Search Results (206)

As global population aging accelerates, the growing burden of age-related diseases is driving a shift in medical research from single-disease treatment to interventions targeting the aging process itself. Organ-specific interventions have emerged as a promising strategy to modulate systemic aging. Among organs, the brain, muscle, and gut have attracted particular attention due to their central roles in neural regulation, metabolic homeostasis, and immune balance. In this review, we focus on these three key organs, systematically summarizing their roles and regulatory mechanisms in organismal aging and discussing how exercise influences the aging process by affecting these organs. Crucially, we propose a novel “local-to-global” regulatory model, positing that preserving homeostasis in these specific tissues is sufficient to orchestrate systemic anti-aging effects. This work represents a conceptual advance by providing the theoretical rationale to move beyond non-specific systemic treatments toward precise, organ-targeted interventions. Full article

Background: Aging exerts a progressive and multifaceted impact on the microcirculatory system, undermining the structural and molecular integrity that sustains endothelial stability across both peripheral and cerebral vascular territories. A sustained shift toward oxidative imbalance, chronic low-grade inflammation, and progressive endothelial exhaustion converges to destabilize microvascular networks, linking peripheral artery disease (PAD) with heightened susceptibility to cerebral microvascular dysfunction and neurovascular decline. As redox homeostasis deteriorates, endothelial cells progressively lose barrier-selective properties, intercellular communication with pericytes weakens, and pro-thrombotic tendencies subtly emerge, creating a permissive environment for early neurovascular injury and impaired cerebrovascular resilience. Methods: This narrative review integrates mechanistic evidence derived from experimental, clinical, and translational studies examining the interplay between oxidative stress, inflammatory signaling cascades, endothelial senescence, and blood–brain barrier (BBB) disruption across peripheral and cerebral microvascular systems. A comparative framework was applied to PAD and cerebral microcirculatory pathology to identify convergent molecular drivers and systemic mechanisms underlying endothelial deterioration. Results: Accumulating evidence demonstrates that oxidative stress disrupts endothelial mitochondrial function, compromises tight junction architecture, and accelerates angiogenic failure. Concurrent inflammatory activation amplifies these alterations through cytokine-mediated endothelial activation, enhanced leukocyte adhesion, and promotion of a pro-thrombotic microenvironment. Progressive endothelial senescence consolidates these insults into a persistent state of microvascular dysfunction characterized by diminished nitric oxide bioavailability, capillary rarefaction, and compromised barrier integrity. Notably, these pathological features are shared between PAD and the aging cerebral circulation, reinforcing the concept of a unified systemic microvascular aging phenotype. Conclusions: Microvascular failure in the aging brain should be understood as an extension of systemic endothelial deterioration driven by oxidative stress, chronic inflammation, and senescence-associated vascular exhaustion. Recognizing the shared molecular architecture linking peripheral and cerebral microcirculatory dysfunction offers a strategic framework for developing targeted therapeutic interventions aimed at restoring endothelial resilience, stabilizing BBB integrity, and preserving neurovascular homeostasis in aging populations. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. Recent findings suggest that long-term and heavy alcohol consumption can aggravate several pathological processes associated with AD, whereas the impact of light or moderate consumption remains uncertain. Excessive alcohol exposure impairs the structure and function of key brain regions involved in cognition, particularly the hippocampus, prefrontal cortex, amygdala, cerebellum, Basolateral amygdala (BLA), and hypothalamus. Several studies indicate that chronic alcohol consumption affects the brain by multiple mechanisms like increased oxidative stress, microglial activation, neuroinflammation, microtubule instability, tau hyperphosphorylation, and modified amyloid-β turnover. Disruption of cholinergic transmission further contributes to memory deficits and neuronal susceptibility. These alcohol-related alterations closely resemble core features of AD pathology and may accelerate disease progression. Although some epidemiological studies report the potential benefits of low alcohol intake, their interpretation is limited by inconsistent definitions of drinking patterns and the influence of confounding variables. Overall, current evidence supports a dose-dependent relationship in which alcoholism increases vulnerability to AD-related neurodegeneration. Reducing harmful alcohol use may therefore represent a practical approach to lowering long-term dementia risk. This review summarizes the current mechanisms of alcohol induced neuronal damage across different brain regions. Prolonged alcohol consumption accelerates cerebral aging by enhancing oxidative stress, neuroinflammation, disrupting tau protein degradations, and other neuronal damages that intersect with the pathogenesis of AD. Full article

