Improved multifidelity Monte Carlo estimators based on normalizing flows and dimensionality reduction techniques.

We study the problem of multifidelity uncertainty propagation for computationally expensive models. In particular, we consider the general setting where the high-fidelity and low-fidelity models have a dissimilar parameterization both in terms of number of random inputs and their probability distributions, which can be either known in closed form or provided through samples. We derive novel multifidelity Monte Carlo estimators which rely on a shared subspace between the high-fidelity and low-fidelity models where the parameters follow the same probability distribution, i.e., a standard Gaussian. We build the shared space employing normalizing flows to map different probability distributions into a common one, together with linear and nonlinear dimensionality reduction techniques, active subspaces and autoencoders, respectively, which capture the subspaces where the models vary the most. We then compose the existing low-fidelity model with these transformations and construct modified models with an increased correlation with the high-fidelity model, which therefore yield multifidelity estimators with reduced variance. A series of numerical experiments illustrate the properties and advantages of our approaches.

Oral probiotic therapy improves motor function in a rodent model of sensorimotor stroke.

Ischemic stroke is a debilitating neurological disease with few effective therapeutics. Previous work has shown that oral probiotic treatment prior to stroke can attenuate cerebral infarction and neuroinflammation, highlighting the gut-microbiota-brain axis as a novel therapeutic target. Whether a more clinically relevant, post-stroke, administration of probiotics can improve stroke outcomes is unknown. In this study, we examined the effect of post-stroke oral probiotic therapy on motor behavior in the pre-clinical mouse endothelin-1 (ET-1) model of sensorimotor stroke. We found that post-stroke oral probiotic therapy with Cerebiome® (Lallemand, Montreal, Canada), containing B. longum R0175 and L. helveticus R0052, improved functional recovery and changed the composition of the post-stroke gut microbiota. Interestingly, oral Cerebiome® administration did not result in alterations of lesion volume or the number of CD8+/Iba1+ cells in the injured tissue. Overall, these findings suggest that probiotic treatment following injury can improve sensorimotor function.

Outcome of liver cancer patients with SARS-CoV-2 infection: An International, Multicentre, Cohort Study.

BACKGROUND & AIMS: Information about the impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in patients with liver cancer is lacking. This study characterizes the outcomes and mortality risk in this population. METHODS: Multicentre retrospective, cross-sectional, international study of liver cancer patients with SARS-CoV-2 infection registered between February and December 2020. Clinical data at SARS-CoV-2 diagnosis and outcomes were registered. RESULTS: Two hundred fifty patients from 38 centres were included, 218 with hepatocellular carcinoma (HCC) and 32 with intrahepatic cholangiocarcinoma (iCCA). The median age was 66.5 and 64.5 years, and 84.9% and 21.9% had cirrhosis in the HCC and iCCA cohorts respectively. Patients had advanced cancer stage at SARS-CoV-2 diagnosis in 39.0% of the HCC and 71.9% of the iCCA patients. After a median follow-up of 7.20 (IQR: 1.84-11.24) months, 100 (40%) patients have died, 48% of the deaths were SARS-CoV-2-related. Forty (18.4%) HCC patients died within 30-days. The death rate increase was significantly different according to the BCLC stage (6.10% [95% CI 2.24-12.74], 11.76% [95% CI 4.73-22.30], 20.69% [95% CI 11.35-31.96] and 34.52% [95% CI 17.03-52.78] for BCLC 0/A, B, C and D, respectively; p = .0017). The hazard ratio was 1.45 (95% CI 0.49-4.31; p = .5032) in BCLC-B versus 0/A, and 3.13 (95% CI 1.29-7.62; p = .0118) in BCLC-C versus 0/A in the competing risk Cox regression model. Nineteen out of 32 iCCA (59.4%) died, and 12 deaths were related to SARS-CoV-2 infection. CONCLUSIONS: This is the largest cohort of liver cancer patients infected with SARS-CoV-2. It characterizes the 30-day mortality risk of SARS-CoV-2 infected patients with HCC during this period.

