Biofluid-based biomarkers have transformed neurodegenerative disease research and care, providing insights into the molecular underpinnings of Alzheimer's disease (AD) and other neurodegenerative dementias. This Review provides an update on recent developments in biofluid-based biomarkers for amyloid-β (Aβ) pathology, tau pathology, neurodegeneration, glial reactivity, α-synuclein pathology, TAR DNA-binding protein 43 (TDP-43) pathology, synaptic pathophysiology and cerebrovascular disease-pathologies and processes that are all relevant to neurodegenerative dementias. Complementing longstanding cerebrospinal assays, improved technologies now facilitate the detection of molecules linked to neurodegenerative brain changes at very low concentrations in the blood. This promises to complement the clinical evaluation of suspected neurodegenerative disease in healthcare with molecular phenotyping biomarkers that will help to link the clinical symptoms to ongoing pathophysiological processes in the brain and improve how patients are referred to specialty clinics for initiation and monitoring of molecularly targeted treatments. Clinically relevant breakthroughs such as the use of anti-Aβ monoclonal antibodies to address Aβ pathology in AD serve as important proof-of-concept examples of how the field is advancing toward molecularly informed prevention and treatment. This Review provides an overview of the most established biofluid-based biomarkers currently in use and offers practical guidance on their interpretation and implementation in clinical settings.

The vacuum is now understood to have a rich and complex structure, characterized by fluctuating energy fields1 and a condensate of virtual quark-antiquark pairs. The spontaneous breaking of the approximate chiral symmetry2, signalled by the nonvanishing quark condensate ⟨qq¯⟩ , is dynamically generated through topologically nontrivial gauge configurations such as instantons3. The precise mechanism linking the chiral symmetry breaking to the mass generation associated with quark confinement4 remains a profound open question in quantum chromodynamics (QCD)-the fundamental theory of strong interaction. High-energy proton-proton collisions could liberate virtual quark-antiquark pairs from the vacuum that subsequently undergo confinement to form hadrons, whose properties could serve as probes into QCD confinement and the quark condensate. Here we report evidence of spin correlations in ΛΛ¯ hyperon pairs inherited from spin-correlated strange quark-antiquark virtual pairs. Measurements by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory reveal a relative polarization signal of (18 ± 4)% that links the virtual spin-correlated quark pairs from the QCD vacuum to their final-state hadron counterparts. Crucially, this correlation vanishes when the hyperon pairs are widely separated in angle, consistent with the decoherence of the quantum system. Our findings provide a new experimental model for exploring the dynamics and interplay of quark confinement and entanglement.

Fast and reliable validation of new designs in complex physical systems such as batteries is critical to accelerating technological innovation. However, battery development remains bottlenecked by the high time and energy costs required to evaluate the lifetime of new designs1,2. Notably, existing lifetime forecasting approaches require datasets containing battery lifetime labels for target designs to improve accuracy and cannot make reliable predictions before prototyping, thus limiting rapid feedback3,4. Here we introduce Discovery Learning, a scientific machine learning approach that integrates active learning5, physics-guided learning6 and zero-shot learning7 into a human-like reasoning loop, drawing inspiration from educational psychology. Discovery Learning can learn from historical battery designs and reduce the need for prototyping, thereby predicting the lifetime of new designs from minimal experiments. To test Discovery Learning, we present industrial-grade battery data comprising 123 large-format lithium-ion pouch cells, including diverse material-design combinations and cycling protocols. Trained on public datasets of cell designs different from ours, Discovery Learning achieves 7.2% test error in predicting cycle life using physical features from the first 50 cycles of 51% of cell prototypes. Under conservative assumptions, this results in savings of 98% in time and 95% in energy compared with conventional practices. Discovery Learning represents a key advance in accurate and efficient battery lifetime prediction and, more broadly, helps realize the promise of machine learning to accelerate scientific discovery8.