Anatomical study for elucidating the stabilization mechanism in the trapeziometacarpal joint.

To determine the pathogenesis of trapeziometacarpal (TMC) joint instability, which leads to osteoarthritis, we investigated the anatomical relationships among the surrounding ligaments, muscles (first dorsal interosseous [FDI] and opponens pollicis [OPP]), and joint capsule. We examined the bone morphology and cortical bone thickening in 25 cadaveric thumbs using micro-computed tomography and performed macroscopic and histological analyses. The dorsal trapezium had a tubercle with cortical bone thickening, corresponding to the attachment of the FDI aponeurosis intermingled with the joint capsule. Radially, the thin joint capsule was observed to underlie the muscular part of the OPP. Therefore, the dorsal ligaments, which have been previously considered static stabilizers, could be interpreted as parts of the capsuloaponeurotic complex consisting of the FDI aponeurosis and joint capsule. In the radial aspect, muscular OPP activation may be essential for TMC joint stabilization. Our findings may contribute to the appropriate management of TMC osteoarthritis.

Medial patellofemoral ligament is a part of the vastus medialis obliquus and vastus intermedius aponeuroses attaching to the medial epicondyle.

PURPOSE: This study aimed to investigate the bony surface characteristic of the femoral attachment of the medial patellofemoral ligament (MPFL) and the correlation between the relevant layered structures, including muscular aponeurosis and the joint capsule, which contribute to patellofemoral joint (PFJ) stability. METHODS: The morphology of the medial aspect of the medial condyle using micro-computed tomography and analysed cortical bone thickening in 24 knees was observed. For the macroscopic and histological analyses, 21 and 3 knees were allocated, respectively. The Kruskal-Wallis one-way analysis of variance test with Dunn post hoc testing was performed for statistical analysis. RESULTS: At the level of the adductor tubercle, there were no significant differences in cortical bone thickness. At the level of the medial epicondyle (MEC), cortical bone thickness was considerably greater than that in other areas of the medial condyle (mean ± standard deviation, 0.60 ± 0.20 mm; p < 0.0001). Macroscopic analysis revealed that the deep aponeurosis of the vastus medialis obliquus and the tendinous arch of the vastus intermedius distally formed the composite membrane and adjoined to the joint capsule to firmly attach to MEC, which was located at 41.3 ± 5.7 mm posterior and 14.2 ± 3.1 mm superior to the joint cartilage. Histological analysis showed a composite membrane and adjoining capsule attached to MEC via fibrocartilage. CONCLUSION: MPFL could be interpreted as part of the deep aponeurosis of the vastus medialis obliquus (VMO) and the tendinous arch of the vastus intermedius, which combined with the joint capsule to attach to MEC. The cortical bone thickening indicated that the tensile stresses were loaded on MEC in aged cadavers. Involvement of VMO and vastus intermedius aponeuroses in restored graft of MPFL could utilise the dynamic stability of surrounding muscles to mimic a native structure.

Analysis of Spastic Gait in Patients With Cervical Myelopathy Using the Timed Up and Go Test With a Laser Range Sensor.

STUDY DESIGN: Cross-sectional study. OBJECTIVE: This study aimed to objectively evaluate spastic gait and reveal its novel characteristics via analysis of gait in patients with cervical myelopathy (CM) using the Timed Up and Go (TUG) test with a laser range sensor. SUMMARY OF BACKGROUND DATA: Among patients with CM, spastic gait is a common diagnostic symptom; thus, objective assessments of spastic gait would be useful for the diagnosis of CM and recognition of disease status. Although spastic gait has been objectively evaluated in previous studies, the methods employed in those studies are not suitable for clinical settings. METHODS: In total, 37 and 24 participants were recruited for a control group and CM group, respectively. CM was diagnosed by spine surgeons. We developed a laser TUG test, in which the position and velocity of both the legs were captured. The parameter values for both groups were statistically compared, and odds ratios were calculated using logistic regression analyses. RESULTS: The total TUG-test time, time to stand up, time to first step, number of steps, and trajectory error for the CM group were significantly higher than those for the control group, whereas the average velocity and average stride length for the CM group were significantly lower than those for the control group. There was a significant independent association between the total TUG-test time and CM. The optimal cutoff point of the total test time for CM risk was approximately 9 seconds. CONCLUSION: Through the use of the laser TUG test, we were able to identify characteristics of spastic gait, which leads to difficulty in standing and taking the first step, wobbling while walking, and an increased risk of falling. We found that the risk of CM was higher if the individual took longer than 9 seconds to complete the TUG test.Level of Evidence: 4.

High-Dimensional Analysis of Finger Motion and Screening of Cervical Myelopathy With a Noncontact Sensor: Diagnostic Case-Control Study.

BACKGROUND: Cervical myelopathy (CM) causes several symptoms such as clumsiness of the hands and often requires surgery. Screening and early diagnosis of CM are important because some patients are unaware of their early symptoms and consult a surgeon only after their condition has become severe. The 10-second hand grip and release test is commonly used to check for the presence of CM. The test is simple but would be more useful for screening if it could objectively evaluate the changes in movement specific to CM. A previous study analyzed finger movements in the 10-second hand grip and release test using the Leap Motion, a noncontact sensor, and a system was developed that can diagnose CM with high sensitivity and specificity using machine learning. However, the previous study had limitations in that the system recorded few parameters and did not differentiate CM from other hand disorders. OBJECTIVE: This study aims to develop a system that can diagnose CM with higher sensitivity and specificity, and distinguish CM from carpal tunnel syndrome (CTS), a common hand disorder. We then validated the system with a modified Leap Motion that can record the joints of each finger. METHODS: In total, 31, 27, and 29 participants were recruited into the CM, CTS, and control groups, respectively. We developed a system using Leap Motion that recorded 229 parameters of finger movements while participants gripped and released their fingers as rapidly as possible. A support vector machine was used for machine learning to develop the binary classification model and calculated the sensitivity, specificity, and area under the curve (AUC). We developed two models, one to diagnose CM among the CM and control groups (CM/control model), and the other to diagnose CM among the CM and non-CM groups (CM/non-CM model). RESULTS: The CM/control model indexes were as follows: sensitivity 74.2%, specificity 89.7%, and AUC 0.82. The CM/non-CM model indexes were as follows: sensitivity 71%, specificity 72.87%, and AUC 0.74. CONCLUSIONS: We developed a screening system capable of diagnosing CM with higher sensitivity and specificity. This system can differentiate patients with CM from patients with CTS as well as healthy patients and has the potential to screen for CM in a variety of patients.