategies, providing theoretical foundations and directional guidance for developing more effective anti-tumor immunotherapies.

Humans

Catecholaminergic Contributions to Inhibitory Control Following Physical Fatigue: Behavioral and Neurophysiological Findings.

Acute physical fatigue can impair cognitive control, yet its underlying neurochemical mechanisms remain unclear. This study investigated whether catecholaminergic modulation influences behavioral and neural markers of inhibitory control following physical fatigue. Eighteen healthy, recreationally active adults (9 males, 9 females; 23.4 ± 2.2 years) completed a randomized, triple-blind, placebo-controlled crossover study. On separate visits, participants received methylphenidate (MPH; 20 mg; a dopamine and noradrenaline reuptake inhibitor), reboxetine (REB; 8 mg; a noradrenaline reuptake inhibitor), or placebo. Physical fatigue was induced by repeated bilateral leg extensions to task failure. Cognitive performance was assessed before and after physical fatigue using a Go/No-Go task with electroencephalographic recording. Behavioral outcomes included reaction time and accuracy, while event-related potentials measured neural stages of response execution and inhibition (N2 and P3). Mixed-effects models were used for statistical analysis. For No-Go trials, a significant MPH × Time interaction was observed for accuracy (p = 0.008), with improved post-fatigue performance following MPH administration (p = 0.048). At the neural level, MPH was associated with shorter fronto-central No-Go N2 latency (p = 0.038) and altered fatigue-related changes in No-Go P3 latency (p = 0.047). REB did not produce comparable behavioral or neural effects. These findings provide pharmacological evidence that catecholaminergic mechanisms contribute to inhibitory control following physical fatigue. The differential effects of MPH and REB suggest that selective noradrenergic enhancement alone is insufficient to maintain inhibitory control following physical fatigue. Instead, the findings implicate broader dopaminergic and noradrenergic mechanisms, potentially involving alterations in the temporal dynamics of inhibitory processing. TRIAL REGISTRATION: (G095422N and identifier NCT05880342).

Adult

Integrative multi-omics reveals a fibroblast-centered, ZFHX3-prioritized regulatory framework linking sick sinus syndrome and atrial fibrillation.

OBJECTIVE: To define shared genetic and multi-scale mechanisms underlying comorbidity between sick sinus syndrome (SSS) and atrial fibrillation (AF). METHODS: We integrated genome-wide association study (GWAS) summary statistics for SSS and AF with Genotype-Tissue Expression (GTEx) expression and splicing quantitative trait loci (eQTL/sQTL), atrial single-cell and spatial transcriptomics, and epigenomics. We identified trait-relevant tissues and pathways, prioritized shared cell types, quantified genome-wide and local genetic sharing, detected joint loci by cross-trait meta-analysis, and linked loci to regulatory programs via colocalization and cell-prioritized co-expression networks. RESULTS: Both traits showed strongest enrichment in cardiac tissue, especially Heart Atrial Appendage. Fibroblasts from the left atrial appendage were consistently prioritized as the key shared cell population. SSS and AF displayed significant positive genome-wide genetic correlation, with multiple locally shared regions, including six major loci. Cross-trait meta-analysis identified eight joint-phenotype SNPs implicating four susceptibility genes. ZFHX3 was the leading tissue-cell-gene candidate, acting as a hub in fibroblast co-expression modules and colocalizing with cardiac regulatory signals. CONCLUSION: Shared liability for SSS and AF is highly tissue- and cell-specific, converging on regulatory networks in atrial appendage fibroblasts, with ZFHX3 serving as a central mechanistic and biomarker node.

Humans

One Year After a Cyberattack: Lessons Learned and Dosimetric Analysis of Contingency Radiotherapy Plans.

PURPOSE: Cyberattacks on health care institutions pose significant risks to patient care, particularly in radiotherapy departments, which are heavily reliant on digital systems. This study examines the impact of a ransomware attack on our hospital and evaluates the effectiveness of the contingency measures implemented to resume radiotherapy treatments. METHODS AND MATERIALS: Following the cyberattack, our radiotherapy department faced a complete shutdown. After an initial estimate considering a shutdown of several weeks, a contingency plan was executed, including manual patient data retrieval and collaboration with a backup hospital. Contingency plans were prepared and delivered within hours, despite a partial lack of information. These plans allowed some patients to restart treatment 3 days after the attack. A dosimetric analysis was performed for the contingency plans, including various pathologies, mainly glioblastoma, head and neck cancers, and lung cancer. We compared the original and contingency plans in terms of dose coverage to the clinical target volume, biological effective dose, and their clinical impact as assessed at the 1‑year follow‑up after the cyberattack. RESULTS: Treatments resumed within 12 days at our hospital. Patients with glioblastoma showed good target coverage because of generous margins, resulting in favorable outcomes. In head and neck cases, the lack of detailed imaging led to significant target volume misses, suggesting that more conservative initial treatments could have been beneficial. Lung cases demonstrated accurate peripheral lesion targeting but faced challenges in central lesions because of the absence of positron emission tomography information. In most cases, the approach of using a contingency plan, even with limited information, led to a higher biological effective dose than would have been achieved if treatment had been stopped until full recovery at our hospital. CONCLUSIONS: The study highlights the critical importance of robust contingency planning in radiotherapy departments, emphasizing the need for backup systems and tailored approaches based on tumor location and available diagnostic information. These lessons emphasize that preparedness for digital disruptions should not focus exclusively on information and technology infrastructure.

