437 research outputs found

    Staphylococcal scalded skin syndrome in adults with obesity and type 2 diabetes: A case series

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    : Staphylococcal scalded skin syndrome (SSSS) primarily affects children and rarely adults with immunodepression. We describe two cases of adults diagnosed with SSSS with no additional cause of immunological compromise other than obesity and uncontrolled diabetes. An increased risk of infection should be considered in cases of obesity and diabetes

    Cor triatriatum dexter: a rare incidentaloma

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    Cor triatriatum dexter (CTD) is an extremely rare finding resulting from the persistence of the right valve of sinus venosus. It is a congenital cardiac anomaly defined by an abnormal septation of the atrium leading to inflow obstruction to the respective ventricle. Multimodal diagnostic modalities are necessary to characterize it for an optimal patient management. We report the case of a 68-year-old woman who presented to our clinic for further feedback of ventricular ectopic beats

    Thermal-induced phase transitions in self-assembled mesostructured films studied by small-angle X-ray scattering

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    Two examples of phase transition in self-assembled mesostructured hybrid thin films are reported. The materials have been synthesized using tetraethoxysilane as the silica source hydrolyzed with or without the addition of methyltriethoxysilane. The combined use of transmission electron microscopy, small-angle X-ray scattering and computer simulation has been introduced to achieve a clear identification of the organized phases. A structural study of the self-assembled mesophases as a function of thermal treatment has allowed the overall phase transition to be followed. The initial symmetries of mesophases in as-deposited films have been linked to those observed in samples after thermal treatment. The monodimensional shrinkage of silica films during calcination has induced a phase transition from face-centered orthorhombic to body-centered cubic. In hybrid films, instead, the phase transition has not involved a change in the unit cell but a contraction of the cell parameter normal to the substrate

    Microperimetric evaluation and predictive factors of visual recovery after successful inverted internal limiting membrane-flap technique for macular hole in high myopic eyes

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    IntroductionInverted Internal Limiting Membrane (ILM)-flap technique demonstrated its effectiveness, in terms of anatomical closure rate and visual acuity recovery for high myopic macular holes. We evaluated macular function after a successful inverted ILM-flap for macular holes in high myopic eyes (hMMH) using microperimetry to predict visual prognosis.MethodsA retrospective study on 23 eyes of 23 patients after surgical closure of hMMH, was performed. All patients underwent inverted ILM-flap and gas tamponade. Cataract surgery was performed in phakic eyes. Study outcomes including best-corrected visual acuity (BCVA), retinal sensitivity (RS) at central 12¬į, central retinal sensitivity (CRS) at central 4¬į and mean deviation (MD), and fixation behavior as bivariate contour ellipse area (BCEA, degrees2) measured by microperimetry, were evaluated over 6‚ÄČmonths. A mixed-effects model was used to evaluate and compare the repeated measurements of outcomes between phakic and pseudophakic eyes. A regression model was performed to assess the relationship between BCVA at 6‚ÄČmonths and independent variables.ResultsOverall mean BCVA improved from 0.98‚ÄȬĪ‚ÄČ0.21 logMAR at baseline to 0.47‚ÄȬĪ‚ÄČ0.31 logMAR at the last follow-up (p‚ÄČ<‚ÄČ0.001). Over 6‚ÄČmonths, overall sensitivity measurements improved (RS, p‚ÄČ=‚ÄČ0.001; CRS, p‚ÄČ<‚ÄČ0.0001; MD, p‚ÄČ=‚ÄČ0.03), and the BCEA decreased in dimension, although not significantly (p‚ÄČ‚Č•‚ÄČ0.05). The mixed model revealed a significantly better effect of inverted ILM-flap combined with cataract surgery on BCVA and CRS in phakic eyes than inverted ILM-flap alone in pseudophakic ones. The regression model revealed a relationship of 6-month BCVA with pre-operative BCVA (ő≤‚ÄČ=‚ÄČ0.60, p‚ÄČ=‚ÄČ0.02) and RS (ő≤‚ÄČ=‚ÄČ‚ąí0.03, p‚ÄČ=‚ÄČ0.01).ConclusionThe inverted ILM-flap technique significantly improved visual acuity and retinal sensitivity after the hMMH closure, particularly when combined with cataract extraction. Pre-operative visual acuity and retinal sensitivity at central 12¬į may predict post-surgical visual acuity

    Mortality and pulmonary complications in patients undergoing surgery with perioperative SARS-CoV-2 infection: an international cohort study

