Background: Currently there are no reported series determining the Covid-19 infected lung cancer patient´s characteristics and outcome that allow us to clarify strategies to protect our patients. In our study we determine whether exists differences in cumulative incidence and severity of Covid-19 infection between lung cancer patients visiting our Medical Oncology department and the reference population of our center (320,000 people), in the current epicenter of the pandemic in Europe (Madrid, Spain). We also describe clinical and demographic factors associated with poor prognosis and Covid-19 treatment outcomes. Patients and methods: We retrospectively reviewed 1878 medical records of all Covid-19 patients who were admitted at Hospital Universitario Infanta Leonor of Madrid between March 5, 2020 and April 7, 2020, in order to detect cumulative incidence of Covid-19 in lung cancer patients. We also described Covid-19 treatment outcome, mortality and associated risk factors using univariate and multivariate logistic regression analysis. Results: 17/1878 total diagnosis in our center had lung cancer (0.9 %) versus 1878/320,000 of the total reference population (p = 0.09). 9/17 lung cancer patients with Covid-19 diagnosis died (52.3 %) versus 192/ 1878 Covid-19 patients in our center (p < 0.0001). Dead lung cancer patients were elderly compared to survivors: 72 versus 64.5 years old (p = 0.12). Combined treatment with hydroxychloroquine and azithromycin improves the outcome of Covid-19 in lung cancer patients, detecting only 1/6 deaths between patients under this treatment versus others treatment, with statistical significance in the univariate and multivariate logistic regression (OR 0.04, p = 0.018). Conclusions: Lung cancer patients have a higher mortality rate than general population. Combined hydroxychloroquine and azithromycin treatment seems like a good treatment option. It is important to try to minimize visits to hospitals (without removing their active treatments) in order to decrease nosocomial transmission.

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Salinity is one of the most important stress factors in crop production, particularly in arid regions. This research focuses on the effect of salinity on the growth of lettuce plants; three solutions with different levels of salinity were considered and compared (S1 = 50, S2 = 100 and S3 = 150 mM NaCl) with a control solution (Ct = 0 mM NaCl). The osmotic potential and water content of the leaves were measured, and hyperspectral images of the surfaces of 40 leaves (10 leaves per treatment) were taken after two weeks of growth. The mean spectra of the leaves (n = 32,000) were pre-processed by means of a Savitzky-Golay algorithm and standard normal variate normalization. Principal component analysis was then performed on a calibration set of 28 mean spectra, yielding an initial model for salinity effect detection. A second model was subsequently proposed based on an index computing an approximation to the second derivative at the red edge region. Both models were applied to all the hyperspectral images to obtain the corresponding artificial images, distinguishing between the 28 that were used to extract the calibration mean spectra and the rest that constituted an external validation. Those virtual images were studied using analysis of variance in order to compare their ability for detecting salinity effects on the leaves. Both models showed significant differences between each salinity level, and the hyperspectral images allowed observations of the distribution of the salinity effects on the leaf surfaces, which were more intense in the areas distant from the veins. However, the index-based model is simpler and easier to apply because it is based solely on the reflectance at three different wavelengths, thus allowing for the implementation of less expensive multispectral devices.

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