Random forests highlight the combined effect of environmental heavy metals exposure and genetic damages for cardiovascular diseases

Heavy metals are a dangerous source of pollution due to their toxicity, permanence in the environment and chemical nature. It is well known that long-term exposure to heavy metals is related to several chronic degenerative diseases (cardiovascular diseases, neoplasms, neurodegenerative syndromes, etc.). In this work, we propose a machine learning framework to evaluate the severity of cardiovascular diseases (CVD) from Human scalp hair analysis (HSHA) tests and genetic analysis and identify a small group of these clinical features mostly associated with the CVD risk. Using a private dataset provided by the DD Clinic foundation in Caserta, Italy, we cross-validated the classification performance of a Random Forests model with 90 subjects affected by CVD. The proposed model reached an AUC of 0.78 ± 0.01 on a three class classification problem. The robustness of the predictions was assessed by comparison with different cross-validation schemes and two state-ofthe-art classifiers, such as Artificial Neural Network and General Linear Model. Thus, is the first work that studies, through a machine learning approach, the tight link between CVD severity, heavy metal concentrations and SNPs. Then, the selected features appear highly correlated with the CVD phenotype, and they could represent targets for future CVD therapies

Hormonal therapy with megestrol in inoperable hepatocellular carcinoma characterized by variant oestrogen receptors

Variant liver oestrogen receptor transcripts in hepatocellular carcinoma are associated with aggressive clinical course and unresponsiveness to tamoxifen. To evaluate the impact on survival and on tumour growth of megestrol (progestin drug acting at post-receptorial level) we enrolled 45 patients with HCC characterized by variant liver oestrogen receptors in a prospective, randomized study with megestrol vs. placebo. Presence of variant oestrogen receptors was determined by RT/PCR. 24 patients were randomized to no treatment and 21 to therapy with megestrol 160 mg day−1. Results were analysed by Kaplan-Meier and Cox methods. Survival of hepatocellular carcinoma characterized by variant oestrogen receptors was extremely poor (median survival 7 months); megestrol significantly improved survival (18 months) (P = 0.0090). Tumour growth at one year was significantly slowed down in megestrol-treated patients (P = 0.0212). Bilirubin levels, presence of portal thrombosis, HBV aetiology and treatment were identified at univariate analysis as factors significantly associated with survival; at multivariate analysis, only megestrol therapy (P = 0.0003), presence of HBV infection (P = 0.0009) and presence of portal vein thrombosis (P = 0.0051) were factors independently related with survival. (1) Megestrol slows down the aggressive tumour growth of patients with hepatocellular carcinoma characterized by variant estrogen receptors and (2) is also able to favourably influence the course of disease, more than doubling median survival. © 2001 Cancer Research Campaignhttp://www.bjcancer.co

Piecewise smooth systems near a co-dimension 2 discontinuity manifold: can one say what should happen?

We consider a piecewise smooth system in the neighborhood of a co-dimension 2 discontinuity manifold $\Sigma$. Within the class of Filippov solutions, if $\Sigma$ is attractive, one should expect solution trajectories to slide on $\Sigma$. It is well known, however, that the classical Filippov convexification methodology is ambiguous on $\Sigma$. The situation is further complicated by the possibility that, regardless of how sliding on $\Sigma$ is taking place, during sliding motion a trajectory encounters so-called generic first order exit points, where $\Sigma$ ceases to be attractive. In this work, we attempt to understand what behavior one should expect of a solution trajectory near $\Sigma$ when $\Sigma$ is attractive, what to expect when $\Sigma$ ceases to be attractive (at least, at generic exit points), and finally we also contrast and compare the behavior of some regularizations proposed in the literature. Through analysis and experiments we will confirm some known facts, and provide some important insight: (i) when $\Sigma$ is attractive, a solution trajectory indeed does remain near $\Sigma$, viz. sliding on $\Sigma$ is an appropriate idealization (of course, in general, one cannot predict which sliding vector field should be selected); (ii) when $\Sigma$ loses attractivity (at first order exit conditions), a typical solution trajectory leaves a neighborhood of $\Sigma$; (iii) there is no obvious way to regularize the system so that the regularized trajectory will remain near $\Sigma$ as long as $\Sigma$ is attractive, and so that it will be leaving (a neighborhood of) $\Sigma$ when $\Sigma$ looses attractivity. We reach the above conclusions by considering exclusively the given piecewise smooth system, without superimposing any assumption on what kind of dynamics near $\Sigma$ (or sliding motion on $\Sigma$) should have been taking place.Comment: 19 figure

Extended Adjuvant Endocrine Treatment in Luminal Breast Cancers in the Era of Genomic Tests

In patients with early-stage endocrine receptor-positive (ER+) breast cancer (BC), adjuvant endocrine therapy (ET) for 5 years is the standard of care. However, for some patients, the risk of recurrence remain high for up to 15 years after diagnosis and extended ET beyond 5 years may be a reasonable option. Nevertheless, this strategy significantly increases the occurrence of side effects. Here we summarize the available evidence from randomized clinical trials on the efficacy and safety profile of extended ET and discuss available clinical and genomic tools helpful to select eligible patients in daily clinical practice

Effect of Interleukin-1 Blockade With Anakinra on Leukocyte Count in Patients With ST-Segment Elevation Acute Myocardial Infarction

Leukocytosis is a common finding in patients with ST elevation myocardial infarction (STEMI) and portends a poor prognosis. Interleukin 1-β regulates leukopoiesis and pre-clinical studies suggest that anakinra (recombinant human interleukin-1 [IL-1] receptor antagonist) suppresses leukocytosis in myocardial infarction. However, the effect of IL-1 blockade with anakinra on leukocyte count in patients with STEMI is unknown. We reviewed the white blood cell (WBC) and differential count of 99 patients enrolled in a clinical trial of anakinra (n = 64) versus placebo (n = 35) for 14 days after STEMI. A complete blood cell count with differential count were obtained at admission, and after 72 h, 14 days and 3 months. After 72 h from treatment, anakinra compared to placebo led to a statistically significant greater percent reduction in total WBC count (− 35% [− 48 to − 24] vs. − 21% [− 34 to − 10], P = 0.008), absolute neutrophil count (− 48% [− 60 to − 22] vs. − 27% [− 46 to − 5], P = 0.004) and to an increase in absolute eosinophil count (+ 50% [0 to + 100] vs. 0% [− 50 to + 62], P = 0.022). These changes persisted while on treatment at 14 days and were no longer apparent at 3 months after treatment discontinuation. We found that in patients with STEMI IL-1 blockade with anakinra accelerates resolution of leukocytosis and neutrophilia. This modulation may represent one of the mechanisms by which IL-1 blockade improves clinical outcomes

Differential branching fraction and angular analysis of the decay B0→K∗0μ+μ−

The angular distribution and differential branching fraction of the decay B 0→ K ∗0 μ + μ − are studied using a data sample, collected by the LHCb experiment in pp collisions at s√=7 TeV, corresponding to an integrated luminosity of 1.0 fb−1. Several angular observables are measured in bins of the dimuon invariant mass squared, q 2. A first measurement of the zero-crossing point of the forward-backward asymmetry of the dimuon system is also presented. The zero-crossing point is measured to be q20=4.9±0.9GeV2/c4 , where the uncertainty is the sum of statistical and systematic uncertainties. The results are consistent with the Standard Model predictions