A comparative study of IL-33 and its receptor ST2 in a C57BL/6 J mouse model of pulmonary Cryptococcus neoformans infection.

It has been reported that IL-33 receptor ST2 deficiency mitigates Cryptococcus neoformans (C. neoformans) pulmonary infection in BALB/c mice. IL-33 may modulate immune responses in ST2-dependent and ST2-independent manners. The host genetic background (i.e., BALB/c, C57BL/6 J) influences immune responses against C. neoformans. In the present study, we aimed to explore the roles of IL-33 and ST2 in pulmonary C. neoformans-infected mice on a C57BL/6 J genetic background. C. neoformans infection increased IL-33 expression in lung tissues. IL-33 deficiency but not ST2 deficiency significantly extended the survival time of C. neoformans-infected mice. In contrast, either IL-33 or ST2 deficiency reduced fungal burdens in lung, spleen and brain tissues from the mice following C. neoformans intratracheal inoculation. Similarly, inflammatory responses in the lung tissues were more pronounced in both the IL-33-/- and ST2-/- infected mice. However, mucus production was decreased in IL-33-/- infected mice alone, and the level of IL-5 in bronchoalveolar lavage fluid (BALF) was substantially decreased in the IL-33-/- infected mice but not ST2-/- infected mice. Moreover, IL-33 deficiency but not ST2 deficiency increased iNOS-positive macrophages. At the early stage of infection, the reduced pulmonary fungal burden in the IL-33-/- and ST2-/- mice was accompanied by increased neutrophil infiltration. Collectively, IL-33 regulated pulmonary C. neoformans infection in an ST2-dependent and ST2-independent manner in C57BL/6 J mice.

G2Φnet: Relating genotype and biomechanical phenotype of tissues with deep learning.

Many genetic mutations adversely affect the structure and function of load-bearing soft tissues, with clinical sequelae often responsible for disability or death. Parallel advances in genetics and histomechanical characterization provide significant insight into these conditions, but there remains a pressing need to integrate such information. We present a novel genotype-to-biomechanical phenotype neural network (G2Φnet) for characterizing and classifying biomechanical properties of soft tissues, which serve as important functional readouts of tissue health or disease. We illustrate the utility of our approach by inferring the nonlinear, genotype-dependent constitutive behavior of the aorta for four mouse models involving defects or deficiencies in extracellular constituents. We show that G2Φnet can infer the biomechanical response while simultaneously ascribing the associated genotype by utilizing limited, noisy, and unstructured experimental data. More broadly, G2Φnet provides a powerful method and a paradigm shift for correlating genotype and biomechanical phenotype quantitatively, promising a better understanding of their interplay in biological tissues.

Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems.

Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous descriptions are coupled together for predicting the system's response. The solver with lower fidelity (coarse) is responsible for simulating domains with homogeneous features, whereas the expensive high-fidelity (fine) model describes microscopic features with refined discretization, often making the overall cost prohibitively high, especially for time-dependent problems. In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver. DeepONet is trained offline using data acquired from the fine solver for learning the underlying and possibly unknown fine-scale dynamics. It is then coupled with standard PDE solvers for predicting the multiscale systems with new boundary/initial conditions in the coupling stage. The proposed framework significantly reduces the computational cost of multiscale simulations since the DeepONet inference cost is negligible, facilitating readily the incorporation of a plurality of interface conditions and coupling schemes. We present various benchmarks to assess the accuracy and efficiency, including static and time-dependent problems. We also demonstrate the feasibility of coupling of a continuum model (finite element methods, FEM) with a neural operator, serving as a surrogate of a particle system (Smoothed Particle Hydrodynamics, SPH), for predicting mechanical responses of anisotropic and hyperelastic materials. What makes this approach unique is that a well-trained over-parametrized DeepONet can generalize well and make predictions at a negligible cost.

Fatigue fracture of nearly elastic hydrogels.

Polyacrylamide hydrogels are highly stretchable and nearly elastic. Their stress-stretch curves exhibit small hysteresis, and change negligibly after many loading cycles. Polyacrylamide is used extensively in applications, and is the primary network for many types of tough hydrogels. Recent experiments have shown that polyacrylamide hydrogels are susceptible to fatigue fracture, but available data are limited. Here we study fatigue fracture of polyacrylamide hydrogels of various water contents. We form polymer networks in all samples under the same conditions, and then obtain hydrogels of 96, 87, 78, and 69 wt% of water by solvent exchange. We measure the crack extension under cyclic loads, and the fracture energy under monotonic loading. For the hydrogels of the four water contents, the fatigue thresholds are 4.3, 8.4, 20.5, and 64.5 J m-2, and the fracture energies are 18.9, 71.2, 289, and 611 J m-2. The measured thresholds agree well with the predictions of the Lake-Thomas model for hydrogels of high water content, but not in the case of low water content. It is hoped that further basic studies will soon follow to aid the development of fatigue-resistant hydrogels.

Analyses of internal structures and defects in materials using physics-informed neural networks.

