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We settle the last open case of Kuznetsov’s conjecture on the derived categories of Fano threefolds. Contrary to the original conjecture, we prove the Kuznetsov components of quartic double solids and Gushel–Mukai threefolds are never equivalent, as recently shown independently by Zhang. On the other hand, we prove the modified conjecture asserting their deformation equivalence. Our proof of nonequivalence combines a categorical Enriques-K3 correspondence with the Hodge theory of categories. Along the way, we obtain a categorical description of the periods of Gushel–Mukai varieties, which we use to resolve a conjecture of Kuznetsov and the second author on the birational categorical Torelli problem, as well as to give a simple proof of a theorem of Debarre and Kuznetsov on the fibers of the period map. Our proof of deformation equivalence relies on results of independent interest about obstructions to enhancing group actions on categories.

Physical chemistry, chemical physics 24(38), 23119 - 23127 (2022). doi:10.1039/D2CP02383K special issue: "Ions, electrons, coincidences and dynamics: Festschrift for John H.D. Eland", We report the X-ray absorption of isolated H3O+ cations at the O 1s edge. The molecular ions were prepared in a flowing afterglow ion source which was designed for the production of small water clusters, protonated water clusters, and hydrated ions. Isolated H2O+ cations have been analyzed for comparison. The spectra show significant differences in resonance energies and widths compared to neutral H2O with resonances shifting to higher energies by as much as 10 eV and resonance widths increasing by as much as a factor of 5. The experimental results are supported by time-dependent density functional theory calculations performed for both molecular cations, showing a good agreement with the experimental data. The spectra reported here could enable the identification of the individual molecules in charged small water clusters or liquid water using X-ray absorption spectroscopy., Published by RSC Publ., Cambridge

We formulate a version of the integral Hodge conjecture for categories, prove the conjecture for two-dimensional Calabi–Yau categories which are suitably deformation equivalent to the derived category of a K3 or abelian surface, and use this to deduce cases of the usual integral Hodge conjecture for varieties. Along the way, we prove a version of the variational integral Hodge conjecture for families of two-dimensional Calabi–Yau categories, as well as a general smoothness result for relative moduli spaces of objects in such families. Our machinery also has applications to the structure of intermediate Jacobians, such as a criterion in terms of derived categories for when they split as a sum of Jacobians of curves.

Infectious endocarditis is associated with substantial in-hospital mortality of 15%-20%. Effective management requires coordination between multiple medical and surgical subspecialties, which can often lead to disjointed care. Previous European studies have identified multidisciplinary endocarditis teams as a tool for reducing endocarditis mortality.The multidisciplinary endocarditis team was formed in May 2018. The group developed an evidence-based algorithm for management of endocarditis that was used to provide recommendations for hospitalized patients over a 1-year period. Mortality outcomes were then retroactively assessed and compared to a historical control utilizing propensity matching.Between June 2018 and June 2019 the team provided guideline-based recommendations on 56 patients with Duke Criteria-definite endocarditis and at least 1 American Heart Association indication for surgery. The historical control included 68 patients with definite endocarditis and surgical indications admitted between July 1, 2014, and June 30, 2015. In-hospital mortality decreased significantly from 29.4% in 2014-2015 to 7.1% in 2018-2019 (P.0001). There was a non-significant increase in the rate of surgical intervention after implementation of the team (41.2% vs 55.4%; P = 0.12). Propensity score matching demonstrated similar results.Implementation of a multidisciplinary endocarditis team was associated with a significant 1-year decrease in all-cause in-hospital mortality for patients with definite endocarditis and surgical indications, in the presence of notable differences between the 2 studied cohorts. In conjunction with previous studies demonstrating their effectiveness, these data support the idea that widespread adoption of endocarditis teams in North America could improve outcomes for this patient population.

BackgroundClostridioides difficile infection (CDI) is a leading cause of health care–associated infection and may result in organ dysfunction, colectomy, and death. Published risk scores to predict severe complications from CDI demonstrate poor performance upon external validation. We hypothesized that building and validating a model using geographically and temporally distinct cohorts would more accurately predict risk for complications from CDI.MethodsWe conducted a multicenter retrospective cohort study of adults diagnosed with CDI. After randomly partitioning the data into training and validation sets, we developed and compared 3 machine learning algorithms (lasso regression, random forest, stacked ensemble) with 10-fold cross-validation to predict disease-related complications (intensive care unit admission, colectomy, or death attributable to CDI) within 30 days of diagnosis. Model performance was assessed using the area under the receiver operating curve (AUC).ResultsA total of 3646 patients with CDI were included, of whom 217 (6%) had complications. All 3 models performed well (AUC, 0.88–0.89). Variables of importance were similar across models, including albumin, bicarbonate, change in creatinine, non-CDI-related intensive care unit admission, and concomitant non-CDI antibiotics. Sensitivity analyses indicated that model performance was robust even when varying derivation cohort inclusion and CDI testing approach. However, race was an important modifier, with models showing worse performance in non-White patients.ConclusionsUsing a large heterogeneous population of patients, we developed and validated a prediction model that estimates risk for complications from CDI with good accuracy. Future studies should aim to reduce the disparity in model accuracy between White and non-White patients and to improve performance overall.