Development of recombinant human granulocyte colony-stimulating factor (nartograstim) production process in Escherichia coli compatible with industrial scale and with no antibiotics in the culture medium.

The granulocyte colony-stimulating factor (G-CSF) is a hematopoietic cytokine that has important clinical applications for treating neutropenia. Nartograstim is a recombinant variant of human G-CSF. Nartograstim has been produced in Escherichia coli as inclusion bodies (IB) and presents higher stability and biological activity than the wild type of human G-CSF because of its mutations. We developed a production process of nartograstim in a 10-L bioreactor using auto-induction or chemically defined medium. After cell lysis, centrifugation, IB washing, and IB solubilization, the following three refolding methods were evaluated: diafiltration, dialysis, and direct dilution in two refolding buffers. Western blot and SDS-PAGE confirmed the identity of 18.8-kDa bands as nartograstim in both cultures. The auto-induction medium produced 1.17 g/L and chemically defined medium produced 0.95 g/L. The dilution method yielded the highest percentage of refolding (99%). After refolding, many contaminant proteins precipitated during pH adjustment to 5.2, increasing purity from 50 to 78%. After applying the supernatant to cation exchange chromatography (CEC), nartograstim recovery was low and the purity was 87%. However, when the refolding solution was applied to anion exchange chromatography followed by CEC, 91%-98% purity and 2.2% recovery were obtained. The purification process described in this work can be used to obtain nartograstim with high purity, structural integrity, and the expected biological activity. KEY POINTS: • Few papers report the final recovery of the purification process from inclusion bodies. • The process developed led to high purity and reasonable recovery compared to literature. • Nartograstim biological activity was demonstrated in mice using a neutropenia model.

Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks.

We propose a novel approach to generate samples from the conditional distribution of patient-specific cardiovascular models given a clinically aquired image volume. A convolutional neural network architecture with dropout layers is first trained for vessel lumen segmentation using a regression approach, to enable Bayesian estimation of vessel lumen surfaces. This network is then integrated into a path-planning patient-specific modeling pipeline to generate families of cardiovascular models. We demonstrate our approach by quantifying the effect of geometric uncertainty on the hemodynamics for three patient-specific anatomies, an aorto-iliac bifurcation, an abdominal aortic aneurysm and a sub-model of the left coronary arteries. A key innovation introduced in the proposed approach is the ability to learn geometric uncertainty directly from training data. The results show how geometric uncertainty produces coefficients of variation comparable to or larger than other sources of uncertainty for wall shear stress and velocity magnitude, but has limited impact on pressure. Specifically, this is true for anatomies characterized by small vessel sizes, and for local vessel lesions seen infrequently during network training.

Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics.

Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.

Light-actuated contactless macro motors exploiting Bénard-Marangoni convection.

Near-infrared light is commonly used to move small objects floating on water by exploiting the Bénard-Marangoni convection. This is because infrared light is absorbed well by water and the induced thermal gradients are responsible for the objects' motion. However, visible light was recently used to move macroscopic objects on the free liquid surfaces. In this work, we show the use of visible light to rotate symmetric millimeter-sized objects. Those objects represent light-driven macro motors that are able to work in a continuous or step-by-step mode. We studied light intensity's effects on our system's angular velocity and estimated the entire process's conversion efficiency.

Experience with Sorafenib in 3 Hospitals in Sao Paulo.

