In the early stages of cutaneous malignant melanoma (MM), it is extremely difficult to predict adequately the risk from hematogenic and lymphatic metastasis. We investigate whether the immunohistochemical expression of Ki-67 and estrogen receptor beta (ER&bgr;) in cells of MM could predict the status of regional lymph nodes. A total of 55 tissue samples of primary cutaneous melanomas with known status of regional lymph nodes were retrospectively evaluated for Ki-67 and ER&bgr; expression by quantitative immunohistochemistry and then correlated with the status of regional lymph nodes and relevant clinicopathologic parameters. The ER&bgr;-positive expression was detected in 38 of 55 tumors (69.09%). The Clark level showed a strong correlation with ER&bgr; expression, as well as pT stage. All cases of MM showed Ki-67-positive expression and an elevated Ki-67 expression was strongly associated with increased Breslow thickness, Clark level, ulceration, lymphovascular invasion, number of mitosis, and pT stage. Logistic regression analysis showed that when ER&bgr; levels increase by 1%, the risk of positive lymph nodes decreases by 7% (odds ratio=0.930; 95% confidence interval, 0.87-0.99; P=0.036), and, when the Ki-67 expression increases by 1%, the risk of lymph nodes’ positivity increases by 10% (odds ratio=1.108; 95% confidence interval, 1.02-1.19; P=0.009). Correlation between expression of Ki-67 and ER&bgr; and the status of lymph nodes has better prognostic significance than the relationship between melanoma thickness and the status of lymph nodes. Our study showed a significant prognostic value of Ki-67 expression in predicting the behavior of MM and the potential prognostic significance of ER&bgr;.

Knowing accurately the dynamic parameters of a manipulated object is required for common coordination strategies in physical human-robot interaction. Bias in object dynamics results in inaccurately calculated robot wrenches, which may disturb the human during interaction and bias the recognition of the human motion intention. This paper presents an identification strategy of object dynamics for physical human-robot interaction, which allows the tracking of desired human motion and inducing the motions necessary for parameter identification. The estimation of object dynamics is performed online and the estimator minimizes the least square error between the measured and estimated wrenches acting on the object. Identification-relevant motions are derived by analyzing the persistence of excitation condition, necessary for estimation convergence. Such motions are projected in the null space of the partial grasp matrix, relating the human and the robot redundant motion directions, to avoid disturbance of the human desired motion. The approach is evaluated in a physical human-robot object manipulation scenario.