Abstract Hereditary spastic paraplegia (HSP) comprises a genetically heterogeneous group of neurodegenerative disorders characterized by progressive lower-limb spasticity and weakness. Although HSP genes have traditionally been classified according to a single mode of inheritance, increasing evidence indicates that several genes can cause disease through both autosomal dominant and autosomal recessive inheritance, complicating molecular diagnosis, variant interpretation, and genetic counseling. Here, we present the first comprehensive review of dual inheritance in HSP by systematically evaluating the published literature through June 2026. Patients carrying monoallelic or biallelic pathogenic variants were identified, and classically dominant HSP genes were further assessed for evidence supporting recessive disease. Six genes ( ALDH18A1 , ERLIN2 , KIF1A , REEP2 , UCHL1 , and SPTSSA ) were confirmed as established dual-inheritance HSP genes, while supporting evidence for recessive inheritance was identified for ATL1 , SPAST , RTN2 , BSCL2 , and SPTAN1 . Dual inheritance was shown to arise through diverse molecular mechanisms, including haploinsufficiency, dominant-negative effects, gene dosage, and allele-specific functional consequences. Although biallelic variants generally resulted in earlier onset and more severe phenotypes, genotype–phenotype correlations varied considerably among genes. Comparative analyses also identified gene-specific patterns of variant distribution, recurrent mutational hotspots, and domain enrichment. Together, these findings provide a unified framework for understanding dual inheritance in HSP and highlight the evolving nature of inheritance patterns as genomic evidence continues to expand. Recognizing dual inheritance has important implications for molecular diagnosis, variant interpretation, and genetic counseling and supports considering biallelic pathogenic variants even in genes traditionally regarded as exclusively autosomal dominant, particularly in patients with early-onset or unusually severe disease.
Abstract Stroke triggers a complex secondary injury cascade, in which neuroinflammation plays a central role. The glymphatic system is a perivascular clearance network that facilitates cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange and removes metabolic waste from the brain. Recent studies suggest that glymphatic dysfunction is not simply a consequence of stroke, but also contributes to disease progression. Impaired glymphatic clearance promotes the accumulation of damage-associated molecular patterns, inflammatory mediators, and toxic metabolites within the brain parenchyma, sustaining both innate and adaptive immune responses. At the same time, inflammatory signaling, immune cell infiltration, and vascular injury progressively disrupt perivascular architecture and aquaporin-4 (AQP4) polarization, leading to further impairment of glymphatic transport. As a result, glymphatic dysfunction and neuroinflammation reinforce each other, contributing to cerebral edema, persistent inflammation, and neurological deterioration after stroke. In this review, we summarize current evidence linking glymphatic dysfunction to neuroinflammation in both ischemic and hemorrhagic stroke. We discuss the molecular and cellular mechanisms underlying this interaction and preclinical therapeutic approaches that may help preserve or restore glymphatic function after stroke. A better understanding of the relationship between glymphatic dysfunction and neuroinflammation may reveal new therapeutic opportunities for improving neurological recovery after stroke.
Abstract Brain age prediction has emerged as a promising approach for characterizing interindividual differences in brain aging. However, the increasing complexity of deep learning models has created a substantial gap between predictive performance and interpretability. Explainable artificial intelligence (XAI) has, therefore, become increasingly important for identifying the neuroanatomical features underlying brain age predictions. This review systematically examines interpretable deep learning approaches for brain age prediction. A systematic search of PubMed, Web of Science, and ScienceDirect identified 32 eligible studies, which were organized into post-hoc explanation methods and intrinsically interpretable models. We further propose a four-dimensional operational framework encompassing Algorithm Reliability, Robustness and Reproducibility, Biological Validation, and Clinical Utility. The reviewed literature indicates a transition from localized feature attribution toward generative, disentangled, attention-based, and anatomically meaningful representations. However, interpretation remains sensitive to methodological and imaging-related factors, while brain age gap (BAG) is also influenced by age dependency, calibration, reference-population composition, and site heterogeneity. Future research should emphasize standardized evaluation and prospective clinical validation to develop reliable and clinically meaningful brain age biomarkers.
Abstract Peripheral immune alterations are widely reported in Parkinson’s disease (PD), but their biological meaning and clinical utility remain uncertain because circulating immune profiles are shaped by aging, latent infection, medication exposure, comorbidity, disease stage, and preanalytical conditions. This concept-driven narrative review examines how technically valid peripheral immune findings should be biologically interpreted and clinically qualified through an immune-context phenotyping framework. Available human evidence suggests alterations in neutrophil- and monocyte-related inflammatory indices and cellular states, α-synuclein–responsive T-cell pathways, cytotoxic and regulatory T-cell states, B-cell phenotypic alterations, and peripheral–central immune associations. Much of this evidence, however, derives from cross-sectional and cohort-dependent studies, and within the human evidence considered in this review, PD specificity remains insufficiently established for most candidate signals. An observed blood difference may reflect altered production, survival, differentiation, redistribution, denominator effects, systemic inflammatory context, or measurement-related distortion rather than a single disease mechanism. Immune-context phenotyping, therefore, integrates trait, state, disease-trajectory, measurement, and treatment contexts with biological identifiability, disease-control comparisons, cross-compartment corroboration, repeated sampling, and external validation. The framework adapts established biomarker concepts to distinguish diagnostic, prognostic, monitoring, predictive, pharmacodynamic/response, and treatment-selection claims without treating peripheral target engagement, CNS engagement, pathway modification, and clinical benefit as equivalent. Based on the human evidence considered in this review, no peripheral immune phenotype appears ready for stand-alone clinical implementation in PD. The proposed framework defines a testable path from descriptive peripheral immune associations toward biologically coherent, temporally informative, and clinically qualified phenotypes.
Abstract Locomotor impairment is one of the most devastating consequences of spinal cord injury (SCI) and cannot be adequately overcome by existing rehabilitation methods. Numerous studies in spinalized mammals have demonstrated that serotonin, dopamine, and noradrenaline receptors located on spinal cord cells below the injury level play an important role in locomotor recovery. Hence, these monoamine receptors (MARs) represent promising targets for pharmacological rehabilitation following SCI, but no drug targeting them has yet proven sufficiently effective for locomotor recovery or reached clinical use. This review summarizes current data on the spinal localization of MARs, their postinjury changes (expression and function), and the role of specific receptor subtypes and their combinations in regulating kinematic, kinetic, and electromyographic locomotor parameters in intact and spinalized mammals. Specifically, the studies reviewed here show that some MARs are important for locomotor control in healthy animals, while the role of others becomes apparent only after SCI. Data on changes in receptor expression following SCI are contradictory and strongly depend on the experimental protocol, animal species, and sex, as well as on the type of sample analyzed. Finally, targeting different receptor subtypes affects distinct locomotor parameters, whereas simultaneous targeting of multiple receptors (e.g., 5-HT1AR + 5-HT2A/2CR + D1R + α2AR) can produce a synergistic effect. Therefore, the development of highly selective drugs and knowledge of post-traumatic changes occurring in receptors will enable targeting of a narrow spectrum of receptors and correction of individual locomotor parameters depending on the degree of their impairment.