INTRODUCTION AND AIM: Sorafenib has been the standard of care for first-line treatment of advanced hepatocellular carcinoma, a complex disease that affects an extremely heterogenous population. Thereby requiring multidisciplinary individualized treatment strategies that match the disease characteristics and the patients' specific needs. MATERIAL AND METHODS: Data for 175 patients who received sorafenib for hepatocellular carcinoma in three different hospitals in Sao Paulo, Brazil over a span of nine years were retrospectively analyzed. RESULTS: The median age was 62 years. Percentages of patients with Child-Pugh A, B and C liver cirrhosis were 61%, 31% and 5%, respectively. Approximately half of the patients had Barcelona Clinic Liver Cancer stage B disease, and the other half had stage C. The median treatment duration was 253 days. Sorafenib dose was reduced to 400 mg/day in 41% of the patients due to toxicity. Overall objective response rate as per Response Evaluation Criteria in Solid Tumors and its modified version was 39%. Patients who received transarterial chemoembolization (TACE) at any point during sorafenib therapy were significantly more likely to experience an objective response. After a median follow-up of 339 days, the median overall survival was 380 days. Child-Pugh cirrhosis, tumor response and concomitant chemoembolization were independent prognostic factors for overall survival in multivariate analysis. CONCLUSION: Our results suggest that, in experienced hands, sorafenib therapy may benefit carefully selected hepatocellular carcinoma patients for whom other therapies are initially contraindicated, including those patients with Child-Pugh B liver function and those patients who are subsequently treated with concomitant TACE.

Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts.

Coronary artery bypass graft surgery (CABG) is performed on more than 400,000 patients annually in the U.S. However, saphenous vein grafts (SVGs) implanted during CABG exhibit poor patency compared to arterial grafts, with failure rates up to 40% within 10 years after surgery. Differences in mechanical stimuli are known to play a role in driving maladaptation and have been correlated with endothelial damage and thrombus formation. As these quantities are difficult to measure in vivo, multi-scale coronary models offer a way to quantify them, while accounting for complex coronary physiology. However, prior studies have primarily focused on deterministic evaluations, without reporting variability in the model parameters due to uncertainty. This study aims to assess confidence in multi-scale predictions of wall shear stress and wall strain while accounting for uncertainty in peripheral hemodynamics and material properties. Boundary condition distributions are computed by assimilating uncertain clinical data, while spatial variations of vessel wall stiffness are obtained through approximation by a random field. We developed a stochastic submodeling approach to mitigate the computational burden of repeated multi-scale model evaluations to focus exclusively on the bypass grafts. This produces a two-level decomposition of quantities of interest into submodel contributions and full model/submodel discrepancies. We leverage these two levels in the context of forward uncertainty propagation using a previously proposed multi-resolution approach. The time- and space-averaged wall shear stress is well estimated with a coefficient of variation of <35%, but ignorance about the spatial distribution on the wall elastic modulus and thickness lead to large variations in an objective measure of wall strain, with coefficients of variation up to 100%. Sensitivity analysis reveals how the interactions between the flow and material parameters contribute to output variability.

Hygromycin B hypersensitive (hhy) mutants implicate an intact trans-Golgi and late endosome interface in efficient Tor1 vacuolar localization and TORC1 function.

Saccharomyces cerevisiae vacuoles are functionally analogous to mammalian lysosomes. Both also serve as physical platforms for Tor Complex 1 (TORC1) signal transduction, the master regulator of cellular growth and proliferation. Hygromycin B is a eukaryotic translation inhibitor. We recently reported on hygromycin B hypersensitive (hhy) mutants that fail to grow at subtranslation inhibitory concentrations of the drug and exhibit vacuolar defects (Banuelos et al. in Curr Genet 56:121-137, 2010). Here, we show that hhy phenotype is not due to increased sensitivity to translation inhibition and establish a super HHY (s-HHY) subgroup of genes comprised of ARF1, CHC1, DRS2, SAC1, VPS1, VPS34, VPS45, VPS52, and VPS54 that function exclusively or inclusively at trans-Golgi and late endosome interface. Live cell imaging of s-hhy mutants revealed that hygromycin B treatment disrupts vacuolar morphology and the localization of late endosome marker Pep12, but not that of late endosome-independent vacuolar SNARE Vam3. This, along with normal post-late endosome trafficking of the vital dye FM4-64, establishes that severe hypersensitivity to hygromycin B correlates specifically with compromised trans-Golgi and late endosome interface. We also show that Tor1p vacuolar localization and TORC1 anabolic functions, including growth promotion and phosphorylation of its direct substrate Sch9, are compromised in s-hhy mutants. Thus, an intact trans-Golgi and late endosome interface is a requisite for efficient Tor1 vacuolar localization and TORC1 function.

