Epigenetic alterations may be critical non‑genetic drivers of cancer evolution. With growing development of epigenetic‑targeting drugs, epigenetic therapies have entered different phases, including preclinical studies, early trials, or FDA‑approval. However, clinical efficacy is often compromised by poor target specificity, unfavorable pharmacokinetics, and off‑target toxicity. To address these issues, the convergence of epigenetics and nanotechnology has arisen. In this review, we systematically elucidate how epigenetic dysregulation, centered on DNA methylation, histone modifications, RNA modifications, and non-coding RNAs, drives TME reprogramming and therapeutic resistance. Building on this foundation, we delve into how rationally designed advanced drug delivery systems can effectively improve bioavailability, targeting specificity, and controlled release of epigenetic drugs. We further summarize recent breakthroughs of epigenetic-targeted nanomedicine for synergistic cancer therapy, including its integration with chemotherapy, radiotherapy, molecularly targeted therapy, immunotherapy, and emerging therapeutic modalities. Finally, we discuss the challenges and future directions in translating cutting-edge findings from this interdisciplinary field into effective and personalized cancer therapeutic strategies.

Focal to bilateral tonic-clonic seizures (FBTCS) is a severe form of seizure associated with various adverse events. This study aimed to characterize abnormalities in the resting-state brain network related to FBTCS and use those findings to fit machine learning models for individual-level identification of patients with FBTCS. T1-weighted and resting-state functional magnetic resonance imaging (rfMRI) data were acquired from 84 patients with FBTCS (FBTCS+), 47 patients without FBTCS (FBTCS-), and 81 matched healthy controls (HCs). Amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and degree centrality (DC) were calculated across whole brain and compared among 3 groups. Brain regions with significant differences between FBTCS+ and FBTCS- groups were seeded for resting-state functional connectivity (rs-FC) analysis. Four models were employed to classify FBTCS+ from FBTCS- patients at the individual level. Compared to HCs, both FBTCS+ and FBTCS- patients exhibited diffuse alterations in ALFF, ReHo, and DC, with similar patterns but more significant and widespread in FBTCS+ patients. Direct comparison demonstrated significant increase of DC in the ipsilateral temporal pole, with rs-FC increase to the ipsilateral lingual gyrus and the contralateral temporal pole and superior temporal gyrus, in the FBTCS+ patients relative to FBTCS- patients. Using significant differences as features, four classifiers performed well to distinguish FBTCS+ patient from FBTCS- patient, achieving an average AUC of 0.76. Ipsilateral temporal pole showed increased neural activity and hyper-connection to the temporo-occipital regions in FBTCS+ patients, which provide additional insights for FBTCS and carry individual-level information for sensitive identification of FBTCS+ patient.

Background:Major depressive disorder (MDD) is heterogeneous in clinical presentation and treatment response. The COORDINATE-MDD consortium identified two magnetic resonance imaging (MRI)-derived neuroanatomical profiles: dimension 1 (D1), with relatively preserved gray and white matter, and dimension 2 (D2), showing widespread reductions aligned with immunometabolic profile. Profiles were associated with distinct responses to selective serotonin reuptake inhibitor (SSRI) antidepressant and placebo (PLA). In this study, we examined electrophysiological correlates of the neuroanatomical profiles and their relationship to treatment outcome. Methods:Baseline resting-state, eyes-closed electroencephalography (EEG) was acquired from 237 medication-free participants with MDD who were in a current depressive episode (155 women; mean age [SD] = 37.47 [13.36] years) from CAN-BIND (Canadian Biomarker Integration Network in Depression) (SSRI) and EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) (SSRI or PLA). EEG features included spectral power, frontal alpha asymmetry (FAA), multiscale sample entropy, and intersite phase clustering. Effects of profile (D1 and D2) and clinical outcome (responder, nonresponder; defined as ≥50% symptom improvement) were examined with age, sex, and site as covariates. Results:No significant electrophysiological differences were observed after covariate adjustment. However, among participants who subsequently responded to treatment, D1 showed greater baseline alpha power in frontal and central regions and lower relative delta posteriorly compared with D2. In PLA-treated responders, D2 showed spectral slowing, elevated low-frequency power, reduced gamma, and coarse-scale entropy compared with D1. Baseline FAA was lower in responders than nonresponders, independent of the neuroanatomical profile. Conclusions:EEG differences between MRI-defined neuroanatomical profiles emerged in relation to clinical outcome. D1 was associated with electrophysiological patterns consistent with flexible, globally regulated cortical dynamics in SSRI responders, whereas D2 showed a distinct pattern in PLA responders, indicating partially separable neural mechanisms underlying pharmacological and PLA treatment effects.

