4d CT to assess spinal instability in developmental anomaly of posterior arch of atlas

Four-dimensional (4D) CT uniquely allows cinematic visualization of the entirety of joint motion throughout dynamic movement, which can reveal subtle or transient internal joint derangements not evident on static images. As developmental anomalies of the posterior arch can predispose to cervical spinal instability and neurological morbidity, precise assessment of spinal movement during motion is of clinical relevance. We describe the use of 4D-CT in a subject with partial absence of posterior C1 arch. This, to our knowledge, is the first such report. In at-risk individuals, 4D-CT has the potential to enable an assessment of spinal instability with a higher level of clarity and, in this sense, its more routine implementation may be a future direction.

Dynamic Knee Joint Line Orientation Is Not Predictive of Tibio-Femoral Load Distribution During Walking

Some approaches in total knee arthroplasty aim for an oblique joint line to achieve an even medio-lateral load distribution across the condyles during the stance phase of gait. While there is much focus on the angulation of the joint line in static frontal radiographs, precise knowledge of the associated dynamic joint line orientation and the internal joint loading is limited. The aim of this study was to analyze how static alignment in frontal radiographs relates to dynamic alignment and load distribution, based on direct measurements of the internal joint loading and kinematics. A unique and novel combination of telemetrically measured in vivo knee joint loading and simultaneous internal joint kinematics derived from mobile fluoroscopy (“CAMS-Knee dataset”) was employed to access the dynamic alignment and internal joint loading in 6 TKA patients during level walking. Static alignment was measured in standard frontal postoperative radiographs while external adduction moments were computed based on ground reaction forces. Both static and dynamic parameters were analyzed to identify correlations using linear and non-linear regression. At peak loading during gait, the joint line was tilted laterally by 4°–7° compared to the static joint line in most patients. This dynamic joint line tilt did not show a strong correlation with the medial force (R2: 0.17) or with the mediolateral force distribution (pseudo R2: 0.19). However, the external adduction moment showed a strong correlation with the medial force (R2: 0.85) and with the mediolateral force distribution (pseudo R2: 0.78). Alignment measured in static radiographs has only limited predictive power for dynamic kinematics and loading, and even the dynamic orientation of the joint line is not an important factor for the medio-lateral knee load distribution. Preventive and rehabilitative measures should focus on the external knee adduction moment based on the vertical and horizontal components of the ground reaction forces.

Computational Modeling: Human Dynamic Model

Improvements in quantitative measurements of human physical activity are proving extraordinarily useful for studying the underlying musculoskeletal system. Dynamic models of human movement support clinical efforts to analyze, rehabilitate injuries. They are also used in biomechanics to understand and diagnose motor pathologies, find new motor strategies that decrease the risk of injury, and predict potential problems from a particular procedure. In addition, they provide valuable constraints for understanding neural circuits. This paper describes a physics-based movement analysis method for analyzing and simulating bipedal humanoid movements. The model includes the major body segments and joints to report human movements' energetic components. Its 48 degrees of freedom strike a balance between very detailed models that include muscle models and straightforward two-dimensional models. It has sufficient accuracy to analyze and synthesize movements captured in real-time interactive applications, such as psychophysics experiments using virtual reality or human-in-the-loop teleoperation of a simulated robotic system. The dynamic model is fast and robust while still providing results sufficiently accurate to be used to animate a humanoid character. It can also estimate internal joint forces used during a movement to create effort-contingent stimuli and support controlled experiments to measure the dynamics generating human behaviors systematically. The paper describes the innovative features that allow the model to integrate its dynamic equations accurately and illustrates its performance and accuracy with demonstrations. The model has a two-foot stance ability, capable of generating results comparable with an experiment done with subjects, and illustrates the uncontrolled manifold concept. Additionally, the model's facility to capture large energetic databases opens new possibilities for theorizing as to human movement function. The model is freely available.

An Unusual Terrible Triad Variant Associated with an Essex-Lopresti Injury

Essex-Lopresti injuries and terrible triad injuries of the elbow are rare injuries that typically result from high-energy trauma such as falling from a height or a motor vehicle collision. However, the combination of an Essex-Lopresti injury and terrible triad injury is unique and poses a significant challenge for treatment as these injuries are independently associated with poor functional outcomes if they are not acutely diagnosed. We describe a case of a 19-year-old who presented with an unusual variant of a terrible triad injury associated with an Essex-Lopresti injury. The patient had a distal radioulnar joint (DRUJ) and elbow dislocation, a radial head and coronoid process fracture, and a distal radius fracture. Almost a reverse Essex-Lopresti, this injury was successfully managed with open reduction and repair of the distal radius, radial head, and damaged ligaments in the elbow, along with an internal joint stabilizer (IJS).