Humans

Theta-range SEEG stimulation for intracranial mapping: extending conventional 1-Hz and 50-Hz protocols.

OBJECTIVE: Electrical brain stimulations (EBS) are central to epileptic network identification and functional mapping during stereo-electroencephalography, yet stimulation frequencies remain empirical, and standardized across patients and brain regions, producing false negatives and false positives, and potentially compromising surgical outcome. We prospectively investigated theta-range EBS (7 Hz) in the temporal lobe, a prominent physiological frequency band in this region, and compared it with conventional 1-Hz and 50-Hz protocols. METHODS: We analyzed 1,408 temporal EBS in 25 drug-resistant epileptic patients. Epileptic responses (afterdischarges, seizures) and clinical signs were assessed across the epileptic network and temporal structures (amygdala, hippocampus, neocortex, parahippocampal gyrus, white matter), and confronted to stimulation parameters (frequency, intensity, duration, total charge). RESULTS: At matched intensity and duration, 7-Hz EBS were associated with a higher occurrence of afterdischarges and clinical signs than 1-Hz EBS in several temporal structures. Effects on usual seizure induction were less consistent. Comparisons with 50 Hz showed no systematic significant differences, with responses observed at one or both frequencies depending on structure and outcome. When controlling for total charge, frequency-related differences were attenuated. Some effects were sporadically observed at both intermediate frequency and charge quantity. CONCLUSIONS: EBS responses emerge from the interaction between all electrical parameters and anatomical location and local excitability, and can hardly be disentangled. However, 7-Hz EBS can provide complementary clinical information during temporal-lobe mapping, with a targeted approach. SIGNIFICANCE: These findings support targeted evaluation of broader stimulation parameter spaces, including intermediate frequencies, rather than routine reliance on fixed low- and high-frequency protocols alone.

Humans

Micro- and nanoplastics-induced neurotoxicity: a CNS-centered, evidence-graded adverse outcome pathway framework based on systematic weight-of-evidence assessment.

Micro- and nanoplastics (MPs/NPs) are ubiquitous anthropogenic particulate pollutants posing emerging threats to human neurological health. Severe heterogeneity in particle physicochemical properties, environmental aging status, exposure paradigms and experimental platforms has created persistent mechanistic uncertainties in MP/NP neurotoxicology, hindering reliable hazard characterization and risk translation. Here, we systematically consolidate empirical toxicological evidence and construct a dedicated central nervous system (CNS)-targeted adverse outcome pathway (AOP) network integrated with rigorous weight-of-evidence (WoE) grading to elucidate the hierarchical, particle-specific toxic cascades underlying MP/NP-induced neural injury. Our synthesis overturns the conventional linear toxicity paradigm, demonstrating that MPs/NPs trigger neurotoxicity via a complex multi-input mechanistic network. We definitively establish oxidative stress as a robust early convergent key event-rather than a universal molecular initiating event-orchestrating ROS overproduction, lipid peroxidation, mitochondrial dysfunction, and neuroinflammation to propagate neuronal damage. This core module is driven by five distinct particulate upstream triggers: particle-biomolecule interfacial perturbation, corona-facilitated cellular internalization, plastic-associated chemical leaching, aging-derived free radical reactivity, and gut-borne systemic neurotoxic signaling. Downstream pathogenic outcomes encompass glial overactivation, neurotransmitter dyshomeostasis, autophagy-lysosome dysfunction, metabolic reprogramming, regulated neuronal cell death, and behavioral impairments. Tiered WoE analysis confirms strong validation for early oxidative/inflammatory cascades, moderate support for gut-brain axis crosstalk and intracellular trafficking disruption, and nascent evidence for synaptic dysfunction and neurodegeneration-linked proteostatic defects. Extrapolation to human health risk remains constrained by the frequent use of high-dose exposure paradigms, limited validated data on internal dosimetry in the human brain, discrepancies between effective concentrations in experimental models and environmentally relevant human tissue burdens, and insufficient causal validation of distal adverse outcomes. We highlight key research priorities including aged mixed-particle exposure systems, leachate-controlled assays, quantitative internal dose evaluation, and mechanistic intervention verification. This evidence-stratified AOP framework resolves longstanding mechanistic ambiguities in particulate neurotoxicity, providing a standardized, causality-based foundation for future mechanistic exploration and health risk assessment of global plastic pollution.