Mental disorders are frequently associated with accelerated brain aging, yet the diagnostic and classificatory utility of brain age remains uncertain. This study aimed to evaluate the diagnostic value of brain age across multiple mental disorders and to identify the underlying neural mechanisms. Articles published through November 2025 were retrieved from PubMed, Web of Science, and Embase, resulting in 68 eligible studies covering DSM-5 diagnostic categories. We compared brain age across disorders and extracted key contributing brain regions. The largest effect was observed in schizophrenia spectrum disorders (Cohen's d = 3.49, 95 % CI 2.62-4.37, p < 0.05), followed by neurocognitive disorders (Cohen's d = 3.27, 95 % CI 2.31-4.24, p < 0.05), mood disorders (Cohen's d = 1.41, 95 % CI 0.69-2.14, p < 0.05), and neurodevelopmental disorders (mean = 0.60). Analysis of covariance indicated a significant effect of diagnostic category on brain age (F = 5.13, p = 0.004), and Bonferroni tests further confirmed intergroup differences (p < 0.05). The central executive, default mode, and salience networks, emerged as a common system implicated across disorders, although the relative contributions differed by diagnosis. Overall, these findings suggest that brain age may serve as a biomarker for the diagnosis and classification of mental disorders.

Abstract Background Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Parkinson’s disease (PD) is increasingly recognized as a brain network-disconnection syndrome. However, there is little consistent evidence on multimodal global topological alterations and their diagnostic value. We systematically searched PubMed, Embase and Web of Science up to March 2025 for articles reporting brain network topology in PD, to which we applied a multilevel random-effects meta-analyses with robust variance estimation to account for statistical dependencies. Our case-control meta-analysis included 80 studies (42 fMRI, 25 dMRI, 10 EEG, 4 sMRI, 3 others) involving 3736 PD patients and 2384 healthy controls. Compared to controls, PD patients showed lower structural and functional network segregation, especially when cognitively impaired. Structural network integration was also lower in PD, such deficits appearing to correlate with disease progression. Drug and network construction strategies were identified as potential moderating factors. Our diagnostic meta-analysis of 10 studies yielded a pooled diagnostic odds ratio of 16.4 and a pooled area under the curve of 0.86, with better diagnostic performance observed in studies using combined network metrics. These results support the clinical relevance of topological metrics in PD as potential biomarkers for disease characterization, prognosis and patient stratification, and underscore the importance of methodological harmonization and prospective validation in future research.

Major depressive disorder (MDD) is associated with widespread alterations in functional brain networks across the lifespan. However, heterogeneity in atypical brain development among patients with MDD remains largely uncharacterized. Using a multisite resting-state functional MRI dataset consisting of 1,105 MDD patients and 1,065 healthy controls, we constructed a harmonized multicenter brain age prediction model based on individualized functional topography and identified two patient subgroups with positive or negative brain age gaps (BAGs). In patients with a positive BAG (BAG+), expansion of the salience network (SAL) into the dorsolateral prefrontal and ventrolateral prefrontal cortices, in addition to contraction of the sensorimotor and dorsal attention networks (DAN), contributes to accelerated brain aging. Conversely, in the negative BAG (BAG-) group, SAL expansion into the orbitofrontal cortex (OFC) and contraction of the visual and sensorimotor networks (SMN) were linked to delayed brain development. These subgroups also exhibited distinct neurodevelopmental trajectories. Clinically, BAG+ patients showed stronger associations between higher-order network topography and mood symptoms, whereas BAG- patients exhibited links between visual/default mode network topography and insomnia. At the molecular level, both groups showed enrichment of genes related to synaptic signaling but displayed distinct expression patterns and divergent expression trajectories in key neurodevelopmental gene sets. Notably, antidepressant treatment modulated the brain in ways that were specific to each subgroup. These findings reveal heterogeneous neurodevelopmental profiles in MDD with distinct biological and clinical signatures, offering insights into personalized precision medicine for this disorder.

