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Introduction The objective of this study was to determine the burden of influenza disease in patients with or without diabetes in a population of American adults to understand the benefits of seasonal vaccination.Research design and methods We performed a retrospective cohort study using electronic medical records totaling 1,117,263 from two Louisiana healthcare providers spanning January 2012 through December 2017. Adults 18 years or older with two or more records within the study period were included. The primary outcome quantified was influenza-related diagnosis during inpatient (IP) or emergency room (ER) visits and risk reduction with the timing of immunization.Results Influenza-related IP or ER visits totaled 0.0122–0.0169 events per person within the 2013–2016 influenza seasons. Subjects with diabetes had a 5.6-fold more frequent influenza diagnosis for IP or ER visits than in subjects without diabetes or 3.7-fold more frequent when adjusted for demographics. Early immunization reduced the risk of influenza healthcare utilization by 66% for subjects with diabetes or 67% for subjects without diabetes when compared with later vaccination for the 2013–2016 influenza seasons. Older age and female sex were associated with a higher incidence of influenza, but not a significant change in risk reduction from vaccination.Conclusions The risk for influenza-related healthcare utilization was 3.7-fold higher if patients had diabetes during 2013–2016 influenza seasons. Early immunization provides a significant benefit to adults irrespective of a diabetes diagnosis. All adults, but particularly patients with diabetes, should be encouraged to get the influenza vaccine at the start of the influenza season.

Abstract Objective The impact of comorbidities and biomarkers on COVID-19 severity vary by sex but have not yet been verified in population-based studies. We examined the association of comorbidities, inflammatory biomarkers, and severe outcomes in men and women hospitalized for COVID-19. Design This is a retrospective cohort analysis based on the National COVID Cohort Collaborative (N3C). We included 574,391 adult patients admitted for COVID-19 at hospitals or emergency rooms between 01/01/2020 and 12/31/2021. Methods We defined comorbidities at or before the first admission for COVID-19 by Charlson Comorbidity Index (CCI) and CCI components. We used the averaged lab values taken within 15 days before or after the admission date to measure biomarkers including c-reactive protein (CRP), ferritin, procalcitonin, N-terminal pro b-type natriuretic peptide (NT proBNP), d-dimer, absolute lymphocyte counts, absolute neutrophil counts, and platelets. Our primary outcome was all-cause mortality; secondary outcomes were invasive mechanical ventilation (IMV) and hospital length of stay (LOS). We used logistic regression adjusted for age, race, ethnicity, visit type, and medications to assess the association of comorbidities, biomarkers, and mortality disaggregating by sex. Results Moderate to severe liver disease, renal disease, metastatic solid tumor, and myocardial infarction were the top four fatal comorbidities among patients who were hospitalized for COVID-19 (adjusted odds ratio [aOR] > 2). These four comorbid conditions remained the most lethal in both sexes, with a higher magnitude of risk in women than in men (p-interaction 2). The association between the abnormal biomarkers and death was stronger in women than in men (p-interaction

ObjectiveAdvancements in fluoroscopy-assisted procedures have increased radiation exposure among cardiologists. Radiation has been linked to cardiovascular complications but its effect on cardiac rhythm, specifically, is underexplored.MethodsDemographic, social, occupational, and medical history information was collected from board-certified cardiologists via an electronic survey. Bivariate and multivariable logistic regression analyses were performed to assess the risk of atrial arrhythmias (AA).ResultsWe received 1,478 responses (8.8% response rate) from cardiologists, of whom 85.4% were male, and 66.1% were ≤65 years of age. Approximately 36% were interventional cardiologists and 16% were electrophysiologists. Cardiologists > 50 years of age, with > 10,000 hours (h) of radiation exposure, had a significantly lower prevalence of AA vs. those with ≤10,000 h (11.1% vs. 16.7%, p = 0.019). A multivariable logistic regression was performed and among cardiologists > 50 years of age, exposure to > 10,000 radiation hours was significantly associated with a lower likelihood of AA, after adjusting for age, sex, diabetes mellitus, hypertension, and obstructive sleep apnea (adjusted OR 0.57; 95% CI 0.38–0.85, p = 0.007). The traditional risk factors for AA (age, sex, hypertension, diabetes mellitus, and obstructive sleep apnea) correlated positively with AA in our data set. Cataracts, a well-established complication of radiation exposure, were more prevalent in those exposed to > 10,000 h of radiation vs. those exposed to ≤10,000 h of radiation, validating the dependent (AA) and independent variables (radiation exposure), respectively.ConclusionAA prevalence may be inversely associated with radiation exposure in Cardiologists based on self-reported data on diagnosis and radiation hours. Large-scale prospective studies are needed to validate these findings.