Characterizing internal structures and defects in materials is a challenging task, often requiring solutions to inverse problems with unknown topology, geometry, material properties, and nonlinear deformation. Here, we present a general framework based on physics-informed neural networks for identifying unknown geometric and material parameters. By using a mesh-free method, we parameterize the geometry of the material using a differentiable and trainable method that can identify multiple structural features. We validate this approach for materials with internal voids/inclusions using constitutive models that encompass the spectrum of linear elasticity, hyperelasticity, and plasticity. We predict the size, shape, and location of the internal void/inclusion as well as the elastic modulus of the inclusion. Our general framework can be applied to other inverse problems in different applications that involve unknown material properties and highly deformable geometries, targeting material characterization, quality assurance, and structural design.

Mid- to Long-Term Clinical and Echocardiographic Effects of Post-procedural Permanent Pacemaker Implantation After Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis.

Aims: To date, the prognostic effects of permanent pacemaker implantation (PPI) after transcatheter aortic valve replacement (TAVR) remain controversial. The purpose of this meta-analysis was to investigate the mid- (1 year) to long-term (> 1 year) clinical and echocardiographic effects of post-procedural PPI in patients after TAVR. Methods: PubMed, Embase, Web of Science, and Cochrane Library databases were systematically searched from the establishment of databases up to 1 December 2021. Studies comparing clinical and echocardiographic outcomes between patients with and without post-TAVR PPI of ≥ 1-year follow-up were collected for further meta-analysis. Results: A total of 39 studies comprising of 83,082 patients were included in this meta-analysis. At mid-term follow-up (1 year), the pooled results demonstrated a higher risk of all-cause mortality in patients with post-procedural PPI than those without following TAVR (relative risk (RR), 1.17; 95% CI, 1.10-1.24; P < 0.00001). No significant differences were observed in cardiovascular mortality (RR, 0.86; 95% CI, 0.71-1.03; P = 0.10) or heart failure rehospitalization (RR, 0.91; 95% CI, 0.58-1.44; P = 0.69) at 1-year follow-up. At long-term follow-up (> 1 year), post-TAVR PPI had negative effects on all-cause mortality (RR, 1.18; 95% CI, 1.09-1.28; P < 0.0001) and heart failure rehospitalization (RR, 1.42; 95% CI, 1.18-1.71; P = 0.0002). There was no difference in long-term cardiovascular mortality between the two groups (RR, 1.15; 95% CI, 0.97-1.36; P = 0.11). Left ventricular ejection fraction (LVEF) was not significantly different at baseline (mean difference, 1.40; 95% CI, -0.13-2.93; P = 0.07), but was significantly lower in the PPI group at 1-year follow-up (mean difference, -3.57; 95% CI, -4.88 to -2.26; P < 0.00001). Conclusion: Our meta-analysis provides evidence that post-TAVR PPI has negative clinical and echocardiographic effects on patients at mid- to long-term follow-up. Further studies are urgently needed to explore the cause of these complications and optimize the treatment and management of patients requiring permanent pacing after TAVR. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021289935], identifier [CRD42021289935].

Simulating progressive intramural damage leading to aortic dissection using DeepONet: an operator-regression neural network.

Aortic dissection progresses mainly via delamination of the medial layer of the wall. Notwithstanding the complexity of this process, insight has been gleaned by studying in vitro and in silico the progression of dissection driven by quasi-static pressurization of the intramural space by fluid injection, which demonstrates that the differential propensity of dissection along the aorta can be affected by spatial distributions of structurally significant interlamellar struts that connect adjacent elastic lamellae. In particular, diverse histological microstructures may lead to differential mechanical behaviour during dissection, including the pressure-volume relationship of the injected fluid and the displacement field between adjacent lamellae. In this study, we develop a data-driven surrogate model of the delamination process for differential strut distributions using DeepONet, a new operator-regression neural network. This surrogate model is trained to predict the pressure-volume curve of the injected fluid and the damage progression within the wall given a spatial distribution of struts, with in silico data generated using a phase-field finite-element model. The results show that DeepONet can provide accurate predictions for diverse strut distributions, indicating that this composite branch-trunk neural network can effectively extract the underlying functional relationship between distinctive microstructures and their mechanical properties. More broadly, DeepONet can facilitate surrogate model-based analyses to quantify biological variability, improve inverse design and predict mechanical properties based on multi-modality experimental data.

Fine Particulate Matter (PM2.5) Promotes CD146 Expression in Alveolar Epithelial Cells and Cryptococcus neoformans Pulmonary Infection.

Air pollution is a leading cause of increasing infectious lung diseases. Pulmonary cryptococcosis is a fatal fungal pneumonia in acquired immunodeficiency syndrome patients. In some cases, the pathogen Cryptococcus neoformans also develops dormant nodules in immunocompetent individuals. In the present study, we demonstrated that fine particulate matter (PM2.5) increased CD146 expression in alveolar epithelial cells and promoted C. neoformans pulmonary infection. Aryl hydrocarbon receptor (AhR) signaling was required for increased expression of CD146 in epithelial cells treated with PM2.5. In a murine model of pulmonary infection, PM2.5 promoted fungal infection, and CD146 deficiency decreased the fugal burden of C. neoformans. Our study may highlight the importance of air pollution to lung mycosis and CD146 as a target for preventing infectious lung diseases.