Search Results (212)

Due to their clinical heterogeneity, nonspecific symptoms, and the limitations of existing biomarkers and imaging modalities, metabolic brain diseases (MBDs), such as mitochondrial encephalopathies, lysosomal storage disorders, and glucose metabolism syndromes, pose significant diagnostic challenges. This review examines the growing potential of cell-free DNA (cfDNA) derived from cerebrospinal fluid (CSF) epigenetic profiling as a dynamic, cell-type-specific, minimally invasive biomarker approach for MBD diagnosis and monitoring. We review important technological platforms and their use in identifying CNS-specific DNA methylation patterns indicative of neuronal injury, neuroinflammation, and metabolic reprogramming, including cfMeDIP-seq, enzymatic methyl sequencing (EM-seq), and targeted bisulfite sequencing. By synthesizing current findings across disorders such as MELAS, Niemann–Pick disease, Gaucher disease, GLUT1 deficiency syndrome, and diabetes-associated cognitive decline, we highlight the superior diagnostic and prognostic resolution offered by CSF cfDNA methylation signatures relative to conventional CSF markers or neuroimaging. We also address technical limitations, interpretive challenges, and translational barriers to clinical implementation. Ultimately, this review explores CSF cfDNA epigenetic analysis as a liquid biopsy modality. The central objective is to assess whether epigenetic profiling of CSF-derived cfDNA can serve as a reliable and clinically actionable biomarker for improving the diagnosis and longitudinal monitoring of metabolic brain diseases. Full article

Background/Objective: Breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) has affected more than 1700 women with textured breast implants. About 80% of patients present with fluid (seroma) around their implant. BIA-ALCL can be cured by surgery alone when confined to the seroma and lining of the peri-implant capsule. To address the need for early detection, we developed a rapid point of care (POC) lateral flow assay (LFA) to identify lymphoma in seromas. Methods: We compared 28 malignant seromas to 23 benign seromas using both ELISA and LFA. LFA test lines (TL) and control lines (CL) were visualized and measured with imaging software and the TL/CL ratio for each sample was calculated. Results: By visual exam, the sensitivity for detection of CA9 was 93% and specificity 78%, while the positive predictive value was 84% and negative predictive value 90%. Quantitative image analysis increased the positive predictive value to 96% while the negative predictive value reduced to 79%. Conclusions: We conclude that CA9 is a sensitive biomarker for detection and screening of patients for BIA-ALCL in patients who present with seromas of unknown etiology. The CA9 LFA can potentially replace ELISA, flow cytometry and other tests requiring specialized equipment, highly trained personnel, larger amounts of fluid and delay in diagnosis of BIA-ALCL. Full article

Background: Optical Coherence Tomography has become a common imaging technique that enables a non-invasive and detailed visualization of the retina and allows for the identification of various diseases. Through the advancement of technology, the volume and complexity of OCT data have rendered manual analysis infeasible, creating the need for automated means of detection. Methods: This study investigates the ability of state-of-the-art object detection models, including the latest YOLO versions (from v8 to v12), YOLO-World, YOLOE, and RT-DETR, to accurately detect pathological biomarkers in two retinal OCT datasets. The AROI dataset focuses on fluid detection in Age-related Macular Degeneration, while the OCT5k dataset contains a wide range of retinal pathologies. Results: The experiments performed show that YOLOv12 offers the best balance between detection accuracy and computational efficiency, while YOLOE manages to consistently outperform all other models across both datasets and most classes, particularly in detecting pathologies that cover a smaller area. Conclusions: This work provides a comprehensive benchmark of the capabilities of state-of-the-art object detection for medical applications, specifically for identifying retinal pathologies from OCT scans, offering insights and a starting point for the development of future automated solutions for analysis in a clinical setting. Full article

