C. Bridel, W. V. van Wieringen, H. Zetterberg
et al.
Importance Neurofilament light protein (NfL) is elevated in cerebrospinal fluid (CSF) of a number of neurological conditions compared with healthy controls (HC) and is a candidate biomarker for neuroaxonal damage. The influence of age and sex is largely unknown, and levels across neurological disorders have not been compared systematically to date. Objectives To assess the associations of age, sex, and diagnosis with NfL in CSF (cNfL) and to evaluate its potential in discriminating clinically similar conditions. Data Sources PubMed was searched for studies published between January 1, 2006, and January 1, 2016, reporting cNfL levels (using the search terms neurofilament light and cerebrospinal fluid) in neurological or psychiatric conditions and/or in HC. Study Selection Studies reporting NfL levels measured in lumbar CSF using a commercially available immunoassay, as well as age and sex. Data Extraction and Synthesis Individual-level data were requested from study authors. Generalized linear mixed-effects models were used to estimate the fixed effects of age, sex, and diagnosis on log-transformed NfL levels, with cohort of origin modeled as a random intercept. Main Outcome and Measure The cNfL levels adjusted for age and sex across diagnoses. Results Data were collected for 10 059 individuals (mean [SD] age, 59.7 [18.8] years; 54.1% female). Thirty-five diagnoses were identified, including inflammatory diseases of the central nervous system (n = 2795), dementias and predementia stages (n = 4284), parkinsonian disorders (n = 984), and HC (n = 1332). The cNfL was elevated compared with HC in a majority of neurological conditions studied. Highest levels were observed in cognitively impaired HIV-positive individuals (iHIV), amyotrophic lateral sclerosis, frontotemporal dementia (FTD), and Huntington disease. In 33.3% of diagnoses, including HC, multiple sclerosis, Alzheimer disease (AD), and Parkinson disease (PD), cNfL was higher in men than women. The cNfL increased with age in HC and a majority of neurological conditions, although the association was strongest in HC. The cNfL overlapped in most clinically similar diagnoses except for FTD and iHIV, which segregated from other dementias, and PD, which segregated from atypical parkinsonian syndromes. Conclusions and Relevance These data support the use of cNfL as a biomarker of neuroaxonal damage and indicate that age-specific and sex-specific (and in some cases disease-specific) reference values may be needed. The cNfL has potential to assist the differentiation of FTD from AD and PD from atypical parkinsonian syndromes.
E. O. Olufunmilayo, M. B. Gerke-Duncan, R. Holsinger
Neurodegenerative disorders constitute a substantial proportion of neurological diseases with significant public health importance. The pathophysiology of neurodegenerative diseases is characterized by a complex interplay of various general and disease-specific factors that lead to the end point of neuronal degeneration and loss, and the eventual clinical manifestations. Oxidative stress is the result of an imbalance between pro-oxidant species and antioxidant systems, characterized by an elevation in the levels of reactive oxygen and reactive nitrogen species, and a reduction in the levels of endogenous antioxidants. Recent studies have increasingly highlighted oxidative stress and associated mitochondrial dysfunction to be important players in the pathophysiologic processes involved in neurodegenerative conditions. In this article, we review the current knowledge of the general effects of oxidative stress on the central nervous system, the different specific routes by which oxidative stress influences the pathophysiologic processes involved in Alzheimer’s disease, Parkinson’s disease, Amyotrophic Lateral Sclerosis and Huntington’s disease, and how oxidative stress may be therapeutically reversed/mitigated in order to stall the pathological progression of these neurodegenerative disorders to bring about clinical benefits.
Abstract
Background
This study aimed to cross-compare European countries’ responsiveness to their populations’ mental health (MH) needs.
Methods
For the EU-27 countries and the United Kingdom, the 2023 Headway Initiative collected data on 15 key performance indicators (KPIs) in responsiveness in healthcare, including workforce, facilities, quality of care, and MH expenditure, and 14 KPIs in responsiveness in workplaces, schools, and society. Bivariate correlations between Headway-transformed KPI scores, which were standardised in a 1–10 Likert Scale (1: worst performance; 10: best performance), tested for putative associations.
