Spatiotemporal analysis of electronic cigarette discussion on Twitter/X using natural language processing.

Cocaine is used by an estimated 25 million individuals aged 15–64 worldwide, and use prevalence has continued to increase over the past decade, accompanied by a rising mortality rate associated with cocaine use disorder (CUD) []. CUD is characterized by compulsive drug-seeking and a high rate of relapse []. Remaining abstinent when cocaine use has transitioned into CUD is a critical therapeutic challenge []. The abstinent state engages multiple neural systems involved in executive control, decision-making, emotional regulation, and arousal [, ]. Therapeutic interventions that target both motivational drive for cocaine use and the affective dysregulation associated with withdrawal may provide a promising therapeutic strategy [, ]. However, despite decades of research, no approved pharmacological treatments for CUD are currently available, highlighting the need for novel treatment strategies. One promising approach involves targeting M1 muscarinic receptors, as the development of highly selective M1 modulators has revealed potential for regulating addictive behaviors. VU0364572, a reported bitopic partial agonist of the M1 muscarinic receptor [–], has shown promising efficacy in preclinical models. In CUD-related behaviors in rodents, VU0364572 significantly reduces cocaine self-administration and drug-seeking behavior for at least four weeks post-treatment. The behavior is accompanied by a sustained reduction in cocaine-evoked glutamate release in the nucleus accumbens (NAc) []. However, the cellular and synaptic mechanisms underlying VU0364572’s effects within circuits known to play a role in CUD remain largely unknown.