Background: The clinical–radiological paradox in multiple sclerosis (MS) underscores the need for biomarkers that better reflect neurodegenerative pathology. Serum neurofilament light chain (sNfL) is a dynamic marker of neuroaxonal injury, while brain volumetry provides structural assessment of disease impact. However, the precise link between sNfL and regional atrophy patterns, as well as their combined utility for patient stratification and prediction, remains underexplored. Objective: This study aimed to establish a multimodal biomarker framework by integrating sNfL with comprehensive volumetric MRI to define neurodegenerative endophenotypes and predict neuroaxonal injury using Bayesian inference and machine learning. Methods: In a cohort of 57 MS patients, sNfL levels were measured using single-molecule array (Simoa) technology. Brain volumes for 42 regions were quantified via automated deep learning segmentation (mdbrain software). We employed (1) Bayesian correlation to quantify evidence for sNfL–volumetric associations; (2) mediation analysis to test whether grey matter atrophy mediates the EDSS–sNfL (Expanded Disability Status Scale) relationship; (3) unsupervised K-means clustering to identify patient subtypes based on combined sNfL–volumetric profiles; and (4) supervised machine learning (Elastic Net and Random Forest regression) to predict sNfL from volumetric features. Results: Bayesian analysis revealed strong evidence linking sNfL to total grey matter volume (r = −0.449, BF₁₀ = 0.022) and lateral ventricular volume (r = 0.349, BF₁₀ = 0.285). Mediation confirmed that grey matter atrophy significantly mediates the relationship between EDSS and sNfL (indirect effect = 0.45, 95% CI [0.20, 0.75]). Unsupervised clustering identified three distinct endophenotypes: “High Neurodegeneration” (elevated sNfL, severe atrophy, high disability), “Moderate Injury,” and “Benign Volumetry” (low sNfL, preserved volumes, mild disability). Supervised models predicted sNfL with high accuracy (R² = 0.65), identifying total grey matter volume, ventricular volume, and age as top predictors. Conclusions: This integrative multi-method analysis demonstrates that sNfL is robustly associated with global grey matter and ventricular volumes, and that these measures define clinically meaningful neurodegenerative subtypes in MS. Machine learning confirms that a concise set of volumetric features can effectively predict neuroaxonal injury. These findings advance a pathobiology-driven subtyping framework and provide a validated model for using routine MRI volumetry to assess neuroaxonal health, with implications for prognosis and personalised therapeutic strategies.

Background: The clinical-radiological paradox in multiple sclerosis (MS) underscores the need for biomarkers that better reflect neurodegenerative pathology. Serum neurofilament light chain (sNfL) is a dynamic marker of neuroaxonal injury, while brain volumetry provides structural assessment of disease impact. However, the precise link between sNfL and regional atrophy patterns, and their combined utility for patient stratification and prediction, remains underexplored. Objective: To establish a multimodal biomarker framework by integrating sNfL with comprehensive volumetric MRI to define neurodegenerative endophenotypes and predict neuroaxonal injury using Bayesian inference and machine learning. Methods: In a cohort of 57 MS patients, sNfL levels were measured using single-molecule array (Simoa) technology. Brain volumes for 42 regions were quantified via automated deep-learning segmentation (mdbrain software). We employed: 1) Bayesian correlation to quantify evidence for sNfL-volumetric associations; 2) mediation analysis to test whether gray matter atrophy mediates the EDSS–sNfL relationship; 3) unsupervised K-means clustering to identify patient subtypes based on combined sNfL-volumetric profiles; and 4) supervised machine learning (Elastic Net and Random Forest regression) to predict sNfL from volumetric features. Results: Bayesian analysis revealed strong evidence linking sNfL to total gray matter volume (r = -0.449, BF₁₀ = 0.022) and lateral ventricular volume (r = 0.349, BF₁₀ = 0.285). Mediation confirmed that gray matter atrophy significantly mediates the relationship between EDSS and sNfL (indirect effect = 0.45, 95% CI [0.20, 0.75]). Unsupervised clustering identified three distinct endophenotypes: "High Neurodegeneration" (elevated sNfL, severe atrophy, high disability), "Moderate Injury," and "Benign Volumetry" (low sNfL, preserved volumes, mild disability). Supervised models predicted sNfL with high accuracy (R² = 0.65), identifying total gray matter volume, ventricular volume, and age as top predictors. Conclusions: This integrative multi-method analysis demonstrates that sNfL is robustly associated with global gray matter and ventricular volumes, and that these measures define clinically meaningful neurodegenerative subtypes in MS. Machine learning confirms that a concise set of volumetric features can effectively predict neuroaxonal injury. These findings advance a pathobiology-driven subtyping framework and provide a validated model for using routine MRI volumetry to assess neuroaxonal health, with implications for prognosis and personalized therapeutic strategies.

