A systematic review of machine learning applications in predicting opioid associated adverse events.

Machine learning has increasingly been applied to predict opioid-related harms due to its ability to handle complex interactions and generating actionable predictions. This review evaluated the types and quality of ML methods in opioid safety research, identifying 44 studies using supervised ML through searches of Ovid MEDLINE, PubMed and SCOPUS databases. Commonly predicted outcomes included postoperative opioid use (n = 15, 34%) opioid overdose (n = 8, 18%), opioid use disorder (n = 8, 18%) and persistent opioid use (n = 5, 11%) with varying definitions.

Childhood Maltreatment and Somatic Symptoms in Adulthood: Establishing a New Research Pathway.

Background: Somatic symptoms, such as chronic pain, fatigue, and gastrointestinal disturbances, are commonly reported in individuals with a history of childhood maltreatment (CM), which includes various forms of abuse and neglect experienced before age 18. Although CM is strongly associated with somatic symptoms, the specific relationships between CM subtypes and these symptoms, as well as the mechanisms connecting them, remain insufficiently understood. This review examines the complex interaction between CM and somatic symptoms, which often coexist with mental disorders and significantly impact quality of life and healthcare systems.

Atp1a2 and Kcnj9 Are Candidate Genes Underlying Sensitivity to Oxycodone-Induced Locomotor Activation and Withdrawal-Induced Anxiety-Like Behaviors in C57BL/6 Substrains.

Opioid use disorder is heritable, yet its genetic etiology is largely unknown. C57BL/6J and C57BL/6NJ mouse substrains exhibit phenotypic diversity in the context of limited genetic diversity which together can facilitate genetic discovery. Here, we found C57BL/6NJ mice were less sensitive to oxycodone (OXY)-induced locomotor activation versus C57BL/6J mice in a conditioned place preference paradigm.

Inferring structure of cortical neuronal networks from activity data: A statistical physics approach.

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve over time, spontaneously or under stimulation. It requires a method for inferring the structure and composition of a network from neuronal activities. Tracking the evolution of networks and their changing functionality will provide invaluable insight into the occurrence of plasticity and the underlying learning process.

Sub-5 Ångstrom Porosity Tuning in Calixarene-Derived Porous Liquids via Supramolecular Complexation Construction.

Sub-Ångstrom-level porosity engineering, which is appealing in gas separations, has been demonstrated in solid carbon, polymer, and framework materials but rarely achieved in the liquid phase. In this work, a gas molecular sieving effect in the liquid phase at sub-5 Ångstrom scale is created via sophisticated porosity tuning in calixarene-derived porous liquids (PLs). Type II PLs are constructed via supramolecular complexation between the sodium salts of calixarene derivatives and crown ether solvents.