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Background The cessation of opioid use can cause withdrawal symptoms. People often continue opioid misuse to avoid these symptoms. Many people who use opioids self-treat withdrawal symptoms with a range of substances. Little is known about the substances that people use or their effects. Objective The aim of this study is to validate a methodology for identifying the substances used to treat symptoms of opioid withdrawal by a community of people who use opioids on the social media site Reddit. Methods We developed a named entity recognition model to extract substances and effects from nearly 4 million comments from the r/opiates and r/OpiatesRecovery subreddits. To identify effects that are symptoms of opioid withdrawal and substances that are potential remedies for these symptoms, we deduplicated substances and effects by using clustering and manual review, then built a network of substance and effect co-occurrence. For each of the 16 effects identified as symptoms of opioid withdrawal in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, we identified the 10 most strongly associated substances. We classified these pairs as follows: substance is a Food and Drug Administration–approved or commonly used treatment for the symptom, substance is not often used to treat the symptom but could be potentially useful given its pharmacological profile, substance is a home or natural remedy for the symptom, substance can cause the symptom, or other or unclear. We developed the Withdrawal Remedy Explorer application to facilitate the further exploration of the data. Results Our named entity recognition model achieved F1 scores of 92.1 (substances) and 81.7 (effects) on hold-out data. We identified 458 unique substances and 235 unique effects. Of the 130 potential remedies strongly associated with withdrawal symptoms, 54 (41.5%) were Food and Drug Administration–approved or commonly used treatments for the symptom, 17 (13.1%) were not often used to treat the symptom but could be potentially useful given their pharmacological profile, 13 (10%) were natural or home remedies, 7 (5.4%) were causes of the symptom, and 39 (30%) were other or unclear. We identified both potentially promising remedies (eg, gabapentin for body aches) and potentially common but harmful remedies (eg, antihistamines for restless leg syndrome). Conclusions Many of the withdrawal remedies discussed by Reddit users are either clinically proven or potentially useful. These results suggest that this methodology is a valid way to study the self-treatment behavior of a web-based community of people who use opioids. Our Withdrawal Remedy Explorer application provides a platform for using these data for pharmacovigilance, the identification of new treatments, and the better understanding of the needs of people undergoing opioid withdrawal. Furthermore, this approach could be applied to many other disease states for which people self-manage their symptoms and discuss their experiences on the web.

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When developing a new compound, potential side effects on the central nervous system (CNS) should be systematically investigated to determine the drug’s safety, e.g., in respect of operating machinery or driving a car. The present study investigated CNS effects of ketanserin, a newly developed S2-serotonergic antagonist, in hypertensive patients. A multidimensional research strategy was used combining pharmaco-EEG and pharmacopsychological methods. The investigation consisted of two separate double-blind, randomized, and placebo-controlled studies, which were, however, planned together and in part included the same patients. The first study was carried out in 2 × 24 patients receiving chronic treatment with 2 × 20 mg ketanserin for 2 weeks, followed by 2 × 40 mg for a further 3 weeks. The second study was performed in 2 × 20 patients after subacute administration of 20 mg twice daily for six days. A multidimensional research strategy was employed to investigate CNS effects on functional and performance measures. Vigilance-related parameters, such as the alpha slow wave index and the absolute delta power, were assessed with pharmaco electroencephalography. Critical flicker-fusion frequency served as a measure of central sedation. Psychomotor performance and concentration tests were used to detect CNS effects which might impair car-driving ability. In addition, subjective well-being and adverse drug reactions were recorded. Ketanserin proved to be a safe drug to lower blood pressure. Only after chronic treatment were there slight indications that ketanserin could have a sedating and inhibiting action on the CNS. However, the differences between placebo and the ketanserin group, and the alterations within each group, were so minimal that they were not considered clinically relevant. A negative effect of ketanserin on car-driving ability is not very likely. The results show that the model was sensitive enough to detect CNS effects with sufficient certainty.

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Data-driven decision making is becoming an integral part of manufacturing companies. Data is collected and commonly used to improve efficiency and produce high quality items for the customers. IoT-based and other forms of object tracking are an emerging tool for collecting movement data of objects/entities (e.g. human workers, moving vehicles, trolleys etc.) over space and time. Movement data can provide valuable insights like process bottlenecks, resource utilization, effective working time etc. that can be used for decision making and improving efficiency.

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