Alzheimer’s disease (AD) is the most common neurodegenerative disorder, yet effective disease-modifying therapies remain elusive. Here, we apply a semantic logic framework of Gene Ontology-based protein function prediction to identify ferroptosis-inhibitory proteins with potential neuroprotective activity. Guided by these predictions, we engineer selenium-modified apolipoprotein A1 supramolecular nanoparticles (Se-NDs) that intrinsically display glutathione peroxidase 4 (GPX4)-like catalytic activity and exhibit high brain penetrance. Se-ND suppresses lipid peroxidation, restores redox balance, and blocks ferroptotic neuronal death, thereby promoting mitochondrial repair and synaptic integrity and reversing structural and functional deficits underlying cognitive decline. Moreover, Se-ND synergizes with anti-inflammatory interventions and other AD-associated pathways. Our findings identify ferroptosis inhibition as a critical disease-modifying mechanism and establish Se-ND as a promising therapeutic avenue for Alzheimer’s disease.
Microbial biophotovoltaics (BPVs) are an emerging renewable energy technology that harnesses photosynthetic microorganisms to generate electric current. Despite the rapid growth of the field, the lack of standardized testing and reporting methods currently prevents the benchmarking of devices, slowing technological progress. This article provides a critical analysis of the experimental practices used in BPV research, highlighting the significant variability in device configurations, light sources, electrode materials, biomass quantification methods, and electrochemical measurement protocols reported in the literature. It further considers the historical standardization of protocols for conventional photovoltaics (PVs) and the resulting acceleration of the maturation and market adoption of PV technologies. Drawing inspiration from conventional PVs, the article outlines key steps toward BPV standardization, including defining common testing conditions, methods for data normalization, shared reference systems, and round-robin testing protocols. Establishing consensus in the testing protocols would significantly improve reproducibility and enable meaningful comparisons across laboratories, accelerating the development and deployment of BPVs.
Li metal batteries are challenged by anode instability issues originating from Li surface utilization heterogeneities, which emerge during the earliest stages of cycling, propagate throughout operation, and ultimately drive cell-to-cell performance variations and failure. However, heterogeneity issues remain under-investigated due to mainstream studies’ reliance on electrodes with small areas and few replicates. Here, we evaluated 32 Li || NMC811 pouch cells, using either as-purchased or mechanically homogenized Li via brushing. The brushed group exhibits narrower distributions of capacity trajectories and lifetimes, along with a 35% increase in median lifetime relative to the as-purchased group. Additionally, millimeter-scale variations in chemical compositions are observed on the as-purchased Li surface and result in localized utilization patterns after initial plating and stripping, whereas such inhomogeneity is eliminated after brushing. This work establishes a link between early-stage Li utilization and cell performance consistency, providing a pathway for evaluating and improving reliability in large-format battery production.
Degree-day methods are essential for estimating building heating and cooling energy demands. We investigate the sensitivity of cooling and heating degree-day models, CDD26 and HDD18, at national and regional scales across mainland China. Non-observational datasets overestimate national heating demand while severely underestimating cooling demand. Compared with current building codes, substituting estimation indicators and calculation models amplifies national cooling demand by 29.7%–100.6%, whereas national heating demand variations are −5.6% to 1.8%. Estimation methods prominently affect cooling demand in hot-summer zones, fundamentally reshaping the cooling energy structures of HSCWZ and CZ. While heating demand in warmer zones exhibits higher sensitivity to estimation indicators and calculation models, this sensitivity does not alter their overarching heating energy structures. Structurally, ASHRAE’s Tavg formulation increases indicator variability, whereas UKMO mitigates this variability but systematically inflates CDD26 and HDD18. Tavg benchmarking paired with asymmetric Tmax and Tmin dominant coefficients structurally control degree-day sensitivity across calculation models.
Refractive index (RI)-matching-based tissue clearing techniques render intact biological tissues optically transparent, enabling 3D in situ interrogation of nervous systems from whole-brain to single-cell scales. We systematically evaluate three mainstream clearing strategies (hydrophobic, hydrophilic, and hydrogel-based) and summarize their promising applications in neural circuit tracing, tumor research, stem cell therapy, and artificial intelligence (AI) integration. Here, we report that this technology has evolved from a niche imaging tool to an integrated multimodal biotechnological platform. Despite core challenges, including inherent physicochemical trade-offs, optical limits, multi-omics integration biases, and insufficient clinical standardization, future advances in multimodal integration, AI algorithms, and targeted in vivo clearing will drive life science research from static structural mapping toward systematic dissection of dynamic biological processes.
