BackgroundThe anterior cruciate ligament (ACL) plays an important role in stabilizing translation and rotation of the tibia relative to the femur. ACL injury alters knee kinematics and usually links to the alternation of gait patterns. The aim of this study is to develop a new method to distinguish between gait patterns of patients with anterior cruciate ligament deficient (ACL-D) knees and healthy controls with ACL-intact (ACL-I) knees based on nonlinear features and neural networks. Therefore ACL injury will be automatically and objectively detected.MethodsFirst knee rotation and translation parameters are extracted and phase space reconstruction (PSR) is employed. The properties associated with the gait system dynamics are preserved in the reconstructed phase space. For the purpose of classification of ACL-D and ACL-I knee gait patterns, three-dimensional (3D) PSR together with Euclidean distance computation has been used. These measured parameters show significant difference in gait dynamics between the two groups and have been utilized to form a feature set. Neural networks are then constructed to identify gait dynamics and are utilized as the classifier to distinguish between ACL-D and ACL-I knee gait patterns based on the difference of gait dynamics between the two groups.ResultsExperiments are carried out on a database containing 18 patients with ACL injury and 28 healthy controls to assess the effectiveness of the proposed method. By using the twofold and leave-one-subject-out cross-validation styles, the correct classification rates for ACL-D and ACL-I knees are reported to be 91.3 and 95.65, respectively.ConclusionCompared with other state-of-the-art methods, the results demonstrate that gait alterations in the presence of ACL deficiency can be detected with superior performance. The proposed method is a potential candidate for the automatic and non-invasive classification between patients with ACL deficiency and healthy subjects.
Objectives
To investigate the association of eight variants of four matrix metalloproteinase (MMP) genes with ischemic stroke (IS) and whether interactions among these single nucleotide polymorphisms (SNPs) increases the risk of IS.
Methods
Among 547 patients with ischemic stroke and 350 controls, matrix‐assisted laser desorption/ionization time of flight mass spectrometry was used to examine eight variants arising from four different genes, including MMP‐1 (rs1799750), MMP‐2 (rs243865, rs2285053, rs2241145), MMP‐9 (rs17576), and MMP‐12 (rs660599, rs2276109, and rs652438). Gene–gene interactions were employed using generalized multifactor dimensionality reduction (GMDR) methods.
Results
The frequency of rs17576 was significantly higher in IS patients than in controls (p = .033). Logistic regression analysis revealed the AG and GG genotypes of rs17576 to be associated with a higher risk for IS, with the odds ratio and 95% confidence interval being 2.490 (1.251–4.959) and 2.494 (1.274–4.886), respectively. GMDR analysis showed a significant SNP‐SNP interaction between rs17576 and rs660599 (the testing balanced accuracy was 53.70% and cross‐validation consistency was 8/10, p = .0107). Logistic regression analysis showed the interaction between rs17576 and rs660599 to be an independent risk factor for IS with an odds ratio of 1.568 and a 95% confidence interval of 1.152–2.135.
Conclusion
An MMP‐9 rs17576 polymorphism is associated with increased IS risk in the Han Hakka population and interaction between MMP‐9 rs17576 and MMP‐12 rs660599 is associated with increased IS risk as well.
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