Results
Responsiveness in healthcare: Sweden (10), Denmark (8.8), and Finland (8.3) showed the best performance, while Romania (1.0), Slovakia (1.1), and Latvia and Bulgaria (1.2) had the poorest performance. Responsiveness in workplaces: schools, and society, Germany (10.0), France (9.1), and Denmark (9.1) were the most responsive countries, while Greece and Slovakia (1.0) had the poorest responsiveness. MH status total scores negatively correlated with global scores on responsiveness in healthcare (r = −0.34, p = .075), workplaces (r = −0.46, p = .014), schools (r = −0.59, p = .003), and society (r = −0.53, p = .003) – poorer MH status, greater responsiveness.
Conclusions
European countries significantly differed in their responsiveness to the populations’ MH needs, although the real effectiveness of their MH policies remains to be elucidated. Whether more responsive countries, which achieved poorer MH outcomes, successfully met greater preexisting MH needs, they failed to do so, or the relationship is driven by other third variables (e.g., quality of MH assessment) requires future investigation.
Sara Bosticardo, Matteo Battocchio, Mario Ocampo-Pineda
et al.
Investigating myelin integrity within multiple sclerosis (MS) lesions and in normal-appearing white matter is crucial for understanding demyelination and remyelination processes. While most approaches assess global myelin changes or compare lesions with homologous regions in healthy controls, they do not allow direct within-tract comparisons between lesional and non-lesional tissue.We introduce the tractography-informed myelin estimate (TIME), a novel map designed to quantify tract-specific myelin loss. TIME integrates tractography with myelin-sensitive imaging, such as myelin volume fraction, to compare lesional and non-lesional segments within the same white matter tract. By modeling local deviations from the expected myelin volume fraction signal along streamlines, TIME captures tract-specific myelin damage while accounting for within-tract variability. TIME is based on a microstructure-informed tractography framework, with an extra compartment to model signal loss caused by lesions.We evaluated TIME in 159 MS patients, assessing its association with neurological disability at baseline and longitudinally over a median follow-up of two years. At baseline, higher myelin loss captured by TIME was significantly associated with worse disability (β = 0.14, p = 0.015). Longitudinally, greater baseline disability predicted faster TIME-quantified myelin loss, which was in turn associated with a higher risk of disability worsening. In contrast, lesion-averaged myelin volume fraction showed no significant associations with either baseline disability or its progression.TIME provides a detailed, tract-specific assessment of myelin damage, providing greater sensitivity than conventional metrics, highlighting its potential as a biomarker in MS.
Computer applications to medicine. Medical informatics, Neurology. Diseases of the nervous system
Bruno Henrique Carneiro Costa Filho, Victor Ting Po Chy, João Augusto de Macedo Cavalcanti de Albuquerque
et al.
Abstract Background Sneddon syndrome, a rare, non-inflammatory thrombotic vasculopathy characterized by livedo racemosa and cerebrovascular disease. Case presentation We present a case series of six women diagnosed with Sneddon syndrome. We conducted a thorough analysis of clinical, radiological, and laboratory data, including results of prothrombotic and autoimmune screening. Our findings emphasize the importance of considering Sneddon syndrome as a potential cause of stroke, particularly in young women, and underscore the necessity of a comprehensive dermatological examination when evaluating stroke etiology. Additionally, we provide a comprehensive literature review of the clinical manifestations, radiological and histopathological findings, as well as treatment options. Conclusion A thorough dermatological examination can aid in early detection of Sneddon syndrome and may change the course of treatment of stroke in young adults.
