The Impact of Routine Molecular Screening for SARS-CoV-2 in Patients Receiving Anticancer Therapy: An Interim Analysis of the Observational COICA Study

Introduction: Cancer aggravates COVID-19 prognosis. Nosocomial transmission of SARS-CoV-2 is particularly frequent in cancer patients, who need to attend hospitals regularly. Since March 2020, all cancer patients having access to the Oncology Unit at the "Andrea Tortora" Hospital (Pagani, Salerno – referred to as "the Hospital") as inpatients or outpatients receiving intravenous therapy have been screened for SARS-CoV-2 using RT-PCR nasal swab. The ongoing COICA (COVID-19 infection in cancer patients) study is an ambispective, multicenter, observational study designed to assess the prognosis of SARS-CoV-2 infection in cancer patients. The aim of the study presented here was to explore potential differences in COVID-19-related outcomes among screening-detected versus nonscreening-detected SARS-CoV-2-infected patients. Methods: The COICA study enrolled cancer patients who had received any anticancer systemic therapy within 3 months since the day they tested positive for SARS-CoV-2 on RT-PCR. The target accrual is 128 patients, and the study was approved by the competent Ethics Committee. Only the subgroup of patients enrolled at the Hospital was considered in this unplanned interim analysis. Logistic regression analysis was used to evaluate the association of screening-based versus nonscreening-based diagnosis. Results: Since March 15, 2020, until August 15, 2021, a total of 931 outpatients and 230 inpatients were repeatedly screened for SARS-CoV-2 using RT-PCR nasal swab at the Hospital. Among these, 71 asymptomatic patients were positive on routine screening and 5 patients were positive for SARS-CoV-2 outside the institutional screening. Seven patients died because of COVID-19. At univariate analysis, nonscreening- versus screening-detected SARS-CoV-2 infection was associated with significantly higher odds of O2 therapy (OR = 16.2; 95% CI = 2.2–117.1; p = 0.006), hospital admission (OR = 31.5; 95% CI = 3.1–317.8; p = 0.003), admission to ICU (OR = 23.0; 95% CI = 2.4–223.8; p = 0.007), and death (OR = 8.8; 95% CI = 1.2–65.5; p = 0.034). Conclusion: Routine screening with RT-PCR may represent a feasible and effective strategy in reducing viral circulation and possibly COVID-19 mortality in patients with active cancer having repeated access to hospital facilities

Performance of the First ANTARES Detector Line

In this paper we report on the data recorded with the first Antares detector line. The line was deployed on the 14th of February 2006 and was connected to the readout two weeks later. Environmental data for one and a half years of running are shown. Measurements of atmospheric muons from data taken from selected runs during the first six months of operation are presented. Performance figures in terms of time residuals and angular resolution are given. Finally the angular distribution of atmospheric muons is presented and from this the depth profile of the muon intensity is derived.Comment: 14 pages, 9 figure

Time calibration of the ANTARES neutrino telescope

The ANTARES deep-sea neutrino telescope comprises a three-dimensional array of photomultipliers to detect the Cherenkov light induced by upgoing relativistic charged particles originating from neutrino interactions in the vicinity of the detector. The large scattering length of light in the deep sea facilitates an angular resolution of a few tenths of a degree for neutrino energies exceeding 10 TeV. In order to achieve this optimal performance, the time calibration procedures should ensure a relative time calibration between the photomultipliers at the level of similar to 1 ns. The methods developed to attain this level of precision are described

A search for neutrino emission from the Fermi bubbles with the ANTARES telescope

Analysis of the Fermi-LAT data has revealed two extended structures above and below the Galactic Centre emitting gamma rays with a hard spectrum, the so-called Fermi bubbles. Hadronic models attempting to explain the origin of the Fermi bubbles predict the emission of high-energy neutrinos and gamma rays with similar fluxes. The ANTARES detector, a neutrino telescope located in the Mediterranean Sea, has a good visibility to the Fermi bubble regions. Using data collected from 2008 to 2011 no statistically significant excess of events is observed and therefore upper limits on the neutrino flux in TeV range from the Fermi bubbles are derived for various assumed energy cutoffs of the source

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

The positioning system of the ANTARES Neutrino Telescope

The ANTARES neutrino telescope, located 40km off the coast of Toulon in the Mediterranean Sea at a mooring depth of about 2475m, consists of twelve detection lines equipped typically with 25 storeys. Every storey carries three optical modules that detect Cherenkov light induced by charged secondary particles (typically muons) coming from neutrino interactions. As these lines are flexible structures fixed to the sea bed and held taut by a buoy, sea currents cause the lines to move and the storeys to rotate. The knowledge of the position of the optical modules with a precision better than 10cm is essential for a good reconstruction of particle tracks. In this paper the ANTARES positioning system is described. It consists of an acoustic positioning system, for distance triangulation, and a compass-tiltmeter system, for the measurement of the orientation and inclination of the storeys. Necessary corrections are discussed and the results of the detector alignment procedure are described