Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method

Alzheimer disease (AD) is the fourth major cause of death in the elderly following cancer, heart disease and cerebrovascular disease. Finding candidate causal genes can help in the design of Gene targeted drugs and effectively reduce the risk of the disease. Complex diseases such as AD are usually caused by multiple genes. The Genome-wide association study (GWAS), has identified the potential genetic variants for most diseases. However, because of linkage disequilibrium (LD), it is difficult to identify the causative mutations that directly cause diseases. In this study, we combined expression quantitative trait locus (eQTL) studies with the GWAS, to comprehensively define the genes that cause Alzheimer disease. The method used was the Summary Mendelian randomization (SMR), which is a novel method to integrate summarized data. Two GWAS studies and five eQTL studies were referenced in this paper. We found several candidate SNPs that have a strong relationship with AD. Most of these SNPs overlap in different data sets, providing relatively strong reliability. We also explain the function of the novel AD-related genes we have discovered

BdBG: a bucket-based method for compressing genome sequencing data with dynamic de Bruijn graphs

Dramatic increases in data produced by next-generation sequencing (NGS) technologies demand data compression tools for saving storage space. However, effective and efficient data compression for genome sequencing data has remained an unresolved challenge in NGS data studies. In this paper, we propose a novel alignment-free and reference-free compression method, BdBG, which is the first to compress genome sequencing data with dynamic de Bruijn graphs based on the data after bucketing. Compared with existing de Bruijn graph methods, BdBG only stored a list of bucket indexes and bifurcations for the raw read sequences, and this feature can effectively reduce storage space. Experimental results on several genome sequencing datasets show the effectiveness of BdBG over three state-of-the-art methods. BdBG is written in python and it is an open source software distributed under the MIT license, available for download at https://github.com/rongjiewang/BdBG

Identification of preoperative radiological risk factors for reoperation following percutaneous endoscopic lumbar decompression to treat degenerative lumbar spinal stenosis

BackgroundThis study aimed to identify radiological risk factors associated with reoperation after percutaneous transforaminal endoscopic decompression (PTED) for degenerative lumbar spinal stenosis (DLSS).MethodsThe preoperative clinical data of 527 consecutive patients with DLSS who underwent PTED were retrospectively reviewed. Overall, 44 patients who underwent reoperation were matched for age, sex, body mass index, and surgical segment to 132 control patients with excellent or good clinical outcomes. Radiological characteristics were compared between the groups using independent sample t-tests and Pearson's chi-square tests. A predictive model was established based on multivariate logistic regression analysis.ResultsThe analyses revealed significant differences in the presence of lumbosacral transitional vertebra (LSTV, 43.2% vs. 17.4%, p = 0.001), the number of levels with senior-grade disc degeneration (2.57 vs. 1.96, p = 0.018) and facet degeneration (1.91 vs. 1.25 p = 0.002), and the skeletal muscle index (SMI, 849.7 mm2/m2 vs. 1008.7 mm2/m2, p < 0.001) between patients in the reoperation and control groups. The results of the logistic analysis demonstrated that LSTV (odds ratio [OR] = 2.734, 95% confidence interval [CI]:1.222–6.117, p < 0.014), number of levels with senior-grade facet degeneration (OR = 1.622, 95% CI:1.137–2.315, p = 0.008), and SMI (OR = 0.997, 95% CI:0.995–0.999, p = 0.001) were associated with reoperation after PTED. The application of the nomogram based on these three factors showed good discrimination (area under the receiver operating characteristic curve 0.754, 95% CI 0.670–0.837) and good calibration.ConclusionLSTV, more levels with senior-grade facet degeneration, and severe paraspinal muscle atrophy are independent risk factors for reoperation after PTED. These factors can thus be used to predict reoperation risk and to help tailor treatment plans for patients with DLSS

Integration of multiple-omics data to reveal the shared genetic architecture of educational attainment, intelligence, cognitive performance, and Alzheimer’s disease