Background and Clinical Significance: Parry–Romberg syndrome (PRS), also known as progressive hemifacial atrophy, is a rare disorder characterized by progressive unilateral hemifacial atrophy, with potential involvement of the cranial bones and the brain. Although neurological manifestations are frequently described, central nervous system involvement may be subclinical and detectable only through neuroimaging. Owing to its rarity and the lack of standardized diagnostic criteria, most available data derive from case reports and small case series. Case Presentation: We report the case of a 38-year-old female patient diagnosed with PRS (stage 2 according to the Guerrerosantos classification), with onset in childhood characterized by left parietal alopecia, followed by progressive left-sided hemifacial atrophy and a linear “en coup de sabre” lesion. Neurological examination was normal, with no history of seizures or focal deficits. Brain MRI revealed ipsilateral cutaneous, subcutaneous, muscular, and osseous atrophy, as well as atrophy of the left parotid and submandibular glands. Additionally, subcortical white matter lesions were identified in the left frontal lobe in the absence of hemispheric cerebral atrophy. Conclusions: This case highlights a significant radioclinical dissociation, demonstrating that central nervous system involvement may occur even in clinically stable and paucisymptomatic forms of PRS. This disease may be associated with subclinical intracranial abnormalities, underscoring the need for systematic neuroimaging evaluation even in the absence of neurological manifestations. Based on the available literature and the specific features of the present case, we propose a practical clinical framework and imaging algorithm to facilitate early diagnosis and to contribute to the standardization of the diagnostic approach in this rare disorder.

The Background: monoclonal antibody therapies represent high-efficacy treatment options for relapsing forms of multiple sclerosis (MS). However, the absence of direct head-to-head randomized trials and the use of heterogeneous comparators across pivotal studies complicate comparative effectiveness assessments. While network meta-analysis (NMA) offers a framework to integrate evidence, the fragmented structure of the available evidence base precludes a conventional NMA with global indirect comparisons and treatment ranking. Methods: A systematic review with qualitative assessment of treatment effects of randomized controlled trials evaluating monoclonal antibody therapies in relapsing forms of multiple sclerosis was conducted. Annualized relapse rate (ARR) was analyzed as the primary outcome, and six-month confirmed disability progression (CDP) as the key secondary outcome. Network geometry and connectivity were explicitly assessed for each outcome prior to quantitative synthesis. Analyses were restricted to comparator-defined connected components of the evidence base, and indirect comparisons across disconnected components were not performed. Sensitivity analyses, including descriptive analyses in progressive multiple sclerosis, were conducted where appropriate. Results: nine randomized controlled trials involving 6762 patients were included. For ARR, the evidence network was fragmented into three disconnected components defined by placebo-, interferon beta-1a-, and teriflunomide-controlled trials. Within connected sub-networks, monoclonal antibody therapies consistently demonstrated substantial reductions in ARR relative to their respective comparators, with overlapping confidence intervals suggesting broadly comparable relapse suppression among high-efficacy agents. For CDP, network connectivity was more limited, and treatment effects were more heterogeneous. Significant reductions in disability progression were observed for some agents within comparator-specific networks, while uncertainty remained for others. Due to network disconnection, no global treatment ranking was performed. Conclusions: this study provides a transparent synthesis of randomized evidence on monoclonal antibody therapies in relapsing MS. By explicitly accounting for network connectivity and comparator heterogeneity, the analysis avoids unsupported indirect comparisons and global treatment hierarchies. The findings support robust relapse suppression across monoclonal antibody therapies within comparable trial frameworks, while highlighting heterogeneity in disability outcomes. These results illustrate the importance of contextual interpretation in comparative effectiveness research in MS.

