Development and Multicenter Validation of a Novel Immune-Inflammation-Based Nomogram to Predict Survival in Western Resectable Gastric and Gastroesophageal Junction Adenocarcinoma (GEA): The NOMOGAST

Background. More than 50% of operable GEA relapse after curative-intent resection. We aimed at externally validating a nomogram to enable a more accurate estimate of individualized risk in resected GEA. Methods. Medical records of a training cohort (TC) and a validation cohort (VC) of patients undergoing radical surgery for c/uT2-T4 and/or node-positive GEA were retrieved, and potentially interesting variables were collected. Cox proportional hazards in univariate and multivariate regressions were used to assess the effects of the prognostic factors on OS. A graphical nomogram was constructed using R software’s package Regression Modeling Strategies (ver. 5.0-1). The performance of the prognostic model was evaluated and validated. Results. The TC and VC consisted of 185 and 151 patients. ECOG:PS > 0 (p < 0.001), angioinvasion (p < 0.001), log (Neutrophil/Lymphocyte ratio) (p < 0.001), and nodal status (p = 0.016) were independent prognostic values in the TC. They were used for the construction of a nomogram estimating 3- and 5-year OS. The discriminatory ability of the model was evaluated with the c-Harrell index. A 3-tier scoring system was developed through a linear predictor grouped by 25 and 75 percentiles, strengthening the model’s good discrimination (p < 0.001). A calibration plot demonstrated a concordance between the predicted and actual survival in the TC and VC. A decision curve analysis was plotted that depicted the nomogram’s clinical utility. Conclusions. We externally validated a prognostic nomogram to predict OS in a joint independent cohort of resectable GEA; the NOMOGAST could represent a valuable tool in assisting decision-making. This tool incorporates readily available and inexpensive patient and disease characteristics as well as immune-inflammatory determinants. It is accurate, generalizable, and clinically effectivex

The DREAMS experiment onboard the Schiaparelli module of the ExoMars 2016 mission: Design, performances and expected results

The first of the two missions foreseen in the ExoMars program was successfully launched on 14th March 2016. It included the Trace Gas Orbiter and the Schiaparelli Entry descent and landing Demonstrator Module. Schiaparelli hosted the DREAMS instrument suite that was the only scientific payload designed to operate after the touchdown. DREAMS is a meteorological station with the capability of measuring the electric properties of the Martian atmosphere. It was a completely autonomous instrument, relying on its internal battery for the power supply. Even with low resources (mass, energy), DREAMS would be able to perform novel measurements on Mars (atmospheric electric field) and further our understanding of the Martian environment, including the dust cycle. DREAMS sensors were designed to operate in a very dusty environment, because the experiment was designed to operate on Mars during the dust storm season (October 2016 in Meridiani Planum). Unfortunately, the Schiaparelli module failed part of the descent and the landing and crashed onto the surface of Mars. Nevertheless, several seconds before the crash, the module central computer switched the DREAMS instrument on, and sent back housekeeping data indicating that the DREAMS sensors were performing nominally. This article describes the instrument in terms of scientific goals, design, working principle and performances, as well as the results of calibration and field tests. The spare model is mature and available to fly in a future mission