Background Major depressive disorder (MDD) is a heterogeneous psychiatric disorder characterized by significant cognitive impairments, which severely affect the treatment outcomes and social functioning of patients. However, little is known about the brain functional topological changes in different cognitive subgroups of MDD. Methods A total of 164 first-episode drug-naïve MDD patients and 68 healthy controls (HC) underwent neuropsychological assessments and resting-state functional magnetic resonance imaging. K-means clustering was employed to identify potential cognitive subgroups of MDD. A recently developed individualized approach parcellated the cerebral cortex into 92 regions of interest, and whole-brain functional networks were constructed by thresholding the Pearson correlation matrices derived from these regions. Topological properties were assessed using static and dynamic graph-theoretical analyses. Results Two cognitive subgroups of MDD were identified: a cognitively impaired subgroup (MDD-CI) and a cognitively preserved subgroup (MDD-CP) with performance comparable to HC. No differences were observed in global network metrics. MDD-CI showed a decrease in the node degree at the right lateral occipitotemporal junction in the visual network, which correlated with poorer visual memory. Dynamic graph analyses revealed no significant alterations after Bonferroni correction. Conclusion Findings highlight cognitive heterogeneity in MDD and show that reduced degree in the visual network is associated with visual memory deficits.

Behavioral inhibition, a stable temperament characterized by fear, withdrawal, and hypervigilance towards novelty, is a transdiagnostic predisposition to psychopathology which can have negative consequences across the lifespan. Numerous studies have examined brain function in behavioral inhibition, but have not reached a consistent conclusion. To address this gap, we used anisotropic effect-size signed differential mapping (AES-SDM) to conduct a whole-brain voxel-based meta-analysis of functional magnetic resonance imaging studies using behavioral inhibition tasks. We analyzed 18 eligible studies of 699 healthy individuals. We found that behavioral inhibition was associated with increased activation in the right dorsal anterior cingulate cortex and left amygdala, and decreased activation in the left inferior frontal gyrus, left middle temporal gyrus and right insula. Meta-regression analysis revealed a positive correlation between the proportion of females and neural activation in the right supplementary motor area and right inferior occipital gyrus. These comprehensive findings suggest that neural patterns implicated in cognitive control, emotion regulation, and reward processing may reflect neural signatures of behavioral inhibition and may provide novel insights into the pathways linking this temperament to later psychopathology.

BACKGROUND:Inflammation has been implicated in psychosis, but its role in individuals at clinical (CHR) and genetic (GHR) high-risk remains unclear. We therefore conducted a network meta-analysis (NMA) to compare circulating cytokine levels across CHR, GHR, and healthy control (HC) groups. METHODS:We systematically searched multiple databases up to February 2025, extracting cytokine levels (plasma/serum) from CHR, GHR, and HC groups. Standardized mean differences (SMDs) with 95% confidence intervals (CIs) were estimated using random-effects models. Given that no direct head-to-head comparisons between CHR and GHR were available, indirect comparisons were performed through the common comparator (HC). The transitivity assumption was assessed by comparing key study and participant characteristics across comparisons. RESULTS:Thirty studies were included (CHR: 1601, GHR: 675, HC: 1980). NMA estimates indicated higher IL-6 levels in CHR compared with GHR, while IL-6 and IL-1β levels were lower in GHR compared with HC. In pairwise subgroup analyses, CHR converters showed higher IL-13 levels than non-converters. The evidence network was sparse and star-shaped, with all CHR-GHR estimates relying exclusively on indirect comparisons. CONCLUSIONS:This study represents the first NMA to synthesize cytokine alterations in individuals at high risk for psychosis using indirect evidence. Elevated IL-6 in CHR individuals suggests immune activation, whereas reduced IL-6 in GHR may reflect a distinct immune profile. Increased IL-13 levels in converters highlight potential involvement of Th2-related pathways during transition to psychosis. However, the sparse nature of the evidence network necessitates cautious interpretation of the findings, and larger, standardized multi-center studies are required for confirmation.

Background:Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and/or reliance on summary-level data. Methods:Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results:MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions:Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.