To extend healthy life expectancy in an aging society, it is crucial to prevent various diseases at pre-disease states. Although dynamical network biomarker theory has been developed for pre-disease detection, mathematical frameworks for pre-disease treatment have not been well established. Here I propose a control theory-based approach for pre-disease treatment, named Markov chain sparse control (MCSC), where time evolution of a probability distribution on a Markov chain is described as a discrete-time linear system. By designing a sparse controller, a few candidate states for intervention are identified. The validity of MCSC is demonstrated using numerical simulations and real-data analysis.
Niccolò McConnell, Pardeep Vasudev, Daisuke Yamada
et al.
Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE demonstrates fast convergence during fine-tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine-tuning data. Pretrained using self-supervised learning on over 98,000 thoracic LDCTs, including the UK's largest LCS initiative to date and 27 public datasets, TANGERINE achieves state-of-the-art performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while generalising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource-intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open-source lightweight design lays the foundation for rapid integration into next-generation medical imaging tools that could transform LCS initiatives, allowing them to pivot from a singular focus on lung cancer detection to comprehensive respiratory disease management in high-risk populations.
Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for discovering disease relationships by analyzing over 1300 disease-condition pairs using GenoMAS, a fully automated agentic AI system. Beyond identifying robust gene-level overlaps, we develop a novel pathway-based similarity framework that integrates multi-database enrichment analysis to quantify functional convergence across diseases. The resulting disease similarity network reveals both known comorbidities and previously undocumented cross-category links. By examining shared biological pathways, we explore potential molecular mechanisms underlying these connections-offering functional hypotheses that go beyond symptom-based taxonomies. We further show how background conditions such as obesity and hypertension modulate transcriptomic similarity, and identify therapeutic repurposing opportunities for rare diseases like autism spectrum disorder based on their molecular proximity to better-characterized conditions. In addition, this work demonstrates how biologically grounded agentic AI can scale transcriptomic analysis while enabling mechanistic interpretation across complex disease landscapes. All results are publicly accessible at github.com/KeeeeChen/Pathway_Similarity_Network.
Shuchao Duan, Amirhossein Dadashzadeh, Alan Whone
et al.
Automated facial expression quality assessment (FEQA) in neurological disorders is critical for enhancing diagnostic accuracy and improving patient care, yet effectively capturing the subtle motions and nuances of facial muscle movements remains a challenge. We propose to analyse facial landmark trajectories, a compact yet informative representation, that encodes these subtle motions from a high-level structural perspective. Hence, we introduce Trajectory-guided Motion Perception Transformer (TraMP-Former), a novel FEQA framework that fuses landmark trajectory features for fine-grained motion capture with visual semantic cues from RGB frames, ultimately regressing the combined features into a quality score. Extensive experiments demonstrate that TraMP-Former achieves new state-of-the-art performance on benchmark datasets with neurological disorders, including PFED5 (up by 6.51%) and an augmented Toronto NeuroFace (up by 7.62%). Our ablation studies further validate the efficiency and effectiveness of landmark trajectories in FEQA. Our code is available at https://github.com/shuchaoduan/TraMP-Former.
Dmitrii Seletkov, Sophie Starck, Ayhan Can Erdur
et al.
Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/
ObjectiveDespite the widespread application of non-pharmacological therapies in treating cancer-related insomnia, a comprehensive assessment of these methods is lacking. This study aims to compare the efficacy of 11 non-pharmacological interventions for cancer-related insomnia, providing a theoretical basis for clinicians in choosing treatment methods.MethodsWe searched five databases, including the Cochrane Central Register of Controlled Trials, PubMed, Embase, Wiley Library, and Web of Science, for relevant randomized controlled trials. Included studies involved patients diagnosed with cancer-related insomnia, employed non-pharmacological treatments, and reported outcomes using the PSQI and ISI. Bayesian statistical methods were used for the network meta-analysis, and statistical processing was performed using Review Manager 5.4 and Stata 14.0 software. The results were thoroughly analyzed and evaluated, and publication bias was assessed using funnel plot tests.ResultsOur study included 41 randomized controlled trials, comprising 11 different non-pharmacological interventions (3,541 participants), the network analysis identifying Electroacupuncture as the most effective, with a SUCRA value of 92.2% in ISI, this was followed by Professionally administered Cognitive behavioral therapy for insomnia(PCBT-I) and Mindfulness-based cognitive therapy(MBCT), with SUCRA values of 78.4 and 64.1%, respectively. Traditional Cognitive behavioral therapy for insomnia(CBT-I) and VCBT-I showed lower efficacy with SUCRA values of 55.9 and 55.2%, respectively. Exercise interventions and control groups had the lowest efficacy, with SUCRA values of 24.0 and 16.1%. Using PSQI as the outcome measure, Massage therapy ranked highest in improving sleep quality with a SUCRA value of 92.2%, followed by Professionally administered Cognitive behavioral therapy for insomnia (PCBT-I) and Electroacupuncture. League tables indicated significant improvements in sleep outcomes for Electroacupuncture and MT compared to control groups, with Electroacupuncture (EA) showing an MD of −7.80 (95% CI: −14.45, −1.15) and MT an MD of −4.23 (CI: −8.00, −0.46).ConclusionConsidering both outcome indicators, Electroacupuncture was significantly effective in alleviating the severity of insomnia, while MT was most effective in improving sleep quality. Therefore, in the non-pharmacological interventions for cancer-related insomnia, Electroacupuncture and MT May be particularly effective choices. Future research should further explore the specific mechanisms of action of these interventions and their efficacy in different patient groups.
Alzheimers disease (AD) is a severe neurological brain disorder. It is not curable, but earlier detection can help improve symptoms in a great deal. The machine learning based approaches are popular and well motivated models for medical image processing tasks such as computer-aided diagnosis. These techniques can improve the process for accurate diagnosis of AD. In this paper, we investigate the performance of these techniques for AD detection and classification using brain MRI and PET images from the OASIS database. The proposed system takes advantage of the artificial neural network and support vector machines as classifiers, and principal component analysis as a feature extraction technique. The results indicate that the combined scheme achieves good accuracy and offers a significant advantage over the other approaches.
Many uncontrollable disease outbreaks of the past exposed several vulnerabilities in the healthcare systems worldwide. While advancements in technology assisted in the rapid creation of the vaccinations, there needs to be a pressing focus on the prevention and prediction of such massive outbreaks. Early detection and intervention of an outbreak can drastically reduce its impact on public health while also making the healthcare system more resilient. The complexity of disease transmission dynamics, influence of various directly and indirectly related factors and limitations of traditional approaches are the main bottlenecks in taking preventive actions. Specifically, this paper implements deep learning algorithms and LLM's to predict the severity of infectious disease outbreaks. Utilizing the historic data of several diseases that have spread in India and the climatic data spanning the past decade, the insights from our research aim to assist in creating a robust predictive system for any outbreaks in the future.
Selestine Melchane, Youssef Elmir, Farid Kacimi
et al.
Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.
IntroductionAutism spectrum disorders (ASDs) are a group of neurodevelopmental disorders characterized by core symptoms of impaired social interaction and communication. The pathological mechanism and treatment are not clear and need further study. Our previous study found that the deletion of high-risk gene Autism Susceptibility 2 (AUTS2) in mice led to dentate gyrus (DG) hypoplasia that highly associated with impaired social novelty recognition. Here we aim to improve the social deficit through increasing the neurogenesis in the subgranular zone (SGZ) and expanding the newborn granule neurons in DG.MethodsThree approaches including repeated oxytocin administration, feeding in enriched environment and overexpression of cyclin-dependent kinase 4 (Cdk4)-CyclinD1 complex in DG neural stem cells (NSCs) at the post-weaning stage were conducted.ResultsWe found that the number of EdU labeled proliferative NSCs or retrovirus labeled newborn neurons was significantly increased after manipulations. The social recognition deficit was also significantly improved.DiscussionOur findings suggested a possible strategy to restore the social deficit through expansion of newborn neurons in hippocampus, which might provide a new insight into the treatment of autism.