Search Results (659)

Regenerative medicine is becoming more widely integrated with longevity-oriented and preventive care as populations age and chronic degenerative diseases burden healthcare systems. Mesenchymal stem cell (MSC) therapies have progressed from experimental interventions to approved products, yet scalability, safety, cost, and regulatory complexity constrain widespread implementation in medical wellness contexts. The predominant therapeutic effects of MSCs are mediated via paracrine mechanisms, leading to cell-free approaches based on the MSC secretome—a complex mixture of bioactive factors including all types of biomolecules and assemblies thereof, such as exosomes. These acellular products offer compelling advantages: multiple batches from single-donor sources, standardized dosing, reduced allogeneic cell risks, and shorter outpatient-compatible administration. Preclinical and clinical data indicate that secretome-based products exert potent regenerative effects in osteoarthritis, chronic wounds, stroke, traumatic brain injury, and neurodegenerative diseases. This review examines the evolution from cell-based to cell-free regenerative strategies, focusing on human umbilical cord Wharton’s jelly MSC secretome for precision longevity medicine. It compares MSC therapies with secretome- and exosome-based formulations across mechanistic, manufacturing, safety, practical and regulatory dimensions. Regional perspectives highlight Southeast Asia, and especially Thailand, as an emerging regenerative-longevity hub. Finally, it outlines the preventive patient journey integrating cell-free interventions within multi-modal programs aimed at extending healthspan. Full article

Humans, and most other late Homo species, are characterized by large brains and bodies. However, the discovery of two small-brained Homo species—H. floresiensis and Homo naledi—has cast doubts on large brain size as a defining feature of our genus. We reevaluated brain and body size scaling using data for 225 extant primates and 16 fossil hominid taxa, including one of the most diminutive species in genus Homo, H. floresiensis. Brain and body size are tightly correlated in genus Homo, varying along a positively allometric slope (R² = 0.84, F(1,5) = 33, p < 0.01) that is significantly different from the slope characterizing extant primates (R² = 0.94, F(1,222) = 3294, p < 0.001). Both small-bodied Homo floresiensis and Homo naledi have endocranial volumes (ECVs) that are consistent with their body size given the scaling relationship that characterizes genus Homo. Paired ECV and body mass estimates demonstrate considerable overlap of brain:body size proportions across fossil hominid taxa. Earlier hominids, Ardipithecus ramidus and Australopithecus anamensis, are characterized by ancestral brain:body size scaling; we discuss the hypothesis that a fundamental biological shift ca. 3 Ma altered the trajectory of encephalization—potentially linked to changes in fetal growth and gestation in Pleistocene fossil hominids—and may be directly implicated in the evolution of complex symbolic behavior in our lineage. Full article

Background: Traumatic head injury (THI) includes a diverse range of hemorrhagic brain lesions (HBL), which are distinct phenotypes with characteristic pathophysiological mechanisms. Computed tomography (CT) is the cornerstone of the initial assessment and diagnosis; however, its sensitivity is limited, especially in mild head injury. Blood-derived biomarkers, including Neuron-Specific Enolase (NSE) and S-100B, have been extensively studied; however, their efficacy in distinguishing HBL subtypes remains unclear. We evaluated whether circulating serum levels of S-100B and NSE can discriminate between distinct intracranial HBLs and extracranial hemorrhagic lesions (ECH). Methods: This is an interim analysis of a prospective, randomized, double-blind clinical trial including 434 adult patients with blunt THI. HBL phenotypes identified by CT scan included subarachnoid hemorrhage (SAH), subdural hematoma (SDH), epidural hematoma (EDH), and brain contusion (BC). Unique lesions were considered while overlapping lesions were excluded. Subgaleal hematoma (SGH) was included as an example of ECH. Serum S-100B was assessed within 6 h post-injury, while serum NSE was evaluated at admission, 24 h, and 48 h thereafter. Serum NSE and inflammatory cytokines were quantified in duplicates using a Human Magnetic Luminex 5-plex assay, while serum S-100B concentrations were measured separately. Serum epinephrine concentrations were quantified using an ELISA. Biomarker profiles were analyzed based on lesion phenotype, lesion multiplicity, injury pattern, and clinical outcomes, including hospital length of stay (HLOS) and the Glasgow Outcome Scale—Extended (GOSE). Results: Admission median S-100B levels were higher in patients with SAH (495 pg/mL) and lower in those with SGH (191 pg/mL); however, they did not show statistically significant difference among HBL phenotypes. They were significantly higher in patients with polytrauma TBI (420 pg/mL) compared to isolated TBI (258 pg/mL). Baseline and 48 h NSE concentrations were significantly higher in SDH (25,089 and 28,438 pg/mL) than in other THI lesions (p = 0.04). There were no statistically significant changes in NSE values over time across all THI lesions except for SDH in which they raised more after 48 h (p = 0.02). They had a significant drop in polytrauma over the time (p = 0.001). Compared to intracranial lesions, S-100 B levels were significantly lower in SGH and in skull fractures without intracranial hematomas. Both S-100B and NSE levels were elevated in individuals with unfavorable GOSE scores. Conclusions: In this secondary exploratory analysis, elevated serum NSE and S-100B levels discriminate between extra- and intracranial lesions and appear to represent distinct but complementary aspects of THI, indicating neuronal damage and its temporal evolution, and predicting clinical and functional outcomes. The present findings reflect association and not causation. Future studies incorporating larger or multicenter cohorts, volumetric imaging, and long-term outcomes are required to validate and refine biomarker-guided algorithms for personalized THI care. Full article