As a central hub of emotional processing, alterations in amygdala functional connectivity (FC) have garnered significant attention in both major depressive disorder (MDD) and bipolar disorder (BD), which holds promise in identifying differential biomarkers and highlighting their similarities. However, current findings are limited by inconsistency. To address this, we conducted a comparative and conjunction analysis using the Seed-based d Mapping (SDM) toolbox to examine amygdala FC alterations in MDD and BD patients. Our results revealed distinct amygdala FC alterations between MDD and BD were primarily identified in the left temporal pole, cingulate cortex, and left supramarginal gyrus, while shared amygdala FC abnormalities were particularly observed in the fronto-limbic regions and occipitotemporal gyrus. These findings highlight both commonalities and differences in amygdala FC alterations across MDD and BD, providing insights into the underlying pathophysiology of mood disorders and offering potential neural biomarkers for differential diagnosis, thereby aiding in the improvement of treatment strategies.

Attention-deficit/hyperactivity disorder (ADHD) is characterized by considerable clinical heterogeneity. This study investigates whether normative modelling of topological properties derived from brain morphometry similarity networks can provide robust stratification markers for ADHD children. Leveraging multisite neurodevelopmental datasets (discovery: 446 ADHD, 708 controls; validation: 554 ADHD, 123 controls), we constructed morphometric similarity networks and developed normative models for three topological metrics: degree centrality, nodal efficiency, and participation coefficient. Through semi-supervised clustering, we delineated putative biotypes and examined their clinical profiles. We further contextualized brain profiles of these biotypes in terms of their neurochemical and functional correlates using large-scale databases, and assessed model generalizability in an independent cohort. ADHD exhibited atypical hub organization across all three topological metrics, with significant case-control differences primarily localized to a covarying multi-metric component in the orbitofrontal cortex. Three biotypes emerged: one characterized by severe overall symptoms and longitudinally persistent emotional dysregulation, accompanied by pronounced topological alterations in the medial prefrontal cortex and pallidum; a second by predominant hyperactivity/impulsivity accompanied by changes in the anterior cingulate cortex and pallidum; and a third by marked inattention with alterations in the superior frontal gyrus. These neural profiles of each biotype showed distinct neurochemical and functional correlates. Critically, the core findings were replicated in an independent validation cohort. Our comprehensive approach reveals three distinct ADHD biotypes with unique clinical-neural patterns, advancing our understanding of ADHD's neurobiological heterogeneity and laying the groundwork for personalized treatment.

Objective Autonomic nervous system (ANS) dysfunction may be a key neuropathological feature of bipolar disorder (BD). There is limited understanding of how perturbations in the ANS manifest and their relation to brain function in youth with a familial risk for BD. Method Galvanic skin response (GSR) and functional magnetic resonance imaging (fMRI) data during a continuous performance task with emotional and neutral distractors (CPT-END) were collected from high-risk youth with depressive and/or anxiety symptoms and healthy controls (HCs). We compared skin conductance response (SCR) values from GSR data collected during imaging as an index of ANS arousal between groups. Then, we computed seed-based functional connectivity maps using generalized psychophysiological interaction (gPPI) analysis, and applied a general linear model (GLM) to examine associations between SCR values and functional connectivity patterns within the high-risk group. Results We included 31 high-risk youth and 15 HCs in our analysis. The high-risk group showed significantly higher maximum (1.29 ± 1.32 μS vs. 0.65 ± 0.62 μS, p = 0.048) and mean (0.26 ± 0.26 μS vs. 0.14 ± 0.11 μS, p = 0.028) SCR values compared to HCs. Within high-risk group, SCR values were not significantly correlated with emotional scores. Mean SCR values in high-risk youth were positively correlated with the functional connectivity between the right ventrolateral prefrontal cortex and both the left amygdala and right thalamus, and these associations were not observed in the HC group. Conclusion Our findings provided preliminary evidence of ANS dysfunction in high-risk youth compared to HCs. Furthermore, functional connectivity patterns in prefrontal-limbic regions may be associated with ANS arousal, offering novel insights into ANS-related neural mechanisms and deepening our understanding of emotional dysregulation from peripheral and central perspectives in these youth. Clinical trial registration information Mechanism of Antidepressant-Related Dysfunctional Arousal in High-Risk Youth; https://clinicaltrials.gov/ct2/show/NCT02553161 Diversity & Inclusion Statement We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants.

