Gene Therapy for Hemophilia A: Where We Stand

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.

Multiple Laplacian Regularized RBF Neural Network for Assessing Dry Weight of Patients With End-Stage Renal Disease

Dry weight (DW) is an important dialysis index for patients with end-stage renal disease. It can guide clinical hemodialysis. Brain natriuretic peptide, chest computed tomography image, ultrasound, and bioelectrical impedance analysis are key indicators (multisource information) for assessing DW. By these approaches, a trial-and-error method (traditional measurement method) is employed to assess DW. The assessment of clinician is time-consuming. In this study, we developed a method based on artificial intelligence technology to estimate patient DW. Based on the conventional radial basis function neural (RBFN) network, we propose a multiple Laplacian-regularized RBFN (MLapRBFN) model to predict DW of patient. Compared with other model and body composition monitor, our method achieves the lowest value (1.3226) of root mean square error. In Bland-Altman analysis of MLapRBFN, the number of out agreement interval is least (17 samples). MLapRBFN integrates multiple Laplace regularization terms, and employs an efficient iterative algorithm to solve the model. The ratio of out agreement interval is 3.57%, which is lower than 5%. Therefore, our method can be tentatively applied for clinical evaluation of DW in hemodialysis patients.

Prediction of Gastric Cancer-Related Proteins Based on Graph Fusion Method

Gastric cancer is a common malignant tumor of the digestive system with no specific symptoms. Due to the limited knowledge of pathogenesis, patients are usually diagnosed in advanced stage and do not have effective treatment methods. Proteome has unique tissue and time specificity and can reflect the influence of external factors that has become a potential biomarker for early diagnosis. Therefore, discovering gastric cancer-related proteins could greatly help researchers design drugs and develop an early diagnosis kit. However, identifying gastric cancer-related proteins by biological experiments is time- and money-consuming. With the high speed increase of data, it has become a hot issue to mine the knowledge of proteomics data on a large scale through computational methods. Based on the hypothesis that the stronger the association between the two proteins, the more likely they are to be associated with the same disease, in this paper, we constructed both disease similarity network and protein interaction network. Then, Graph Convolutional Networks (GCN) was applied to extract topological features of these networks. Finally, Xgboost was used to identify the relationship between proteins and gastric cancer. Results of 10-cross validation experiments show high area under the curve (AUC) (0.85) and area under the precision recall (AUPR) curve (0.76) of our method, which proves the effectiveness of our method.

CRISPR/Cas9-Mediated in vivo Genetic Correction in a Mouse Model of Hemophilia A

Hemophilia A (HA), a common bleeding disorder caused by a deficiency of coagulation factor VIII (FVIII), has long been considered an attractive target for gene therapy studies. However, full-length F8 cDNA cannot be packaged efficiently by adeno-associated virus (AAV) vectors. As the second most prevalent mutation causing severe HA, F8 intron 1 inversion (Inv1) is caused by an intrachromosomal recombination, leaving the majority of F8 (exons 2–26) untranscribed. In theory, the truncated gene could be rescued by integrating a promoter and the coding sequence of exon 1. To test this strategy in vivo, we generated an HA mouse model by deleting the promoter region and exon 1 of F8. Donor DNA and CRISPR/SaCas9 were packaged into AAV vectors and injected into HA mice intravenously. After treatment, F8 expression was restored and activated partial thromboplastin time (aPTT) was shortened. We also compared two liver-specific promoters and two types of integrating donor vectors. When an active promoter was used, all of the treated mice survived the tail-clip challenge. This is the first report of an in vivo gene repair strategy with the potential to treat a recurrent mutation in HA patients.

An update of the current pharmacotherapeutic armamentarium for hemophilia A

INTRODUCTION

For several decades, we have seen unprecedented advances in novel therapy development for hemophilia A. These advances address the unmet need of replacement therapy, and they include the development of recombinant products with improved pharmacokinetics, subcutaneously administered products, and those with better efficacy and safety profiles in hemophilia A management.

AREAS COVERED

In this update of hemophilia A treatment, the author summarizes data from completed standard half-life FVIII products, extended half-life FVIII products and FVIII mimetic studies. All products have an acceptable safety profile. The standard half-life products, EHL-FVIII products and emicizumab are efficacious in the prevention and treatment of bleeds and for EHL-FVIII in the perisurgical setting.

EXPERT OPINION

Advances in pharmacotherapy for hemophilia A have been characterized by changing care goals from supportive care to eliminating infections, preventing inhibitors, and more recently achieving zero bleeds in many patients. While gene therapy has the potential for functional cure in hemophilia A, it has many limitations which need to be addressed. Therefore, pharmacotherapy is likely to remain the mainstay in the management of hemophilia A and promises to get better with currently available therapies. Evolving factor and non-factor replacement therapies may also improve current unmet needs in hemophilia A management.