Malignant diseases represent a major global health challenge and are among the leading causes of death worldwide. Accurate early diagnosis is essential for improving outcomes and combating these conditions effectively. Currently, the diagnosis of malignancies relies heavily on radiological imaging and pathological examinations, which are often invasive and not cost-effective. As such, there is a growing need for non-invasive and accessible methods to detect cancer in its early stages. Tumor markers—biomolecules whose levels increase in malignancy and can be measured in blood or other biological tissues and fluids—offer a promising tool. However, the sensitivity and specificity of currently available tumor markers are insufficient for early detection, limiting their use primarily to disease monitoring rather than diagnosis. While ongoing research continues to identify novel tumor markers, the development of more effective early detection strategies requires more than the discovery of new biomarkers. The continuous monitoring of patients and individuals with a high tumor risk and the personalization of tumor marker interpretation are also critical. In this review, we (i) summarize the most commonly used tumor markers, (ii) examine strategies for developing novel biomarkers, particularly through omics technologies, (iii) explore the potential of continuous monitoring using wearable biosensors for early tumor detection, and (iv) discuss approaches to personalizing tumor marker interpretation to support early diagnosis and improve treatment outcomes. Full article

This pilot study explored whether the ceruloplasmin (CP) and ferritin (FT) levels in ocular fluids could serve as biomarkers for early neurodegenerative diseases (Alzheimer’s, Parkinson’s, and other dementias). CP and FT are known to modulate neurodegenerative tissue responses. We analysed aqueous and vitreous samples from 26 patients (8M/18F, aged 60–85) who were undergoing elective vitreoretinal (VR) surgery. Of these, 14 had idiopathic epiretinal membranes (ERMs), 6 had idiopathic macular holes (MH), and 6 were patients with Alzheimer’s disease (AD) who presented with VR disorders (VRDs). CP, FT, and selected neuroinflammatory mediators such as interferon γ (IFN-γ), interleukin (IL-6), vascular endothelial growth factor (VEGF), nerve growth factor (NGF), and brain-derived neurotrophic factor (BDNF) were quantified. Odds ratio analysis was applied to assess the CP/FT ratio’s association with subretinal drusen. We found distinct CP and FT profiles in VRD samples. In aqueous fluid, the CP increased and the FT decreased in early-stage ERM, which reduced the CP/FT ratio. Similar patterns were observed in vitreous fluid. The CP levels correlated with the VEGF (aqueous), IL-4 (vitreous), NGF, and BDNF levels; FT correlated with IL-6 and NGF. A higher CP/FT ratio was associated with increased risk for neurodegenerative conditions. Our findings support the quantification of CP and FT in ocular fluids as a promising approach for identifying early neurodegenerative changes and suggest that the CP/FT ratio may be linked to drusen imaging and clinical neurodegenerative history. Full article

Alzheimer’s disease (AD) impacts more than half a million people worldwide, with no cure available. The regulatory approval of three anti-amyloid monoclonal antibodies (mAbs), including aducanumab, lecanemab, and donanemab, has established immunotherapy as a therapeutic approach to modify disease progression. Its multifactorial pathology, which involves amyloid-β (Aβ) plaques, tau neurofibrillary tangles, neuroinflammation, and cerebrovascular dysfunction, limits the efficacy of single-target therapies. The restricted blood–brain barrier (BBB) penetration and amyloid-related imaging abnormalities (ARIA), together with small treatment effects, demonstrate the necessity for advanced biologic therapies. Protein engineering advancements have created bispecific antibodies that bind to pathological proteins (e.g., Aβ, tau) and BBB shuttle receptors to boost brain delivery and dual therapeutic effects. This review combines existing information about antibody-based therapy in AD by focusing on bispecific antibody formats and their preclinical and clinical development, as well as biomarker-based patient selection and upcoming combination strategies. The combination of rationally designed bispecific antibodies with fluid and imaging biomarkers could show potential for overcoming existing therapeutic challenges and delivering significant clinical advantages. Full article

