China Three Gorges University

About: China Three Gorges University is a education organization based out in Yichang, China. It is known for research contribution in the topics: Catalysis & Landslide. The organization has 11161 authors who have published 8011 publications receiving 82224 citations. The organization is also known as: Sanxia Daxue.

Autologous bone marrow mesenchymal stem cell therapy in the subacute stage of traumatic brain injury by lumbar puncture.

Abstract: Objectives To explore the clinical therapeutic effects and safety of autologous bone marrow mesenchymal stem cell therapy for traumatic brain injury by lumbar puncture. Materials and methods A total of 97 patients (24 with persistent vegetative state and 73 with disturbance motor activity) who developed a complex cerebral lesion after traumatic brain injury received autologous bone marrow mesenchymal stem cell therapy voluntarily. The stem cells were isolated from the bone marrow of the patients and transplanted into the subarachnoid space by lumbar puncture. Results Fourteen days after cell therapy, no serious complications or adverse events were reported. To a certain extent, 38 of 97 patients (39.2%) improved in the function of brain after transplant (P = .007). Eleven of 24 patients (45.8%) with persistent vegetative state showed posttherapeutic improvements in consciousness (P = .024). Twenty-seven of 73 patients (37.0%) with a motor disorder began to show improvements in motor functions (P = .025). The age of patients and the time elapsed between injury and therapy had effects on the outcomes of the cellular therapy (P .05). Conclusions This study suggests that the bone marrow stem cell therapy is safe and effective on patients with traumatic brain injury complications, such as persistent vegetative state and motor disorder, through lumbar puncture. Young patients improve more easily than older ones. The earlier the cellular therapy begins in the subacute stage of traumatic brain injury, the better the results.

QC-Chain: fast and holistic quality control method for next-generation sequencing data.

Abstract: Next-generation sequencing (NGS) technologies have been widely used in life sciences. However, several kinds of sequencing artifacts, including low-quality reads and contaminating reads, were found to be quite common in raw sequencing data, which compromise downstream analysis. Therefore, quality control (QC) is essential for raw NGS data. However, although a few NGS data quality control tools are publicly available, there are two limitations: First, the processing speed could not cope with the rapid increase of large data volume. Second, with respect to removing the contaminating reads, none of them could identify contaminating sources de novo, and they rely heavily on prior information of the contaminating species, which is usually not available in advance. Here we report QC-Chain, a fast, accurate and holistic NGS data quality-control method. The tool synergeticly comprised of user-friendly tools for (1) quality assessment and trimming of raw reads using Parallel-QC, a fast read processing tool; (2) identification, quantification and filtration of unknown contamination to get high-quality clean reads. It was optimized based on parallel computation, so the processing speed is significantly higher than other QC methods. Experiments on simulated and real NGS data have shown that reads with low sequencing quality could be identified and filtered. Possible contaminating sources could be identified and quantified de novo, accurately and quickly. Comparison between raw reads and processed reads also showed that subsequent analyses (genome assembly, gene prediction, gene annotation, etc.) results based on processed reads improved significantly in completeness and accuracy. As regard to processing speed, QC-Chain achieves 7-8 time speed-up based on parallel computation as compared to traditional methods. Therefore, QC-Chain is a fast and useful quality control tool for read quality process and de novo contamination filtration of NGS reads, which could significantly facilitate downstream analysis. QC-Chain is publicly available at: http://www.computationalbioenergy.org/qc-chain.html.