HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data.

Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.

Anomaly Detection for Medical Images Using Heterogeneous Auto-Encoder.

Anomaly detection is an important task for medical image analysis, which can alleviate the reliance of supervised methods on large labelled datasets. Most existing methods use a pixel-wise self-reconstruction framework for anomaly detection. However, there are two challenges of these studies: 1) they tend to overfit learning an identity mapping between the input and output, which leads to failure in detecting abnormal samples; 2) the reconstruction considers the pixel-wise differences which may lead to an undesirable result. To mitigate the above problems, we propose a novel heterogeneous Auto-Encoder (Hetero-AE) for medical anomaly detection. Our model utilizes a convolutional neural network (CNN) as the encoder and a hybrid CNN-Transformer network as the decoder. The heterogeneous structure enables the model to learn the intrinsic information of normal data and enlarge the difference on abnormal samples. To fully exploit the effectiveness of Transformer in the hybrid network, a multi-scale sparse Transformer block is proposed to trade off modelling long-range feature dependencies and high computational costs. Moreover, the multi-stage feature comparison is introduced to reduce the noise of pixel-wise comparison. Extensive experiments on four public datasets (i.e., retinal OCT, chest X-ray, brain MRI, and COVID-19) verify the effectiveness of our method on different imaging modalities for anomaly detection. Additionally, our method can accurately detect tumors in brain MRI and lesions in retinal OCT with interpretable heatmaps to locate lesion areas, assisting clinicians in diagnosing abnormalities efficiently.

Effects of "fixation-fusion" sequence of lumbar surgery on surgical outcomes for patients with lumbar spinal stenosis: study protocol for a multicenter randomized controlled trial.

BACKGROUND: New-onset neurological symptoms such as numbness and pain in lower extremities might appear immediately after conventional lumbar interbody fusion (LIF) surgery performed in patients with lumbar spinal stenosis. METHODS AND ANALYSIS: This is a multicenter, randomized, open-label, parallel-group, active-controlled trial investigating the clinical outcomes of modified LIF sequence versus conventional LIF sequence in treating patients with lumbar spinal stenosis. A total of 254 eligible patients will be enrolled and randomized in a 1:1 ratio to either modified LIF sequence or conventional LIF sequence group. The primary outcome measure is the perioperative incidence of new-onset lower extremity neurological symptoms, including new adverse events of pain, numbness, and foot drop of any severity. Important secondary endpoints include visual analogue scale (VAS) pain score and lumbar Japanese Orthopaedic Association (JOA) recovery rate. Other safety endpoints will also be evaluated. The safety set used for safety data analysis by the actual surgical treatment received and the full analysis set for baseline and efficacy data analyses according to the intent-to-treat principle will be established as the two analysis populations in the study. CONCLUSION: This study is designed to investigate the clinical outcomes of modified LIF sequences in patients with lumbar spinal stenosis. It aims to provide clinical evidence that the modified "fixation-fusion" sequence of LIF surgery is effective in treating lumbar spinal stenosis. TRIAL REGISTRATION: http://www.chictr.org.cn/index.aspx ID: ChiCTR2100048507.

Multi-omics clustering for cancer subtyping based on latent subspace learning.

The increased availability of high-throughput technologies has enabled biomedical researchers to learn about disease etiology across multiple omics layers, which shows promise for improving cancer subtype identification. Many computational methods have been developed to perform clustering on multi-omics data, however, only a few of them are applicable for partial multi-omics in which some samples lack data in some types of omics. In this study, we propose a novel multi-omics clustering method based on latent sub-space learning (MCLS), which can deal with the missing multi-omics for clustering. We utilize the data with complete omics to construct a latent subspace using PCA-based feature extraction and singular value decomposition (SVD). The data with incomplete multi-omics are then projected to the latent subspace, and spectral clustering is performed to find the clusters. The proposed MCLS method is evaluated on seven different cancer datasets on three levels of omics in both full and partial cases compared to several state-of-the-art methods. The experimental results show that the proposed MCLS method is more efficient and effective than the compared methods for cancer subtype identification in multi-omics data analysis, which provides important references to a comprehensive understanding of cancer and biological mechanisms. AVAILABILITY: The proposed method can be freely accessible at https://github.com/ShangCS/MCLS.