Background: Real-world evidence regarding the effectiveness and safety of anti-calcitonin gene-related peptide (CGRP) monoclonal antibodies in Eastern European populations remains limited. We aimed to evaluate treatment outcomes and safety in Romanian patients with high-frequency episodic migraine (HFEM) and chronic migraine (CM). Methods: We conducted a multicenter retrospective observational cohort study including 113 adults with HFEM (n = 38) or CM (n = 75) who initiated treatment with galcanezumab, erenumab, fremanezumab, or eptinezumab at specialized headache centers in Romania. Monthly migraine days (MMDs) in HFEM and monthly headache days (MHDs) in CM were assessed at the baseline and during follow-up. Treatment outcomes, discontinuation, persistence, and adverse events were evaluated during routine clinical follow-up. Results: At month 6, headache frequency was substantially reduced in both groups, with median MMDs decreasing from 10 to 2 in patients with HFEM and median MHDs decreasing from 25 to 6 in patients with CM. A ≥50% reduction in headache frequency was achieved by 78.9% of patients with HFEM and 64.0% of those with CM. Among the patients with CM, 70.7% met the predefined criterion for conversion to episodic migraine at month 6. During follow-up, 22 patients discontinued treatment. Adverse events were reported in 28 patients (24.8%), were predominantly non-serious, and resulted in treatment discontinuation in 3 patients (2.7%). No serious adverse events were documented. Treatment persistence at 12 months was 82.3%. Conclusions: In this Romanian real-world cohort, anti-CGRP monoclonal antibody treatment was associated with substantial reductions in migraine/headache frequency in patients with HFEM and CM. The treatment was generally well-tolerated, with predominantly non-serious adverse events and a low rate of discontinuation due to adverse events. These findings provide real-world evidence from Romania and support further prospective evaluation of the long-term effectiveness and safety of anti-CGRP monoclonal antibodies.

Background/Objectives: Manganese–Curcumin–Triphenylphosphonium (Mn-C-TPP), a complex designed for mitochondrial targeting, was investigated with ascorbate to assess cytotoxicity and associated cellular responses. We hypothesized that Mn-C-TPP would amplify ascorbate-driven oxidative stress and produce synergistic cytotoxicity in susceptible cancer cell lines. Methods: An inverse, control-normalized SYTOX-derived response index (vehicle = 100; staurosporine = 0) was determined after 72 h in HCT116, A549, HepG2, and normal dermal human fibroblasts (NDHFs). H₂O₂-associated luminescence (ROS-Glo), caspase-3/7 activation, and mitochondrial membrane potential were assessed in selected cancer cell lines. Drug interactions were analyzed using SynergyFinder 3.0 (ZIP model). Results: Mn-C-TPP alone reduced the SYTOX-derived response index in HCT116 and HepG2, whereas A549 and NDHF showed smaller changes. Ascorbate enhanced cytotoxicity at selected combinations. In HCT116, 3 µM Mn-C-TPP plus 100 µM ascorbate decreased the SYTOX-derived response index toward the staurosporine reference. HepG2 responded markedly to 3–7 µM Mn-C-TPP with 100–300 µM ascorbate, whereas pronounced A549 responses occurred mainly at 15–30 µM Mn-C-TPP with 100–300 µM ascorbate. Mean ZIP scores were approximately 32, 54, 6, and 10 for HCT116, HepG2, A549, and NDHF, respectively. The NDHF response index remained close to the vehicle reference at lower combination doses but decreased at higher doses. Cellular assays showed increased H₂O₂-associated luminescence with Mn-C-TPP alone and higher mean signals with selected combinations than with ascorbate alone, together with caspase-3/7 activation and mitochondrial depolarization. Conclusions: These findings demonstrate cell-line- and concentration-dependent cytotoxicity and support further investigation of the proposed mitochondrial redox mechanism.