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Alternative splicing (AS) is a key process in which precursor RNAs produce different mature RNAs, and the disorder of AS is a key factor in promoting cancer development. Compared with coding RNA, studies on the functions of long non-coding RNAs (lncRNAs) are far from enough. In fact, lncRNA is an important participant and regulator in the process of AS. On the one hand, lncRNAs regulate cancer progression as AS products of precursor messenger RNA (mRNA), but on the other hand, precursor lncRNA generates cancer-related abnormal splicing variants through AS. In addition, lncRNAs directly or indirectly regulate the AS events of downstream target genes, thus affecting the occurrence and development of cancer. Here, we reviewed how lncRNAs regulate AS and influence oncogenesis in different ways.

Objective We focus on providing the first comprehensive national dataset on the incidence, injury aetiology and mortality of TSCI in China. Methods A multi-stage stratified cluster sampling method was used. We included TSCI cases from all hospitals in three regions, nine provinces and 27 cities in China via search of electronic medical records and retrospectively analysed the characteristics of TSCI in China from 2009 to 2018. We estimated the incidence of TSCI in the total population and subgroups. Results There were 5954 actual cases in 2009, corresponding to a total estimated TSCI incidence of 45.1 cases per million population (95% CI, 44.0–46.3). There were 10,074 actual cases in 2018, corresponding to a total estimated TSCI incidence of 66.5 cases per million population (95% CI, 65.2–67.8) (P P Conclusions The TSCI incidence increased significantly from 2009 to 2018. The incidence in the elderly populations was consistently high and continues to increase over time. The mortality of TSCI patients in hospitals is relatively low and continues to decrease each year, but elderly individuals remain at a high risk of hospital death.

Given samples from a particular normal class, image novelty detection is aimed at determining whether a query sample is from the normal class. Images from this particular class are termed inliers or normal images, whereas images not belonging to the class are termed novelties. Most novelty detection approaches use deep neural networks. However, few of them are end-to-end, and state-of-the-art neural networks tend to be overconfident in their predictions. This may result in incorrect predictions and may negatively affect novelty detection. In this paper, we propose a novel model termed adversarially learned one-class novelty detection with confidence estimation for image novelty detection. The proposed model consists of a representation and a detection module, which are adversarially trained to collaboratively learn the inlier distribution. Moreover, the model uses confidence estimation so that the detection module can more effective. The proposed model is end-to-end and does not require additional calculations such as novelty scores after training. We conduct comprehensive experiments on four publicly available datasets that are commonly used for novelty detection, and the model is compared with state-of-the-art methods to demonstrate its performance.

In eukaryotes, alternative splicing refers to a process via which a single precursor RNA (pre-RNA) is transcribed into different mature RNAs. Thus, alternative splicing enables the translation of a limited number of coding genes into a large number of proteins with different functions. Although, alternative splicing is common in normal cells, it also plays an important role in cancer development. Alteration in splicing mechanisms and even the participation of non-coding RNAs may cause changes in the splicing patterns of cancer-related genes. This article reviews the latest research on alternative splicing in cancer, with a view to presenting new strategies and guiding future studies related to pathological mechanisms associated with cancer.

Electronic Medical Records (EMRs), which record visits of patients to the hospital, are the main resources for medical data analysis. However, plenty of missing values in EMRs limit the model capability for various researches in healthcare. Recently, many imputation methods have been proposed to address this challenging problem, but they fail to take medical bias into account. Medical bias is a ubiquitous phenomenon that the missingness of medical data is missing not at random because doctors prone to measure features related to the disease of patients. It reflects the physical conditions of patients, which helps impute missing data with accurate and practical values. In this paper, we propose a novel joint recurrent neural network (RNN) model called ImputeRNN, which considers medical bias for EMR imputation. We model the medical bias by an additional RNN based on a mask (missing or not) matrix, whose hidden vectors are incorporated into the model as contexts by a fusion layer. Extensive experiments on two real-world EMR datasets demonstrate that ImputeRNN outperforms state-of-the-art methods on imputation and downstream prediction tasks.

