Development of an Early Prediction Model for Subarachnoid Hemorrhage With Genetic and Signaling Pathway Analysis

Emerging insights into Lipocalin-2: Unraveling its role in Parkinson's Disease

Parkinson's disease (PD) ranks as the second most prevalent neurodegenerative disorder globally, marked by a complex pathogenesis. Lipocalin-2 (LCN2) emerges as a crucial factor during the progression of PD. Belonging to the lipocalin family, LCN2 is integral to several biological functions, including glial cell activation, iron homeostasis regulation, immune response, inflammatory reactions, and oxidative stress mitigation. Substantial research has highlighted marked increases in LCN2 expression within the substantia nigra (SN), cerebrospinal fluid (CSF), and blood of individuals with PD. This review focuses on the pathological roles of LCN2 in neuroinflammation, aging, neuronal damage, and iron dysregulation in PD. It aims to explore the underlying mechanisms of LCN2 in the disease and potential therapeutic targets that could inform future treatment strategies.

Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis

Introduction

Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities.

Methods

Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications.

Results

Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation.

Discussion

These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).

An integrated platform for Brucella with knowledge graph technology: From genomic analysis to epidemiological projection

Motivation:Brucella, the causative agent of brucellosis, is a global zoonotic pathogen that threatens both veterinary and human health. The main sources of brucellosis are farm animals. Importantly, the bacteria can be used for biological warfare purposes, requiring source tracking and routine surveillance in an integrated manner. Additionally, brucellosis is classified among group B infectious diseases in China and has been reported in 31 Chinese provinces to varying degrees in urban areas. From a national biosecurity perspective, research on brucellosis surveillance has garnered considerable attention and requires an integrated platform to provide researchers with easy access to genomic analysis and provide policymakers with an improved understanding of both reported patients and detected cases for the purpose of precision public health interventions.

Results: For the first time in China, we have developed a comprehensive information platform for Brucella based on dynamic visualization of the incidence (reported patients) and prevalence (detected cases) of brucellosis in mainland China. Especially, our study establishes a knowledge graph for the literature sources of Brucella data so that it can be expanded, queried, and analyzed. When similar “epidemiological comprehensive platforms” are established in the distant future, we can use knowledge graph to share its information. Additionally, we propose a software package for genomic sequence analysis. This platform provides a specialized, dynamic, and visual point-and-click interface for studying brucellosis in mainland China and improving the exploration of Brucella in the fields of bioinformatics and disease prevention for both human and veterinary medicine.

Lipocalin-2-Mediated Insufficient Oligodendrocyte Progenitor Cell Remyelination for White Matter Injury After Subarachnoid Hemorrhage via SCL22A17 Receptor/Early Growth Response Protein 1 Signaling

Insufficient remyelination due to impaired oligodendrocyte precursor cell (OPC) differentiation and maturation is strongly associated with irreversible white matter injury (WMI) and neurological deficits. We analyzed whole transcriptome expression to elucidate the potential role and underlying mechanism of action of lipocalin-2 (LCN2) in OPC differentiation and WMI and identified the receptor SCL22A17 and downstream transcription factor early growth response protein 1 (EGR1) as the key signals contributing to LCN2-mediated insufficient OPC remyelination. In LCN-knockdown and OPC EGR1 conditional-knockout mice, we discovered enhanced OPC differentiation in developing and injured white matter (WM); consistent with this, the specific inactivation of LCN2/SCl22A17/EGR1 signaling promoted remyelination and neurological recovery in both atypical, acute WMI due to subarachnoid hemorrhage and typical, chronic WMI due to multiple sclerosis. This potentially represents a novel strategy to enhance differentiation and remyelination in patients with white matter injury.

Review of Artificial Intelligence Applications and Algorithms for Brain Organoid Research

The human brain organoid is a miniature three-dimensional tissue culture that can simulate the structure and function of the brain in an in vitro culture environment. Although we consider that human brain organoids could be used to understand brain development and diseases, experimental models of human brain organoids are so highly variable that we apply artificial intelligence (AI) techniques to investigate the development mechanism of the human brain. Therefore, this study briefly reviewed commonly used AI applications for human brain organoid-magnetic resonance imaging, electroencephalography, and gene editing techniques, as well as related AI algorithms. Finally, we discussed the limitations, challenges, and future study direction of AI-based technology for human brain organoids.