Hydrocephalus is a complex neurological condition marked by abnormal cerebrospinal fluid (CSF) accumulation, often leading to elevated intracranial pressure and structural brain damage. Despite advances in surgical treatment, diagnostic precision and prognosis remain challenging, especially in idiopathic normal pressure hydrocephalus (iNPH). This narrative review aims to synthesize the current knowledge regarding molecular and neuroimaging biomarkers that hold diagnostic, prognostic, and therapeutic significance in hydrocephalus. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and Google Scholar. The inclusion criteria encompassed peer-reviewed studies involving congenital or acquired hydrocephalus and reporting on mechanistic, diagnostic, or monitoring biomarkers. Both established and emerging biomarkers were included, and preclinical findings were considered when translational relevance was apparent. The review highlights a broad spectrum of molecular markers including aquaporins, vascular endothelial growth factor, neurofilaments, glial fibrillary acidic protein, matrix metalloproteinases, and neuroinflammatory markers. The genetic markers associated with ciliogenesis also show promise in subtyping disease. Parallel to molecular advances, neuroimaging techniques, ranging from classic markers like Evans’ index to advanced modalities such as diffusion tensor imaging (DTI), arterial spin labeling (ASL), and glymphatic MRI, provide functional perspectives on hydrocephalus diagnosis and management, while artificial intelligence may further enhance diagnostic algorithms. Molecular and imaging markers could not only increase diagnostic confidence, but also provide information on disease causes and progression. As research progresses, merging various methodologies may result in more accurate diagnoses. Full article

Background/Objectives: Fabry disease (FD) is a lysosomal storage disorder often associated with early-onset neuropathic pain, attributed to small fibre neuropathy (SFN). The dorsal root ganglion (DRG) has emerged as a critical site of early pathophysiological involvement in FD, with structural and functional alterations implicated in the development of neuropathic symptoms. This exploratory study introduces DRG proton density (DRG-PD) as a novel MRI-derived biomarker and evaluates its association with SFN. Methods: Eighty genetically confirmed FD patients underwent high-resolution 3T MRI with DRG-PD quantification at the lumbosacral levels L5 and S1. DRG-PD was derived from B1-corrected multi-echo spin echo sequences and normalised to cerebrospinal fluid intensity. All patients underwent clinical, biochemical and histological evaluation to determine SFN status. Associations between DRG imaging parameters and clinical variables were analysed using correlation and regression models. Diagnostic performance was evaluated using receiver operating characteristic curve analysis. Results: DRG-PD values were significantly increased in patients with classical FD and SFN, demonstrating a large effect size (Cliff’s δ = 0.92) and excellent discriminatory performance (AUC = 0.96). In contrast, DRG volume and T2 relaxation time were not significantly associated with SFN status. DRG-PD remained an independent predictor of SFN in multivariable logistic regression (p = 0.019). Conclusions: DRG-PD is a non-invasive correlate of SFN in classical FD. It may provide superior diagnostic value compared to existing MRI metrics and reflects proximal ganglionic pathology not captured by distal histological assessments. Full article

Parkinson’s disease and related synucleinopathies, including dementia with Lewy bodies and multiple system atrophy, are characterised by the pathological aggregation of the α-synuclein (aSyn) protein in neuronal and glial cells, leading to cellular dysfunction and neurodegeneration. This review synthesizes knowledge of aSyn biology, including its structure, aggregation mechanisms, cellular interactions, and systemic influences. We highlight the structural diversity of aSyn aggregates, ranging from oligomers to fibrils, their strain-like properties, and their prion-like propagation. While the role of prion-like mechanisms in disease progression remains a topic of ongoing debate, these processes may contribute to the clinical heterogeneity of synucleinopathies. Dysregulation of protein clearance pathways, including chaperone-mediated autophagy and the ubiquitin–proteasome system, exacerbates aSyn accumulation, while post-translational modifications influence its toxicity and aggregation propensity. Emerging evidence suggests that immune responses and alterations in the gut microbiome are key modulators of aSyn pathology, linking peripheral processes—particularly those of intestinal origin—to central neurodegeneration. Advances in biomarker development, such as cerebrospinal fluid assays, post-translationally modified aSyn, and real-time quaking-induced conversion technology, hold promise for early diagnosis and disease monitoring. Furthermore, positron emission tomography imaging and conformation-specific antibodies offer innovative tools for visualising and targeting aSyn pathology in vivo. Despite significant progress, challenges remain in accurately modelling human synucleinopathies, as existing animal and cellular models capture only specific aspects of the disease. This review underscores the need for more reliable aSyn biomarkers to facilitate the development of effective treatments. Achieving this goal requires an interdisciplinary approach integrating genetic, epigenetic, and environmental insights. Full article