Adverse outcome pathway

Role of Polygenic Risk Scores in Predicting Cognitive Functioning after Mild Traumatic Brain Injury: A TRACK-TBI Study.

Patients with traumatic brain injury (TBI) and Glasgow Coma Scale scores of 13-15 (historically called mild TBI [mTBI]) commonly experience changes in cognitive functioning, including processing speed, memory, and executive functioning. In a prospective sample (N = 523) of individuals of European descent who had been treated in a U.S. level 1 trauma center for mTBI, we examined the prognostic value of four polygenic risk scores (PRS) for cognitive outcomes at 6-months postinjury. To estimate the impact of mTBI on cognition, primary cognitive outcomes were scaled as z-scores reflecting changes in performance relative to predicted preinjury performance. The PRS examined were previously developed and validated to predict cognition-related outcomes of educational attainment (Education-PRS), intelligence (Intelligence-PRS), and Alzheimer's disease (AD-mild traumatic brain injury (APOE)-PRS and AD + APOE-PRS). Both the Education-PRS and Intelligence-PRS displayed bivariate associations with all four cognitive outcomes (β = 0.19-0.32), whereas neither Alzheimer's disease PRS was significantly associated with any outcome. After controlling for other factors known to predict cognitive outcomes of TBI (e.g., sex, education, mTBI severity defined by a combination of Glasgow Coma Scale scores and the presence/absence of acute intracranial findings on clinical neuroimaging), the Education-PRS and Intelligence-PRS remained independently predictive of verbal episodic memory (β = 0.10-0.16), whereas their associations with processing speed and executive functioning were mostly nonsignificant and were mediated through educational attainment. Looking across primary z-score and secondary raw score outcomes, cognitive outcomes 6 months post-mTBI were good on average, and PRS made small independent contributions to outcome prediction. The mediation model findings may support theories of cognitive reserve, which propose that individuals with stronger preinjury cognitive processing abilities (often estimated by educational history) can better compensate for TBI. Moreover, findings indicate that PRS may contribute modestly to multivariable models predicting cognitive function after TBI.

Humans

Comfort and Usability of Digital Versus Conventional Custom-Fit Mouthguards: A Randomized Clinical Trial.

BACKGROUND/OBJECTIVES: The use of mouthguards protects teeth and the supporting tissues against impacts. Digital impressions and 3D-printed models can reduce manufacturing steps and minimize discomfort. This study evaluated the fit, comfort, and usability of custom-made mouthguards, obtained by conventional and digital workflow, in amateur athletes. MATERIALS AND METHODS: Amateur athletes were recruited for this randomized, double-blind, crossover clinical study. Each participant received two 4-mm-thick custom-fit mouthguards made of ethylene vinyl acetate (EVA) sheets using two protocols: conventional impression using alginate to produce dental stone cast (CMP) and intraoral digital scanning to produce 3D-printed resin models (DMP). Each mouthguard was worn during all training and matches over 3&#x2009;months. The order of mouthguard use was randomly assigned. Patient satisfaction with the impression protocol and mouthguard use, model accuracy, and mouthguard fit were assessed. RESULTS: Overall, 46 participants completed the study. The DMP protocol required less execution time (p&#x2009;=&#x2009;0.023), caused less discomfort (p&#x2009;=&#x2009;0.018), anxiety (p&#x2009;=&#x2009;0.008), nausea (p&#x2009;=&#x2009;0.027), difficulty breathing (p&#x2009;<&#x2009;0.001), and unpleasant taste (p&#x2009;=&#x2009;0.006) compared to the CMP protocol. The pain (p&#x2009;=&#x2009;0.971) perceived by the participants was similar in both impression protocols. The CMP-mouthguards were considered to be better fitting by the majority of participants (p&#x2009;=&#x2009;0.027). Between-group comparisons revealed that DMP-mouthguard caused less discomfort immediately postadaptation (p&#x2009;=&#x2009;0.034), with no significant differences observed at 30 or 90&#x2009;days. CONCLUSIONS: The digital workflow demonstrated to be efficient in the fabrication of mouthguards. Digital impression was more comfortable, requiring less execution time, with more accurate printed models and produced mouthguards with reported comfort and fit levels similar to those manufactured conventionally.

