Successful management of interstitial lung disease in dermatomyositis complicated by malignancy: a case-based review.

Dermatomyositis (DM) is associated with interstitial lung disease (ILD) and malignancy. However, the coexistence of ILD and malignancy (DM-ILD-malignancy) is rare, and limited information exists regarding its management. Herein, we report the case of a 70-year-old man who developed DM with rapidly progressive ILD and advanced gastric cancer and provide a literature review of managing DM-ILD-malignancy. The patient presented with typical DM skin rashes and shortness of breath, which worsened within 1 month, without muscular symptoms. Additionally, the patient tested negative for myositis-specific autoantibodies (MSAs). Computed tomography revealed ILD and advanced gastric cancer, which was confirmed on endoscopic examination to be a poorly differentiated adenocarcinoma. Although the patient's ILD progressed rapidly, surgical treatment of the cancer was prioritized. Prednisolone (PSL) 0.5 mg/kg was initiated 3 days before surgery and increased to 1 mg/kg at 7 days postoperative. Remarkable improvement in the skin rash and ILD was observed, and the PSL dose was tapered without immunosuppressants. A literature review revealed that anti-melanoma differentiation-associated gene 5 and anti-aminoacyl transfer RNA synthetase antibodies are the predominant MSAs in DM-ILD-malignancy, and the optimal treatment should be determined based on several factors, including ILD patterns, and malignancy type and stage. In particular, lung cancer may be a risk factor for the acute exacerbation of ILD, and preceding immunosuppression would be useful. Furthermore, prioritizing surgery for gastric cancer is effective because of its paraneoplastic nature.

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.