Measuring mental well-being in Norway: validation of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS).

BACKGROUND: Mental well-being is an important, yet understudied, area of research, partly due to lack of appropriate population-based measures. The Warwick-Edinburgh Mental Well-being Scale (WEMWBS) was developed to meet the needs for such a measure. This article assesses the psychometric properties of the Norwegian version of the WEMWBS, and its short-version (SWEMWBS) among a sample of primary health care patients who participated in the evaluation of Prompt Mental Health Care (PMHC), a novel Norwegian mental health care program aimed to increase access to treatment for anxiety and depression. METHODS: Forward and back-translations were conducted, and 1168 patients filled out an electronic survey including the WEMWBS, and other mental health scales. The original dataset was randomly divided into a training sample (≈70%) and a validation sample (≈30%). Parallel analysis and confirmatory factor analysis were carried out to assess construct validity and precision. The final models were cross-validated in the validation sample by specifying a model with fixed parameters based on the estimates from the trainings set. Criterion validity and measurement invariance of the (S)WEMWBS were examined as well. RESULTS: Support was found for the single factor hypothesis in both scales, but similar to previous studies, only after a number of residuals were allowed to correlate (WEMWBS: CFI = 0.99; RMSEA = 0.06, SWEMWBS: CFI = .99; RMSEA = 0.06). Further analyses showed that the correlated residuals did not alter the meaning of the underlying construct and did not substantially affect the associations with other variables. Precision was high for both versions of the WEMWBS (>.80), and scalar measurement invariance was obtained for gender and age group. The final measurement models displayed adequate fit statistics in the validation sample as well. Correlations with other mental health scales were largely in line with expectations. No statistically significant differences were found in mean latent (S)WEMWBS scores for age and gender. CONCLUSION: Both WEMWBS scales appear to be valid and precise instruments to measure mental well-being in primary health care patients. The results encourage the use of mental well-being as an outcome in future epidemiological, clinical, and evaluation studies, and may as such be valuable for both research and public health practice.

Automated Tuning for Parameter Identification and Uncertainty Quantification in Multi-scale Coronary Simulations.

Atherosclerotic coronary artery disease, which can result in coronary artery stenosis, acute coronary artery occlusion, and eventually myocardial infarction, is a major cause of morbidity and mortality worldwide. Non-invasive characterization of coronary blood flow is important to improve understanding, prevention, and treatment of this disease. Computational simulations can now produce clinically relevant hemodynamic quantities using only non-invasive measurements, combining detailed three dimensional fluid mechanics with physiological models in a multiscale framework. These models, however, require specification of numerous input parameters and are typically tuned manually without accounting for uncertainty in the clinical data, hindering their application to large clinical studies. We propose an automatic, Bayesian, approach to parameter estimation based on adaptive Markov chain Monte Carlo sampling that assimilates non-invasive quantities commonly acquired in routine clinical care, quantifies the uncertainty in the estimated parameters and computes the confidence in local predicted hemodynamic indicators.

Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics.

Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.

A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis.

The substantial computational cost of high-fidelity models in numerical hemodynamics has, so far, relegated their use mainly to offline treatment planning. New breakthroughs in data-driven architectures and optimization techniques for fast surrogate modeling provide an exciting opportunity to overcome these limitations, enabling the use of such technology for time-critical decisions. We discuss an application to the repair of multiple stenosis in peripheral pulmonary artery disease through either transcatheter pulmonary artery rehabilitation or surgery, where it is of interest to achieve desired pressures and flows at specific locations in the pulmonary artery tree, while minimizing the risk for the patient. Since different degrees of success can be achieved in practice during treatment, we formulate the problem in probability, and solve it through a sample-based approach. We propose a new offline-online pipeline for probabilistic real-time treatment planning which combines offline assimilation of boundary conditions, model reduction, and training dataset generation with online estimation of marginal probabilities, possibly conditioned on the degree of augmentation observed in already repaired lesions. Moreover, we propose a new approach for the parametrization of arbitrarily shaped vascular repairs through iterative corrections of a zero-dimensional approximant. We demonstrate this pipeline for a diseased model of the pulmonary artery tree available through the Vascular Model Repository.