Degenerative joint disease remains a leading cause of global disability, with early diagnosis posing a significant clinical challenge due to its gradual onset and symptom overlap with other musculoskeletal disorders. This review focuses on emerging diagnostic strategies by synthesizing evidence specifically from studies that integrate biochemical biomarkers, advanced imaging techniques, and machine learning models relevant to osteoarthritis. We evaluate the diagnostic utility of cartilage degradation markers (e.g., CTX-II, COMP), inflammatory cytokines (e.g., IL-1β, TNF-α), and synovial fluid microRNA profiles, and how they correlate with quantitative imaging readouts from T2-mapping MRI, ultrasound elastography, and dual-energy CT. Furthermore, we highlight recent developments in radiomics and AI-driven image interpretation to assess joint space narrowing, osteophyte formation, and subchondral bone changes with high fidelity. The integration of these datasets using multimodal learning approaches offers novel diagnostic phenotypes that stratify patients by disease stage and risk of progression. Finally, we explore the implementation of these tools in point-of-care diagnostics, including portable imaging devices and rapid biomarker assays, particularly in aging and underserved populations. By presenting a unified diagnostic pipeline, this article advances the future of early detection and personalized monitoring in joint degeneration. Full article

Background: Endometriosis is a common gynecological disease linked to significant diagnostic challenges. Cadherin 12 (CDH12), as a member of adhesion molecules, is supposed to be involved in the pathogenesis of this disease and therefore can be a potential biomarker candidate. Methods: In this study, we analyzed the concentration of CDH12 in plasma and peritoneal fluid samples collected from women with endometriosis and controls, using surface plasmon resonance imaging (SPRi). We collected plasma samples from 96 women and peritoneal fluid from 73 women after laparoscopy due to symptoms/ultrasound findings suggestive of endometriosis. The diagnosis was confirmed histologically. In the collected samples, we measured the concentrations of CDH12 using a novel technique utilizing an SPRi biosensor. Results: We found that peritoneal fluid CDH12 concentrations were lower in women with infertility compared to fertile women. However, we observed no differences in concentration of CDH12 between endometriosis and control groups in both plasma and peritoneal fluid. Additionally, in a study group of patients with confirmed endometriosis, we observed a significant positive correlation of CDH12 concentrations with patients’ age. Overall, plasma concentrations of CDH12 were significantly greater as compared to levels found in peritoneal fluid. Conclusion: Cadherin 12 has not been confirmed to show direct diagnostic potential for endometriosis using the SPRi method, at least in our cohort of patients. Full article

Background: Mild traumatic brain injury (mTBI), commonly known as concussion, is a major public health issue characterized by subtle neuronal damage that traditional imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI) often fail to detect. Fluid biomarkers have emerged as promising diagnostic and prognostic tools for mTBI. Objectives: This umbrella meta-analysis aims to evaluate the diagnostic accuracy and clinical utility of the key fluid biomarkers, S100B, glial fibrillary acidic protein (GFAP), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1, neurofilament light chain (NfL)) and tau protein, in detecting mTBI and to clarify their roles as screening, confirmatory, and complementary indicators. Methods: A systematic review was performed using PubMed, Web of Science, Scopus, and Cochrane to identify the published meta-analyses that assessed the biomarkers in mTBI. Sensitivity, specificity, and diagnostic odds ratios were then calculated using random-effects models. Heterogeneity was evaluated with the I² statistic, and publication bias was assessed via funnel plots. The results of S100B demonstrated high sensitivity (91.6%) but low specificity (42.4%), making it an effective rule-out biomarker to minimize unnecessary CT scans. In contrast, GFAP exhibited moderate sensitivity (84.5%) with improved specificity (61.0%), supporting its role in confirming mTBI diagnoses. UCH-L1 revealed a sensitivity of 86.7% alongside low specificity (37.3%), indicating its potential as a complementary marker. Additionally, the NfL levels were notably elevated in sports-related concussions, while the diagnostic utility of tau protein remains inconclusive due to limited available data. Conclusions: The findings underscore the clinical promise of fluid biomarkers in the management of mTBI. S100B and GFAP are particularly valuable as screening and confirmatory markers, respectively. Nonetheless, further standardization of biomarker thresholds and additional longitudinal studies are necessary to validate the roles of UCH-L1, NfL, and Tau protein. The integration of these biomarkers into a multimodal diagnostic panel may enhance mTBI detection accuracy and facilitate improved patient stratification and management. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. Methods: The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers—including cerebrospinal fluid (CSF) amyloid-beta 42 (A${\beta }_{42}$), amyloid positron emission tomography (amyloid PET), cerebrospinal fluid Tau protein (CSF Tau), and magnetic resonance imaging with fluorodeoxyglucose positron emission tomography (MRI FDG-PET)—were analyzed simultaneously to compute the cognitive impairment (CI) score of instances, dynamically adjusted for age. Higher CSF A${\beta }_{42}$ levels consistently demonstrated a protective effect, while elevated amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. To demonstrate its applicability, we applied the full four-stage framework—comprising data aggregation and cleaning, sigmoid-based synthetic biomarker simulation with descriptive analysis, parameter accumulation modeling, and correlation-driven CI classification—on a curated dataset of 307 instances (ages 10–110) from Kaggle, the Alzheimer’s Disease Neuroimaging Initiative (ANDI), and the Open Access Series of Imaging Studies (OASIS) to evaluate age-specific stratification of preclinical AD risk. Results: The study highlights the model’s potential to identify individuals in risk zones from a pool of 150 instances, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Conclusions: Future work aims to validate the model using longitudinal, inclusive, real-world datasets and expand its predictive capacity through machine learning techniques and integrating genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer’s disease by providing a scalable computational tool for early risk assessment and intervention planning. Full article

