Evolutionarily conserved human targets of adenosine to inosine RNA editing

A-to-I RNA editing by ADARs is a post-transcriptional mechanism for expanding the proteomic repertoire. Genetic recoding by editing was so far observed for only a few mammalian RNAs that are predominantly expressed in nervous tissues. However, as these editing targets fail to explain the broad and severe phenotypes of ADAR1 knockout mice, additional targets for editing by ADARs were always expected. Using comparative genomics and expressed sequence analysis, we identified and experimentally verified four additional candidate human substrates for ADAR-mediated editing: FLNA, BLCAP, CYFIP2 and IGFBP7. Additionally, editing of three of these substrates was verified in the mouse while two of them were validated in chicken. Interestingly, none of these substrates encodes a receptor protein but two of them are strongly expressed in the CNS and seem important for proper nervous system function. The editing pattern observed suggests that some of the affected proteins might have altered physiological properties leaving the possibility that they can be related to the phenotypes of ADAR1 knockout mice

Performance analysis of a machine learning flagging system used to identify a group of individuals at a high risk for colorectal cancer.

Individuals with colorectal cancer (CRC) have a tendency to intestinal bleeding which may result in mild to severe iron deficiency anemia, but for many colon cancer patients hematological abnormalities are subtle. The fecal occult blood test (FOBT) is used as a pre-screening test whereby those with a positive FOBT are referred to colonscopy. We sought to determine if information contained in the complete blood count (CBC) report coud be processed automatically and used to predict the presence of occult colorectal cancer (CRC) in the setting of a large health services plan. Using the health records of the Maccabi Health Services (MHS) we reviewed CBC reports for 112,584 study subjects of whom 133 were diagnosed with CRC in 2008 and analysed these with the MeScore tool. The odds ratio for being diagnosed with CRC in 2008 was calculated with regards to the MeScore, using cutoff levels of 97% and 99% percentiles. For individuals in the highest one percentile, the odds ratio for CRC was 21.8 (95% CI 13.8 to 34.2). For the majority of the individuals with cancer, CRC was not suspected at the time of the blood draw. Frequent use of anticoagulants, the presence of other gastrointestinal pathologies and non-GI malignancies were assocaitged with false positive MeScores. The MeScore can help identify individuals in the population who would benefit most from CRC screening, including those with no clinical signs or symptoms of CRC

Prediction of Influenza Complications: Development and Validation of a Machine Learning Prediction Model to Improve and Expand the Identification of Vaccine-Hesitant Patients at Risk of Severe Influenza Complications

Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783–0.789], compared with 0.694 [0.690–0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823–0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system

Schema of the study design.

<p>Schema of the study design.</p

Cancers diagnosed in cohort from 2008 to 2012 according to test score as determined in 2007.

<p>Cancers diagnosed in cohort from 2008 to 2012 according to test score as determined in 2007.</p