Social distancing causally impacts the spread of SARS-CoV-2: a U.S. nationwide event study.

We assess the causal impact of social distancing on the spread of SARS-CoV-2 in the U.S. using the quasi-natural experimental setting created by the spontaneous relaxation of social distancing behavior brought on by the protests that erupted across the nation following George Floyd's tragic death on May 25, 2020. Using a difference-in-difference specification and a balanced sample covering the [- 30, 30] day event window centered on the onset of protests, we document an increase of 1.34 cases per day, per 100,000 population, in the SARS-CoV-2 incidence rate in protest counties, relative to their propensity score matching non-protest counterparts. This represents a 26.8% increase in the incidence rate relative to the week preceding the protests. We find that the treatment effect only manifests itself after the onset of the protests and our placebo tests rule out the possibility that our findings are attributable to chance. Our research informs policy makers and provides insights regarding the usefulness of social distancing as an intervention to minimize the spread of SARS-CoV-2.

Deep learning of chest X-rays can predict mechanical ventilation outcome in ICU-admitted COVID-19 patients.

The COVID-19 pandemic repeatedly overwhelms healthcare systems capacity and forced the development and implementation of triage guidelines in ICU for scarce resources (e.g. mechanical ventilation). These guidelines were often based on known risk factors for COVID-19. It is proposed that image data, specifically bedside computed X-ray (CXR), provide additional predictive information on mortality following mechanical ventilation that can be incorporated in the guidelines. Deep transfer learning was used to extract convolutional features from a systematically collected, multi-institutional dataset of COVID-19 ICU patients. A model predicting outcome of mechanical ventilation (remission or mortality) was trained on the extracted features and compared to a model based on known, aggregated risk factors. The model reached a 0.702 area under the curve (95% CI 0.707-0.694) at predicting mechanical ventilation outcome from pre-intubation CXRs, higher than the risk factor model. Combining imaging data and risk factors increased model performance to 0.743 AUC (95% CI 0.746-0.732). Additionally, a post-hoc analysis showed an increase performance on high-quality than low-quality CXRs, suggesting that using only high-quality images would result in an even stronger model.

Cutaneous Leukocytoclastic Vasculitis Induction Following ChAdOx1 nCoV-19 Vaccine.

The coronavirus disease 2019 (COVID-19) pandemic is an unprecedented event, and in order to control its spread and minimize its damages, all efforts are immediately mobilized. Mass vaccination is considered a promising solution to combat this universal issue. However, given the urgent need for vaccine production, some of the side effects may not have been presented during trials and will only appear during the mass vaccination. Limited vasculitis cases have been reported so far following vaccination against COVID-19. We present a case of cutaneous leukocytoclastic vasculitis (LCV) induced following the first dose of the ChAdOx1 nCoV-19 vaccine in an otherwise healthy individual.

Asymptomatic Left Ventricular Malignant Psammomatous Melanotic Schwannoma.

Malignant psammomatous melanotic schwannoma (MPMS) is a rare type of tumour, occasionally reported to occur with mediastinal involvement. Histopathologic similarities with melanoma may lead to a wrong diagnosis, but distinguishing between types of tumours is mandatory for adequate management and prognosis. MPMS may be aggressive and manifest unpredictable behavior, with a poor midterm prognosis despite benign histopathologic features. We discuss the challenges that come with a diagnosis of MPMS, and the rationale for our treatment strategy, in this first report regarding MPMS involving the left heart ventricle.

Le schwannome mélanotique psammomateux malin (SMPM) est un type de tumeur rare qui est à l'occasion observé au niveau du médiastin. Ses similitudes histologiques avec le mélanome peuvent conduire à un diagnostic erroné, mais il est impératif de savoir faire la distinction entre ces types de tumeur pour optimiser la prise en charge et le pronostic. Le SMPM peut être agressif et avoir une évolution imprévisible, avec un pronostic défavorable à moyen terme malgré des caractéristiques histopathologiques bénignes. Dans cette première étude de cas de SMPM présentant une atteinte du ventricule gauche, nous décrivons les défis posés par un diagnostic de SMPM et justifions notre stratégie de traitement.

Modeling of vascular space occupancy and BOLD functional MRI from first principles using real microvascular angiograms.