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    Background: The impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on postoperative recovery needs to be understood to inform clinical decision making during and after the COVID-19 pandemic. This study reports 30-day mortality and pulmonary complication rates in patients with perioperative SARS-CoV-2 infection. Methods: This international, multicentre, cohort study at 235 hospitals in 24 countries included all patients undergoing surgery who had SARS-CoV-2 infection confirmed within 7 days before or 30 days after surgery. The primary outcome measure was 30-day postoperative mortality and was assessed in all enrolled patients. The main secondary outcome measure was pulmonary complications, defined as pneumonia, acute respiratory distress syndrome, or unexpected postoperative ventilation. Findings: This analysis includes 1128 patients who had surgery between Jan 1 and March 31, 2020, of whom 835 (74¬∑0%) had emergency surgery and 280 (24¬∑8%) had elective surgery. SARS-CoV-2 infection was confirmed preoperatively in 294 (26¬∑1%) patients. 30-day mortality was 23¬∑8% (268 of 1128). Pulmonary complications occurred in 577 (51¬∑2%) of 1128 patients; 30-day mortality in these patients was 38¬∑0% (219 of 577), accounting for 81¬∑7% (219 of 268) of all deaths. In adjusted analyses, 30-day mortality was associated with male sex (odds ratio 1¬∑75 [95% CI 1¬∑28‚Äď2¬∑40], p\textless0¬∑0001), age 70 years or older versus younger than 70 years (2¬∑30 [1¬∑65‚Äď3¬∑22], p\textless0¬∑0001), American Society of Anesthesiologists grades 3‚Äď5 versus grades 1‚Äď2 (2¬∑35 [1¬∑57‚Äď3¬∑53], p\textless0¬∑0001), malignant versus benign or obstetric diagnosis (1¬∑55 [1¬∑01‚Äď2¬∑39], p=0¬∑046), emergency versus elective surgery (1¬∑67 [1¬∑06‚Äď2¬∑63], p=0¬∑026), and major versus minor surgery (1¬∑52 [1¬∑01‚Äď2¬∑31], p=0¬∑047). Interpretation: Postoperative pulmonary complications occur in half of patients with perioperative SARS-CoV-2 infection and are associated with high mortality. Thresholds for surgery during the COVID-19 pandemic should be higher than during normal practice, particularly in men aged 70 years and older. Consideration should be given for postponing non-urgent procedures and promoting non-operative treatment to delay or avoid the need for surgery. Funding: National Institute for Health Research (NIHR), Association of Coloproctology of Great Britain and Ireland, Bowel and Cancer Research, Bowel Disease Research Foundation, Association of Upper Gastrointestinal Surgeons, British Association of Surgical Oncology, British Gynaecological Cancer Society, European Society of Coloproctology, NIHR Academy, Sarcoma UK, Vascular Society for Great Britain and Ireland, and Yorkshire Cancer Research

    Machine learning approaches for parameter reweighting in MC samples of top quark production in CMS

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    In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured data. Many MC samples have to be generated to account for theoretical systematic uncertainties, at a significant computational cost. Therefore, the MC statistic becomes a limiting factor for most measurements and the significant computational cost of these programs a bottleneck in most physics analyses. In this contribution, the Deep neural network using Classification for Tuning and Reweighting (DCTR) approach is evaluated for the reweighting of two systematic uncertainties in MC simulations of top quark pair production within the CMS experiment. DCTR is a method, based on a Deep Neural Network (DNN) technique, to reweight simulations to different model parameters by using the full kinematic information in the event. This methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample.In particle physics, Monte Carlo (MC) event generators are needed to compare theory to the measured data. Many MC samples have to be generated to account for theoretical systematic uncertainties, at a significant computational cost. Therefore, the MC statistic becomes a limiting factor for most measurements and the significant computational cost of these programs a bottleneck in most physics analyses. In this contribution, the Deep neural network using Classification for Tuning and Reweighting (DCTR) approach is evaluated for the reweighting of two systematic uncertainties in MC simulations of top quark pair production within the CMS experiment. DCTR is a method, based on a Deep Neural Network (DNN) technique, to reweight simulations to different model parameters by using the full kinematic information in the event. This methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample

    Machine learning approaches for parameter reweighting for MC samples of top quark production in CMS

    No full text
    In high-energy particle physics, complex Monte Carlo (MC) simulations are needed to compare theory predictions to measurable quantities. Many and large MC samples are needed to be generated to take into account all the systematics. Therefore, the MC statistics (and hence the MC modeling uncertainties) become a limiting factor for most measurements. Moreover, the significant computational cost of these programs becomes a bottleneck in most physics analyses. Therefore, it is extremely important to find a way to reduce the MC samples generated to decrease the MC statistical uncertainties and lower the computational cost. In these proceedings, we evaluate an approach called Deep neural network using Classification for Tuning and Reweighting (DCTR). DCTR is a method based on a Deep Neural Network (DNN) to reweight simulations to different models or model parameters and fit simulations, using the full kinematic information in the event. This reweighting methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample. In this way, the MC statistical uncertainties and the computational cost are both reduced. Moreover, unlike the standard reweighting, in which the ratio in bins of two histograms at truth level is performed, multidimensional and unbinned information can be used as inputs to the DNN. In addition, DCTR can perform tasks that are not possible with other current existing methods, such as continuous reweighting as a function of any MC parameter, simultaneous reweighting of more MC parameters and tuning MC simulations to the data. We test the method on MC simulations of top quark pair production, which we reweight to different SM parameter values and to different QCD models.In high-energy particle physics, complex Monte Carlo (MC) simulations are needed to compare theory predictions to measurable quantities. Many and large MC samples are needed to be generated to take into account all the systematics. Therefore, the MC statistics (and hence the MC modeling uncertainties) become a limiting factor for most measurements. Moreover, the significant computational cost of these programs becomes a bottleneck in most physics analyses. Therefore, it is extremely important to find a way to reduce the MC samples generated to decrease the MC statistical uncertainties and lower the computational cost. In these proceedings, we evaluate an approach called Deep neural network using Classification for Tuning and Reweighting (DCTR). DCTR is a method based on a Deep Neural Network (DNN) to reweight simulations to different models or model parameters and fit simulations, using the full kinematic information in the event. This reweighting methodology avoids the need for simulating the detector response multiple times by incorporating the relevant variations in a single sample. In this way, the MC statistical uncertainties and the computational cost are both reduced. Moreover, unlike the standard reweighting, in which the ratio in bins of two histograms at truth level is performed, multidimensional and unbinned information can be used as inputs to the DNN. In addition, DCTR can perform tasks that are not possible with other current existing methods, such as continuous reweighting as a function of any MC parameter, simultaneous reweighting of more MC parameters and tuning MC simulations to the data. We test the method on MC simulations of top quark pair production, which we reweight to different SM parameter values and to different QCD models
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