Energy Consumption has been increasing at an alarming rate due to the growing energy need. More and more non-renewable sources are harvested to fulfill the energy demand resulting in and rising environmental health issues. However, harvesting Solar and Wind energy is considered as the best alternative in generating energy as these resources are renewable. Hybrid Renewable Energy System (HRES) has been grabbed the attention recently, as it involves with renewable, environmentally friendly sources to generate energy. The limitation of single Renewable Energy (RE) system is overcome by systems such as HRES. Even though it has been introduced different sizing and optimization techniques, due to the lack of system function, it had posed issues in calculating the optimized cost of a hybrid system considering the solar, wind resources and load demand as the optimization of the system cannot be predicted accurately. The aim of this research was to obtain optimization of a Hybrid PV-wind system in term of sizing and cost over the 20 years of the period of interest. The simulation of the PV-Wind Hybrid system using MATLAB for the verification purpose. This work includes detailed calculation using the Life Cycle Cost method for identifying all possible combinations. The combination of eleven Solar Panels, one Wind Turbine and nine Batteries was identified as the optimal Combination with LCC of RM 221,329.97 and has been verified using simulation results. Lastly, a sensitivity test was carried out using the exiting results of verified by the simulation to identify the most deterministic system in affecting LCC of the Hybrid system. Further, total Cost distribution for the Optimized hybrid PV-Wind system was conducted and identified that 50% of system cost was contributed by the Wind turbine. Determination of LCC, was done as a combination of Component and Operation costs. It was identified that Replacement cost contributed the highest while Wind turbine showed the highest Operation cost from the system cost. Thus, this work was included with the sensitivity test assuming 10% price increment for each component and it was concluded that price changes in Wind turbine results the greatest difference in LCC while further verified with the results of the simulation.

A novel deep-learning paradigm for synthesis imaging by radio interferometry in astronomy was recently proposed, dubbed “Residual-to-Residual DNN series for high-Dynamic range imaging” (R2D2). In this work, we start by shedding light on R2D2's algorithmic structure, interpreting it as a learned version of CLEAN with minor cycles substituted with a deep neural network (DNN) whose training is iteration-specific. We then proceed with R2D2's first demonstration on real data, for monochromatic intensity imaging of the radio galaxy Cygnus A from S -band observations with the Very Large Array. We show that the modeling power of R2D2's learning approach enables delivering high-precision imaging, superseding the resolution of CLEAN, and matching the precision of modern optimization and plug-and-play algorithms, respectively uSARA and AIRI. Requiring few major-cycle iterations only, R2D2 provides a much faster reconstruction than uSARA and AIRI, known to be highly iterative, and is at least as fast as CLEAN.

Radio-interferometric imaging entails solving high-resolution high-dynamic-range inverse problems from large data volumes. Recent image reconstruction techniques grounded in optimization theory have demonstrated remarkable capability for imaging precision, well beyond CLEAN’s capability. These range from advanced proximal algorithms propelled by handcrafted regularization operators, such as the SARA family, to hybrid plug-and-play (PnP) algorithms propelled by learned regularization denoisers, such as AIRI. Optimization and PnP structures are however highly iterative, which hinders their ability to handle the extreme data sizes expected from future instruments. To address this scalability challenge, we introduce a novel deep-learning approach, dubbed “Residual-to-Residual DNN series for high-Dynamic-range imaging” or in short R2D2. R2D2's reconstruction is formed as a series of residual images, iteratively estimated as outputs of deep neural networks (DNNs) taking the previous iteration’s image estimate and associated data residual as inputs. It thus takes a hybrid structure between a PnP algorithm and a learned version of the matching pursuit algorithm that underpins CLEAN. We present a comprehensive study of our approach, featuring its multiple incarnations distinguished by their DNN architectures. We provide a detailed description of its training process, targeting a telescope-specific approach. R2D2's capability to deliver high precision is demonstrated in simulation, across a variety of image and observation settings using the Very Large Array. Its reconstruction speed is also demonstrated: with only a few iterations required to clean data residuals at dynamic ranges up to 10 ^5 , R2D2 opens the door to fast precision imaging. R2D2 codes are available in the BASPLib ( https://basp-group.github.io/BASPLib/ ) library on GitHub.