PURPOSE: The vascular space occupancy (VASO) is a functional MRI technique for probing cerebral blood volume changes noninvasively, including during neuronal activation in humans. An important consideration when implementing VASO is the BOLD effect in the signal. Assessing the physical origin of this BOLD contamination and the capabilities of correction methods could improve the quantification of cerebral blood volume changes with VASO. METHODS: Given the heterogeneity of cerebral microvascular architecture, the vascular geometry within an MRI voxel can influence both BOLD and VASO signals. To investigate this effect, 3D high-resolution images of mouse cerebral vasculature measured with two-photon microscopy were used to model BOLD and VASO signals from first principles using Monte Carlo diffusion of water protons. Quantitative plots of VASO together with intravascular and extravascular BOLD signals as a function of TE at B0 fields 1.5 T to 14 T were obtained. RESULTS: The BOLD contamination of the VASO response was on the order of 50% for gradient echo and 5% for spin echo at 7 T and TE = 6 ms and significantly increased with TE and B0 . Two currently used correction schemes were shown to account for most of this contamination and recover accurate relative signal changes, with optimal correction obtained using TEs as short as possible. CONCLUSION: These results may provide useful information for optimizing sequence parameters in VASO and BOLD functional MRI, leading the way to a wider application of these techniques in healthy and diseased brain.

Binding symmetry and surface flexibility mediate antibody self-association.

Solution stability is an important factor in the optimization of engineered biotherapeutic candidates such as monoclonal antibodies because of its possible effects on manufacturability, pharmacology, efficacy and safety. A detailed atomic understanding of the mechanisms governing self-association of natively folded protein monomers is required to devise predictive tools to guide screening and re-engineering along the drug development pipeline. We investigated pairs of affinity-matured full-size antibodies and observed drastically different propensities to aggregate from variants differing by a single amino-acid. Biophysical testing showed that antigen-binding fragments (Fabs) from the aggregating antibodies also reversibly associated with equilibrium dissociation constants in the low-micromolar range. Crystal structures (PDB accession codes 6MXR, 6MXS, 6MY4, 6MY5) and bottom-up hydrogen-exchange mass spectrometry revealed that Fab self-association occurs in a symmetric mode that involves the antigen complementarity-determining regions. Subtle local conformational changes incurred upon point mutation of monomeric variants foster formation of complementary polar interactions and hydrophobic contacts to generate a dimeric Fab interface. Testing of popular in silico tools generally indicated low reliabilities for predicting the aggregation propensities observed. A structure-aggregation data set is provided here in order to stimulate further improvements of in silico tools for prediction of native aggregation. Incorporation of intermolecular docking, conformational flexibility, and short-range packing interactions may all be necessary features of the ideal algorithm.

Design and Characterization of Protein E-PilA, a Candidate Fusion Antigen for Nontypeable Haemophilus influenzae Vaccine.

Nontypeable Haemophilus influenzae (NTHi) is a pathogen known for being a frequent cause of acute otitis media in children and respiratory tract infections in adults with chronic obstructive pulmonary disease. In the present study, a vaccine antigen based on the fusion of two known NTHi adhesive proteins, protein E (PE) and a pilin subunit (PilA), was developed. The quality of the combined antigen was investigated through functional, biophysical, and structural analyses. It was shown that the PE and PilA individual structures are not modified in the PE-PilA fusion and that PE-PilA assembles as a dimer in solution, reflecting PE dimerization. PE-PilA was found to bind vitronectin by enzyme-linked immunosorbent assay, as isolated PE does. Disulfide bridges were conserved and homogeneous, which was determined by peptide mapping and top-down analysis of PE, PilA, and PE-PilA molecules. Finally, the PE-PilA crystal showed a PE entity with a three-dimensional (3D) structure similar to that of the recently published isolated PE, while the structure of the PilA entity was similar to that of a 3D model elaborated from two other type 4 pilin subunits. Taken together, our observations suggest that the two tethered proteins behave independently within the chimeric molecule and display structures similar to those of the respective isolated antigens, which are important characteristics for eliciting optimal antibody-mediated immunity. PE and PilA can thus be further developed as a single fusion protein in a vaccine perspective, in the knowledge that tethering the two antigens does not perceptibly compromise the structural attributes offered by the individual antigens.

