Rapid Prototypable Biomimetic Peristalsis Bioreactor Capable of Concurrent Shear and Multi-Axial Strain.

Peristalsis is a nuanced mechanical stimulus comprised of multi-axial strain (radial and axial strain) and shear stress. Forces associated with peristalsis regulate diverse biological functions including digestion, reproductive function, and urine dynamics. Given the central role peristalsis plays in physiology and pathophysiology, we were motivated to design a bioreactor capable of holistically mimicking peristalsis. We engineered a novel rotating screw-drive based design combined with a peristaltic pump, in order to deliver multi-axial strain and concurrent shear stress to a biocompatible polydimethylsiloxane (PDMS) membrane "wall." Radial indentation and rotation of the screw drive against the wall demonstrated multi-axial strain evaluated via finite element modeling. Experimental measurements of strain using piezoelectric strain resistors were in close alignment with model-predicted values (15.9 ± 4.2% vs. 15.2% predicted). Modeling of shear stress on the "wall" indicated a uniform velocity profile and a moderate shear stress of 0.4 Pa. Human mesenchymal stem cells (hMSCs) seeded on the PDMS "wall" and stimulated with peristalsis demonstrated dramatic changes in actin filament alignment, proliferation, and nuclear morphology compared to static controls, perfusion, or strain, indicating that hMSCs sensed and responded to peristalsis uniquely. Lastly, significant differences were observed in gene expression patterns of calponin, caldesmon, smooth muscle actin, and transgelin, corroborating the propensity of hMSCs toward myogenic differentiation in response to peristalsis. Collectively, our data suggest that the peristalsis bioreactor is capable of generating concurrent multi-axial strain and shear stress on a "wall." hMSCs experience peristalsis differently than perfusion or strain, resulting in changes in proliferation, actin fiber organization, smooth muscle actin expression, and genetic markers of differentiation. The peristalsis bioreactor device has broad utility in the study of development and disease in several organ systems.

A machine learning model to estimate myocardial stiffness from EDPVR.

In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters [Formula: see text] and [Formula: see text] associated with fiber direction ([Formula: see text] and [Formula: see text]). After conducting permutation feature importance analysis, the ML performance further improved for [Formula: see text] ([Formula: see text]), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases.

Stress-Measure Dependence of Phase Transformation Criterion under Finite Strains: Hierarchy of Crystal Lattice Instabilities for Homogeneous and Heterogeneous Transformations.

Hierarchy of crystal lattice instabilities leading to a first-order phase transformation (PT) is found, which consists of PT instability described by the order parameter and elastic instabilities under different prescribed stress measures. After PT instability and prior to the elastic instability, an unexpected continuous third-order PT was discovered, which is followed by a first-order PT after the elastic instability. Under prescribed compressive second Piola-Kirchhoff stress, PT is third order until completion; it occurs without hysteresis and dissipation, properties that are ideal for various applications. For heterogeneous perturbations and PT, first-order PT occurs when the first elastic instability criterion (among criteria corresponding to different stress measures) is met inside the volume, surprisingly independent of the stress measure prescribed at the boundary.

Comparison of Intra-Socket Bupivacaine Administration Versus Oral Mefenamic Acid Capsule for Postoperative Pain Management Following Removal of Impacted Mandibular Third Molars.

PURPOSE: Surgical removal of impacted third molar teeth is one of the most common surgical procedures performed in oral and maxillofacial surgery. Postoperative pain is a common and predictable occurrence after maxillofacial surgery. MATERIALS AND METHODS: This randomized double-blind clinical trial was conducted with a crossover design in which each patient served as his or her own control. Forty-six patients with similar bilateral impacted lower third molars were selected. In each patient, the intervention and control sides of the mandible were randomly determined at the end of surgery. If the removed tooth was in the intervention side, then the patient would receive bupivacaine and a placebo of mefenamic acid. If the impacted tooth was in the control side, then the patient would receive a mefenamic acid capsule and a placebo of bupivacaine. Pain severity was assessed using a visual analog scale. Data were analyzed using paired-sample t test and a P value less than .05 was considered statistically significant. RESULTS: Of 46 participants originally recruited, 43 were included in the present study. The mean postoperative pain score in patients who received bupivacaine was increased to a maximum 4 hours, with marked improvements after this time. The mean intensity of pain after administration of bupivacaine was lower than that of mefenamic acid capsules at different time points. Statistical analysis showed a relevant difference in pain intensity between the 2 study groups. CONCLUSION: The results of the present study showed that local administration of bupivacaine relieves postoperative pain after surgical removal of impacted third molar teeth.