Pretreated MSCs with IronQ Transplantation Attenuate Microglia Neuroinflammation via the cGAS-STING Signaling Pathway

Background

Intracerebral hemorrhage (ICH), a devastating form of stroke, is characterized by elevated morbidity and mortality rates. Neuroinflammation is a common occurrence following ICH. Mesenchymal stem cells (MSCs) have exhibited potential in treating brain diseases due to their anti-inflammatory properties. However, the therapeutic efficacy of MSCs is limited by the intense inflammatory response at the transplantation site in ICH. Hence, enhancing the function of transplanted MSCs holds considerable promise as a therapeutic strategy for ICH. Notably, the iron-quercetin complex (IronQ), a metal-quercetin complex synthesized through coordination chemistry, has garnered significant attention for its biomedical applications. In our previous studies, we have observed that IronQ exerts stimulatory effects on cell growth, notably enhancing the survival and viability of peripheral blood mononuclear cells (PBMCs) and MSCs. This study aimed to evaluate the effects of pretreated MSCs with IronQ on neuroinflammation and elucidate its underlying mechanisms.

Methods

The ICH mice were induced by injecting the collagenase I solution into the right brain caudate nucleus. After 24 hours, the ICH mice were randomly divided into four subgroups, the model group (Model), quercetin group (Quercetin), MSCs group (MSCs), and pretreated MSCs with IronQ group (MSCs+IronQ). Neurological deficits were re-evaluated on day 3, and brain samples were collected for further analysis. TUNEL staining was performed to assess cell DNA damage, and the protein expression levels of inflammatory factors and the cGAS-STING signaling pathway were investigated and analyzed.

Results

Pretreated MSCs with IronQ effectively mitigate neurological deficits and reduce neuronal inflammation by modulating the microglial polarization. Moreover, the pretreated MSCs with IronQ suppress the protein expression levels of the cGAS-STING signaling pathway.

Conclusion

These findings suggest that pretreated MSCs with IronQ demonstrate a synergistic effect in alleviating neuroinflammation, thereby improving neurological function, which is achieved through the inhibition of the cGAS-STING signaling pathway.

Novel histone post-translational modifications in Alzheimer's disease: current advances and implications

Alzheimer's disease (AD) has a complex pathogenesis, and multiple studies have indicated that histone post-translational modifications, especially acetylation, play a significant role in it. With the development of mass spectrometry and proteomics, an increasing number of novel HPTMs, including lactoylation, crotonylation, β-hydroxybutyrylation, 2-hydroxyisobutyrylation, succinylation, and malonylation, have been identified. These novel HPTMs closely link substance metabolism to gene regulation, and an increasing number of relevant studies on the relationship between novel HPTMs and AD have become available. This review summarizes the current advances and implications of novel HPTMs in AD, providing insight into the deeper pathogenesis of AD and the development of novel drugs.

Sd-net: a semi-supervised double-cooperative network for liver segmentation from computed tomography (CT) images

INTRODUCTION

The automatic segmentation of the liver is a crucial step in obtaining quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This task is challenging due to the frequent presence of noise and sampling artifacts in computerized tomography (CT) images, as well as the complex background, variable shapes, and blurry boundaries of the liver. Standard segmentation of medical images based on full-supervised convolutional networks demands accurate dense annotations. Such a learning framework is built on laborious manual annotation with strict requirements for expertise, leading to insufficient high-quality labels.

METHODS

To overcome such limitation and exploit massive weakly labeled data, we relaxed the rigid labeling requirement and developed a semi-supervised double-cooperative network (SD- Net). SD-Net is trained to segment the complete liver volume from preoperative abdominal CT images by using limited labeled datasets and large-scale unlabeled datasets. Specifically, to enrich the diversity of unsupervised information, we construct SD-Net consisting of two collaborative network models. Within the supervised training module, we introduce an adaptive mask refinement approach. First, each of the two network models predicts the labeled dataset, after which adaptive mask refinement of the difference predictions is implemented to obtain more accurate liver segmentation results. In the unsupervised training module, a dynamic pseudo-label generation strategy is proposed. First each of the two models predicts unlabeled data and the better prediction is considered as pseudo-labeling before training.

RESULTS AND DISCUSSION

Based on the experimental findings, the proposed method achieves a dice score exceeding 94%, indicating its high level of accuracy and its suitability for everyday clinical use.

Bibliometric analysis of global literature productivity in systemic lupus erythematosus from 2013 to 2022

BACKGROUND

Bibliometric analysis is a mature method for quantitative evaluation of academic productivity. In view of the rapid development of research in the field of systemic lupus erythematosus (SLE) in the past decade, we used bibliometric methods to comprehensively analyze the literature in the field of SLE from 2013 to 2022.

METHODS

The relevant literature in the field of SLE from 2013 to 2022 was screened in the Web of Science Core Collection database. After obtaining and sorting out the data, CiteSpace and VOSviewer software were used to visualize the relevant data, and SPSS software was used for scientific statistics.

RESULTS

A total of 18,450 publications were included in this study. The number of articles published over the past 10 years has generally shown an upward trend, while Altmetric attention scores have also shown a clear upward trend in general and in most countries. Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers. The USA, China, Japan, Italy, and the UK are the most productive countries, but China and Japan are significantly inferior to other countries in terms of research influence. Four of the top ten authors are at the center of the collaboration network. LUPUS is the most contributing journal. The theme of systemic lupus erythematosus research mainly focuses on the pathogenesis, treatment, and management of SLE, and the emerging trend is related research on machine learning and immune cells.

CONCLUSION

This study shows the research status of SLE, clarifies the main contributors in this field, discusses and analyzes the research hotspots and trends in this field, and provides reference for further research in this field to promote the development of SLE research. Key Points • Through bibliometric analysis, Altmetric analysis, and visual analysis, we reveal the global productivity characteristics of SLE-related papers in the past 10 years. • The number of global SLE-related studies has shown a significant increase, indicating that SLE is still a hot topic and deserves further study. • Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers, and the attention of related literature among non-professional researchers is increasing. • The theme of SLE research mainly focuses on the pathogenesis, treatment, and management of SLE. The emerging trend is machine learning and immune cells, which may provide new strategies for the diagnosis and treatment of SLE in the future.