Dependence of the MR signal on the magnetic susceptibility of blood studied with models based on real microvascular networks.

PURPOSE: The primary goal of this study was to estimate the value of ß , the exponent in the power law relating changes of the transverse relaxation rate and intra-extravascular local magnetic susceptibility differences as ΔR2∗∝(Δχ)ß . The secondary objective was to evaluate any differences that might exist in the value of ß obtained using a deoxyhemoglobin-weighted Δχ distribution versus a constant Δχ distribution assumed in earlier computations. The third objective was to estimate the value of ß that is relevant for methods based on susceptibility contrast agents with a concentration of Δχ higher than that used for BOLD fMRI calculations. METHODS: Our recently developed model of real microvascular anatomical networks is used to extend the original simplified Monte-Carlo simulations to compute ß from the first principles. RESULTS: Our results show that ß=1 for most BOLD fMRI measurements of real vascular networks, as opposed to earlier predictions of ß=1 .5 using uniform Δχ distributions. For perfusion or fMRI methods based on contrast agents, which generate larger values for Δχ , ß=1 for B0≤ 9.4 T, whereas at 14 T ß can drop below 1 and the variation across subjects is large, indicating that a lower concentration of contrast agent with a lower value of Δχ is desired for experiments at high B0 . CONCLUSION: These results improve our understanding of the relationship between R2* and the underlying microvascular properties. The findings will help to infer the cerebral metabolic rate of oxygen and cerebral blood volume from BOLD and perfusion MRI, respectively.

Early Renal Replacement Therapy Versus Standard Care in the ICU: A Systematic Review, Meta-Analysis, and Cost Analysis.

OBJECTIVE:: Renal replacement therapy (RRT) is the treatment of choice for severe acute kidney injury, but there are no firm guidelines as to the time of initiation of RRT in the critically ill. The primary objective of this study is to determine 1-month mortality rates of early versus late dialysis in critical care. As secondary end points, we provide a cost analysis of early versus late RRT initiation, intensive care unit (ICU) length of stay (LOS), hospital LOS, and number of patients on dialysis at day 60 postrandomization. DATA SOURCES:: We identified all randomized controlled trials (RCTs) through EMLINE and MEDBASE that examined adult patients admitted to critical care who were randomized to receiving early dialysis versus standard of care. STUDY SELECTION:: Inclusion criteria: (1) RCTs conducted after the year 2000, (2) the population evaluated had to be adults admitted to ICU, (3) the intervention had to be early RRT versus standard care, and (4) outcomes had to measure patient mortality. DATA EXTRACTION:: Two independent investigators reviewed search results and identified appropriate studies. Information was extracted using standardized case report forms. DATA SYNTHESIS:: Overall, 7 RCTs were included with a total of 1400 patients. Early RRT showed no survival benefit when compared to standard treatment (odds ratio [OR], 0.90 95% confidence interval [95% CI] 0.70-1.15, P = .39). There was no significant difference in length of hospital stay in patients with early RRT (-1.55 days [95% CI -4.75 to 1.65, P = .34]), in length of ICU stay (-0.79 days [95% CI -2.09 to 0.52], P = .24), or proportion of patients on dialysis at day 60 (OR 0.93 [95% CI 0.62 to 1.43], P = .79). Per patient, there is likely a small increase in costs (

Automatic Graph-Based Modeling of Brain Microvessels Captured With Two-Photon Microscopy.

Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network complexity and two-photon sensitivity limitations with depth. In this paper, we propose a fully automatic processing pipeline to address this issue. The modeling scheme consists of a fully-convolution neural network to segment microvessels, a three-dimensional surface model generator, and a geometry contraction algorithm to produce graphical models with a single connected component. Based on a quantitative assessment using NetMets metrics, at a tolerance of 60 µm, false negative and false positive geometric error 19 rates are 3.8% and 4.2%, respectively, whereas false nega- 20 tive and false positive topological error rates are 6.1% and 4.5%, respectively. Our qualitative evaluation confirms the efficiency of our scheme in generating useful and accurate graphical models.

Evaluation of an evidence-based medicine educational intervention in a regional medical campus.

