Diagnosis of Alzheimer Disease and Tauopathies on Whole-Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm.

Neuropathologic assessment during autopsy is the gold standard for diagnosing neurodegenerative disorders. Neurodegenerative conditions, such as Alzheimer disease (AD) neuropathological change, are a continuous process from normal aging rather than categorical; therefore, diagnosing neurodegenerative disorders is a complicated task. We aimed to develop a pipeline for diagnosing AD and other tauopathies, including corticobasal degeneration (CBD), globular glial tauopathy, Pick disease, and progressive supranuclear palsy. We used a weakly supervised deep learning-based approach called clustering-constrained-attention multiple-instance learning (CLAM) on the whole-slide images (WSIs) of patients with AD (n = 30), CBD (n = 20), globular glial tauopathy (n = 10), Pick disease (n = 20), and progressive supranuclear palsy (n = 20), as well as nontauopathy controls (n = 21). Three sections (A: motor cortex; B: cingulate gyrus and superior frontal gyrus; and C: corpus striatum) that had been immunostained for phosphorylated tau were scanned and converted to WSIs. We evaluated 3 models (classic multiple-instance learning, single-attention-branch CLAM, and multiattention-branch CLAM) using 5-fold cross-validation. Attention-based interpretation analysis was performed to identify the morphologic features contributing to the classification. Within highly attended regions, we also augmented gradient-weighted class activation mapping to the model to visualize cellular-level evidence of the model's decisions. The multiattention-branch CLAM model using section B achieved the highest area under the curve (0.970 ± 0.037) and diagnostic accuracy (0.873 ± 0.087). A heatmap showed the highest attention in the gray matter of the superior frontal gyrus in patients with AD and the white matter of the cingulate gyrus in patients with CBD. Gradient-weighted class activation mapping showed the highest attention in characteristic tau lesions for each disease (eg, numerous tau-positive threads in the white matter inclusions for CBD). Our findings support the feasibility of deep learning-based approaches for the classification of neurodegenerative disorders on WSIs. Further investigation of this method, focusing on clinicopathologic correlations, is warranted.

Functionalizing Separator by Coating a Lithiophilic Metal for Dendrite-Free Anode-free Lithium Metal Batteries.

A stable anode-free lithium metal battery (AFLMB) is accomplished by the adoption of a facile fabricated amorphous antimony (Sb)-coated separator (SbSC). The large specific surface area of the separator elevates lithium (Li)-Sb alloy kinetic, improving Li wetting ability on pristine copper current collector (Cu). When tested with LiNi0.8 Mn0.1 Co0.1 O2 (NMC811) as cathode, the full cell with SbSC demonstrates low nucleation overpotential with compact, dendrite-free and homogeneous Li plating, and exhibits a notable lithium inventory retention rate (LIRR) of 99.8 % with capacity retention of 93.6 % after 60 cycles at 0.5 C-rate. Conversely, full cells containing pristine separator/Cu (i. e., SC) and pristine separator/Sb-coated current collector (i. e., SSbC) display poor cycling performances with low LIRRs. Density functional theory corroborates the nucleation behaviours observed during in-situ half-cell Li deposition. Functionalizing polymeric separator by metallic coating in AFLMB is a novel approach in improving the cycle life of an AFLMB by promoting homogeneous Li plating behavior. This innovative approach exemplifies a promising applicability for uniform Li-plating behavior to achieve a longer cycle life in AFLMB.