Diagnostic markers articles from across Nature Portfolio

Single-cell manipulation and processing techniques and improvements in mass spectrometry sensitivity make single-cell proteomic profiling feasible. This study presents a label-free approach for the characterisation of native N-glycans of single mammalian cells and ng-level blood isolates, demonstrating the potential to detect cell surface glycome changes at the single-cell level in health or disease.

Metabolic implications in AD are unclear. Here, authors found significant correlations between cognitive impairment and metabolic features in a Chinese aging cohort (n = 1397). The study highlights ammonia disturbance as a potential therapeutic target for AD.

Digital health technologies (DHTs) can transform neurological assessments, improving quality and continuity of care. In the United States, the Food & Drug Administration (FDA) oversees the safety and efficacy of these technologies, employing a detailed regulatory process that classifies devices based on risk and requires rigorous review and post-market surveillance. Following FDA approval, DHTs enter the Current Procedural Terminology, Relative Value Scale Update Committee, and Centers for Medicare & Medicaid Services coding and valuation processes leading to coverage and payment decisions. DHT adoption is challenged by rapid technologic advancements, an inconsistent evidence base, marketing discrepancies, ambiguous coding guidance, and variable health insurance coverage. Regulators, policymakers, and payers will need to develop better methods to evaluate these promising technologies and guide their deployment. This includes striking a balance between patient safety and clinical effectiveness versus promotion of innovation, especially as DHTs increasingly incorporate artificial intelligence. Data validity, cybersecurity, risk management, societal, and ethical responsibilities should be addressed. Regulatory advances can support adoption of these promising tools by ensuring DHTs are safe, effective, accessible, and equitable.

Staging Alzheimer’s disease on the basis of the disease’s biological underpinnings might help with stratification and prognostication, both in the clinical setting and in clinical trials. We propose a staging model based on only five biomarkers, which are related to amyloid-β and tau pathologies in different ways and can be measured with a single sample of cerebrospinal fluid.

In recent years the intersection of wearable technologies and machine learning (ML) based deep learning (DL) approaches have highlighted their potential in sleep research. Yet, a recent study published in NPJ Digital Medicine highlights the generalization limitations of DL models in sleep-wake classification using actigraphy data. Here, this article discusses some of the challenges and opportunities presented by domain adaptation and self-supervised learning (SSL), innovative methodologies that use large-scale unlabeled data to bolster the generalizability of DL models in sleep assessment. These approaches not only improve sleep-wake classification but also hold promise for extending to more comprehensive sleep stage classification, potentially advancing the field of automated sleep assessment through efficient and user-friendly wearable monitoring systems.