A deep convolutional neural network for efficient microglia detection

Swimming short fibrous nasal drops achieving intraventricular administration

Adequate drug delivery across the blood-brain barrier (BBB) is a critical factor in treating central nervous system (CNS) disorders. Inspired by swimming fish and the microstructure of the nasal cavity, this study is the first to develop swimming short fibrous nasal drops that can directly target the nasal mucosa and swim in the nasal cavity, which can effectively deliver drugs to the brain. Briefly, swimming short fibrous nasal drops with charged controlled drug release were fabricated by electrospinning, homogenization, the π-π conjugation between indole group of fibers, the benzene ring of leucine-rich repeat kinase 2 (LRRK2) inhibitor along with charge-dipole interaction between positively charged poly-lysine (PLL) and negatively charged surface of fibers; this enabled these fibers to stick to nasal mucosa, prolonged the residence time on mucosa, and prevented rapid mucociliary clearance. In vitro, swimming short fibrous nasal drops were biocompatible and inhibited microglial activation by releasing an LRRK2 inhibitor. In vivo, luciferase-labelled swimming short fibrous nasal drops delivered an LRRK2 inhibitor to the brain through the nasal mucosa, alleviating cognitive dysfunction caused by sepsis-associated encephalopathy by inhibiting microglial inflammation and improving synaptic plasticity. Thus, swimming short fibrous nasal drops is a promising strategy for the treatment of CNS diseases.

Annotated dataset for training deep learning models to detect astrocytes in human brain tissue

Astrocytes, a type of glial cell, significantly influence neuronal function, with variations in morphology and density linked to neurological disorders. Traditional methods for their accurate detection and density measurement are laborious and unsuited for large-scale operations. We introduce a dataset from human brain tissues stained with aldehyde dehydrogenase 1 family member L1 (ALDH1L1) and glial fibrillary acidic protein (GFAP). The digital whole slide images of these tissues were partitioned into 8730 patches of 500 × 500 pixels, comprising 2323 ALDH1L1 and 4714 GFAP patches at a pixel size of 0.5019/pixel, furthermore 1382 ADHD1L1 and 311 GFAP patches at 0.3557/pixel. Sourced from 16 slides and 8 patients our dataset promotes the development of tools for glial cell detection and quantification, offering insights into their density distribution in various brain areas, thereby broadening neuropathological study horizons. These samples hold value for automating detection methods, including deep learning. Derived from human samples, our dataset provides a platform for exploring astrocyte functionality, potentially guiding new diagnostic and treatment strategies for neurological disorders.