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Patients with type 1 diabetes and their physicians have long desired a fully closed-loop artificial pancreas (AP) system that can alleviate the burden of blood glucose regulation. Although deep reinforcement learning (DRL) methods theoretically enable adaptive insulin dosing control, they face numerous challenges, including safety and training efficiency, which have hindered their clinical application. This paper proposes a safe and efficient adaptive insulin delivery controller based on DRL.

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Background: Insulinoma is a neuroendocrine tumor, the main manifestation of which is hypoglycemia. However, the symptoms of hypoglycemia can be non-specific for a long time, especially outside provocative conditions, and quite often the tumor manifests from a life-threatening condition - hypoglycemic coma. In this regard, timely laboratory diagnosis of insulinoma and determination of its aggressive course is one of the priorities in modern researches.

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Non-islet cell tumor hypoglycemia (NICTH) is a paraneoplastic syndrome associated with non-mesenchymal-derived and epithelial tumors. A 37-year-old male with stage IVB hepatocellular carcinoma (HCC) and pulmonary metastases presented with recurrent hypoglycemia despite glucose supplementation. Laboratory findings revealed low insulin growth factor 1 (IGF-1) (15 ng/mL), elevated insulin growth factor 2 (IGF-2) (395 ng/ml), and an IGF-2:IGF-1 ratio of 26:1, consistent with NICTH.

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Synthetic antidiabetic drugs are often associated with various adverse side effects, including hypoglycemia, nausea, gastrointestinal disturbances, headaches, and even liver damage. In contrast, plant-derived natural antidiabetic bioactive compounds typically exhibit lower toxicity and fewer side effects and have been reported to aid effectively in diabetes management. These plant extracts regulate diabetes by restoring pancreatic function, enhancing insulin secretion, inhibiting intestinal glucose absorption, and facilitating insulin dependent metabolism.

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To evaluate real-world outcomes in adults with type 1 diabetes initiating open-source automated insulin delivery systems (OS-AID). Adults with type 1 diabetes who commenced OS-AID, between May 2016 and April 2021, across 12 centers in the United Kingdom were included. Anonymized clinical data, collected during routine clinical care between December 2019 and November 2023, were submitted to a secure web-based tool within the National Health Service network.

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