Background Many models have been developed to predict severe outcomes from Clostridioides difficile infection (CDI). These models are usually developed at a single institution and largely are not externally validated. Our aim in this study was to validate previously published risk scores in a multicenter cohort of patients with CDI. Methods This was a retrospective study on 4 inpatient cohorts with CDI from 3 distinct sites: the universities of Michigan (2010–2012 and 2016), Chicago (2012), and Wisconsin (2012). The primary composite outcome was admission to an intensive care unit, colectomy, and/or death attributed to CDI within 30 days of positive testing. Both within each cohort and combined across all cohorts, published CDI severity scores were assessed and compared to each other and the Infectious Diseases Society of America (IDSA) guideline definitions of severe and fulminant CDI. Results A total of 3646 patients were included for analysis. Including the 2 IDSA guideline definitions, 14 scores were assessed. Performance of scores varied within each cohort and in the combined set (mean area under the receiver operator characteristic curve [AuROC], 0.61; range, 0.53–0.66). Only half of the scores had performance at or better than IDSA severe and fulminant definitions (AuROCs of 0.64 and 0.63, respectively). Most of the scoring systems had more false than true positives in the combined set (mean, 81.5%; range, 0%–91.5%). Conclusions No published CDI severity score showed stable, good predictive ability for adverse outcomes across multiple cohorts/institutions or in a combined multicenter cohort.

BackgroundClostridioides difficile infection (CDI) is a leading cause of healthcare-associated infections and may result in organ dysfunction, colectomy, and death. We recently showed that published risk scores to predict severe complications from CDI demonstrate poor performance upon external validation. We hypothesized that building and validating a model using geographically and temporally distinct cohorts would more accurately identify patients at risk for complicated CDI.MethodsWe conducted a multi-center retrospective cohort study of adult subjects diagnosed with CDI in the US. After randomly partitioning the data into training/validation set, we developed and compared three machine learning algorithms (Lasso regression, random forest, stacked ensemble models) with 10-fold cross-validation that used structured EHR data collected within 48 hours of CDI diagnosis to predict disease-related complications from CDI (intensive care unit admission, colectomy, or death attributable to CDI within 30 days of diagnosis). Model performance was assessed using area under the receiver operating curve (AUC).ResultsA total of 3,762 patients with CDI were included of which 218 (5.8%) had complications. Lasso regression, random forest, and stacked ensemble models all performed well with AUC ranging between 0.89-0.9. Variables of importance were similar across models, including albumin, bicarbonate, change in creatinine, systolic blood pressure, non-CDI-related ICU admission, and concomitant non-CDI antibiotics. Sensitivity analyses indicated that model performance was robust even when varying derivation cohort inclusion and CDI testing approach.ConclusionUsing a large heterogeneous population of patients, we have developed and validated a prediction model based on structured EHR data that accurately estimates risk for complications from CDI.Key PointsMachine learning models using structured electronic health records can be leveraged to accurately predict risk of severe complications related to Clostridiodes difficile infection, including intensive care unit admission, colectomy, and/or death.

Background Coronary catheterization with angiography is often performed prior to surgical valve replacement in infectious endocarditis. There are no existing data as to whether this intervention is clinically necessary or leads to a change in surgical management. In order to determine the frequency with which coronary angiography impacts surgical management in infectious endocarditis, we conducted a retrospective review of surgically managed endocarditis cases at a tertiary care medical center. Methods Utilizing the institutional Society of Thoracic Surgeon's database, we identified 598 patients with surgically managed endocarditis between April 29, 2011 and December 31, 2018. Patient variables were recorded, including risk factors for coronary artery disease, whether the patient received coronary angiography prior to surgery, and if the patient underwent coronary artery bypass grafting as part of their valve surgery. Results There were 430 patients who received coronary catheterization with angiography prior to surgical valve replacement for infectious endocarditis, and 168 patients proceeded to surgery without coronary angiography. Nine percent of patients underwent coronary artery bypass grafting at the time of valve replacement as a result of coronary angiography findings. There was no significant difference in 30-day mortality for patients with endocarditis who underwent coronary angiography when compared with those who did not receive coronary angiography (2.6 vs 2.4%; P = 0.89). Conclusions Left heart catheterization with coronary angiography prior to surgical valve replacement leads to coronary artery bypass grafting in the minority of infective endocarditis patients.