The development of the human brain starts with the orchestrated expression of our genes during embryogenesis. Non-protein-coding DNA sequences (gene promoters and enhancers) dynamically interact to form a three-dimensional (3D) network, orchestrating gene expression. We discuss novel perspectives on how DNA sequence variants within regulatory DNA, identified by whole-genome sequencing (WGS), contribute to the development of neurodevelopmental disorders (NDDs), including autism spectrum disorders (ASDs). We discuss two recent models explaining the evolution of a subset of regulatory sequences, Human Accelerated DNA Regions (HARs), proposed to be involved in the evolution of uniquely human brain features through their participation in the 3D interactions network. We connect this with the recent proposal that rare, recessive inherited sequence variants within HARs, interacting with distant target genes in neural cells, represent risk factors for the development of ASDs. The SOX2 transcription factor, whose heterozygous mutation causes NDDs, shapes the noncoding-DNA interaction network in neural cells, and binds DNA together with FOS, whose recognition sequence is enriched within HARs carrying human-specific substitutions modulating enhancer activity. SOX2 also binds regulatory regions (including HARs) carrying ASD-associated mutations. We highlight research directions based on these findings, which will hopefully improve our understanding of the connection between SOX2-dependent gene regulatory networks, NDDs, and brain evolution. Full article

Improving absolute accuracy in industrial manipulators remains difficult because rigid-body kinematic calibration cannot fully represent configuration-dependent non-geometric effects. Drawing inspiration from biological brain–body co-adaptation, this study presents an Evolutionary Neuro-Physical Hybrid (Evo-NPH) framework in which rigid geometric parameters and neural compensator weights are treated as a single co-evolving decision vector. In the offline phase, a Dual-Strategy Adaptive Differential Evolution (DS-ADE) optimizer performs global joint identification using complementary exploration–exploitation behaviors and success-history inheritance, analogous to morphology-control co-evolution in biological systems. In the online phase, a Neuro-Physical Hybrid Jacobian (NPHJ) solver augments the analytical Jacobian with gradients from a Graph Kolmogorov–Arnold Network (GKAN), enabling sensorimotor-like real-time compensation on the learned physical manifold. Experiments on an ABB IRB 120 manipulator with 600 configurations (500 training, 100 testing) report a testing distance-residual RMSE of 0.62 mm, STD of 0.59 mm, and MAX of 0.83 mm. Relative to the uncalibrated baseline, RMSE is reduced by 86.75%; compared with the strongest published baseline, RMSE improves by 23.46%. Ablation results show that joint DS-ADE optimization outperforms a sequential pipeline by 32.6%, and the graph-structured KAN outperforms a parameter-matched MLP by 26.2%. Wilcoxon signed-rank tests ($p<0.001$) confirm statistical significance. Full article

Glial brain tumours, including astrocytoma IDH (Isocitrate Dehydrogenase) mutant and glioblastoma IDH wild-type, are highly malignant brain tumours with poor clinical outcomes. Genomic instability, encompassing microsatellite (MIN) and chromosomal instability (CIN), drives tumour heterogeneity and evolution. In this study, genomic instability was analysed in 85 patients using AP-PCR (Arbitrarily Primed Polymerase Chain Reaction) by comparing tumour and normal tissue (blood) DNA profiles of the same patient. Both types of alterations were present in all analysed samples, contributing almost equally to the total level of genomic instability. The dominant pattern of genomic instability in our cohort was low overall instability, predominantly manifesting as low-degree microsatellite instability. A general decrease in genomic instability was observed with increasing tumour grade. Glioblastoma IDH wild-type was more prevalent in older patients, whereas astrocytoma IDH mutant predominated in younger individuals. Notably, low genomic instability (both MIN and CIN) was associated with poorer survival in patients over 50 years of age. Females, compared to males, exhibited higher MIN in grade 2 tumours and elevated CIN in grade 4 tumours. Our results confirm that genomic instability contributes to tumour progression, MIN being the pivotal factor, and could serve as a prognostic biomarker in malignant gliomas. Full article

