Association of biochemical and clinical parameters with parathyroid adenoma weight. Turkish-Bulgarian endocrine and breast surgery study group, hyperparathyroidism registry study.

BACKGROUND: Primary hyperparathyroidism (pHPT) caused by a single benign parathyroid adenoma is a common endocrine disorder that is affected by regional differences. Living in different geographical regions reveals differences in the laboratory results and pathological findings, but studies on this subject are not sufficient. The article focuses on biochemical and pathological effects of geographical differences in parathyroid adenoma. In addition, the present study seeks to elaborate on treatment methods and effectiveness of screening in geographical area of Bulgaria and Turkey. METHOD: In this prospective study, 159 patients were included from 16 centres. Demographic characteristics, symptoms, biochemical markers and pathologic characteristics were analysed and compared between 8 different regions. RESULTS: Patients from Turkish Black Sea had the highest median serum calcium (Ca) level, whereas patients from Eastern Turkey had the lowest median serum phosphorus (P) level. On the other hand, there was no significant difference between Ca, parathormone (PTH) and P levels according to regions. Patients from Eastern Turkey had the highest adenoma weight, while patients from Bulgaria had the lowest adenoma weight. The weight of adenoma showed statistically significant differences between regions (p < 0.001). There was a correlation between adenoma weight and serum PTH level (p = 0.05) and Ca level (p = 0.035). CONCLUSION: This study has provided a deeper insight into the effect of the regional differences upon clinicopathological changing and biochemical values of pHTP patients with adenoma. Awareness of regional differences will assist in biochemical screening and treatment of this patient group.

Imitation and mirror systems in robots through Deep Modality Blending Networks.

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, which is based on conditional neural processes, can be conditioned on any desired sensory/motor value at any time step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in one-shot by querying the network for all the sampled time points in parallel avoiding the accumulation of prediction errors. Based on simulation experiments with an arm-gripper robot and an RGB camera, we showed that DMBN could make accurate predictions about any missing modality (camera or joint angles) given the available ones outperforming recent multimodal variational autoencoder models in terms of long-horizon high-dimensional trajectory predictions. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect-based imitation behavior. To achieve this mirror-like behavior, our system does not perform a pixel-based template matching but rather benefits from and relies on the common latent space constructed by using both joint and image modalities, as shown by additional experiments. Moreover, we showed that mirror learning (in our system) does not only depend on visual experience and cannot be achieved without proprioceptive experience. Our experiments showed that out of ten training scenarios with different initial configurations, the proposed DMBN model could achieve mirror learning in all of the cases where the model that only uses visual information failed in half of them. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities.

A Soft+Rigid Hybrid Exoskeleton Concept in Scissors-Pendulum Mode: A Suit for Human State Sensing and an Exoskeleton for Assistance.

In this paper, we present a novel concept that can enable the human aware control of exoskeletons through the integration of a soft suit and a robotic exoskeleton. Unlike the state-of-the-art exoskeleton controllers which mostly rely on lumped human-robot models, the proposed concept makes use of the independent state measurements concerning the human user and the robot. The ability to observe the human state independently is the key factor in this approach. In order to realize such a system from the hardware point of view, we propose a system integration frame that combines a soft suit for human state measurement and a rigid exoskeleton for human assistance. We identify the technological requirements that are necessary for the realization of such a system with a particular emphasis on soft suit integration. We also propose a template model, named scissor pendulum, that may encapsulate the dominant dynamics of the human-robot combined model to synthesize a controller for human state regulation. A series of simulation experiments were conducted to check the controller performance. As a result, satisfactory human state regulation was attained, adequately confirming that the proposed system could potentially improve exoskeleton-aided applications.