“You knew you had to be there, it had to be done”: Experiences of health professionals who faced the COVID-19 pandemic in one public hospital in Spain

Introduction: The COVID-19 pandemic highlighted the lack of a government contingency plan for an effective response to an unexpected health crisis. This study uses a phenomenological approach to explore the experience of healthcare professionals during the first three waves of the COVID-19 pandemic in a public health hospital in the Valencia region, Spain. It assesses the impact on their health, coping strategies, institutional support, organizational changes, quality of care, and lessons learned. Methods: We carried out a qualitative study with semi-structured interviews with doctors and nurses from the Preventive Medicine, Emergency, and Internal Medicine Services and the Intensive Care Unit, using the Colaizzi’s 7-step data analysis method. Results: During the first wave, lack of information and leadership led to feelings of uncertainty, fear of infection, and transmission to family members. Continuous organizational changes and lack of material and human resources brought limited results. The lack of space to accommodate patients, along with insufficient training in treating critical patients, and the frequent moving around of healthcare workers, reduced the quality of care. Despite the high levels of emotional stress reported, no sick leave was taken; the high levels of commitment and professional vocation helped in adapting to the intense work rhythms. Healthcare professionals in the medical services and support units reported higher levels of stress, and a greater sense of neglect by their institution than their colleagues in managerial roles. Family, social support, and camaraderie at work were effective coping strategies. Health professionals showed a strong collective spirit and sense of solidarity. This helped them cope with the additional stress and workload that accompanied the pandemic. Conclusion: In the wake of this experience, they highlight the need for a contingency plan adapted to each organizational context. Such a plan should include psychological counseling and continuous training in critical patient care. Above all, it needs to take advantage of the hard-won knowledge born of the COVID-19 pandemi

“You knew you had to be there, it had to be done”: Experiences of health professionals who faced the COVID-19 pandemic in one public hospital in Spain

IntroductionThe COVID-19 pandemic highlighted the lack of a government contingency plan for an effective response to an unexpected health crisis. This study uses a phenomenological approach to explore the experience of healthcare professionals during the first three waves of the COVID-19 pandemic in a public health hospital in the Valencia region, Spain. It assesses the impact on their health, coping strategies, institutional support, organizational changes, quality of care, and lessons learned.MethodsWe carried out a qualitative study with semi-structured interviews with doctors and nurses from the Preventive Medicine, Emergency, and Internal Medicine Services and the Intensive Care Unit, using the Colaizzi’s 7-step data analysis method.ResultsDuring the first wave, lack of information and leadership led to feelings of uncertainty, fear of infection, and transmission to family members. Continuous organizational changes and lack of material and human resources brought limited results. The lack of space to accommodate patients, along with insufficient training in treating critical patients, and the frequent moving around of healthcare workers, reduced the quality of care. Despite the high levels of emotional stress reported, no sick leave was taken; the high levels of commitment and professional vocation helped in adapting to the intense work rhythms. Healthcare professionals in the medical services and support units reported higher levels of stress, and a greater sense of neglect by their institution than their colleagues in managerial roles. Family, social support, and camaraderie at work were effective coping strategies. Health professionals showed a strong collective spirit and sense of solidarity. This helped them cope with the additional stress and workload that accompanied the pandemic.ConclusionIn the wake of this experience, they highlight the need for a contingency plan adapted to each organizational context. Such a plan should include psychological counseling and continuous training in critical patient care. Above all, it needs to take advantage of the hard-won knowledge born of the COVID-19 pandemic

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma

PURPOSE: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell-carcinoma (MRCC) and to evaluate in vitro their mechanism of action in sunitinib resistance. METHODS: We screened 673 microRNAs using TaqMan Low-density-Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n = 41). The most relevant differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenotypes selected from an independent cohort (n = 101). In vitro experiments were conducted to study the role of miR-942 in sunitinib resistance. RESULTS: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p = 0.0074). High expression of miR-942, miR-628-5p, miR-133a, and miR-484 was significantly associated with decreased time to progression and overall survival. These microRNAs were also overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive cell line. MiR-942 overexpression in Caki-2 up-regulates MMP-9 and VEGF secretion which, in turn, promote HBMEC endothelial migration and sunitinib resistance. CONCLUSIONS: We identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity or resistance to sunitinib. MiR-942 was the best predictor of efficacy. We describe a novel paracrine mechanism through which high miR-942 levels in MRCC cells up-regulates MMP-9 and VEGF secretion to enhance endothelial migration and sunitinib resistance. Our results support further validation of these miRNA in clinical confirmatory studies

Mutational screening of newly diagnosed multiple myeloma patients by deep targeted sequencing

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Carbon dioxide (CO2) emissions and adherence to Mediterranean diet in an adult population: the Mediterranean diet index as a pollution level index

Background Research related to sustainable diets is is highly relevant to provide better understanding of the impact of dietary intake on the health and the environment. Aim To assess the association between the adherence to an energy-restricted Mediterranean diet and the amount of CO2 emitted in an older adult population. Design and population Using a cross-sectional design, the association between the adherence to an energy-reduced Mediterranean Diet (erMedDiet) score and dietary CO2 emissions in 6646 participants was assessed. Methods Food intake and adherence to the erMedDiet was assessed using validated food frequency questionnaire and 17-item Mediterranean questionnaire. Sociodemographic characteristics were documented. Environmental impact was calculated through greenhouse gas emissions estimations, specifically CO2 emissions of each participant diet per day, using a European database. Participants were distributed in quartiles according to their estimated CO2 emissions expressed in kg/day: Q1 (= 2.80 kg CO2). Results More men than women induced higher dietary levels of CO2 emissions. Participants reporting higher consumption of vegetables, fruits, legumes, nuts, whole cereals, preferring white meat, and having less consumption of red meat were mostly emitting less kg of CO2 through diet. Participants with higher adherence to the Mediterranean Diet showed lower odds for dietary CO2 emissions: Q2 (OR 0.87; 95%CI: 0.76-1.00), Q3 (OR 0.69; 95%CI: 0.69-0.79) and Q4 (OR 0.48; 95%CI: 0.42-0.55) vs Q1 (reference). Conclusions The Mediterranean diet can be environmentally protective since the higher the adherence to the Mediterranean diet, the lower total dietary CO2 emissions. Mediterranean Diet index may be used as a pollution level index

