Soft tissue artifact distribution on lower limbs during treadmill gait: Influence of skin markers' location on cluster design.

Segment poses and joint kinematics estimated from skin markers are highly affected by soft tissue artifact (STA) and its rigid motion component (STARM). While four marker-clusters could decrease the STA non-rigid motion during gait activity, other data, such as marker location or STARM patterns, would be crucial to compensate for STA in clinical gait analysis. The present study proposed 1) to devise a comprehensive average map illustrating the spatial distribution of STA for the lower limb during treadmill gait and 2) to analyze STARM from four marker-clusters assigned to areas extracted from spatial distribution. All experiments were realized using a stereophotogrammetric system to track the skin markers and a bi-plane fluoroscopic system to track the knee prosthesis. Computation of the spatial distribution of STA was realized on 19 subjects using 80 markers apposed on the lower limb. Three different areas were extracted from the distribution map of the thigh. The marker displacement reached a maximum of 24.9mm and 15.3mm in the proximal areas of thigh and shank, respectively. STARM was larger on thigh than the shank with RMS error in cluster orientations between 1.2° and 8.1°. The translation RMS errors were also large (3.0mm to 16.2mm). No marker-cluster correctly compensated for STARM. However, the coefficient of multiple correlations exhibited excellent scores between skin and bone kinematics, as well as for STARM between subjects. These correlations highlight dependencies between STARM and the kinematic components. This study provides new insights for modeling STARM for gait activity.

Evaluation of a new serological test for the detection of anti-Coxiella and anti-Rickettsia antibodies.

Coxiella burnetii and members of the genus Rickettsia are obligate intracellular bacteria. Since cultivation of these organisms requires dedicated techniques, their diagnosis usually relies on serological or molecular biology methods. Immunofluorescence is considered the gold standard to detect antibody-reactivity towards these organisms. Here, we assessed the performance of a new automated epifluorescence immunoassay (InoDiag) to detect IgM and IgG against C. burnetii, Rickettsia typhi and Rickettsia conorii. Samples were tested with the InoDiag assay. A total of 213 sera were tested, of which 63 samples from Q fever, 20 from spotted fever rickettsiosis, 6 from murine typhus and 124 controls. InoDiag results were compared to micro-immunofluorescence. For acute Q fever, the sensitivity of phase 2 IgG was only of 30% with a cutoff of 1 arbitrary unit (AU). In patients with acute Q fever with positive IF IgM, sensitivity reached 83% with the same cutoff. Sensitivity for chronic Q fever was 100% whereas sensitivity for past Q fever was 65%. Sensitivity for spotted Mediterranean fever and murine typhus were 91% and 100%, respectively. Both assays exhibited a good specificity in control groups, ranging from 79% in sera from patients with unrelated diseases or EBV positivity to 100% in sera from healthy patients. In conclusion, the InoDiag assay exhibits an excellent performance for the diagnosis of chronic Q fever but a very low IgG sensitivity for acute Q fever likely due to low reactivity of phase 2 antigens present on the glass slide. This defect is partially compensated by the detection of IgM. Because it exhibits a good negative predictive value, the InoDiag assay is valuable to rule out a chronic Q fever. For the diagnosis of rickettsial diseases, the sensitivity of the InoDiag method is similar to conventional immunofluorescence.