Circular Clustering in Fuzzy Approximation Spaces for Color Normalization of Histological Images

ACOD1, rather than itaconate, facilitates p62-mediated activation of Nrf2 in microglia post spinal cord contusion

BACKGROUND

Spinal cord injury (SCI)-induced neuroinflammation and oxidative stress (OS) are crucial events causing neurological dysfunction. Aconitate decarboxylase 1 (ACOD1) and its metabolite itaconate (Ita) inhibit inflammation and OS by promoting alkylation of Keap1 to induce Nrf2 expression; however, it is unclear whether there is another pathway regulating their effects in inflammation-activated microglia after SCI.

METHODS

Adult male C57BL/6 ACOD1-/- mice and their wild-type (WT) littermates were subjected to a moderate thoracic spinal cord contusion. The degree of neuroinflammation and OS in the injured spinal cord were assessed using qPCR, western blot, flow cytometry, immunofluorescence, and trans-well assay. We then employed immunoprecipitation-western blot, chromatin immunoprecipitation (ChIP)-PCR, dual-luciferase assay, and immunofluorescence-confocal imaging to examine the molecular mechanisms of ACOD1. Finally, the locomotor function was evaluated with the Basso Mouse Scale and footprint assay.

RESULTS

Both in vitro and in vivo, microglia with transcriptional blockage of ACOD1 exhibited more severe levels of neuroinflammation and OS, in which the expression of p62/Keap1/Nrf2 was down-regulated. Furthermore, silencing ACOD1 exacerbated neurological dysfunction in SCI mice. Administration of exogenous Ita or 4-octyl itaconate reduced p62 phosphorylation. Besides, ACOD1 was capable of interacting with phosphorylated p62 to enhance Nrf2 activation, which in turn further promoted transcription of ACOD1.

CONCLUSIONS

Here, we identified an unreported ACOD1-p62-Nrf2-ACOD1 feedback loop exerting anti-inflammatory and anti-OS in inflammatory microglia, and demonstrated the neuroprotective role of ACOD1 after SCI, which was different from that of endogenous and exogenous Ita. The present study extends the functions of ACOD1 and uncovers marked property differences between endogenous and exogenous Ita.

KEY POINTS

ACOD1 attenuated neuroinflammation and oxidative stress after spinal cord injury. ACOD1, not itaconate, interacted with p-p62 to facilitate Nrf2 expression and nuclear translocation. Nrf2 was capable of promoting ACOD1 transcription in microglia.

Self-Supervised Digital Histopathology Image Disentanglement for Arbitrary Domain Stain Transfer

Diagnosis of cancerous diseases relies on digital histopathology images from stained slides. However, the staining varies among medical centers, which leads to a domain gap of staining. Existing generative adversarial network (GAN) based stain transfer methods highly rely on distinct domains of source and target, and cannot handle unseen domains. To overcome these obstacles, we propose a self-supervised disentanglement network (SDN) for domain-independent optimization and arbitrary domain stain transfer. SDN decomposes an image into features of content and stain. By exchanging the stain features, the staining style of an image is transferred to the target domain. For optimization, we propose a novel self-supervised learning policy based on the consistency of stain and content among augmentations from one instance. Therefore, the process of training SDN is independent on the domain of training data, and thus SDN is able to tackle unseen domains. Exhaustive experiments demonstrate that SDN achieves the top performance in intra-dataset and cross-dataset stain transfer compared with the state-of-the-art stain transfer models, while the number of parameters in SDN is three orders of magnitude smaller parameters than that of compared models. Through stain transfer, SDN improves AUC of downstream classification model on unseen data without fine-tuning. Therefore, the proposed disentanglement framework and self-supervised learning policy have significant advantages in eliminating the stain gap among multi-center histopathology images.

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.

Histological image color normalization using a skewed normal distribution mixed model

Color variation between histological images may influence the performance of computer-aided histological image analysis. Therefore, among the most essential and challenging tasks in histological image analysis are the reduction of the color variation between images and the preservation of the histological information contained in the images. In recent years, many methods have been introduced with respect to the color normalization of histological images. In this study, we introduce a new clustering method referred to as the skewed normal distribution mixed model clustering algorithm. Realizing that the color distribution of hue values approximates the combination of several skewed normal distributions, we propose to use the skewed normal distribution mixture model to analyze the hue distribution. The proposed skewed normal distribution mixture model clustering algorithm includes saturation-weighted hue histograms because it takes into account the saturation and hue information of a particular histogram image, which can diminish the influence of achromatic pixels. Finally, we conducted extensive experiments based on three data sets and compared them with commonly used color normalization methods. The experiments show that the proposed algorithm has better performance in stain separation and color normalization compared to other methods.