Background/Objectives: Alzheimer’s disease (AD) has a prolonged preclinical phase and marked heterogeneity. Brain age and the Brain Age Gap (BAG), derived from neuroimaging and machine learning (ML), offer a non-invasive, system-level indicator of brain integrity, with potential relevance for early detection, risk stratification, and intervention monitoring. This review summarizes the conceptual basis, imaging characteristics, biological relevance, and explores its potential clinical utility of BAG across the AD continuum. Methods: We conducted a narrative synthesis of evidence from morphometric structural magnetic resonance imaging (sMRI), connectivity-based functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI), alongside recent advances in deep learning architectures and multimodal fusion techniques. We further examined associations between BAG and the Amyloid/Tau/Neurodegeneration (A/T/N) framework, neuroinflammation, cognitive reserve, and lifestyle interventions. Results: BAG may reflect neurodegeneration associated with AD, showing greater deviations in individuals with mild cognitive impairment (MCI) and early AD, and is correlated with tau pathology, neuroinflammation, and metabolic or functional network dysregulation. Multimodal and deep learning approaches enhance the sensitivity of BAG to disease-related deviations. Longitudinal BAG changes outperform static BAG in forecasting cognitive decline, and lifestyle or exercise interventions can attenuate BAG acceleration. Conclusions: BAG emerges as a promising, dynamic, integrative, and modifiable complementary biomarker with the potential for assessing neurobiological resilience, disease staging, and personalized intervention monitoring in AD. While further standardization and large-scale validation are essential to support clinical translation, BAG provides a novel systems-level perspective on brain health across the AD continuum. Full article

Background/Objectives: Parkinson’s disease (PD) is an adult-onset neurodegenerative disorder whose pathogenesis is still not completely understood. Several lines of evidence suggest that alterations in epigenetic architecture may contribute to the development of this condition. Here, we present a pilot DNA methylation study from peripheral blood in a cohort of Sicilian PD patients and matched controls. Peripheral tissue analysis has previously been shown to reflect molecular and functional profiles relevant to neurological diseases, supporting their validity as a proxy for studying brain-related epigenetic mechanisms. Methods: We analyzed 20 PD patients and 20 healthy controls (19 males and 21 females overall), matched for sex, with an age range of 60–87 years (mean 72.3 years). Peripheral blood DNA was extracted and processed using the Illumina Infinium MethylationEPIC v2.0 BeadChip, which interrogates over 935,000 CpG sites across the genome, including promoters, enhancers, CpG islands, and other regulatory elements. The assay relies on sodium bisulfite conversion of DNA to detect methylation status at single-base resolution. Results: Epigenome-wide association study (EWAS) data allowed for multiple levels of analysis, including immune cell-type deconvolution, estimation of biological age (epigenetic clocks), quantification of stochastic epigenetic mutations (SEMs) as a measure of epigenomic stability, and differential methylation profiling. Immune cell-type inference revealed an increased but not significant proportion of monocytes in PD patients, consistent with previous reports. In contrast, epigenetic clock analysis did not reveal significant differences in biological age acceleration between cases and controls, partially at odds with earlier studies—likely due to the limited sample size. SEMs burden did not differ significantly between groups. Epivariations reveal genes involved in pathways known to be altered in dopaminergic neuron dysfunction and α-synuclein toxicity. Differential methylation analysis, however, yielded 167 CpG sites, of which 55 were located within genes, corresponding to 54 unique loci. Gene Ontology enrichment analysis highlighted significant overrepresentation of pathways with neurological relevance, including regulation of synapse structure and activity, axonogenesis, neuron migration, and synapse organization. Notably, alterations in KIAA0319, a gene involved in neuronal migration, synaptic formation, and cortical development, have previously been associated with Parkinson’s disease at the gene expression level, while methylation changes in FAM50B have been reported in neurotoxic and cognitive contexts; our data suggest, for the first time, a potential epigenetic involvement of both genes in Parkinson’s disease. Conclusions: This pilot study on a Sicilian population provides further evidence that DNA methylation profiling can yield valuable molecular insights into PD. Despite the small sample size, our results confirm previously reported findings and highlight biological pathways relevant to neuronal structure and function that may contribute to disease pathogenesis. These data support the potential of epigenetic profiling of peripheral blood as a tool to advance the understanding of PD and generate hypotheses for future large-scale studies. Full article