Background: Disability progression in multiple sclerosis (MS) is increasingly recognized as a consequence of large-scale brain network disruption rather than isolated regional damage. Although diffusion tensor imaging (DTI) is the reference method for assessing structural connectivity, its limited availability restricts widespread clinical application. There is therefore a critical need for alternative approaches capable of capturing network-level alterations using routinely acquired MRI data. Objective: This study aimed to determine whether synthetic structural connectivity matrices derived from standard regional volumetric MRI can capture clinically meaningful network alterations in MS and predict subsequent functional progression, particularly upper limb decline. Methods: Regional brain volumetry was obtained from routine T1-weighted MRI using an automated, clinically approved volumetric pipeline. Synthetic structural connectivity matrices were generated by integrating principles of structural covariance, distance-dependent connectivity, and disease-specific vulnerability patterns. Graph-theoretical network metrics were extracted to characterize global and regional topology. Machine learning models including logistic regression, support vector machines, random forests, and gradient boosting were trained to predict clinical progression defined by worsening on the 9-Hole Peg Test. Dimensionality reduction was performed using principal component analysis, and model performance was evaluated using balanced accuracy, AUC-ROC, and resampling-based validation. Feature importance analyses were conducted to identify network vulnerability patterns. Results: Synthetic connectivity networks exhibited biologically plausible properties, including preserved but attenuated small-world organization. Global efficiency showed a strong inverse correlation with disability severity (EDSS). Patients with clinical progression demonstrated marked reductions in network integration and segregation, alongside increased characteristic path length. Machine learning models achieved robust prediction of upper limb functional decline, with ensemble-based methods performing best (balanced accuracy > 80%, AUC-ROC up to 0.85). A limited subset of connections accounted for a disproportionate share of predictive power, predominantly involving frontoparietal associative networks, thalamocortical pathways, and inter-hemispheric connections. In a longitudinal subset, network-level alterations preceded measurable clinical deterioration by several months. Conclusions: Synthetic structural connectivity derived from routine volumetric MRI captures clinically relevant network-level disruption in multiple sclerosis and enables accurate prediction of functional progression. By bridging network neuroscience with widely accessible imaging data, this framework provides a pragmatic alternative for connectomic analysis when diffusion imaging is unavailable and supports a network-based understanding of disease evolution in MS. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, for which there is currently no cure. The causes of AD are still not well understood, although 5% of cases are known to have a genetic origin, associated with pathogenic genetic variants of the APP and PSEN1/2 genes. There is growing evidence that both APP and PSEN1/2 are also essential for proper human brain development and neural/neuronal function. This implies that abnormalities in early brain development could increase neuronal vulnerability to AD later in life. Human cerebral organoids (hCOs), generated from induced pluripotent stem cells (iPSCs) from AD patients, provide an exceptional model for better understanding the cellular and molecular mechanisms involved in human brain development, as well as early neurological alterations in the evolution of AD. This review compiles the main studies in which hCOs are used as a model for studying AD and for the discovery of new biomarkers. We also discuss the advantages and applications of these hCOs for studying the early stages of AD from a neurodevelopmental perspective. Finally, we mention the main current challenges in the use of hCOs for future research into AD. Full article

Motor imagery EEG decoding often relies on static functional connectivity graphs that cannot capture the dynamic, stage-wise reorganization of brain networks during tasks. This paper aims to develop a graph neural network that explicitly simulates this neurodynamic process to improve decoding and provide computational insights. This paper proposes a Structure-Feature Evolution Graph Attention Network (SFE-GAT). Its inter-layer evolution mechanism dynamically co-adapts graph topology and node features, mimicking functional network reorganization. Initialized with phase-locking value connectivity and spectral features, the model uses a graph autoencoder with Monte Carlo sampling to iteratively refine edges and embeddings. On the BCI Competition IV-2a dataset, SFE-GAT achieved 77.70% (subject-dependent) and 66.59% (subject-independent) accuracy, outperforming baselines. Evolved graphs showed sparsification and strengthening of task-critical connections, indicating hierarchical processing. This paper advances EEG decoding through a dynamic graph architecture, providing a computational framework for studying the hierarchical organization of motor cortex activity and linking adaptive graph learning with neural dynamics. Full article

Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, a framework that captures the self-similar and scale-free properties of electrophysiological signals, which is considered to act as an output of complex physiological structures that generate complex processes. Central to this approach is the principle of ‘decomplexification’, whereby aging and disease are associated with a loss of physiological complexity. We discuss key algorithms, particularly Higuchi’s fractal dimension, which is often combined with other nonlinear measures and machine-learning models for real-time analysis of electrophysiological signals. Evidence shows that fractal metrics enable the early detection and monitoring of neurological and psychiatric disorders, outperforming traditional spectral measures. In movement disorders and mood disorders, fractal and nonlinear features show high diagnostic accuracy. Beyond diagnostics, we discuss therapeutic applications, including the prediction of responsiveness to non-invasive brain stimulation. Here, we envisage the evolution of one fractal or nonlinear measure use, to several measures applied, then use it as a feature for machine learning, and then realize that a whole cluster of biomarkers must be used to reflect the state of autonomic profile, which then can be used for ontology-based application profiles that can be machine-actionable. In addition, we discuss the fractal and fractional description of transport processes, which offer innovative improvement for a much more accurate description of physiological reality as a prerequisite for further modeling: for example, this is needed for digital twins to support the clinical translation of fractal analysis for personalized medicine. In essence, if one is trying to mathematically describe or quantify structures or processes in human physiology, fractal and fractional are the supreme and adequate approach to accurately model that reality. Full article

Precision psychiatry aims to overcome clinical heterogeneity by means of biomarkers that allow predicting the clinical evolution and therapeutic response in psychiatric disorders. This literature review addresses its prognostic role and its potential integration into healthcare practice. The main objective was to compile and synthesize current evidence on prognostic biomarkers in psychiatry, evaluating their usefulness in anticipating clinical evolution, therapeutic response, and risk of relapse. A strategic search was carried out on PubMed, selecting original studies that evaluated blood, genetic, epigenetic, neuroimaging, or electrophysiological biomarkers with prognostic value. We included 30 final studies that met the established inclusion and exclusion criteria and were evaluated according to standardized scales (RoB 2, NOS, AXIS). Inflammatory biomarkers showed potential as clinical modulators. Metabolomic, neuroendocrine, and neurotrophic factors reflected specific biological profiles associated with response to treatment or risk of relapse. Functional connectivity and brain morphometry were useful in the therapeutic prediction and stratification of patients. Finally, genetics and epigenetics are consolidated as tools of sensitivity and pharmacological response. Taken together, the findings reveal specific prognostic utility based on the type of biomarker and the patient’s clinical context. Despite the current methodological limitations and scarce replication of studies, prognostic biomarkers represent a step towards a more personalized psychiatry based on biological mechanisms. The future integration of multimodal models will improve clinical decision-making. Full article

Background/Objectives: Decoding affective states from Electroencephalography (EEG) signals is fundamental to non-invasive Brain–Computer Interfaces. Despite recent advances, accurate recognition is impeded by the inherently non-stationary nature of physiological signals and the entanglement of spatio-temporal dynamics within high-dimensional recordings. While Transformers excel at global modeling, they often neglect the continuous dynamical properties of neural signals and suffer from quadratic complexity. Methods: In this paper, we propose the Spatio-Temporal Hybrid Mamba-Attention (STHMA), a framework designed to explicitly disentangle and model EEG dynamics via linear-complexity State Space Models. First, to incorporate domain knowledge, we introduce a Dual-Domain Physics-Aware Embedding module. This module fuses learnable temporal convolutions with explicit frequency-domain spectral features, ensuring fidelity to neurophysiological principles. Second, we propose a novel Decoupled Spatial–Temporal Scanning strategy. By dynamically reconfiguring the serialization of the data tensor, our model strictly separates the learning of instantaneous functional connectivity from the tracking of emotional state evolution, thereby preventing the structural collapse common in 1D sequence models. Results: Extensive experiments on the FACED and SEED-V datasets demonstrate that the STHMA achieves state-of-the-art performance, significantly exceeding the random chance baselines (11.11% for 9-class FACED and 20.00% for 5-class SEED-V). Conclusions: The results validate that combining Physics-Aware Embeddings with decoupled state-space modeling offers a scalable and effective paradigm for EEG emotion recognition. Full article