Effect of a Nutritional and Behavioral Intervention on Energy-Reduced Mediterranean Diet Adherence Among Patients With Metabolic Syndrome: Interim Analysis of the PREDIMED-Plus Randomized Clinical Trial

Key PointsQuestionWhat is the effect of a nutritional and behavioral intervention focused on encouraging an energy-reduced Mediterranean diet and physical activity on the dietary pattern of participants after 12 months? FindingsIn this preliminary analysis of an ongoing randomized clinical trial involving 6874 participants, an intervention focused on encouraging an energy-reduced Mediterranean diet and promoting physical activity, compared with advice to follow an energy-unrestricted Mediterranean diet, resulted in a significant increase in a measure of diet adherence, the 17-item energy-reduced Mediterranean diet score, at 12 months (4.7 points vs 2.5 points; score range, 0-17; minimal clinically important difference, 1 point). MeaningA nutritional and behavioral intervention focused on encouraging an energy-reduced Mediterranean diet and physical activity led to a significant improvement in a measure of diet adherence at 12 months. Further evaluation of the effects on long-term cardiovascular and other health outcomes is needed. ImportanceHigh-quality dietary patterns may help prevent chronic disease, but limited data exist from randomized trials about the effects of nutritional and behavioral interventions on dietary changes. ObjectiveTo assess the effect of a nutritional and physical activity education program on dietary quality. Design, Setting, and ParticipantsPreliminary exploratory interim analysis of an ongoing randomized trial. In 23 research centers in Spain, 6874 men and women aged 55 to 75 years with metabolic syndrome and no cardiovascular disease were enrolled in the trial between September 2013 and December 2016, with final data collection in March 2019. InterventionsParticipants were randomized to an intervention group that encouraged an energy-reduced Mediterranean diet, promoted physical activity, and provided behavioral support (n=3406) or to a control group that encouraged an energy-unrestricted Mediterranean diet (n=3468). All participants received allotments of extra-virgin olive oil (1 L/mo) and nuts (125 g/mo) for free. Main Outcomes and MeasuresThe primary outcome was 12-month change in adherence based on the energy-reduced Mediterranean diet (er-MedDiet) score (range, 0-17; higher scores indicate greater adherence; minimal clinically important difference, 1 point). ResultsAmong 6874 randomized participants (mean [SD] age, 65.0 [4.9] years; 3406 [52%] men), 6583 (96%) completed the 12-month follow-up and were included in the main analysis. The mean (SD) er-MedDiet score was 8.5 (2.6) at baseline and 13.2 (2.7) at 12 months in the intervention group (increase, 4.7 [95% CI, 4.6-4.8]) and 8.6 (2.7) at baseline and 11.1 (2.8) at 12 months in the control group (increase, 2.5 [95% CI, 2.3-2.6]) (between-group difference, 2.2 [95% CI, 2.1-2.4]; P<.001). Conclusions and RelevanceIn this preliminary analysis of an ongoing trial, an intervention that encouraged an energy-reduced Mediterranean diet and physical activity, compared with advice to follow an energy-unrestricted Mediterranean diet, resulted in a significantly greater increase in diet adherence after 12 months. Further evaluation of long-term cardiovascular effects is needed. Trial Registrationisrctn.com Identifier: ISRCTN89898870 This preliminary exploratory analysis of the ongoing PREDIMED-Plus randomized trial reports dietary adherence among Spanish community-dwelling participants with metabolic syndrome randomized to an energy-reduced Mediterranean diet, physical activity, and behavioral support vs an energy-unrestricted Mediterranean diet alone

Enabling planetary science across light-years. Ariel Definition Study Report

Ariel, the Atmospheric Remote-sensing Infrared Exoplanet Large-survey, was adopted as the fourth medium-class mission in ESA's Cosmic Vision programme to be launched in 2029. During its 4-year mission, Ariel will study what exoplanets are made of, how they formed and how they evolve, by surveying a diverse sample of about 1000 extrasolar planets, simultaneously in visible and infrared wavelengths. It is the first mission dedicated to measuring the chemical composition and thermal structures of hundreds of transiting exoplanets, enabling planetary science far beyond the boundaries of the Solar System. The payload consists of an off-axis Cassegrain telescope (primary mirror 1100 mm x 730 mm ellipse) and two separate instruments (FGS and AIRS) covering simultaneously 0.5-7.8 micron spectral range. The satellite is best placed into an L2 orbit to maximise the thermal stability and the field of regard. The payload module is passively cooled via a series of V-Groove radiators; the detectors for the AIRS are the only items that require active cooling via an active Ne JT cooler. The Ariel payload is developed by a consortium of more than 50 institutes from 16 ESA countries, which include the UK, France, Italy, Belgium, Poland, Spain, Austria, Denmark, Ireland, Portugal, Czech Republic, Hungary, the Netherlands, Sweden, Norway, Estonia, and a NASA contribution

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