Alternative splicing (AS) is a key process in which precursor RNAs produce different mature RNAs, and the disorder of AS is a key factor in promoting cancer development. Compared with coding RNA, studies on the functions of long non-coding RNAs (lncRNAs) are far from enough. In fact, lncRNA is an important participant and regulator in the process of AS. On the one hand, lncRNAs regulate cancer progression as AS products of precursor messenger RNA (mRNA), but on the other hand, precursor lncRNA generates cancer-related abnormal splicing variants through AS. In addition, lncRNAs directly or indirectly regulate the AS events of downstream target genes, thus affecting the occurrence and development of cancer. Here, we reviewed how lncRNAs regulate AS and influence oncogenesis in different ways.

Single-cell RNA sequencing (scRNA-seq), a technology that analyzes transcriptomes of complex tissues at single-cell levels, can identify differential gene expression and epigenetic factors caused by mutations in unicellular genomes, as well as new cell-specific markers and cell types. scRNA-seq plays an important role in various aspects of tumor research. It reveals the heterogeneity of tumor cells and monitors the progress of tumor development, thereby preventing further cellular deterioration. Furthermore, the transcriptome analysis of immune cells in tumor tissue can be used to classify immune cells, their immune escape mechanisms and drug resistance mechanisms, and to develop effective clinical targeted therapies combined with immunotherapy. Moreover, this method enables the study of intercellular communication and the interaction of tumor cells and non-malignant cells to reveal their role in carcinogenesis. scRNA-seq provides new technical means for further development of tumor research and is expected to make significant breakthroughs in this field. This review focuses on the principles of scRNA-seq, with an emphasis on the application of scRNA-seq in tumor heterogeneity, pathogenesis, and treatment.

Background: China accounts for nearly one-fifth of the world's population. However, the national epidemiology of traumatic spinal cord injury (TSCI) is still unknown. Therefore, this study aimed to clarify the incidence and epidemiological characteristics of TSCI in China. Methods: We retrospectively reviewed the medical records of patients with spinal cord injury in hospitals and the ability to treat TSCI. TSCI was defined as the clinical manifestation of paraplegia or quadriplegia after trauma. We collected general data, causes of injury, level of injury, severity of injury, multiple injuries, complications and other data through the designed medical record questionnaire, and then performed an analysis according to geographical region, age and gender, and calculated the overall incidence of the whole country and each region. Findings: The study included medical records of 4,134 patients with a male to female ratio of 2·99:1. In 2018, the overall TSCI rate in China was 50·484 cases (95% CI: 50·122, 50·846) per one million persons, and the expected number of cases was 74,890. The highest incidence rate in the East was 53·791 (95% CI: 53·217, 54·365) cases per one million persons, and the expected number of cases was 33,724. The leading cause of injury was high falls (29·58%), the most common level of injury was cervical injury (64·49%). Regarding the severity of injury, the highest percentages of incomplete quadriplegia (55·20%). Interpretation: The estimated overall incidence of TSCI in China is similar to that of the United States. The leading cause of injury in different regions of China was determined. Preventive work should be carried out in a targeted manner. With the ageing population, there are more TSCIs in the elderly population due to low falls. Future public health policies need more strategies to prevent low falls and to widely publicize them. Funding Statement: This study was supported by the Key Program from the National Natural Science Funds (81830077); the General Program from the National Natural Science Funds (81772357);the National Natural Science Foundation of China; the Key Project from the Science and Technology Commission of Shaanxi (2017ZDXM-SF-054); the Department of Health, Shaanxi Province (2018E002); the Major Research from the Xi'an Science and Technology Department (2017123SF/YX017). Declaration of Interests: The authors declare that they have no conflicts of interest. Ethics Approval Statement: The Ethics Committee of Xi'an Honghui Hospital and the ethics committee of each participating centre approved the current study.