Infectious endocarditis is a highly morbid infection that requires coordination of care across medical and surgical specialties, often through the use of a multidisciplinary team model. Multiple studies have demonstrated that such conferences can improve clinical outcomes. However, little is known about physicians' impressions of these groups. We surveyed 126 (response rate of 30%) internal medicine, infectious diseases, cardiology, and cardiac surgery providers 1 year after the implementation of an endocarditis team at the University of Michigan. Ninety-eight percent of physicians felt that the endocarditis team improved communication between specialties. Additionally, over 85% of respondents agreed that the group influenced diagnostic evaluation, reduced management errors, increased access to surgery, and decreased in-hospital mortality for endocarditis patients. These results suggest that multidisciplinary endocarditis teams are valued by physicians as a tool to improve patient care and serve an important role in increasing communication between providers.

Lung cancer is one of the deadliest forms of cancers and is often diagnosed by performing biopsies with the use of a bronchoscope. However, this diagnostic procedure is limited in ability to explore deep into the periphery of the lung where cancer can remain undetected. In this study, we present design, modeling, fabrication, and testing of a one degree of freedom soft robot with integrated diagnostic and interventional capabilities as well as vision sensing. The robot can be deployed through the working channel of commercial bronchoscopes or used as a stand-alone system as it integrates a micro camera to provide vision sensing and controls to the periphery of the lung. The small diameter (2.4 mm) of the device allows navigation in branches deeper in the lung, where current devices have limited reachability. We have performed mechanical characterizations of the robotic platform, including blocked force, maximum bending angle, maximum angular velocity, and workspace, and assessed its performance in

A man in his 20s with Down syndrome presented to cardiology clinic for evaluation of a murmur. He denied symptoms. He had no prior history of invasive cardiac procedures or cardiac surgery. His examination revealed a murmur. An echocardiogram was performed and a diagnostic image is shown in figure 1. Figure 1 Colour M-mode echocardiographic image of the mitral valve. 1. Based on the colour Doppler M-mode echocardiographic image (figure 1), what is the most likely cause …

Radiation dose estimations in the human body are performed using computational reference phantoms, which are anatomical representations of the human body. In previous studies, dose reconstructions have been performed focusing primarily on phantoms in an upright posture, which limits the accuracy of the dose estimations for postures observed in realistic work settings. In this work, the International Commission on Radiological Protection (ICRP) Publication 103 recommendations for monoenergetic neutron plane sources directed downward from above the head (cranial) and upward from below the feet (caudal) for adult female and male reference phantoms were used to calculate organ absorbed and effective dose coefficients. The Phantom with Moving Arms and Legs (PIMAL) and the Monte Carlo N-Particle (MCNP) radiation transport code were used to compute organ-absorbed dose and effective dose coefficients for the upright, half-bent (45°), and full-bent (90°) phantom postures. The doses calculated for each of the articulated positions were compared to those calculated for the upright posture by computing the ratios of the coefficients (45°/upright and 90°/upright). These ratios were used to assess the effectiveness of upright phantoms in providing a comparable estimate when conducting dose estimations and dose reconstructions for articulated positions. This work compiling neutron cranial and caudal posture-specific dose coefficients completes the series of dose coefficients computed for posture-specific ICRP Publication 116 irradiation geometries for monoenergetic photons and neutrons, in addition to cranial and caudal monoenergetic photons. Results reported demonstrated that organ-absorbed dose coefficients for most of the organs in the CRA and CAU irradiation geometries were significantly higher for the bent phantoms than for the upright phantom. Since the upright phantom underestimates the organ-absorbed dose, this demonstrates the impact of posture while performing dose calculations. Organ doses reported in past neutron dose coefficient data were found to omit effects from neutron resonances at energies of 0.435, 1.0, and 3.21 MeV from 16O in tissue. Reported data notes as high as 60% underestimation for neutron organ-absorbed doses, specifically at the neutron resonance energy region omitted by smoothing. Ongoing studies are examining the effect of resonances on reported neutron organ-absorbed dose coefficients in ICRP 116 geometries.