Machine learning versus classical electrocardiographic criteria for echocardiographic left ventricular hypertrophy in a pre-participation cohort

Background: Classical electrocardiographic (ECG) criteria for left ventricular hypertrophy (LVH) are well studied in older populations and patients with hypertension. Their utility in young pre-participation cohorts is unclear.Aims: We aimed to develop machine learning models for detection of echocardiogram-diagnosed LVH from ECG, and compare these models with classical criteria.Methods: Between November 2009 and December 2014, pre-participation screening ECG and subsequent echocardiographic data was collected from 17 310 males aged 16 to 23, who reported for medical screening prior to military conscription. A final diagnosis of LVH was made during echocardiography, defined by a left ventricular mass index >115 g/m2. The continuous and threshold forms of classical ECG criteria (Sokolow–Lyon, Romhilt–Estes, Modified Cornell, Cornell Product, and Cornell) were compared against machine learning models (Logistic Regression, GLMNet, Random Forests, Gradient Boosting Machines) using receiver-operating characteristics curve analysis. We also compared the important variables identified by machine learning models with the input variables of classical criteria.Results: Prevalence of echocardiographic LVH in this population was 0.82% (143/17310). Classical ECG criteria had poor performance in predicting LVH. Machine learning methods achieved superior performance: Logistic Regression (area under the curve [AUC], 0.811; 95% confidence interval [CI], 0.738–0.884), GLMNet (AUC, 0.873; 95% CI, 0.817–0.929), Random Forest (AUC, 0.824; 95% CI, 0.749–0.898), Gradient Boosting Machines (AUC, 0.800; 95% CI, 0.738–0.862).Conclusions: Machine learning methods are superior to classical ECG criteria in diagnosing echocardiographic LVH in the context of pre-participation screening

Clinical and pathological characteristics of Chinese patients with BRCA related breast cancer

Breast cancers related to BRCA mutations are associated with particular biological features. Here we report the clinical and pathological characteristics of breast cancer in Chinese women with and without BRCA mutations and of carriers of BRCA1 mutations compared to BRCA2 mutations. Two hundred and 26 high-risk Hong Kong Chinese women were tested for BRCA mutations, medical information was obtained from medical records, and risk and demographic information was obtained from personal interviews. In this cohort, 28 (12.4%) women were BRCA mutation carriers and among these carriers, 39.3% were BRCA1 and 60.7% were BRCA2 mutations. Mutation carriers were more likely to have a familial history of breast and ovarian cancer, high-grade cancers, and triple negative (TN) cancers. Prevalence of TN was 48.3% in BRCA carriers and 25.6% in non-carriers and was 67.7% in BRCA1 and 35.3% in BRCA2 carriers. Estrogen receptor (ER) negative cancer was significantly associated with BRCA1 mutations, especially in those under 40 years of age. BRCA-related breast cancer in this Chinese population is associated with family history and adverse pathological/prognostic features, with BRCA2 mutations being more prevalent but BRCA1 carriers having more aggressive and TN cancers. Compared to Caucasian populations, prevalence of BRCA2 mutations and TN cancer in BRCA2 mutation carriers in Chinese population are elevated

Cartography of Methicillin-Resistant S. aureus Transcripts: Detection, Orientation and Temporal Expression during Growth Phase and Stress Conditions

BACKGROUND: Staphylococcus aureus is a versatile bacterial opportunist responsible for a wide spectrum of infections. The severity of these infections is highly variable and depends on multiple parameters including the genome content of the bacterium as well as the condition of the infected host. Clinically and epidemiologically, S. aureus shows a particular capacity to survive and adapt to drastic environmental changes including the presence of numerous antimicrobial agents. Mechanisms triggering this adaptation remain largely unknown despite important research efforts. Most studies evaluating gene content have so far neglected to analyze the so-called intergenic regions as well as potential antisense RNA molecules. PRINCIPAL FINDINGS: Using high-throughput sequencing technology, we performed an inventory of the whole transcriptome of S. aureus strain N315. In addition to the annotated transcription units, we identified more than 195 small transcribed regions, in the chromosome and the plasmid of S. aureus strain N315. The coding strand of each transcript was identified and structural analysis enabled classification of all discovered transcripts. RNA purified at four time-points during the growth phase of the bacterium allowed us to define the temporal expression of such transcripts. A selection of 26 transcripts of interest dispersed along the intergenic regions was assessed for expression changes in the presence of various stress conditions including pH, temperature, oxidative shocks and growth in a stringent medium. Most of these transcripts showed expression patterns specific for the defined stress conditions that we tested. CONCLUSIONS: These RNA molecules potentially represent important effectors of S. aureus adaptation and more generally could support some of the epidemiological characteristics of the bacterium

Nasal-type extranodal natural killer/T-cell lymphomas: a clinicopathologic and genotypic study of 42 cases in Singapore

10.1038/modpathol.3800157MODERN PATHOLOGY1791097-1107MODPEUnited State