Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model

Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits the model performance on clinical phenotypes predictions. Moreover, few of current graph learning model is interpretable, which may not be capable to provide biological insights for model outcomes. Here, we propose an interpretable hierarchical signed graph representation learning model to extract graph-level representations from brain functional networks, which can be used for different prediction tasks. In order to further improve the model performance, we also propose a new strategy to augment functional brain network data for contrastive learning. We evaluate this framework on different classification and regression tasks using the data from HCP and OASIS. Our results from extensive experiments demonstrate the superiority of the proposed model compared to several state-of-the-art techniques. Additionally, we use graph saliency maps, derived from these prediction tasks, to demonstrate detection and interpretation of phenotypic biomarkers

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

Coexistence of anti-SOX1 and anti-GABAB receptor antibodies with paraneoplastic limbic encephalitis presenting with seizures and memory impairment in small cell lung cancer: A case report

PurposeParaneoplastic neurological syndromes associated with autoantibodies are rare diseases that cause abnormal manifestations of the nervous system. Early diagnosis of paraneoplastic neurological syndromes paves the way for prompt and efficient therapy.Case reportwe reported a 56-year-old man presenting with seizures and rapidly progressive cognitive impairment diagnosed as paraneoplastic limbic encephalitis (PLE) with anti-SRY-like high-mobility group box-1 (SOX-1) and anti-γ-aminobutyric acid B (GABAB) receptor antibodies and finally confirmed by biopsy as small cell lung cancer (SCLC). At the first admission, brain magnetic resonance imaging (MRI) showed no abnormal signal in bilateral hippocampal regions and no abnormal enhancement of enhanced scan. The serum anti-GABAB receptor antibody was 1:100 and was diagnosed as autoimmune encephalitis (AE). The computed tomography (CT) scans of the chest showed no obvious tumor signs for the first time. Although positron emission tomography-computed tomography (PET-CT) revealed hypermetabolism in the para mid-esophageal, the patient and his family declined to undertake a biopsy. The patient improved after receiving immunoglobulin, antiepileptic therapy, and intravenous methylprednisolone (IVMP) pulse treatment. However, after 4 months, the symptoms reappeared. Brain MRI revealed abnormal signals in the hippocampal regions. Reexamination of the cerebral fluid revealed anti-GABAB receptor and anti-SOX-1 antibodies, which contributed to the diagnosis of PLE. SCLC was found in a para mid-esophageal pathological biopsy. Antiepileptic medications and immunoglobulin were used to treat the patient, and the symptoms were under control.ConclusionOur findings increase the awareness that patients with limbic encephalitis with cognitive dysfunction and epileptic seizures should be enhanced to detect latent malignancy. Our case also highlights the importance of anti-SOX1 antibodies in the detection of underlying neoplasm, particularly SCLC. Our findings raise awareness of the cognitive impairment seen by patients with limbic encephalitis

3D bi-directional transformer U-Net for medical image segmentation

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics

An Exploration of Transformer and Convolution Layers in Medical Image Segmentation

Deep convolutional neural networks (DCNNs) are a popular deep learning technique that has been widely used in segmentation tasks and has received positive feedback. However, DCNN-based frameworks are known to be inadequate in dealing with global relations within imaging features when it comes to segmentation tasks. While several techniques have been proposed to enhance the global reasoning of DCNN, these models are either unable to achieve satisfactory performance compared to traditional fully-convolutional structures or unable to utilize the fundamental advantages of CNN-based networks, namely the ability of local reasoning. In this study, we designed a novel attention mechanism for 3D computation and used it to fully extract the self-attention ability. We proposed a new segmentation framework (called 3DTU) for three-dimensional medical image segmentation tasks, which processes images in an end-to-end manner and performs 3D computation on both the encoder side (with a 3D transformer) and the decoder side (based on a 3D DCNN). In comparison to existing attempts to combine FCNs and global reasoning methods, our framework outperforms several state-of-the-art segmentation methods on two independent datasets consisting of 3D MRI and CT images, as evidenced by experimental results

Inhibition of TIGAR Increases Exogenous p53 and Cisplatin Combination Sensitivity in Lung Cancer Cells by Regulating Glycolytic Flux

The metabolism and apoptosis of tumor cells are important factors that increase their sensitivity to chemotherapeutic drugs. p53 and cisplatin not only induce tumor cell apoptosis, but also regulate the tumor cell metabolism. The TP53-induced glycolysis and apoptosis regulator (TIGAR) can inhibit glycolysis and promote more glucose metabolism in the pentose phosphate pathway. We speculate that the regulation of the TIGAR by the combination therapy of p53 and cisplatin plays an important role in increasing the sensitivity of tumor cells to cisplatin. In this study, we found that the combined treatment of p53 and cisplatin was able to inhibit the mitochondrial function, promote mitochondrial pathway-induced apoptosis, and increase the sensitivity. Furthermore, the expression of the TIGAR was inhibited after a combined p53 and cisplatin treatment, the features of the TIGAR that regulate the pentose phosphate pathway were inhibited, the glucose flux shifted towards glycolysis, and the localization of the complex of the TIGAR and Hexokinase 2 (HK2) on the mitochondria was also reduced. Therefore, the combined treatment of p53 and cisplatin may modulate a glycolytic flux through the TIGAR, altering the cellular metabolic patterns while increasing apoptosis. Taken together, our findings reveal that the TIGAR may serve as a potential therapeutic target to increase the sensitivity of lung cancer A549 cells to cisplatin

Knitted Ti₃C₂Tₓ MXene based fiber strain sensor for human-computer interaction

Fiber-based stretchable electronics with feasibility of weaving into textiles and advantages of light-weight, long-term stability, conformability and easy integration are highly desirable for wearable electronics to realize personalized medicine, artificial intelligence and human health monitoring. Herein, a fiber strain sensor is developed based on the Ti3C2Tx MXene wrapped by poly(vinylidenefluoride-co-trifluoroethylene) (P(VDF-TrFE)) polymer nanofibers prepared via electrostatic spinning. Owing to the good conductivity of Ti3C2Tx and unique 3D reticular structure with wave shape, the resistance of Ti3C2Tx@P(VDF-TrFE) polymer nanofibers changes under external force, thus providing remarkable strain inducted sensing performance. As-fabricated sensor exhibits high gauge factor (GF) of 108.8 in range of 45-66% strain, rapid response of 19 ms, and outstanding durability over 1600 stretching/releasing cycles. The strain sensor is able to monitor vigorous human motions (finger or wrist bending) and subtle physiological signals (blinking, pulse or voice recognition) in real-time. Moreover, a data glove is designed to connect different gestures and expressions to form an intelligent gesture-expression control system, further confirming the practicability of our Ti3C2Tx@P(VDF-TrFE) strain sensors in multifunctional wearable electronic devices.The authors sincerely acknowledge financial support from the National Natural Science Foundation of China (NSFC Grant Nos. 21571080, 51502110)