Detection of Common Anatomical Landmarks and Vertical Trajectories for Freehand Pedicle Screw Placement.

OBJECTIVE: It is clinically important for pedicle screws to be placed quickly and accurately. Misplacement of pedicle screws results in various complications. However, the incidence of complications varies greatly due to the different professional titles of physicians and surgical experience. Therefore, physicians must minimize pedicle screw dislocation. This study aims to compare the three nail placement methods in this study, and explore which method is the best for determining the anatomical landmarks and vertical trajectories. METHODS: This study involved 70 patients with moderate idiopathic scoliosis who had undergone deformity correction surgery between 2018 and 2021. Two spine surgeons used three techniques (preoperative computed tomography scan [CTS], visual inspection-X-freehand [XFH], and intraoperative detection [ID] of anatomical landmarks) to locate pedicle screws. The techniques used include visual inspection for 287 screws in 21 patients, preoperative planning for 346 screws in 26 patients, and intraoperative probing for 309 screws in 23 patients. Observers assessed screw conditions based on intraoperative CT scans (Grade A, B, C, D). RESULTS: There were no significant differences between the three groups in terms of age, sex, and degree of deformity. We found that 68.64% of screws in the XFH group, 67.63% in the CTS group, and 77.99% in the ID group were placed within the pedicle margins (grade A). On the other hand, 6.27% of screws in the XFH group, 4.33% in the CTS group, and 6.15% in the ID group were considered misplaced (grades C and D). The results show that the total amount of upper thoracic pedicle screws was fewer, meanwhile their placement accuracy was lower. The three methods used in this study had similar accuracy in intermediate physicians (P > 0.05). Compared with intermediate physicians, the placement accuracy of three techniques in senior physicians was higher. The intraoperative detection group was better than the other two groups in the good rate and accuracy of nail placement (P < 0.05). CONCLUSION: Intraoperative common anatomical landmarks and vertical trajectories were beneficial to patients with moderate idiopathic scoliosis undergoing surgery. It is an optimal method for clinical application.

Multi-omics computational analysis unveils the involvement of AP-1 and CTCF in hysteresis of chromatin states during macrophage polarization.

Macrophages display extreme plasticity, and the mechanisms and applications of polarization and de-/repolarization of macrophages have been extensively investigated. However, the regulation of macrophage hysteresis after de-/repolarization remains unclear. In this study, by using a large-scale computational analysis of macrophage multi-omics data, we report a list of hysteresis genes that maintain their expression patterns after polarization and de-/repolarization. While the polarization in M1 macrophages leads to a higher level of hysteresis in genes associated with cell cycle progression, cell migration, and enhancement of the immune response, we found weak levels of hysteresis after M2 polarization. During the polarization process from M0 to M1 and back to M0, the factors IRFs/STAT, AP-1, and CTCF regulate hysteresis by altering their binding sites to the chromatin. Overall, our results show that a history of polarization can lead to hysteresis in gene expression and chromatin accessibility over a given period. This study contributes to the understanding of de-/repolarization memory in macrophages.

Imaging specific cell-surface sialylation using DNA dendrimer-assisted FRET.

Sialylation plays a vital role in multiple different physiologic processes, aberrant sialylation is highly related to disease development. Especially in cancer development, changed states of specific cell-surface sialylation implies rich cancer-related information. Therefore, it is necessary to image specific cell-surface sialylation for better understanding biological functions of sialylation. To meet this purpose, we designed a DNA dendrimer-assisted fluorescence resonance energy transfer (FRET) strategy in this work. By labeling multiple FRET donors and acceptors on the target molecules through metabolic oligosaccharide engineering (MOE) and targeted recognition of aptamer-tethered DNA dendrimer, the FRET was significantly improved. With the DNA dendrimer-assisted FRET strategy, specific imaging of cell-surface sialylation on SMMC-7721 and CEM cells were successfully achieved. The obtained FRET signal intensity was approximately four times higher than the control without the assistance of DNA dendrimer. Moreover, this method is competent to monitor changed states of PTK7-specific sialylation induced by tunicamycin. The proposed imaging strategy may provide a powerful tool to explore the physiological roles of specific cell-surface sialylation and the related mechanism of diseases.

