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Simple Summary: Spinal manipulation is recommended by clinical guidelines to treat low back pain. However, our understanding of how spinal manipulation alleviates pain and knowledge of the role that viscoelastic tissue properties play in the dampening of applied spinal manipulation forces in vivo is limited. Superficial and deep intramuscular pressures and applied forces were measured using miniature pressure catheters and a force transducer during low back spinal manipulation using a clinically available spinal manipulation device (Activator V) or a feedback motor in a deeply anesthetized animal model. Viscoelastic properties of muscle and other soft tissues greatly diminished applied spinal manipulative forces as well as intramuscular pressures in deep spinal tissues. These animal model findings may eventually have clinical implications or help to determine how spinal manipulation helps to alleviate muscular low back pain, particularly if it is determined that certain thresholds of deep tissue forces or intramuscular pressure changes are required to initiate a cascade of biological mechanisms resulting in a positive clinical treatment response. Current knowledge regarding biomechanical in vivo deep tissue measures related to spinal manipulation remain somewhat limited. More in vivo animal studies are needed to better understand the effects viscoelastic tissue properties (i.e., dampening) have on applied spinal manipulation forces. This new knowledge may eventually help to determine whether positive clinical outcomes are associated with particular force thresholds reaching superficial and/or deep spinal tissues. A computer-controlled feedback motor and a modified Activator V device with a dynamic load cell attached were used to deliver thrust spinal manipulations at various magnitudes to the L7 spinous process in deeply anesthetized animals. Miniature pressure catheters (Millar SPR-1000) were inserted unilaterally into superficial and deep multifidi muscles. Measurements of applied mechanical forces and superficial/deep multifidi intramuscular pressure changes were recorded during spinal manipulations delivered in vivo. Manipulative forces and net changes in intramuscular pressures reaching deep spinal tissues are greatly diminished by viscoelastic properties of in vivo tissues, which could have possible clinical safety and/or mechanistic implications. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Surgical planning for orthognathic procedures demands swift and accurate biomechanical modeling of facial soft tissues. Efficient simulations are vital in the clinical pipeline, as surgeons may iterate through multiple plans. Biomechanical simulations typically use the finite element method (FEM). Prior works divide FEM simulations into increments to enhance convergence and accuracy. However, this practice elongates simulation time, thereby impeding clinical integration. To accelerate simulations, deep learning (DL) models have been explored. Yet, previous efforts either perform simulations in a single step or neglect the temporal aspects in incremental simulations.This study investigates the use of spatiotemporal incremental modeling for biomechanics simulations of facial soft tissue.We implement the method using a graph neural network. Our method synergizes spatial features with temporal aggregation using DL networks trained on incremental FEM simulations from 17 subjects that underwent orthognathic surgery.Our proposed spatiotemporal incremental method achieved a mean accuracy of 0.37 mm with a mean computation time of 1.52 s. In comparison, a spatial‐only incremental method yielded a mean accuracy of 0.44 mm and a mean computation time of 1.60 s, while a spatial‐only single‐step method yielded a mean accuracy of 0.41 mm and a mean computation time of 0.05 s.Statistical analysis demonstrated that the spatiotemporal incremental method reduced mean errors compared to the spatial‐only incremental method, emphasizing the importance of incorporating temporal information in incremental simulations. Overall, we successfully implemented spatiotemporal incremental learning tailored to simulate soft tissue deformation while substantially reducing simulation time compared to FEM. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses developments on the use of computational methods in orthopaedic biomechanics.

In orthognathic surgical planning for patients with jaw deformities, it is crucial to accurately simulate the changes in facial appearance that follow the bony movement. Compared with the traditional biomechanics-based methods like the finite-element method (FEM), which are both labor-intensive and computationally inefficient, deep learning-based methods offer an efficient and robust modeling alternative. However, current methods do not account for the physical relationship between facial soft tissue and bony structure, causing them to fall short in accuracy compared to FEM. In this work, we propose an Attentive Correspondence assisted Movement Transformation network (ACMT-Net) to predict facial changes by correlating facial soft tissue changes with bony movement through a point-to-point attentive correspondence matrix. To ensure efficient training, we also introduce a contrastive loss for self-supervised pre-training of the ACMT-Net with a k-Nearest Neighbors (k-NN) based clustering. Experimental results on patients with jaw deformities show that our proposed solution can achieve significantly improved computational efficiency over the state-of-the-art FEM-based method with comparable facial change prediction accuracy., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jaime Gateno and Pingkun Yan reports financial support was provided by National Institutes of Health., (Copyright © 2024. Published by Elsevier B.V.)

Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for surgical outcome improvement. We developed a novel incremental simulation approach using finite element method (FEM) with a realistic lip sliding effect to improve the prediction accuracy in the lip region. First, a lip-detailed mesh is generated based on accurately digitized lip surface points. Second, an improved facial soft-tissue change simulation method is developed by applying a lip sliding effect along with the mucosa sliding effect. Finally, the orthognathic surgery initiated soft-tissue change is simulated incrementally to facilitate a natural transition of the facial change and improve the effectiveness of the sliding effects. Our method was quantitatively validated using 35 retrospective clinical data sets by comparing it to the traditional FEM simulation method and the FEM simulation method with mucosa sliding effect only. The surface deviation error of our method showed significant improvement in the upper and lower lips over the other two prior methods. In addition, the evaluation results using our lip-shape analysis, which reflects clinician's qualitative evaluation, also proved significant improvement of the lip prediction accuracy of our method for the lower lip and both upper and lower lips as a whole compared to the other two methods. In conclusion, the prediction accuracy in the clinically critical region, i.e., the lips, significantly improved after applying incremental simulation with realistic lip sliding effect compared with the FEM simulation methods without the lip sliding effect., Competing Interests: Declaration of Competing Interest None., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Purpose: It is clinically important to accurately predict facial soft-tissue changes prior to orthognathic surgery. However, the current simulation methods are problematic, especially in anatomic regions of clinical significance, e.g., the nose, lips, and chin. We developed a new 3-stage finite element method (FEM) approach that incorporates realistic tissue sliding to improve such prediction., Methods: In Stage One, soft-tissue change was simulated, using FEM with patient-specific mesh models generated from our previously developed eFace template. Postoperative bone movement was applied on the patient mesh model with standard FEM boundary conditions. In Stage Two, the simulation was improved by implementing sliding effects between gum tissue and teeth using a nodal force constraint scheme. In Stage Three, the result of the tissue sliding effect was further enhanced by reassigning the soft-tissue-bone mapping and boundary conditions using nodal spatial constraint. Finally, our methods have been quantitatively and qualitatively validated using 40 retrospectively evaluated patient cases by comparing it to the traditional FEM method and the FEM with sliding effect, using a nodal force constraint method., Results: The results showed that our method was better than the other two methods. Using our method, the quantitative distance errors between predicted and actual patient surfaces for the entire face and any subregions thereof were below 1.5 mm. The overall soft-tissue change prediction was accurate to within 1.1 ± 0.3 mm, with the accuracy around the upper and lower lip regions of 1.2 ± 0.7 mm and 1.5 ± 0.7 mm, respectively. The results of qualitative evaluation completed by clinical experts showed an improvement of 46% in acceptance rate compared to the traditional FEM simulation. More than 80% of the result of our approach was considered acceptable in comparison with 55% and 50% following the other two methods., Conclusion: The FEM simulation method with improved sliding effect showed significant accuracy improvement in the whole face and the clinically significant regions (i.e., nose and lips) in comparison with the other published FEM methods, with or without sliding effect using a nodal force constraint. The qualitative validation also proved the clinical feasibility of the developed approach., (© 2017 American Association of Physicists in Medicine.)

Objectives: The purpose of this preliminary study is to determine muscle spindle response characteristics related to the use of 2 solenoid powered clinical mechanically assisted manipulation (MAM) devices., Methods: L6 muscle spindle afferents with receptive fields in paraspinal muscles were isolated in 6 cats. Neural recordings were made during L7 MAM thrusts using the Activator V (Activator Methods Int. Ltd., Phoenix, AZ) and/or Pulstar (Sense Technology Inc., Pittsburgh, PA) devices at their 3 lowest force settings. Mechanically assisted manipulation response measures included (a) the time required post-thrust until the first action potential, (b) differences in mean frequency (MF) and mean instantaneous frequency (MIF) 2 seconds before and after MAM, and (c) the time required for muscle spindle discharge (MF and MIF) to return to 95% of baseline after MAM., Results: Depending on device setting, between 44% to 80% (Pulstar) and 11% to 63% (Activator V) of spindle afferents required >6 seconds to return to within 95% of baseline MF values; whereas 66% to 89% (Pulstar) and 75% to 100% (Activator V) of spindle responses returned to within 95% of baseline MIF in <6 seconds after MAM. Nonparametric comparisons between the 22 N and 44 N settings of the Pulstar yielded significant differences for the time required to return to baseline MF and MIF., Conclusion: Short duration (<10 ms) MAM thrusts decrease muscle spindle discharge with a majority of afferents requiring prolonged periods (>6 seconds) to return to baseline MF activity. Physiological consequences and clinical relevance of described MAM mechanoreceptor responses will require additional investigation., (Copyright © 2017. Published by Elsevier Inc.)

