Measurement of the cross-section for Z → e⁺e^- production in pp collisions at &#8730;<span style="text-decoration:overline">s</span>=7 TeV

A measurement of the cross-section for pp → Z → e+e− is presented using data at s√=7 TeV corresponding to an integrated luminosity of 0.94 fb−1. The process is measured within the kinematic acceptance p T &#62; 20 GeV/c and 2 &#60; η &#60; 4.5 for the daughter electrons and dielectron invariant mass in the range 60–120 GeV/c 2. The cross-section is determined to be σ(pp→Z→e+e−)=76.0±0.8±2.0±2.6pb where the first uncertainty is statistical, the second is systematic and the third is the uncertainty in the luminosity. The measurement is performed as a function of Z rapidity and as a function of an angular variable which is closely related to the Z transverse momentum. The results are compared with previous LHCb measurements and with theoretical predictions from QCD

Tumor burden as possible biomarker of outcome in advanced NSCLC patients treated with immunotherapy: a single center, retrospective, real-world analysis

none11Aim: The role of tumor burden (TB) for patients with non-small cell lung cancer (NSCLC) receiving immunotherapy is still unknown. The aim of this analysis was to analyze the prognostic value of TB in a real-world sample of advanced NSCLC patients. Methods: Sixty-five consecutive patients with advanced NSCLC treated with immunotherapy as first or second line therapy were retrospectively analyzed between August 2015 and February 2018. TB was recorded at baseline considering sites and number of metastases, thoracic vs. extrathoracic disease, measurable disease (MD) vs. not-MD (NMD) and evaluating dimensional aspects as maximum lesion diameter (cut-off = 6.3 cm), sum of the 5 major lesions diameters (cut-off = 14.3 cm), and number of sites of metastases (cut-off > 4). All cut-offs were calculated by receiver operating characteristic curves. Median overall survival (OS) was estimated using Kaplan-Meier method. A Cox regression model was carried out for univariate and multivariate analyses. Results: Median age was 70 years and most patients (86.2%) had a good performance status (PS-Eastern Cooperative Oncology Group 4 were negative prognostic factors (P < 0.0001). Conclusions: This study underlines the negative prognostic impact of specific metastatic sites, presence of NMD and extrathoracic disease in advanced NSCLC patients treated with immunotherapy. However, TB does not appear to affect the outcome of these patients.restrictedEdoardo Lenci; Giulia Marcantognini; Valeria Cognigni; Alessio Lupi; Silvia Rinaldi; Luca Cantini; Ilaria Fiordoliva; Anna Lisa Carloni; Lina Zuccatosta; Stefano Gasparini; Rossana BerardiLenci, Edoardo; Marcantognini, Giulia; Cognigni, Valeria; Lupi, Alessio; Rinaldi, Silvia; Cantini, Luca; Fiordoliva, Ilaria; Lisa Carloni, Anna; Zuccatosta, Lina; Gasparini, Stefano; Berardi, Rossan

Modeling anticancer drug-DNA interactions via mixed QM/MM molecular dynamics simulations

The development of anticancer drugs started over four decades ago, with the serendipitous discovery of the antitumor activity of cisplatin and its successful use in the treatment of various cancer types. Despite the efforts made in unraveling the mechanism of the action of cisplatin, as well as in the rational design of new anticancer compounds, in many cases detailed structural and mechanistic information is still lacking. Many of these drugs exert their anticancer activity by covalently binding to DNA inducing a distortion or simply impeding replication, thus triggering a cellular response, which eventually leads to cell death. A detailed understanding of the structural and electronic properties of drug-DNA complexes and their mechanism of binding is the key step in elucidating the principles of their anticancer activity. At the theoretical level, the description of covalent drug-DNA complexes requires the use of state-of-the-art computer simulation techniques such as hybrid quantum/classical molecular dynamics simulations. In this review we provide a general overview on: drugs which covalently bind to DNA duplexes, the basic concepts of quantum mechanics/molecular mechanics (QM/MM), molecular dynamics methods and a list of selected applications of these simulations to the study of drug-DNA adducts. Finally, the potential and the limitations of this approach to the study of such systems are critically evaluated

MEDTEC Students against Coronavirus: Investigating the Role of Hemostatic Genes in the Predisposition to COVID-19 Severity

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic. Besides virus intrinsic characteristics, the host genetic makeup is predicted to account for the extreme clinical heterogeneity of the disease, which is characterized, among other manifestations, by a derangement of hemostasis associated with thromboembolic events. To date, large-scale studies confirmed that genetic predisposition plays a role in COVID-19 severity, pinpointing several susceptibility genes, often characterized by immunologic functions. With these premises, we performed an association study of common variants in 32 hemostatic genes with COVID-19 severity. We investigated 49,845 single-nucleotide polymorphism in a cohort of 332 Italian severe COVID-19 patients and 1668 controls from the general population. The study was conducted engaging a class of students attending the second year of the MEDTEC school (a six-year program, held in collaboration between Humanitas University and the Politecnico of Milan, allowing students to gain an MD in Medicine and a Bachelor’s Degree in Biomedical Engineering). Thanks to their willingness to participate in the fight against the pandemic, we evidenced several suggestive hits (p &lt; 0.001), involving the PROC, MTHFR, MTR, ADAMTS13, and THBS2 genes (top signal in PROC: chr2:127192625:G:A, OR = 2.23, 95%CI = 1.50–3.34, p = 8.77 × 10−5). The top signals in PROC, MTHFR, MTR, ADAMTS13 were instrumental for the construction of a polygenic risk score, whose distribution was significantly different between cases and controls (p = 1.62 × 10−8 for difference in median levels). Finally, a meta-analysis performed using data from the Regeneron database confirmed the contribution of the MTHFR variant chr1:11753033:G:A to the predisposition to severe COVID-19 (pooled OR = 1.21, 95%CI = 1.09–1.33, p = 4.34 × 10−14 in the weighted analysis)

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present