BACKGROUND: Enhanced educational activities were developed by a regional medical campus (RMC) in order to incorporate evidence-based medicine (EBM) practice in the learning process of medical students. This study aimed to measure the effectiveness of these activities. METHODS: The experimental group was made up of third-year students from the RMC. The comparison group included students from the main campus of the medical school and another of its RMCs. The experimental group received additional training on EBM: one additional hour in class, plus skills development exercises throughout the semester. During the regular academic sessions, clinical questions requiring EBM literature searching skills were incorporated in the curriculum. Tests on knowledge and self-assessment of competencies were administered to all participants at the beginning and at the end of the semester. Data were analyzed using repeated measures analysis of variance and post hoc tests for within and between groups comparison. RESULTS: The Friedman test demonstrated a statistically significant effect of the intervention on knowledge (p <0.0001). The score of the knowledge test was significantly higher for the experimental group, when compared with baseline testing and with the comparison group (p <0.0001). Repeated measures analysis of variance demonstrated a statistically significant effect of the intervention on the score of the self-assessment of competencies (p=0.032). The score for the self-assessment of competencies was significantly higher for the experimental group when compared to baseline score (p <0.0001), but not with respect to the comparison group. CONCLUSION: Our study demonstrated the effectiveness of additional training and longitudinal integrated skills development leading to an increase in medical student knowledge and self-perception of competencies in EBM practice.

Cost Analysis of Noninvasive Helmet Ventilation Compared with Use of Noninvasive Face Mask in ARDS.

Intensive care unit (ICU) costs have doubled since 2000, totalling 108 billion dollars per year. Acute respiratory distress syndrome (ARDS) has a prevalence of 10.4% and a 28-day mortality of 34.8%. Noninvasive ventilation (NIV) is used in up to 30% of cases. A recent randomized controlled trial by Patel et al. (2016) showed lower intubation rates and 90-day mortality when comparing helmet to face mask NIV in ARDS. The population in the Patel et al. trial was used for cost analysis in this study. Projections of cost savings showed a decrease in ICU costs by $2527 and hospital costs by $3103 per patient, along with a 43.3% absolute reduction in intubation rates. Sensitivity analysis showed consistent cost reductions. Projected annual cost savings, assuming the current prevalence of ARDS, were $237538 in ICU costs and $291682 in hospital costs. At a national level, using yearly incidence of ARDS cases in American ICUs, this represents $449 million in savings. Helmet NIV, compared to face mask NIV, in nonintubated patients with ARDS, reduces ICU and hospital direct-variable costs along with intubation rates, LOS, and mortality. A large-scale cost-effectiveness analysis is needed to validate the findings.

The Impact of Palliative Care Consultation in the ICU on Length of Stay: A Systematic Review and Cost Evaluation.

INTRODUCTION: The intensive care unit (ICU) consumes 20% of hospital expenditures and 1% of gross domestic product. Many strategies have been attempted to reduce ICU costs. A systematic review was conducted to evaluate the effect of palliative care (PC) consultations in the ICU on length of stay (LOS) and costs. METHODS: A literature search was performed using PubMed, MEDLINE, EMBASE, and the Cochrane Library. Randomized controlled trials (RCTs), prospective, and retrospective cohort studies looking at PC consultations in adult ICUs published between January 2000 and February 2016 were selected. Independent reviewers assessed the eligibility of studies, extracted data on ICU, hospital LOS, and mortality, and rated each study's quality. The cost was derived from an existing model in the literature; the primary outcome was ICU LOS and the secondary outcomes were direct variable costs, mortality, and hospital LOS. RESULTS: We reviewed 814 abstracts, but only 8 studies met inclusion criteria and were included. The patients with a PC consultation in the ICU, when compared to those who did not, showed a trend toward reduced LOS. This reduction was statistically significant in the higher quality studies. Mortality was similar in both groups. Palliative care consultations also lead to a reduction in costs in 5 of the 8 eligible trials. On average, ICU costs were USD7533 and USD6406 (control vs PC, P < .05) and hospital direct variable costs were USD9518 and USD8971 ( P < .05) per admission. Due to interstudy heterogeneity, all outcomes were described narratively. CONCLUSION: This review demonstrates a trend that PC consultations reduce LOS and costs without impacting mortality. However, due to the small sample sizes and varying degrees of quality of evidence, many questions remain. A large multicenter RCT and formal economic evaluation would be needed for more definitive results.