Introduction: Mechanoreceptor stimulation is theorized to contribute to the therapeutic efficacy of spinal manipulation. Use of mechanically-assisted spinal manipulation (MA-SM) devices is increasing among manual therapy clinicians worldwide. The purpose of this pilot study is to determine the feasibility of recording in vivo muscle spindle responses during a MA-SM in an intervertebral fixated animal model., Methods: Intervertebral fixation was created by inserting facet screws through the left L 5-6 and L 6-7 facet joints of a cat spine. Three L 6 muscle spindle afferents with receptive fields in back muscles were isolated. Recordings were made during MA-SM thrusts delivered to the L 7 spinous process using an instrumented Activator IV clinical device., Results: Nine MA-SM thrusts were delivered with peak forces ranging from 68-122N and with thrust durations of less than 5ms. High frequency muscle spindle discharge occurred during MA-SM. Following the MA-SM, muscle spindle responses included returning to pre-manipulation levels, slightly decreasing for a short window of time, and greatly decreasing for more than 40s., Conclusion: This study demonstrates that recording in vivo muscle spindle response using clinical MA-SM devices in an animal model is feasible. Extremely short duration MA-SM thrusts (<5ms) can have an immediate and/or a prolonged (> 40s) effect on muscle spindle discharge. Greater peak forces during MA-SM thrusts may not necessarily yield greater muscle spindle responses. Determining peripheral response during and following spinal manipulation may be an important step in optimizing its' clinical efficacy. Future studies may investigate the effect of thrust dosage and magnitude.

Minimization schema in nature affects the material arrangements of most objects, independent of scale. The field of cellular solids has focused on the generalization of these natural architectures (bone, wood, coral, cork, honeycombs) for material improvement and elucidation into natural growth mechanisms. We applied this approach for the comparison of a set of complex three-dimensional (3D) architectures containing the same material volume but dissimilar architectural arrangements. Ball and stick representations of these architectures at varied material volumes were characterized according to geometric properties, such as beam length, beam diameter, surface area, space filling efficiency, and pore volume. Modulus, deformation properties, and stress distributions as contributed solely by architectural arrangements was revealed through finite element simulations. We demonstrated that while density is the greatest factor in controlling modulus, optimal material arrangement could result in equal modulus values even with volumetric discrepancies of up to 10%. We showed that at low porosities, loss of architectural complexity allows these architectures to be modeled as closed celled solids. At these lower porosities, the smaller pores do not greatly contribute to the overall modulus of the architectures and that a stress backbone is responsible for the modulus. Our results further indicated that when considering a deposition-based growth pattern, such as occurs in nature, surface area plays a large role in the resulting strength of these architectures, specifically for systems like bone. This completed study represents the first step towards the development of mathematical algorithms to describe the mechanical properties of regular and symmetric architectures used for tissue regenerative applications. The eventual goal is to create logical set of rules that can explain the structural properties of an architecture based solely upon its geometry. The information could then be used in an automatic fashion to generate patient-specific scaffolds for the treatment of tissue defects.

The goal of this area of research is to manipulate the pore space of scaffolds through the application of an intelligent design concept on dissolvable microparticles. To accomplish this goal, we developed an efficient and repeatable process for fabrication of microparticles from multiple materials using a combination of rapid prototyping (RP) and soft lithography. Phase changed 3D printing was used to create masters for PDMS molds. A photocrosslinkable polymer was then delivered into these molds to make geometrically complex 3D microparticles. This repeatable process has demonstrated to generate the objects with greater than 95% repeatability with complete pattern transfer. This process was illustrated for three different shapes of various complexities. The shapes were based on the extrusion of 2D shapes. This may allow simplification of the fabrication process in the future combined with a direct transfer of the findings. Altering the shapes of particles used for porous scaffold fabrication will allow for tailoring of the pore shapes, and therefore their biological function within a porous tissue engineering scaffold. Through permeation experiments, we have shown that the pore geometry may alter the permeability coefficient of scaffolds while influencing mechanical properties to a lesser extent. By selecting different porogen shapes, the nutrition transport and scaffold degradation can be significantly influenced with minimal effect on the mechanical integrity of the construct. In addition, the different shapes may allow a control of drug release by modifying their surface-to-volume ratio, which could modulate drug delivery over time. While soft lithography is currently used with photolithography, its high precision is offset by high cost of production. The employment of RP to a specific resolution offers a much less expensive alternative with increased throughput due to the speed of current RP systems.

Purpose: The goal of this study was to create a model for human mandibular movement prediction based on the geometry of the mandible., Materials and Methods: Ten nonpathologic individuals underwent motion tracking and sagittal radiographs. From the data, a mathematical algorithm for mandibular movement prediction was developed based on mandibular geometry. The algorithm was then used to predict the mandibular movement of a cadaver subject. The algorithm was also validated in a living subject by comparing to recorded mandibular movement., Results: Both mandibular movement predictions were free of bone collisions and showed mandibular movement that mimicked the in vivo situation. Comparisons between the predicted and recorded mandibular movements for the living human subject verified that the prediction model was accurate., Conclusions: The mandibular movement can be predicted based on the mandibular opening radius. The model is validated in living human subjects and shows effectiveness in predictions for cadaver models. Mandibular movement prediction may be a useful tool for physicians as well as investigators who focus on temporomandibular joint research.