Screw-Home Movement of the Tibiofemoral Joint during Normal Gait: Three-Dimensional Analysis

Background

The purpose of this study was to evaluate the screw-home movement at the tibiofemoral joint during normal gait by utilizing the 3-dimensional motion capture technique.

Methods

Fifteen young males and fifteen young females (total 60 knee joints) who had no history of musculoskeletal disease or a particular gait problem were included in this study. Two more markers were attached to the subject in addition to the Helen-Hayes marker set. Thus, two virtual planes, femoral coronal plane (P_f) and tibial coronal plane (P_t), were created by Skeletal Builder software. This study measured the 3-dimensional knee joint movement in the sagittal, coronal, and transverse planes of these two virtual planes (P_f and P_t) during normal gait.

Results

With respect to kinematics and kinetics, both males and females showed normal adult gait patterns, and the mean difference in the temporal gait parameters was not statistically significant (p > 0.05). In the transverse plane, the screw-home movement occurred as expected during the pre-swing phase and the late-swing phase at an angle of about 17°. However, the tibia rotated externally with respect to the femur, rather than internally, while the knee joint started to flex during the loading response (paradoxical screw-home movement), and the angle was 6°.

Conclusions

Paradoxical screw-home movement may be an important mechanism that provides stability to the knee joint during the remaining stance phase. Obtaining the kinematic values of the knee joint during gait can be useful in diagnosing and treating the pathological knee joints.     

Laterality Classification of Fundus Images Using Interpretable Deep Neural Network

In this paper, we aimed to understand and analyze the outputs of a convolutional neural network model that classifies the laterality of fundus images. Our model not only automatizes the classification process, which results in reducing the labors of clinicians, but also highlights the key regions in the image and evaluates the uncertainty for the decision with proper analytic tools. Our model was trained and tested with 25,911 fundus images (43.4% of macula-centered images and 28.3% each of superior and nasal retinal fundus images). Also, activation maps were generated to mark important regions in the image for the classification. Then, uncertainties were quantified to support explanations as to why certain images were incorrectly classified under the proposed model. Our model achieved a mean training accuracy of 99%, which is comparable to the performance of clinicians. Strong activations were detected at the location of optic disc and retinal blood vessels around the disc, which matches to the regions that clinicians attend when deciding the laterality. Uncertainty analysis discovered that misclassified images tend to accompany with high prediction uncertainties and are likely ungradable. We believe that visualization of informative regions and the estimation of uncertainty, along with presentation of the prediction result, would enhance the interpretability of neural network models in a way that clinicians can be benefitted from using the automatic classification system.     

Venom allergen-like protein 28 in Clonorchis sinensis: four epitopes on its surface and the potential role of Cys124 for its conformational stability

Parasitology Research - Venom allergen-like (VAL) proteins are important to host-parasite interactions. We previously demonstrated that a Clonorchis sinensis VAL (CsVAL) protein-derived synthetic...     

Use of 18F-sodium fluoride bone PET for disability evaluation in ankle trauma: a pilot study

Obesity and high radiologic breast density independently increase breast cancer risk. We evaluated the effect of surgical weight loss on mammographic density (MD). Patients undergoing bariatric surgery and screening mammography (MG) were identified, data regarding demographics, comorbidities, calculated and genetic breast cancer risk was collected. Patients had a MG before and after surgery. Fellowship-trained breast radiologists assigned Breast Imaging Reporting and Data System density categories. Patients underwent sleeve gastrectomy (n = 56) or gastric bypass (n = 7), 78% had hypertension, 48% had diabetes. Four had deleterious BRCA mutations, four were calculated high risk. Mean weight loss = 28.7 kg. Mean initial BMI = 44.3 kg/m2 (range:33–77), final BMI = 33.6 kg/m2 (range:20–62;p < 0.01). Density was unchanged in 53, decreased in 1, increased in 9. Of these 9(14%), 5 changed from almost entirely fatty to scattered MD, and 4 changed from scattered MD to heterogeneously dense. Mean weight loss of the 9 with increased MD was greater than the cohort (37.7vs.28.7 kg;p < 0.01). Surgical weight loss increased MD in 14%. Increased MD masks malignancies, patients may benefit from additional screening based on calculated risk assessments that include MD.