Recurrent Ipsilateral C5 Nerve Palsy Associated With Hereditary Neuropathy With Liability to Pressure Palsy.

Hereditary neuropathy with liability to pressure palsy (HNPP) is an autosomal dominant disorder caused by heteroplasmic deletion of the peripheral myelin protein 22 (PMP22) gene. HNPP typically presents with clinical features such as peroneal nerve palsy or cubital tunnel syndrome, which are caused by mechanical compression. Diagnosing cases where neuropathy is absent at the pressure site can be challenging. This is a case study of an 18-year-old man who underwent surgery on the left side of his neck over 10 years ago to remove lymphadenopathy. Following the surgery, he experienced recurrent weakness but only sought medical attention when muscle weakness persisted for longer than a week postoperatively. Upon admission, the patient exhibited neurological symptoms consistent with C5 neuropathy, mainly affecting the deltoid muscles. No serological abnormalities were found to be associated with neuropathy. Neither magnetic resonance imaging nor computed tomography scans detected any lesions around the C5 nerve root. The posture during sleep was believed to cause excessive extension of the C5 nerve root, leading to the assumption that there was some vulnerability in the nerve. A transient sensory loss in the area innervated by the ulnar nerve prompted us to examine the fluorescence in situ hybridization study on the blood sample, which revealed a deletion of the PMP22 gene. The patient was diagnosed with HNPP and was advised to avoid risky postures. Following the implementation of these lifestyle changes, he did not experience any further weakness in his shoulders.

Reinforcement Learning Model With Dynamic State Space Tested on Target Search Tasks for Monkeys: Self-Determination of Previous States Based on Experience Saturation and Decision Uniqueness.

The real world is essentially an indefinite environment in which the probability space, i. e., what can happen, cannot be specified in advance. Conventional reinforcement learning models that learn under uncertain conditions are given the state space as prior knowledge. Here, we developed a reinforcement learning model with a dynamic state space and tested it on a two-target search task previously used for monkeys. In the task, two out of four neighboring spots were alternately correct, and the valid pair was switched after consecutive correct trials in the exploitation phase. The agent was required to find a new pair during the exploration phase, but it could not obtain the maximum reward by referring only to the single previous one trial; it needed to select an action based on the two previous trials. To adapt to this task structure without prior knowledge, the model expanded its state space so that it referred to more than one trial as the previous state, based on two explicit criteria for appropriateness of state expansion: experience saturation and decision uniqueness of action selection. The model not only performed comparably to the ideal model given prior knowledge of the task structure, but also performed well on a task that was not envisioned when the models were developed. Moreover, it learned how to search rationally without falling into the exploration-exploitation trade-off. For constructing a learning model that can adapt to an indefinite environment, the method of expanding the state space based on experience saturation and decision uniqueness of action selection used by our model is promising.