Many cancer-associated deaths result from metastases rather than primary tumors. Growing evidence suggests that DNA methylation alterations are crucial for inducing a plastic phenotype that allows cancer cells to adapt to the metastatic microenvironment. Brain metastases of melanoma (MBM) and glioblastoma (GB) share a neuroectodermal origin and the brain as tissue of residence, but their epigenetic regulation is poorly understood. Aiming at elucidating shared and tumor-distinct features, we analyzed the methylation of MGMT regulatory elements. We focused on MGMT because MGMT promoter methylation is used as a predictive marker for temozolomide response in GB, but its role in MBM has been discussed controversially. By targeting 12 CpG dinucleotides (CpGs) in the promoter, 68 CpGs in intergenic enhancers, and 31 CpGs in intragenic enhancers, we identified shared features, including an L-shaped relationship between promoter methylation and MGMT protein expression and an inverse L-shaped relationship between intragenic enhancer methylation and MGMT protein expression. GB exhibited higher methylation, particularly in promoter and intergenic enhancers, and stronger associations between methylation and overall survival than MBM. These results highlight both conserved and tumor-specific MGMT regulation, reflecting the complexity of epigenetic control in brain malignancies and emphasizing divergent evolution between MBM and GB. Full article

Glioblastoma (GBM) is an extremely aggressive and incurable primary tumor of the brain. GBM is characterized by interpatient and intratumoral heterogeneity, making this cancer particularly resistant to therapy and likely to recur. Mapping the complex dynamics that underpin the development and evolution of gliomas with human-based in vitro models is difficult. This study aimed to generate 3D glioma patient-derived tumor constructs (PTCs) using a clinically relevant, Matrigel-free, hyaluronic acid system, evaluate their suitability in drug screening assays, and determine the stability of their genetic profiles compared to originating tumors. In this study, we utilized a synthetically modified hyaluronic acid and gelatin hydrogel system to generate tumor constructs containing cells from clinical glioma biospecimens. PTCs were characterized phenotypically, after which they were deployed in chemotherapy drug screens using temozolomide (TMZ) and a P53 activator compound. Drug responses of these 3D cultures were compared with 2D cultures, as well as PTCs that were generated after passaging in 2D. RNA sequencing was used to evaluate genetic parity between PTCs or 2D cultures with originating tumor tissues, using The Cancer Genome Atlas (TCGA) GBM subpopulations for subcategorizing. PTCs were created successfully from five World Health Organization (WHO) grade 4, two grade 3, and two grade 2 gliomas. PTCs were maintained with high viability. Chemotherapy drug screens demonstrated that expected TMZ responses were observed for Isocitrate dehydrogenase (IDH) mutant diffuse gliomas while drug response was variable for IDH wildtype GBM PTCs. PTCs demonstrated stable drug response over time, while 2D passaging resulted in significant shifts in drug sensitivity. RNA sequencing revealed maintenance of subpopulation signatures for PTCs which clustered with their originating patient tumor tissue. In contrast, 2D cultures largely clustered together regardless of the patient. Our PTC approach utilizes a defined hydrogel biomaterial system that maintains the genotypic and drug response characteristics of patient tumors making this an ideal ex vivo model for translational applications. Full article

Ketone bodies (KBs) are the only energy substrates oxidized by the brain, whose concentration in the circulation can greatly increase when a physiological situation requires it. For example, when an adult human fasts for two days, circulating KBs rise twenty-fold from ~0.1 to ~2 mM. As a fuel, KBs provide the brain with acetyl-CoA that produces ATP or glutamate, notably in certain brain regions. Remarkably, KBs activate the expression of their own cerebral transporters and KB-utilizing enzymes so that circulating levels determine cerebral utilization of KBs. Throughout evolution, the energetic role of KBs has been crucial for the metabolic homeostasis of humans endowed with a large brain and facing unpredictable periods of food shortage. Paradoxically, the brain of modern, regularly fed humans whose ordinary blood KBs are ~0.1 mM, has access to much fewer circulating sources of energy than that of their distant ancestors. KBs can modify certain proteins post-translationally, for example, histones through lysine-butyrylation. KBs could act as short- or long-term epigenetic messengers. These properties of KBs might allow a fetus to directly sense maternal starvation and adapt their cerebral metabolism to this situation, possibly preparing for nutritional constraints in extra-uterine life. KB transcriptional and epigenetic properties could also enable the postnatal organism to retain a molecular memory of its own starvation episodes. No other energy substrate, such as glucose or lactate, has such capacities. Medicine turned its attention to KBs a century ago. Indeed, KBs are the only energy substrates whose circulating levels can be increased, and nutritional interventions can alter them under free-living conditions. This property opens broad prospects for ketogenic diets (KDs) to prevent or rescue neurodegenerative diseases characterized by glucose hypometabolism, notably Alzheimer’s disease (AD). However, KDs have not yet found real medical applications, for reasons that are discussed. Full article