Sleep disturbances, affecting nearly half of middle-aged adults, have emerged as modifiable determinants of brain health and dementia risk. Recent advances in machine learning applied to MRI enable the estimation of “brain age,” a biomarker that quantifies deviation from normative neural aging. This review synthesizes and critically evaluates converging evidence that poor sleep accelerates biological brain aging, identifies mechanistic pathways, and delineates translational barriers to clinical application. Across large-scale cohorts comprising more than 25,000 participants, suboptimal sleep independently predicts 1–3 years of MRI-derived brain age acceleration, even after adjusting for vascular and metabolic confounders. Objective sleep fragmentation and altered sleep-stage architecture exhibit sleep-specific neuroanatomical signatures, independent of amyloid and tau pathology, while inflammatory, vascular, and glymphatic mechanisms mediate a small fraction of the effect. Experimental sleep deprivation studies demonstrate reversibility of accelerated brain aging, highlighting opportunities for early intervention. Translation to clinical practice is constrained by methodological heterogeneity, reliance on self-reported sleep metrics, limited population diversity, and the absence of randomized intervention trials demonstrating causal reversibility. Addressing these gaps through standardized MRI-based biomarkers, longitudinal mechanistic studies, and interventional trials could establish sleep optimization as a viable neuroprotective strategy for dementia prevention. Full article

Pediatric drug delivery presents unique challenges due to physiological and pharmacological differences across age groups, requiring specialized formulation approaches beyond simple dose adjustments of adult medications. This review synthesizes recent advances in polysaccharide-based pediatric drug delivery and highlights novel findings that may accelerate clinical translation. It summarizes how chitosan, alginate, hyaluronic acid, dextran, modified starches, and other polysaccharides are engineered into nanoparticles, hydrogels, films, and orodispersible/mini-tablet formulations to improve stability, bioavailability, taste masking, and controlled release across neonates to adolescents. These systems can accommodate developmental variations in absorption, distribution, metabolism, and excretion processes across pediatric subpopulations, with particular emphasis on oral and alternative administration routes. Evidence supporting unexpectedly high acceptability of mini-tablets, successful integration of modified polysaccharides in 3D-printed personalized low-dose therapies, and the emergence of blood–brain barrier-penetrating and RGD-functionalized polysaccharide nanocarriers for pediatric oncology are emphasized as novel, clinically relevant trends. This review also addresses regulatory considerations, safety profiles, and future perspectives. By integrating developmental insights with innovative formulation strategies, polysaccharide polymers offer promising solutions to improve medication adherence, safety, and efficacy across the pediatric age spectrum. Full article

Background: Central nervous system (CNS) tumors during pregnancy are rare but present significant diagnostic, therapeutic, and ethical challenges. These include both primary and metastatic lesions, which share overlapping clinical features and management complexities. Their clinical course is influenced by gestational physiological changes, which can mask symptoms and delay diagnosis, thereby increasing maternal and fetal risks. Objective: This review aims to synthesize current evidence on the epidemiology, pathophysiology, clinical presentation, diagnostic strategies, treatment options, prognosis, and ethical considerations related to CNS tumors in pregnant patients. Methods: A comprehensive literature review was conducted, including retrospective and prospective studies, clinical guidelines, and systematic reviews focusing on brain and spinal tumors diagnosed during pregnancy. Particular attention was given to the impact of gestational age, tumor histology, and maternal condition on treatment outcomes. Results: Hormone-sensitive tumors such as meningiomas and prolactinomas may exhibit accelerated growth during pregnancy due to elevated progesterone and prolactin levels. Diagnosis is often delayed due to symptom overlap with normal gestational changes. MRI without contrast remains the imaging modality of choice. Glucocorticoids and selected chemotherapy agents can be cautiously used depending on gestational age. Surgical resection, particularly in the second trimester, has been shown to be safe and effective in appropriate clinical scenarios. Multidisciplinary coordination is essential. Prognosis varies based on tumor type and timing of intervention, with maternal survival prioritized in high-risk situations. Ethical management hinges on patient autonomy, informed consent, and proportionality of medical interventions. Conclusions: CNS tumors during pregnancy require early recognition, individualized treatment planning, and ethical vigilance. Multidisciplinary collaboration is vital to optimizing outcomes for both mother and fetus. Future efforts should focus on developing standardized protocols and expanding evidence through multicenter studies. Full article