Transcranial interference stimulation (TIS) is a non-invasive neuromodulation technique designed to modulate deep brain structures. Although most TIS simulation studies assume isotropic tissue conductivity, white-matter (WM) conductivity is direction dependent. This study investigated how diffusion-tensor-imaging-derived, volume-normalized WM conductivity anisotropy affects the magnitude and spatial distribution of the maximum low-frequency envelope amplitude, here termed maximum modulation depth (MDmax), in individualized TIS models. Six individualized head models were constructed from structural and diffusion MRI data. Isotropic and anisotropic conductivity models were compared under two manually selected proof-of-concept TIS montages designed to produce contrasting modulation-depth directions relative to the dominant fibre orientation of the corpus callosum. For each voxel, MDmax and the corresponding unit modulation-depth direction vector were calculated. Directionality was evaluated in the corpus callosum by comparing this vector with the principal diffusion eigenvector, v₁. To assess whether these effects extended beyond the corpus callosum, additional target-specific analyses were performed in the left M1 and left hippocampus using montages selected in the isotropic model and held fixed between conductivity models. Anisotropic conductivity altered both the magnitude and spatial distribution of the simulated TIS envelope. Whole-brain MDmax was higher in the anisotropic than in the isotropic model under both montages, whereas threshold-defined high-MD volume showed montage-dependent differences. In the corpus callosum body, regional peak modulation depth was higher when the modulation-depth direction vector was more collinear with v₁, but lower when the two directions were approximately orthogonal. Under the target-specific montages, regional peak modulation depth increased modestly in the left primary motor cortex but decreased in the left hippocampus. These findings indicate that anisotropy-related changes are direction- and region-dependent and vary across target-specific montage configurations. Local fibre orientation should therefore be considered when interpreting individualized TIS field models.

Glioblastoma (GBM) is an aggressive brain tumor characterized by limited therapeutic efficacy and challenges in accurate imaging, largely due to its invasive growth, drug resistance, and the restrictive blood-brain barrier (BBB) hindering the delivery of both therapeutic and diagnostic agents. Current GBM treatments and imaging approaches often suffer from insufficient agent penetration into the tumor. Additionally, they frequently exhibit toxicity or poor signal-to-noise ratios. Polysaccharide (PSC)-based polymers, with their inherent biocompatibility, biodegradability, and versatile chemical modifiability, offer a promising platform to overcome these limitations. These natural polymers can be engineered into sophisticated nanocarriers that enhance BBB traversal, enable targeted tumor accumulation of therapeutic payloads and imaging agents Furthermore, they facilitate controlled drug release and improve diagnostic signal generation. Consequently, PSC-based systems can improve therapeutic efficacy and enhance diagnostic accuracy for tumor visualization. Furthermore, they reduce systemic side effects and support multimodal strategies, ranging from single-modality interventions to integrated theranostic systems. This review aims to comprehensively discuss recent advancements, current challenges, and future perspectives of PSC-based nanomedicines in GBM therapy and imaging.

The clinical diagnosis of sentinel lymph node (SLN) metastasis through contrast-enhanced magnetic resonance imaging (MRI) remains challenging. In this work, a pyclen-based amphiphilic gadolinium chelate (GdL) was developed and coassembled with DSPE-PEG2000 to form hybrid micelles for SLN metastasis imaging. The micelles exhibited high relaxivity and kinetic stability, with particle size and relaxivity adjustable by modifying the DSPE-PEG/GdL mass ratio. The DSPE-PEG/GdL micelles (15:1) displayed the highest T1 relaxivity (23.5 Gd mM-1 s-1 at 1.5 T), which is 5.6-fold greater than that of Gd-DOTA, coupled with high kinetic stability. In a mouse model of breast cancer lymph node metastasis, SLN was successfully distinguished at a much lower dosage (0.75 or 1.5 mg Gd kg-1 BW) compared to the clinical dose of 15.7 mg Gd kg-1 BW, as evidenced by differences in MR signal intensity. Overall, the strategy of coassembling pyclen-based Gd chelates with DSPE-PEG2000 offers a feasible approach for developing effective MR probes for lymph node metastasis imaging.