Progression independent of relapse activity (PIRA) is increasingly recognized as a key driver of disability in multiple sclerosis (MS). However, the concept of PIRA remains elusive, with uncertainty surrounding its definition, underlying mechanisms, and methods of quantification. This review examines the current landscape of biomarkers used to predict and measure PIRA, focusing on clinical, imaging, and body fluid biomarkers. Clinical disability scores such as the Expanded Disability Status Scale (EDSS) are widely used, but may lack sensitivity in capturing subtle relapse-independent progression. Imaging biomarkers, including MRI-derived metrics (brain and spinal cord volume loss, chronic active lesions) and optical coherence tomography (OCT) parameters (retinal nerve fiber layer and ganglion cell-inner plexiform layer thinning), offer valuable insights, but often reflect both inflammatory and neurodegenerative processes. Body fluid biomarkers, such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP), are promising indicators of axonal damage and glial activation, but their specificity for PIRA remains limited. This review emphasizes the distinction between predicting PIRA—identifying individuals at risk of future progression—and measuring ongoing PIRA-related disability in real time. We highlight the limitations of current biomarkers in differentiating PIRA from relapse-associated activity and call for a clearer conceptual framework to guide future research. Advancing the precision and utility of PIRA biomarkers will require multimodal approaches, longitudinal studies, and standardized protocols to enable their clinical integration and to improve personalized MS management. Full article

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, characterized by rapid growth and resistance to chemotherapy. Conventional treatments remain largely ineffective, with patient survival averaging around 18 months after diagnosis. Current diagnostic methods rely on invasive tissue biopsies and imaging tests. While traditional biopsies involve extracting tissue samples, their routine use is often limited by surgical risks and the challenge of accessing sensitive brain regions. Liquid biopsy has emerged as a promising noninvasive alternative, analyzing circulating tumor components—such as DNA, RNA, extracellular vesicles, and microRNAs—found in body fluids. This approach enables initial diagnosis and continuous disease monitoring, offering a significant advantage over traditional biopsies, which are impractical for frequent repetition during treatment follow-up. This review highlights recent advances in liquid biopsy-derived biomarkers for the clinical management of GBM. The discussion includes the advantages, limitations, and potential of these biomarkers as tools for early diagnosis and disease monitoring. A narrative review of the literature published over the last decade (2014–2024) was conducted using major health-focused scientific databases. The analysis focuses on evaluating the clinical relevance and applicability of liquid biopsy in GBM, offering insights into its potential as a minimally invasive and effective tool for improving glioblastoma management. Full article