Background/Objectives: Brain metastases (BM) are common in small cell lung cancer (SCLC) and portend poor outcomes. Contemporary determinants of survival in the modern treatment era remain incompletely defined. We evaluated factors associated with overall survival (OS) among patients with SCLC and BM using a recent, nationally representative dataset. Methods: We identified adults diagnosed with SCLC and brain metastases between 2018 and 2020 in the National Cancer Database (NCDB). Demographic, clinical, treatment, and survival data were extracted for analysis. Unadjusted OS was estimated using Kaplan–Meier methods. Multivariable Cox proportional hazards models identified factors associated with mortality, with proportional hazards (PH) assessed using scaled Schoenfeld residuals. Complementary Accelerated Failure Time (AFT) modeling was performed to confirm robustness. Results: Of 62,671 SCLC cases, 11,074 (17.7%) had BM, including 32.6% with brain-only disease. Median overall survival (mOS) was 6.6 months (95% CI, 6.47–6.87); patients with brain-only disease had an mOS of 8.8 months (8.38–9.26), compared with 5.95 months (5.75–6.18) for those with concurrent extracranial metastases. In multivariable analysis, age ≥ 65 years (HR 1.13, p < 0.001) was associated with higher mortality, whereas female sex (HR 0.87, p < 0.001), Black (HR 0.88, p = 0.001), Asian (HR 0.80, p = 0.022), and Hispanic (HR 0.87, p = 0.008) patients had lower hazards. Worse outcomes were associated with public or no insurance, lower income, non-academic facilities, and extracranial metastases; educational attainment was not significant. Proportional hazards assumptions were largely met with minor deviations, and AFT modeling confirmed consistent results. Treatment modality remained independently associated with survival in both models. Conclusions: In this contemporary national cohort, survival among patients with SCLC and brain metastases was influenced by multiple clinical, sociodemographic, and treatment factors, including age, sex, insurance status, facility type, and extent of metastatic disease. Treatment modality remained an independent predictor of survival. These results provide updated real-world benchmarks and highlight the need for prospective studies to define optimal management strategies in this high-risk population. Full article

There is a subgroup of people infected with the SARS-CoV-2 virus who manifest lingering sequelae (LongC), with neurological symptoms (nLongC). We recruited 86 COVID-19 volunteers, 35 of whom were fully recovered (Cov) and 51 who had neurological symptoms (nLongC) 4–53 months after infection and compared them to 51 healthy pre-pandemic controls (HC). Thirty-five percent of nLongC individuals carried the apolipoprotein E4 (APOE4) gene, compared to 11% of Cov. Four plasma proteins, interleukin 1 beta (IL-1β), interleukin 8 (IL-8), glial fibrillary acidic protein (GFAP), and hemopexin, continued to be elevated in both Cov and nLongC compared to HC. Soluble CD14 was elevated in nLongC but not Cov. As a group, IL-1β decreased over time in Cov but not nLongC. Two of the elevated proteins, IL-8 and GFAP, correlated with age, with both Cov and nLongC showing higher levels than HC. Using a combination of four plasma proteins, along with age, body mass index, and APOE4 presence, we were able to achieve an area under the curve (AUC) of 0.81. These results suggest that SARS-CoV-2 infection causes a low-grade inflammatory process that, even months or years after infection, does not return to pre-COVID-19 levels, which may contribute to neurologic sequelae and accelerated aging. Full article

Prolonged hospitalization contributes to a decline in physical function and immobilization. This narrative review aims to explore physical rehabilitation approaches that address the specific characteristics of physical dysfunction in patients with schizophrenia. A literature review was conducted following an electronic search of PubMed for English-language articles published between January 2014 and January 2025. Based on the findings, a framework was constructed to categorize symptoms and physical challenges into three domains: (1) movement disorders and obesity induced by antipsychotic medications, which alter motor performance and lead to compensatory movements; (2) negative symptoms and cognitive impairments, which promote sedentary behavior and result in dysphagia, dynapenia, sarcopenia, and frailty; and (3) accelerated brain aging and disuse syndrome by schizophrenia, which impair neuromotor and cognitive function and increases the risk of physical dependency. These interconnected factors emphasize the need for targeted physical rehabilitation to maintain independence and reduce the risk of hospitalization. This review proposes a multidisciplinary approach involving psychiatrists, physical therapists, and occupational therapists, along with individualized nutritional support, as essential components of comprehensive rehabilitation strategies aimed at improving physical outcomes and reducing early mortality in this population. Full article

