RESUMO
BACKGROUND: Multiple animal models have previously been utilized to investigate anterior fusion techniques, but a mouse model has yet to be developed. The purpose of this study was to develop murine anterior interbody and posterolateral fusion techniques. METHODS: Mice underwent either anterior interbody or posterolateral spinal fusion. A protocol was developed for both procedures, including a description of the relevant anatomy. Samples were subjected to micro-computed tomography to assess fusion success and underwent biomechanical testing with use of 4-point bending. Lastly, samples were fixed and embedded for histologic evaluation. RESULTS: Surgical techniques for anterior interbody and posterolateral fusion were developed. The fusion rate was 83.3% in the anterior interbody model and 100% in the posterolateral model. Compared with a control, the posterolateral model exhibited a greater elastic modulus. Histologic analysis demonstrated endochondral ossification between bridging segments, further confirming the fusion efficacy in both models. CONCLUSIONS: The murine anterior interbody and posterolateral fusion models are efficacious and provide an ideal platform for studying the molecular and cellular mechanisms mediating spinal fusion. CLINICAL RELEVANCE: Given the extensive genetic tools available in murine disease models, use of fusion models such as ours can enable determination of the underlying genetic pathways involved in spinal fusion.
Assuntos
Vértebras Lombares , Fusão Vertebral , Animais , Camundongos , Vértebras Lombares/cirurgia , Fusão Vertebral/métodos , Microtomografia por Raio-X , Osteogênese , Modelos Animais de DoençasRESUMO
Introduction: A pitcher's ability to achieve pitch location precision after a complex series of motions is of paramount importance. Kinematics have been used in analyzing performance benefits like ball velocity, as well as injury risk profile; however, prior utilization of such data for pitch location metrics is limited. Objective: To develop a pitch classifier model utilizing machine learning algorithms to explore the potential relationships between kinematic variables and a pitcher's ability to throw a strike or ball. Methods: This was a descriptive laboratory study involving professional baseball pitchers (n = 318) performing pitching tests under the setting of 3D motion-capture (480 Hz). Main outcome measures included accuracy, sensitivity, specificity, F1 score, positive predictive value (PPV), and negative predictive value (NPV) of the random forest model. Results: The optimized random forest model resulted in an accuracy of 70.0 %, sensitivity of 70.3 %, specificity of 48.5 %, F1 equal to 80.6 %, PPV of 94.3 %, and a NPV of 11.6 %. Classification accuracy for predicting strikes and balls achieved an area under the curve of 0.67. Kinematics that derived the highest % increase in mean square error included: trunk flexion excursion(4.06 %), pelvis obliquity at foot contact(4.03 %), and trunk rotation at hand separation(3.94 %). Pitchers who threw strikes had significantly less trunk rotation at hand separation(p = 0.004) and less trunk flexion at ball release(p = 0.003) compared to balls. The positive predictive value for determining a strike was within an acceptable range, while the negative predictive value suggests if a pitch was determined as a ball, the model was not adequate in its prediction. Conclusions: Kinematic measures of pelvis and trunk were crucial determinants for the pitch classifier sequence, suggesting pitcher kinematics at the proximal body segments may be useful in determining final pitch location.