Growing evidence suggests the effect of educational attainment (EA) on Alzheimer’s disease (AD), but less is known about the shared genetic architecture between them. Here, leveraging genome-wide association studies (GWAS) for AD (N = 21,982/41,944), EA (N = 1,131,881), cognitive performance (N = 257,828), and intelligence (N = 78,308), we investigated their causal association with the linkage disequilibrium score (LDSC) and Mendelian randomization and their shared loci with the conjunctional false discovery rate (conjFDR), transcriptome-wide association studies (TWAS), and colocalization. We observed significant genetic correlations of EA (rg = −0.22, p = 5.07E-05), cognitive performance (rg = −0.27, p = 2.44E-05), and intelligence (rg = −0.30, p = 3.00E-04) with AD, and a causal relationship between EA and AD (OR = 0.74, 95% CI: 0.58–0.94, p = 0.013). We identified 13 shared loci at conjFDR <0.01, of which five were novel, and prioritized three causal genes. These findings inform early prevention strategies for AD

Development of a software system for surgical robots based on multimodal image fusion: study protocol

BackgroundSurgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations.MethodsInitially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated.DiscussionWe will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations

Deep learning-based multimodal image analysis predicts bone cement leakage during percutaneous kyphoplasty: protocol for model development, and validation by prospective and external datasets

BackgroundBone cement leakage (BCL) is one of the most prevalent complications of percutaneous kyphoplasty (PKP) for treating osteoporotic vertebral compression fracture (OVCF), which may result in severe secondary complications and poor outcomes. Previous studies employed several traditional machine learning (ML) models to predict BCL preoperatively, but effective and intelligent methods to bridge the distance between current models and real-life clinical applications remain lacking.MethodsWe will develop a deep learning (DL)-based prediction model that directly analyzes preoperative computed tomography (CT) and magnetic resonance imaging (MRI) of patients with OVCF to accurately predict BCL occurrence and classification during PKP. This retrospective study includes a retrospective internal dataset for DL model training and validation, a prospective internal dataset, and a cross-center external dataset for model testing. We will evaluate not only model’s predictive performance, but also its reliability by calculating its consistency with reference standards and comparing it with that of clinician prediction.DiscussionThe model holds an imperative clinical significance. Clinicians can formulate more targeted treatment strategies to minimize the incidence of BCL, thereby improving clinical outcomes by preoperatively identifying patients at high risk for each BCL subtype. In particular, the model holds great potential to be extended and applied in remote areas where medical resources are relatively scarce so that more patients can benefit from quality perioperative evaluation and management strategies. Moreover, the model will efficiently promote information sharing and decision-making between clinicians and patients, thereby increasing the overall quality of healthcare services

Artificial intelligence automatic measurement technology of lumbosacral radiographic parameters

BackgroundCurrently, manual measurement of lumbosacral radiological parameters is time-consuming and laborious, and inevitably produces considerable variability. This study aimed to develop and evaluate a deep learning-based model for automatically measuring lumbosacral radiographic parameters on lateral lumbar radiographs.MethodsWe retrospectively collected 1,240 lateral lumbar radiographs to train the model. The included images were randomly divided into training, validation, and test sets in a ratio of approximately 8:1:1 for model training, fine-tuning, and performance evaluation, respectively. The parameters measured in this study were lumbar lordosis (LL), sacral horizontal angle (SHA), intervertebral space angle (ISA) at L4–L5 and L5–S1 segments, and the percentage of lumbar spondylolisthesis (PLS) at L4–L5 and L5–S1 segments. The model identified key points using image segmentation results and calculated measurements. The average results of key points annotated by the three spine surgeons were used as the reference standard. The model’s performance was evaluated using the percentage of correct key points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), root mean square error (RMSE), and box plots.ResultsThe model’s mean differences from the reference standard for LL, SHA, ISA (L4–L5), ISA (L5–S1), PLS (L4–L5), and PLS (L5–S1) were 1.69°, 1.36°, 1.55°, 1.90°, 1.60%, and 2.43%, respectively. When compared with the reference standard, the measurements of the model had better correlation and consistency (LL, SHA, and ISA: ICC = 0.91–0.97, r = 0.91–0.96, MAE = 1.89–2.47, RMSE = 2.32–3.12; PLS: ICC = 0.90–0.92, r = 0.90–0.91, MAE = 1.95–2.93, RMSE = 2.52–3.70), and the differences between them were not statistically significant (p > 0.05).ConclusionThe model developed in this study could correctly identify key vertebral points on lateral lumbar radiographs and automatically calculate lumbosacral radiographic parameters. The measurement results of the model had good consistency and reliability compared to manual measurements. With additional training and optimization, this technology holds promise for future measurements in clinical practice and analysis of large datasets