In the recent years, the accelerating global demographic shift toward population aging has been accompanied by a marked increase in the prevalence of neurodegenerative disorders, notably Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, and multiple sclerosis. Among emerging approaches, dietary interventions targeting the gut–brain axis have garnered considerable attention, owing to their potential to modulate key pathogenic pathways underlying neurodegenerative processes. This review synthesizes current concepts in precision nutrition and elucidates neurohumoral, immune, and metabolic regulatory mechanisms mediated by the gut microbiota, including the roles of the vagus nerve, cytokines, short-chain fatty acids, vitamins, polyphenols, and microbial metabolites. Emerging evidence underscores that dysbiotic alterations contribute to compromised barrier integrity, the initiation and perpetuation of neuroinflammatory responses, pathological protein aggregations, and the progressive course of neurodegenerative diseases. Collectively, these insights highlight the gut microbiota as a pivotal target for the development of precision-based dietary strategies in the prevention and mitigation of neurodegenerative disorders. Particular attention is devoted to key bioactive components such as prebiotics, probiotics, psychobiotics, dietary fiber, omega-3 fatty acids, and polyphenols that critically participate in regulating the gut–brain axis. Contemporary evidence on the contribution of the gut microbiota to the pathogenesis of Alzheimer’s disease, Parkinson’s disease, and multiple sclerosis is systematically summarized. The review further discusses the prospects of applying nutrigenomics, chrononutrition, and metagenomic analysis to the development of personalized dietary strategies. The presented findings underscore the potential of integrating precision nutrition with targeted modulation of the gut–brain axis as a multifaceted approach to reducing the risk of neurodegenerative diseases and preserving cognitive health. Full article

The global rise in high-fat diet (HFD) consumption and obesity has raised concerns about their long-term effects on brain health. This review addresses how HFDs, including ketogenic diets (KDs), influence the central nervous system (CNS) and may contribute to neurodegenerative processes. The findings show that prolonged HFD exposure is associated with altered brain metabolism, increased oxidative stress, neuroinflammation, and impaired synaptic plasticity, particularly in regions like the hippocampus and hypothalamus. These changes may affect cognitive function and accelerate neurodegenerative mechanisms linked to disorders such as Alzheimer’s and Parkinson’s disease. While certain types of KD appear to exert neuroprotective effects—such as improved motor outcomes in experimental Parkinson’s disease models—evidence remains inconsistent, and concerns about their long-term safety persist. This review emphasizes that the impact of high-fat nutrition on the CNS depends on fat type, exposure duration, and individual factors such as age and sex. Overall, further research is needed to distinguish between harmful and potentially therapeutic dietary fat patterns and to better understand their influence on brain health across the lifespan. Full article

Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates’ and small children’s brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients’ quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient’s MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI’s interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, pathologically defined by the accumulation of amyloid-β plaques and tau-related neurofibrillary tangles in the brain. It represents a principal driver of cognitive deterioration in middle-aged and elderly populations. Early diagnosis and pharmacological management of the disease markedly improve both the quality and duration of life. Methods: Electroencephalography (EEG) is critical in detecting and analyzing Alzheimer’s disease. The widespread use of mobile EEG devices in recent years has necessitated real-time and effective data processing. However, extracting disease-specific features from EEG data still poses a significant challenge, especially in cases that must be completed quickly. This study aims to determine the frequency bands associated with Alzheimer’s disease in EEG data obtained from multiple channels and to accelerate the detection methods. An accurate classification that requires little computation is the primary goal. Results: EEG recordings of 48 individuals (24 AD and 24 healthy controls (HC)) obtained from Florida State University were divided into Alpha, Beta, Delta, Gamma, and Theta frequency bands; scalograms and spectrograms were generated for each frequency band. The effectiveness of these bands was evaluated using the MobileNetV2 architecture. The results showed that Delta and Beta frequency bands were the most significant for Alzheimer’s detection. By analyzing the features obtained from the Delta and Beta bands using the MobileNetV2 model integrated with the Dual-Attention Mechanism, it was determined that the attention mechanisms improved model performance by 2%. In addition, the use of an SVM classifier with hyperparameters optimized via Optuna resulted in approximately 3% performance improvement, suggesting that hyperparameter tuning may contribute positively to classification accuracy. Furthermore, combining features obtained from these frequency bands increased the detection performance when evaluated with larger datasets. Conclusions: The study demonstrates the potential of frequency band-based analyses and feature fusion methods to increase the accuracy and efficiency of Alzheimer’s diagnosis using EEG data. The results are promising; however, they should be interpreted with caution regarding their generalizability. Full article