Detecting Interactive Gene Groups for Single-Cell RNA-Seq Data Based on Co-Expression Network Analysis and Subgraph Learning.

High-throughput sequencing technologies have enabled the generation of single-cell RNA-seq (scRNA-seq) data, which explore both genetic heterogeneity and phenotypic variation between cells. Some methods have been proposed to detect the related genes causing cell-to-cell variability for understanding tumor heterogeneity. However, most existing methods detect the related genes separately, without considering gene interactions. In this paper, we proposed a novel learning framework to detect the interactive gene groups for scRNA-seq data based on co-expression network analysis and subgraph learning. We first utilized spectral clustering to identify the subpopulations of cells. For each cell subpopulation, the differentially expressed genes were then selected to construct a gene co-expression network. Finally, the interactive gene groups were detected by learning the dense subgraphs embedded in the gene co-expression networks. We applied the proposed learning framework on a real cancer scRNA-seq dataset to detect interactive gene groups of different cancer subtypes. Systematic gene ontology enrichment analysis was performed to examine the detected genes groups by summarizing the key biological processes and pathways. Our analysis shows that different subtypes exhibit distinct gene co-expression networks and interactive gene groups with different functional enrichment. The interactive genes are expected to yield important references for understanding tumor heterogeneity.

3D skeletonization feature based computer-aided detection system for pulmonary nodules in CT datasets.

Pulmonary nodule detection has a significant impact on early diagnosis of lung cancer. To effectively detect pulmonary nodules from interferential vessels in chest CT datasets, this paper proposes a novel 3D skeletonization feature, named as voxels remove rate. Based on this feature, a computer-aided detection system is constructed to validate its performance. The system mainly consists of five stages. Firstly, the lung tissues are segmented by a global optimal active contour model, which can extract all structures (including juxta-pleural nodules) in the lung region. Secondly, thresholding, 3D binary morphological operations, and 3D connected components labeling are utilized to extract candidates of pulmonary nodules. Thirdly, combining the voxels remove rate with other nine existing 3D features (including gray features and shape features), the extracted candidates are characterized. Then, prior anatomical knowledge is utilized for preliminary screening of numerous invalid nodule candidates. Finally, false positives are reduced by support vector machine. Our system is evaluated on early stage lung cancer subjects obtained from the publicly available LIDC-IDRI database. The result shows the proposed 3D skeletonization feature is a useful indicator that efficiently differentiates lung nodules from the other suspicious structures. The computer-aided detection system based on this feature can detect various types of nodules, including solitary, juxta-pleural and juxta-vascular nodules.

Global optimal hybrid geometric active contour for automated lung segmentation on CT images.

Lung segmentation on thoracic CT images plays an important role in early detection, diagnosis and 3D visualization of lung cancer. The segmentation accuracy, stability, and efficiency of serial CT scans have a significant impact on the performance of computer-aided detection. This paper proposes a global optimal hybrid geometric active contour model for automated lung segmentation on CT images. Firstly, the combination of global region and edge information leads to high segmentation accuracy in lung regions with weak boundaries or narrow bands. Secondly, due to the global optimality of energy functional, the proposed model is robust to the initial position of level set function and requires fewer iterations. Thus, the stability and efficiency of lung segmentation on serial CT slices can be greatly improved by taking advantage of the information between adjacent slices. In addition, to achieve the whole process of automated segmentation for lung cancer, two assistant algorithms based on prior shape and anatomical knowledge are proposed. The algorithms not only automatically separate the left and right lungs, but also include juxta-pleural tumors into the segmentation result. The proposed method was quantitatively validated on subjects from the publicly available LIDC-IDRI and our own data sets. Exhaustive experimental results demonstrate the superiority and competency of our method, especially compared with the typical edge-based geometric active contour model.