Dual targeting of isoleucyl-tRNA synthetase in Trypanosoma brucei is mediated through alternative trans-splicing.

Aminoacyl-tRNA synthetases catalyze the aminoacylation of tRNAs with their cognate amino acids. They are an essential part of each translation system and in eukaryotes are therefore found in both the cytosol and mitochondria. Thus, eukaryotes either have two distinct genes encoding the cytosolic and mitochondrial isoforms of each of these enzymes or a single gene encoding dually localized products. Trypanosomes require trans-splicing of a cap containing leader sequence onto the 5'-untranslated region of every mRNA. Recently we speculated that alternative trans-splicing could lead to the expression of proteins having amino-termini of different lengths that derive from the same gene. We now demonstrate that alternative trans-splicing, creating a long and a short spliced variant, is the mechanism for dual localization of trypanosomal isoleucyl-tRNA synthetase (IleRS). The protein product of the longer spliced variant possesses an amino-terminal presequence and is found exclusively in mitochondria. In contrast, the shorter spliced variant is translated to a cytosol-specific isoform lacking the presequence. Furthermore, we show that RNA stability is one mechanism determining the differential abundance of the two spliced isoforms.

Meta-analysis of risk factors for posttraumatic stress disorder in myocardial infarction.

BACKGROUND: The aim of this study was to identify the risk factors for posttraumatic stress disorder in patients with myocardial infarction. METHODS: Cohort, case-control, and cross-sectional studies on posttraumatic stress disorder (PTSD) in patients with myocardial infarction were searched from PubMed, Embase, Cochrane Library, Web of Science, China Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Technology Journal database. The Newcastle-Ottawa Quality Assessment Scale was used to score the quality of the included literature in the cohort and case-control studies, and the cross-sectional studies were scored using the American Agency for Health Care Quality and Research cross-sectional study quality evaluation criteria. The literature was screened independently by 2 researchers, and if there was no consensus, the inclusion was decided by a third party. The extraction content included first author, publication year, sample size, PTSD assessment tool, PTSD assessment time, PTSD incidence, influencing factors, and study type. Meta-analysis of data was performed using Stata17.0 software. RESULTS: Ten studies were included, including 2 cohort studies, 7 cross-sectional studies, and 1 case-control study, with a total sample size of 2371 patients, including 26 influencing factors. The results of meta-analysis showed that the prevalence of PTSD in patients with myocardial infarction was 21.2%. Statistically significant influencing factors were gender (odd ratio [OR] = 3.124), neuroticism score (OR = 2.069), and age (OR = 0.913). CONCLUSIONS: The prevalence of PTSD in patients with myocardial infarction in China is higher than that in other countries. Female and neurotic personality are risk factors for developing PTSD in patients with myocardial infarction, and old age is protective factor for developing PTSD in patients with myocardial infarction. Targeted measures should be taken to prevent and reduce the occurrence and development of PTSD in patients with myocardial infarction in the future.

Classification of neurofibromatosis-related dystrophic or nondystrophic scoliosis based on image features using Bilateral CNN.

PURPOSE: We developed a system that can automatically classify cases of scoliosis secondary to neurofibromatosis type 1 (NF1-S) using deep learning algorithms (DLAs) and improve the accuracy and effectiveness of classification, thereby assisting surgeons with the auxiliary diagnosis. METHODS: Comprehensive experiments in NF1 classification were performed based on a dataset consisting 211 NF1-S (131 dystrophic and 80 nondystrophic NF1-S) patients. Additionally, 100 congenital scoliosis (CS), 100 adolescent idiopathic scoliosis (AIS) patients, and 114 normal controls were used for experiments in primary classification. For identification of NF1-S with nondystrophic or dystrophic curves, we devised a novel network (i.e., Bilateral convolutional neural network [CNN]) utilizing a bilinear-like operation to discover the similar interest features between whole spine AP and lateral x-ray images. The performance of Bilateral CNN was compared with spine surgeons, conventional DLAs (i.e., VGG-16, ResNet-50, and Bilinear CNN [BCNN]), recently proposed DLAs (i.e., ShuffleNet, MobileNet, and EfficientNet), and Two-path BCNN which was the extension of BCNN using AP and lateral x-ray images as inputs. RESULTS: In NF1 classification, our proposed Bilateral CNN with 80.36% accuracy outperformed the other seven DLAs ranging from 61.90% to 76.19% with fivefold cross-validation. It also outperformed the spine surgeons (with an average accuracy of 77.5% for the senior surgeons and 65.0% for the junior surgeons). Our method is highly generalizable due to the proposed methodology and data augmentation. Furthermore, the heatmaps extracted by Bilateral CNN showed curve pattern and morphology of ribs and vertebrae contributing most to the classification results. In primary classification, our proposed method with an accuracy of 87.92% also outperformed all the other methods with varied accuracies between 52.58% and 83.35% with fivefold cross-validation. CONCLUSIONS: The proposed Bilateral CNN can automatically capture representative features for classifying NF1-S utilizing AP and lateral x-ray images, leading to a relatively good performance. Moreover, the proposed method can identify other spine deformities for auxiliary diagnosis.