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.

PEMA v2: addressing metabarcoding bioinformatics analysis challenges

Environmental DNA (eDNA) and metabarcoding have launched a new era in bio- and eco-assessment over the last years (Ruppert et al. 2019). The simultaneous identification, at the lowest taxonomic level possible, of a mixture of taxa from a great range of samples is now feasible; thus, the number of eDNA metabarcoding studies has increased radically (Deiner and 2017). While the experimental part of eDNA metabarcoding can be rather challenging depending on the special characteristics of the different studies, computational issues are considered to be its major bottlenecks. Among the latter, the bioinformatics analysis of metabarcoding data and especially the taxonomy assignment of the sequences are fundamental challenges. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available. However, each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy; thus, tuning bioinformatics analysis has proved itself fundamental (Kamenova 2020). The computation capacity of high-performance computing systems (HPC) is frequently required for such analyses. On top of that, the non perfect completeness and correctness of the reference taxonomy databases is another important issue (Loos et al. 2020). Based on third-party tools, we have developed the Pipeline for Environmental Metabarcoding Analysis (PEMA), a HPC-centered, containerized assembly of key metabarcoding analysis tools. PEMA combines state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune thoroughly each study thanks to roll-back checkpoints and on-demand partial pipeline execution features (Zafeiropoulos 2020). Once PEMA was released, there were two main pitfalls soon to be highlighted by users. PEMA supported 4 marker genes and was bounded by specific reference databases. In this new version of PEMA the analysis of any marker gene is now available since a new feature was added, allowing classifiers to train a user-provided reference database and use it for taxonomic assignment. Fig. 1 shows the taxonomy assignment related PEMA modules; all those out of the dashed box have been developed for this new PEMA release. As shown, the RDPClassifier has been trained with Midori reference 2 and has been added as an option, classifying not only metazoans but sequences from all taxonomic groups of Eukaryotes for the case of the COI marker gene. A PEMA documentation site is now also available. PEMA.v2 containers are available via the DockerHub and SingularityHub as well as through the Elixir Greece AAI Service. It has also been selected to be part of the LifeWatch ERIC Internal Joint Initiative for the analysis of ARMS data and soon will be available through the Tesseract VRE.

The Effects of Mechanical Scale on Neural Control and the Regulation of Joint Stability

Recent work has demonstrated how the size of an animal can affect neural control strategies, showing that passive viscoelastic limb properties have a significant role in determining limb movements in small animals but are less important in large animals. We extend that work to consider effects of mechanical scaling on the maintenance of joint integrity; i.e., the prevention of aberrant contact forces within joints that might lead to joint dislocation or cartilage degradation. We first performed a literature review to evaluate how properties of ligaments responsible for joint integrity scale with animal size. Although we found that the cross-sectional area of the anterior cruciate ligament generally scaled with animal size, as expected, the effects of scale on the ligament’s mechanical properties were less clear, suggesting potential adaptations in passive contributions to the maintenance of joint integrity across species. We then analyzed how the neural control of joint stability is altered by body scale. We show how neural control strategies change across mechanical scales, how this scaling is affected by passive muscle properties and the cost function used to specify muscle activations, and the consequences of scaling on internal joint contact forces. This work provides insights into how scale affects the regulation of joint integrity by both passive and active processes and provides directions for studies examining how this regulation might be accomplished by neural systems.

A contact problem of bending of two beams with the internal joint

The joint bending of two Bernoulli–Euler’s beams is considered. Each beam has one end fixed and the other free. The beams have the different lengths and thicknesses. The long beam is loaded by the concentrated force. This beam is composite as it includes the internal joint. There is the frictionless unilateral contact between the beams. The elastic lines of the beams are to be found. This problem is reduced to finding of the density of forces of interaction between the beams and the constant that describes the unknown term in the displacement of the unrestrained part of the composite beam. The mathematical formulation of this contact problem is propounded. The density is assumed to be the sum of piecewise continuous function and delta-functions describing the concentrated forces. The uniqueness of the solution of the problem is proved and the analytical solution is constructed. Two possible contact patterns are found out. The former is contact at one point at the end of the short beam. The latter is contact at the same point and at one more point located at the unrestrained part of the composite beam. The coordinate of this point is the root of the cubic equation. The obtained analytical solution is used for the optimization of the structure. The optimization problem is to find the beams thicknesses that minimize the maximum stress for the given loading, beams lengths and the overall deflection. This problem is solved numerically for some values of the given parameters. The hypothesis of the equal-stressed optimum structure is set up on the basis of the numerical results. This hypothesis enables to construct the analytical solution of the optimization problem.