Aspergillus fumigatus AcuM regulates both iron acquisition and gluconeogenesis.

Relatively few transcription factors that govern the virulence of Aspergillus fumigatus are known. We constructed 11 A. fumigatus transcription factor mutants and screened them for altered virulence in Galleria mellonella larvae. We discovered that the zinc cluster transcription factor, AcuM, is essential for maximal virulence in this model, as well as in murine models of haematogenously disseminated and invasive pulmonary aspergillosis. Transcriptional profiling experiments suggested that AcuM suppresses sreA and induces hapX to stimulate expression of genes involved in both reductive iron assimilation and siderophore-mediated iron uptake. Consistent with these results, a ΔacuM mutant had reduced iron incorporation, decreased extracellular siderophore production and impaired capacity to grow under iron-limited conditions. Interestingly, an Aspergillus nidulansΔacuM mutant had normal extracellular siderophore production and growth under iron-limited conditions, indicating that AcuM does not govern iron acquisition in this organism. A. fumigatus AcuM also regulated genes involved in gluconeogenesis, and the ΔacuM mutant had impaired growth on gluconeogenic carbon sources. Deletion of sreA in the ΔacuM mutant restored iron uptake, extracellular siderophore production and virulence, but not the defect in gluconeogenesis. Thus, AcuM represses SreA and thereby induces iron acquisition, a process that is essential for the maximal virulence of A. fumigatus.

Aspergillus fumigatus MedA governs adherence, host cell interactions and virulence.

In medically important fungi, regulatory elements that control development and asexual reproduction often govern the expression of virulence traits. We therefore cloned the Aspergillus fumigatus developmental modifier MedA and characterized its role in conidiation, host cell interactions and virulence. As in the model organism Aspergillus nidulans, disruption of medA in A. fumigatus dramatically reduced conidiation. However, the conidiophore morphology was markedly different between the two species. Further, gene expression analysis suggested that MedA governs conidiation through different pathways in A. fumigatus compared with A. nidulans. The A. fumigatusDeltamedA strain was impaired in biofilm production and adherence to plastic, as well as adherence to pulmonary epithelial cells, endothelial cells and fibronectin in vitro. The DeltamedA strain also had reduced capacity to damage pulmonary epithelial cells, and stimulate pro-inflammatory cytokine mRNA and protein expression. Consistent with these results, the A. fumigatusDeltamedA strain also exhibited reduced virulence in both an invertebrate and a mammalian model of invasive aspergillosis. Collectively, these results suggest that the downstream targets of A. fumigatus MedA mediate virulence, and may provide novel therapeutic targets for invasive aspergillosis.

Role of Aspergillus fumigatus DvrA in host cell interactions and virulence.

The transcription factors that regulate Aspergillus fumigatus interactions with host cells and virulence are incompletely defined. We investigated the role of the putative C2H2 transcription factor DvrA in governing these processes. Although DvrA was identified by its limited homology to Candida albicans Bcr1, a ΔdvrA mutant strain of A. fumigatus had wild-type adherence to host constituents in vitro. However, it had increased capacity to damage both endothelial cells and a pulmonary epithelial cell line compared to the ability of the wild-type strain and a ΔdvrA::dvrA-complemented strain. This increase in damage required direct contact between the mutant and host cells. The ΔdvrA mutant also stimulated greater CCL20, interleukin-8, and tumor necrosis factor mRNA expression in a pulmonary epithelial cell line compared to levels induced by the control strains. Also, it was resistant to nikkomycin Z, suggesting an altered cell wall composition. As predicted by these in vitro results, the ΔdvrA mutant had increased virulence and stimulated a greater pulmonary inflammatory response than the wild-type strain and ΔdvrA::dvrA-complemented strains in the nonneutropenic mouse model of invasive pulmonary aspergillosis. These results indicate that DvrA influences A. fumigatus virulence as well as its capacity to damage host cells and stimulate a proinflammatory response.

Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction.

Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.