Autonomic dysreflexia in a case of radiation myelopathy and cisplatin-induced polyneuropathy

Effects of Medications on Heat Loss Capacity in Chronic Disease Patients: Health Implications Amidst Global Warming

Pharmacological agents used to treat or manage diseases can modify the level of heat strain experienced by chronically ill and elderly patients via different mechanistic pathways. Human thermoregulation is a crucial homeostatic process that maintains body temperature within a narrow range during heat stress through dry (i.e., increasing skin blood flow) and evaporative (i.e., sweating) heat loss, as well as active inhibition of thermogenesis, which is crucial to avoid overheating. Medications can independently and synergistically interact with aging and chronic disease to alter homeostatic responses to rising body temperature during heat stress. This review focuses on the physiologic changes, with specific emphasis on thermolytic processes, associated with medication use during heat stress. The review begins by providing readers with a background of the global chronic disease burden. Human thermoregulation and aging effects are then summarized to give an understanding of the unique physiologic changes faced by older adults. The effects of common chronic diseases on temperature regulation are outlined in the main sections. Physiologic impacts of common medications used to treat these diseases are reviewed in detail, with emphasis on the mechanisms by which these medications alter thermolysis during heat stress. The review concludes by providing perspectives on the need to understand the effects of medication use in hot environments, as well as a summary table of all clinical considerations and research needs of the medications included in this review. SIGNIFICANCE STATEMENT: Long-term medications modulate thermoregulatory function, resulting in excess physiological strain and predisposing patients to adverse health outcomes during prolonged exposures to extreme heat during rest and physical work (e.g., exercise). Understanding the medication-specific mechanisms of altered thermoregulation has importance in both clinical and research settings, paving the way for work toward refining current medication prescription recommendations and formulating mitigation strategies for adverse drug effects in the heat in chronically ill patients.

Predicting adverse drug effects: A heterogeneous graph convolution network with a multi-layer perceptron approach

We apply a heterogeneous graph convolution network (GCN) combined with a multi-layer perceptron (MLP) denoted by GCNMLP to explore the potential side effects of drugs. Here the SIDER, OFFSIDERS, and FAERS are used as the datasets. We integrate the drug information with similar characteristics from the datasets of known drugs and side effect networks. The heterogeneous graph networks explore the potential side effects of drugs by inferring the relationship between similar drugs and related side effects. This novel in silico method will shorten the time spent in uncovering the unseen side effects within routine drug prescriptions while highlighting the relevance of exploring drug mechanisms from well-documented drugs. In our experiments, we inquire about the drugs Vancomycin, Amlodipine, Cisplatin, and Glimepiride from a trained model, where the parameters are acquired from the dataset SIDER after training. Our results show that the performance of the GCNMLP on these three datasets is superior to the non-negative matrix factorization method (NMF) and some well-known machine learning methods with respect to various evaluation scales. Moreover, new side effects of drugs can be obtained using the GCNMLP.