Cancer is one of the most challenging health problems facing humankind. Conventional treatments such as surgery, chemotherapy and radiotherapy can usually have a positive patient outcome in the early-stage tumours without metastasis. However, late-stage cancers can have high mortality; therefore, urging scientists and clinicians to investigate novel treatment approaches. Novel cancer treatment methods provide specific treatment for patients with different cancer types and substantially elevate the survival rate, prognosis, and quality of life, commonly with a much lower immune-related side effect. Monoclonal antibodies are one of the fastest growing novel treatments with high target specificity and high therapeutic selectivity, enabling the treatment of advanced cancers with a low autoimmune inflammatory reaction. Chimeric antigen receptor (CAR)-T is another novel treatment which edits target T lymphocytes for high target specificity and high therapeutic selectivity. However, challenges exist, such as cytokine release syndrome resulting in immunotoxicity problems, including neurotoxicity and brain oedema. Cancer is a genetic disease, and gene-editing technologies, such as CRISPR/Cas9, enable gene modification to cure the disease completely in gene therapy. Although limitations, such as off-target effect and DNA-damaged toxicity can cause immunotoxicity problems, multiple derivatives of the CRISPR system can provide long-term cancer treatment. This review discusses conventional and novel cancer treatment methods and explains challenges which need to be addressed before such treatments are widely available in cancer treatment.
Autism Spectrum Disorder (ASD) is a neurological developmental disorder characterized by repetitive behaviors accompanied by communication and social interaction impairment.In this paper, the effect of prenatal stress factors on ASD was investigated by collecting single-cell RNA sequencing and calculation. The results suggest that stress responses, such as obesity-related stress and oxidative stress, may be related to neuronal death and contribute to the development of ASD. In addition, studies have shown that stress response genes decline over time under low temperature conditions.
Pseudomonas aeruginosa is an abundant bacterium in nature. Several P. aeruginosa strains are capable of producing a wide range of secondary metabolites with broad-spectrum high antimicrobial activity. This study aims to identify antimicrobial activity genes of P. aeruginosa SWUC02 by whole-genome sequencing analysis. Whole genome sequencing of P. aeruginosa SWUC02 was proceeded with the BGISEQ-500 sequencing system. The genome of P. aeruginosa SWUC02 is a single circular chromosome with 6,404,055 bp. 6,214 coding sequences were identified by RAST. We compared P. aeruginosa SWUC02 against two reference strains: P. aeruginosa LV and P. aeruginosa PAO1, according to their antimicrobial activity. Our result shows that several antimicrobial gene clusters including bacteriocins, secondary metabolites, and siderophores are shared among P. aeruginosa SWUC02 and the two reference strains. The notable difference arises in secondary metabolite gene clusters where we identified pyocyanine and thanamycin in P. aeruginosa SWUC02 genome, which are absent from the reference strains. P. aeruginosa SWUC02 shows the potential for being a prospective antagonist and warrants a follow up study for antimicrobial compound production.
Coarctation of aorta (CoA) is a critical congenital malformation which can lead to serious complications such as hypertension, heart failure and even shock in severe case. The effective diagnosis and surgical management of CoA require accurate aortic inner diameter measurement mainly based on cardiovascular computed tomography (CT). Due to the traditional manual aortic inner diameter measurement is labor-intensive and susceptible to observer’s expertise, the deep learning (DL) enabled aortic segmentation based aortic inner diameter measurement methods have been investigated. However, most existing DL based aortic segmentation methods ignore the edge information and spatial consistency, which lead to poor segmentation performances. To solve this problem, we propose an edge enhancement and contextual fusion network, called ECN, which can enhance edge information and utilize the contextual relationships of CT slices so as to improve aortic segmentation. Simulation results show that the proposed algorithm outperforms the compared DL algorithms in dice score (0.9370) and 95% Hausdorff distance (1.3383mm) in our private patient-specific CoA dataset. Moreover, the proposed ECN based aortic inner diameter measurement achieves low bias and high correlation with the results measured by doctors.
Reconstructing gene regulatory networks based on time-series gene expression data is a huge challenge in the field of systems biology. However, the accuracy of traditional methods can be further improved. In practical situations, the gene regulatory network topology and the dynamic rules of genes are not observable. Therefore, reconstructing the underlying network structure and dynamics from observed time-series gene expression data is an important task. In this work, we introduce a new framework, Gumbel Graph Network (GGN), a model-free, data-driven deep learning framework for accomplishing the reconstruction of gene regulatory networks and the reconstruction of gene dynamics. Our model consists of two co-trained parts: a network generator, which generates a discrete network using Gumbel Softmax technique; and a dynamic learner, which uses the generated network and single-step trajectory values to predict the state at the next moment. According to the experimental results on multiple simulated and real datasets, GGN has better performance and stability compared with other state-of-the-art algorithms.
