RNAI MEDIATED GENE SILENCING OF EIF3A: A POSSIBLE SOLUTION TO CONTROL BREAST CANCER

Objective: The eukaryotic translational initiation factor 3A (eIF3A) is reported to be over expressed in most breast cancer cells. In the present study, our aim is to suppress the over expression of eIF3A in human breast cancer MCF-7 cell line using gene silencing technique (RNA interference (RNAi)).Methods: The artificial microRNA (amiRNA) targeting eIF3A gene was constructed by incorporating short interference RNA (siRNA) sequences against eIF3A gene into endogenous microRNA30 (miR-30) and cloned into pcDNA3.1 vector. The amiRNA containing plasmid was then transfected into MCF-7 cell line and the expression of eIF3A was examined by RT-PCR. The cytotoxicity of plasmid with amiRNA targeting eIF3A on MCF–7 cells was evaluated by MTT assay.Results: The amiRNA construct significantly inhibited eIF3A gene expression and reduce the cell viability of MCF-7 cell line.Conclusion: The usage of modified endogenous amiRNA in vector based expression system with significant gene silencing efficiency suggests that RNAi based gene silencing method can be considered as one of the effective means to control cancer.Â

The PEARL score predicts 90-day readmission or death after hospitalisation for acute exacerbation of COPD.

BACKGROUND: One in three patients hospitalised due to acute exacerbation of COPD (AECOPD) is readmitted within 90 days. No tool has been developed specifically in this population to predict readmission or death. Clinicians are unable to identify patients at particular risk, yet resources to prevent readmission are allocated based on clinical judgement. METHODS: In participating hospitals, consecutive admissions of patients with AECOPD were identified by screening wards and reviewing coding records. A tool to predict 90-day readmission or death without readmission was developed in two hospitals (the derivation cohort) and validated in: (a) the same hospitals at a later timeframe (internal validation cohort) and (b) four further UK hospitals (external validation cohort). Performance was compared with ADO, BODEX, CODEX, DOSE and LACE scores. RESULTS: Of 2417 patients, 936 were readmitted or died within 90 days of discharge. The five independent variables in the final model were: Previous admissions, eMRCD score, Age, Right-sided heart failure and Left-sided heart failure (PEARL). The PEARL score was consistently discriminative and accurate with a c-statistic of 0.73, 0.68 and 0.70 in the derivation, internal validation and external validation cohorts. Higher PEARL scores were associated with a shorter time to readmission. CONCLUSIONS: The PEARL score is a simple tool that can effectively stratify patients' risk of 90-day readmission or death, which could help guide readmission avoidance strategies within the clinical and research setting. It is superior to other scores that have been used in this population. TRIAL REGISTRATION NUMBER: UKCRN ID 14214

A framework for feature extraction from hospital medical data with applications in risk prediction

Background: Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Results: Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). Conclusions: The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks

Replication of Epstein-Barr Virus Primary Infection in Human Tonsil Tissue Explants

Epstein-Barr virus (EBV) may cause a variety of virus-associated diseases, but no antiviral agents have yet been developed against this virus. Animal models are thus indispensable for the pathological analysis of EBV-related infections and the elucidation of therapeutic methods. To establish a model system for the study of EBV infection, we tested the ability of B95–8 virus and recombinant EBV expressing enhanced green fluorescent protein (EGFP) to replicate in human lymphoid tissue. Human tonsil tissues that had been surgically removed during routine tonsillectomy were sectioned into small blocks and placed on top of collagen sponge gels in culture medium at the air-interface, then a cell-free viral suspension was directly applied to the top of each tissue block. Increasing levels of EBV DNA in culture medium were observed after 12–15 days through 24 days post-infection in tissue models infected with B95–8 and EGFP-EBV. Expression levels of eight EBV-associated genes in cells collected from culture medium were increased during culture. EBV-encoded small RNA-positive cells were detected in the interfollicular areas in paraffin-embedded sections. Flow cytometric analyses revealed that most EGFP+ cells were CD3− CD56− CD19+ HLA-DR+, and represented both naïve (immunoglobulin D+) and memory (CD27+) B cells. Moreover, EBV replication in this model was suppressed by acyclovir treatment in a dose-dependent manner. These data suggest that this model has potential for use in the pathological analysis of local tissues at the time of primary infection, as well as for screening novel antiviral agents