Magnetic resonance fingerprinting based on realistic vasculature in mice.

Magnetic resonance fingerprinting (MRF) was recently proposed as a novel strategy for MR data acquisition and analysis. A variant of MRF called vascular MRF (vMRF) followed, that extracted maps of three parameters of physiological importance: cerebral oxygen saturation (SatO2), mean vessel radius and cerebral blood volume (CBV). However, this estimation was based on idealized 2-dimensional simulations of vascular networks using random cylinders and the empirical Bloch equations convolved with a diffusion kernel. Here we focus on studying the vascular MR fingerprint using real mouse angiograms and physiological values as the substrate for the MR simulations. The MR signal is calculated ab initio with a Monte Carlo approximation, by tracking the accumulated phase from a large number of protons diffusing within the angiogram. We first study the identifiability of parameters in simulations, showing that parameters are fully estimable at realistically high signal-to-noise ratios (SNR) when the same angiogram is used for dictionary generation and parameter estimation, but that large biases in the estimates persist when the angiograms are different. Despite these biases, simulations show that differences in parameters remain estimable. We then applied this methodology to data acquired using the GESFIDE sequence with SPIONs injected into 9 young wild type and 9 old atherosclerotic mice. Both the pre injection signal and the ratio of post-to-pre injection signals were modeled, using 5-dimensional dictionaries. The vMRF methodology extracted significant differences in SatO2, mean vessel radius and CBV between the two groups, consistent across brain regions and dictionaries. Further validation work is essential before vMRF can gain wider application.

Modeling of Cerebral Oxygen Transport Based on In vivo Microscopic Imaging of Microvascular Network Structure, Blood Flow, and Oxygenation.

Oxygen is delivered to brain tissue by a dense network of microvessels, which actively control cerebral blood flow (CBF) through vasodilation and contraction in response to changing levels of neural activity. Understanding these network-level processes is immediately relevant for (1) interpretation of functional Magnetic Resonance Imaging (fMRI) signals, and (2) investigation of neurological diseases in which a deterioration of neurovascular and neuro-metabolic physiology contributes to motor and cognitive decline. Experimental data on the structure, flow and oxygen levels of microvascular networks are needed, together with theoretical methods to integrate this information and predict physiologically relevant properties that are not directly measurable. Recent progress in optical imaging technologies for high-resolution in vivo measurement of the cerebral microvascular architecture, blood flow, and oxygenation enables construction of detailed computational models of cerebral hemodynamics and oxygen transport based on realistic three-dimensional microvascular networks. In this article, we review state-of-the-art optical microscopy technologies for quantitative in vivo imaging of cerebral microvascular structure, blood flow and oxygenation, and theoretical methods that utilize such data to generate spatially resolved models for blood flow and oxygen transport. These "bottom-up" models are essential for the understanding of the processes governing brain oxygenation in normal and disease states and for eventual translation of the lessons learned from animal studies to humans.

The roadmap for estimation of cell-type-specific neuronal activity from non-invasive measurements.

The computational properties of the human brain arise from an intricate interplay between billions of neurons connected in complex networks. However, our ability to study these networks in healthy human brain is limited by the necessity to use non-invasive technologies. This is in contrast to animal models where a rich, detailed view of cellular-level brain function with cell-type-specific molecular identity has become available due to recent advances in microscopic optical imaging and genetics. Thus, a central challenge facing neuroscience today is leveraging these mechanistic insights from animal studies to accurately draw physiological inferences from non-invasive signals in humans. On the essential path towards this goal is the development of a detailed 'bottom-up' forward model bridging neuronal activity at the level of cell-type-specific populations to non-invasive imaging signals. The general idea is that specific neuronal cell types have identifiable signatures in the way they drive changes in cerebral blood flow, cerebral metabolic rate of O2 (measurable with quantitative functional Magnetic Resonance Imaging), and electrical currents/potentials (measurable with magneto/electroencephalography). This forward model would then provide the 'ground truth' for the development of new tools for tackling the inverse problem-estimation of neuronal activity from multimodal non-invasive imaging data.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'.