The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are frequently pattern-agnostic, i.e. variable in shape, size and location, which can not be effectively matched by traditional CNN-based methods with local and regular receptive fields. To address the above issues, we propose a shape-scale co-awareness network (S ² CA-Net) for brain tumor segmentation, which can efficiently learn shape-aware and scale-aware features simultaneously to enhance pattern-agnostic representations. Primarily, three key components are proposed to accomplish the co-awareness of shape and scale. The Local-Global Scale Mixer (LGSM) decouples the extraction of local and global context by adopting the CNN-Former parallel structure, which contributes to obtaining finer hierarchical features. The Multi-level Context Aggregator (MCA) enriches the scale diversity of input patches by modeling global features across multiple receptive fields. The Multi-Scale Attentive Deformable Convolution (MS-ADC) learns the target deformation based on the multiscale inputs, which motivates the network to enforce feature constraints both in terms of scale and shape for optimal feature matching. Overall, LGSM and MCA focus on enhancing the scale-awareness of the network to cope with the size and location variations, while MS-ADC focuses on capturing deformation information for optimal shape matching. Finally, their effective integration prompts the network to perceive variations in shape and scale simultaneously, which can robustly tackle the variations in patterns of brain tumors. The experimental results on BraTS 2019, BraTS 2020, MSD BTS Task and BraTS2023-MEN show that S ² CA-Net has superior overall performance in accuracy and efficiency compared to other state-of-the-art methods. Code: https://github.com/jiangyu945/S2CA-Net.

Moderate or severe burns are potentially devastating injuries that can even cause death, and many of them occur every year. Infection prevention, anti-inflammation, pain management and administration of growth factors play key roles in the treatment of burn wounds. Novel therapeutic strategies under development, such as nanotherapeutics, are promising prospects for burn wound treatment. Nanotherapeutics, including metallic and polymeric nanoformulations, have been extensively developed to manage various types of burns. Both human and animal studies have demonstrated that nanotherapeutics are biocompatible and effective in this application. Herein, we provide comprehensive knowledge of and an update on the progress of various nanoformulations for the treatment of burn wounds.

Burn injury is a devastating disease with high incidence of disability and mortality. The cognitive dysfunctions, such as memory defect, are the main neurological sequelae influencing the life quality of burn-injured patients. The post-burn cognitive dysfunctions are related to the primary peripheral factors and the secondary cerebral inflammation, resulting in the destruction of blood-brain barrier (BBB), as is shown on Computed Tomography (CT) and magnetic resonance imaging examinations. As part of the neurovascular unit, BBB is vital to the nutrition and homeostasis of the central nervous system (CNS) and undergoes myriad alterations after burn injury, causing post-burn cognitive defects. The diagnosis and treatment of cognitive dysfunctions as burn injury sequelae are of great importance. In this review, we address the major manifestations and interventions of post-burn cognitive defects, as well as the mechanisms involved in memory defect, including neuroinflammation, destruction of BBB, and hormone imbalance.

Fat mass and obesity-associated protein (FTO) is the key demethylase that reverses the abnormally altered N6-methyladenosine (m6A) modification in eukaryotic cells under environmental pollutants exposure. Arsenic is an environmental metalloid and can cause severe symptoms in human mainly through drinking water. However, there is no specific treatment for its toxic effects due to the uncovered mechanisms. We previously revealed that exposure to arsenic increased the level of m6A via down-regulation of FTO, which might serve as a potential target for intervention against arsenic-related disorders. In this study, our results demonstrated that chronic exposure to arsenic significantly disrupted the intestinal barrier and microenvironment. Also, this administration resulted in the enhancement of m6A modification and the reduction of FTO expression in the intestine. By using both CRISPR/Cas9-based FTO knock-in strategy and adeno-associated virus (AAV)-mediated overexpression of FTO in the intestine, we established for the first time that up-regulation of FTO remarkably ameliorated arsenic-induced disruption of intestinal barriers and altered microenvironment of mice. We also firstly identified a dominant gut microbial species, Desulfovibrio fairfieldensis, which was sharply reduced in arsenic-exposed mice, was able to proceed arsenic-induced neurobehavioral impairments by declining the levels of its major metabolite hydrogen sulfide. Administration of Desulfovibrio fairfieldensis could significantly alleviate the neurotoxicity of arsenic. Intriguingly, the beneficial effects of FTO against arsenic neurotoxicity possibly occurred through a novel gut-brain communication via Desulfovibrio fairfieldensis and its produced hydrogen sulfide. Collectively, these findings will provide new ideas for understanding the mechanisms of arsenic-induced toxic effects from a gut-brain communication perspective, and will assist the development of explicit intervention strategy via regulation of a new potential target FTO for prevention and treatment against arsenic-related both intestinal and neurological disorders.

Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3 T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3 T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7 T MRI synthesis to improve hippocampal subfield segmentation in 3 T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3 T MRI and 7 T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7 T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3 T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3 T MRI.

This study sought to determine whether the permeability related parameter K^trans, derived from computed tomography perfusion (CTP) imaging, can predict hemorrhagic transformation (HT) in patients with acute ischemic stroke who receive intra-arterial thrombolysis. Data from patients meeting the criterion were examined. CTP was performed and K^trans maps were used to assess the permeability values in HT and non-HT regions. A receiver operating characteristic (ROC) curve was calculated, showing the sensitivity and specificity of K^trans for predicting HT risk. Composite images were produced to illustrate the spatial correlations among perfusion, permeability changes and HT. This study examined 41 patients. Twenty-six patients had hemorrhagic infarction and 15 had parenchymal hemorrhage. The mean K^trans value in HT regions was significantly lower than that in the non-HT regions (0.26 ± 0.21/min vs. 0.78 ± 0.64/min; P < 0.001). The ROC curve analysis identified an optimal cutoff value of 0.334/min for K^trans to predict HT risk. Composite images suggested ischemic regions with low permeability, or the mismatch area of low perfusion and high permeability, more likely have HT. HT regions after intra-arterial thrombolysis had lower permeability values on K^trans maps. The mismatch area of lower perfusion and higher permeability are more likely to develop HT.