Humans

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6&#xa0;h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6&#xa0;h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-&#x3ba;B cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6&#xa0;h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides

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

PaNDA: Efficient Optimization of Phylogenetic Diversity in Networks.

Phylogenetic diversity (PD) plays an important role in biodiversity, conservation, and evolutionary studies by measuring the diversity of a set of taxa based on their phylogenetic relationships. In phylogenetic trees, a subset of k taxa with maximum PD can be found by a simple and efficient greedy algorithm. However, this algorithmic tractability is lost when considering phylogenetic networks, which incorporate reticulate evolutionary events such as hybridization and horizontal gene transfer. To address this challenge, we introduce PaNDA (Phylogenetic Network Diversity Algorithms), the first software package and interactive graphical user-interface for exploring, visualizing, and maximizing diversity in phylogenetic networks. PaNDA includes a novel algorithm to find a subset of k taxa with maximum diversity, running in polynomial time for networks of bounded scanwidth, a measure of tree-likeness of a network that grows slower than the well-known level measure. This algorithm considers the variant of PD on networks in which the branch lengths of all paths from the root to the selected taxa contribute towards their diversity. We demonstrate the scalability of this algorithm on simulated networks, successfully analyzing level-15 networks with up to 200 taxa in seconds. We also provide a proof-of-concept analysis using a phylogenetic network on Xiphophorus species, illustrating how the tool can support diversity studies based on real genomic data. The software is easily installable and freely available at https://github.com/nholtgrefe/panda. Additionally, we extend the definition of PD to semi-directed phylogenetic networks, which are mixed graphs increasingly used in phylogenetic analysis to model uncertainty of the root location. We prove that finding a subset of k taxa with maximum diversity remains NP-hard on semi-directed networks, but do present a polynomial-time algorithm for networks with bounded level.

network

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61&#xa0;nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE&#xa0;=&#xa0;0.0377&#xa0;mg/kg, RPD&#xa0;=&#xa0;5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves

Ultra-high-field 7T MRI reveals neural abnormalities of attention networks in relation to cognitive impairment in hypertension.

Hypertension is a significant risk factor for cognitive impairment (CI), yet the corresponding neural network abnormalities remain underexplored. In this study, we examined the associations among global and domain-specific cognitive dysfunction, neuroimaging measures, and blood pressure in a subgroup of hypertensive patients with CI (N&#xa0;=&#xa0;41) from a randomized controlled trial who underwent ultra-high-field 7&#xa0;T MRI. Structural atrophy related to CI was localized to regions overlapping the attention networks. Both whole-brain and within-network dysfunction of the attention networks were associated with worse global cognitive performance. Notably, hyperconnectivity within key attention network hubs, including the right anterior insula and posterior intraparietal sulcus, was associated with declined processing speed in hypertensive patients, mediating the association between pulse pressure and processing speed. These findings provide new insights into the neural pathophysiology of hypertension-related CI and suggest potential network-based targets for intervention.

Magnetic Resonance Imaging

Efferocytosis regulatory factors in atherosclerosis: A preclinical systematic review.

BACKGROUND: Impaired efferocytosis is a key driver of plaque instability during atherosclerosis progression. Efficient clearance of apoptotic cells through efferocytosis relies on the coordinated action of multiple regulatory factors. METHODS: PubMed, Web of Science, ScienceDirect, OVID MEDLINE, and Scopus were searched for studies published up to February 7, 2026. Eligible preclinical studies were systematically reviewed to identify endogenous factors that regulate efferocytosis in atherosclerosis. Clinical evidence was also incorporated to enable a preliminary translational assessment of these regulatory factors. RESULTS: Thirty-five endogenous regulatory factors were identified from 36 included studies, and their functional roles across distinct stages of efferocytosis were characterized. Notably, metabolic regulators such as PKM2, PFKFB3, GLS1, and Drp1 were involved in distinct efferocytosis stages. This suggests that metabolic reprogramming may provide the metabolic support require for efficient efferocytosis and inflammation resolution. Ten factors were supported by preliminary clinical evidence consistent with preclinical data. PKM2 was the only candidate biomarker with prospective observational data. However, its independent predictive value still requires validation in multicenter prospective studies. CONCLUSIONS: This review provides a systematic synthesis of 35 endogenous efferocytosis regulators and elucidates their regulatory network in atherosclerosis based on a functional stage framework. Metabolic reprogramming is identified as a central hub linking efferocytosis efficiency to inflammation resolution. This review offers a new theoretical basis for efferocytosis-targeted intervention strategies.

Animals

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7&#xa0;&#xd7;&#xa0;108&#xa0;CFU/mL and a low detection limit of 1.66&#xa0;CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19%&#xa0;&#x223c;&#xa0;104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer