Predicting heroin potency from the analysis of paraphernalia: A tool for overdose prevention projects.

In recent years, fatal and non-fatal heroin-related overdoses have increased in northeastern Italy, and the change in potency of heroin available at street level has been identified as a prominent factor associated with acute toxicity. Two very different products, high-potency and low-potency heroin were becoming available on the street, and no clear morphological characteristics could be used to easily distinguish them. A theoretical model for predicting heroin potency from rapid analysis of cigarette filters was developed as part of an overdose prevention project. The model was derived from the analysis of real heroin samples and exploits the common presence of caffeine in heroin as an adulterant. It was tested on laboratory prepared filters, real filters used to prepare heroin injections, and other paraphernalia. The model showed strong predictive ability and was used to implement a rapid alert system to inform drug users and healthcare institutions about the potency of heroin or other psychoactive substances circulating in the area. Cigarette filters were used as standard material, but other paraphernalia were successfully tested. The developed model is a dynamic tool whose parameters can be updated according to the market characteristics, so it can be useful for laboratories involved in drug analysis and similar prevention programs.

Metabolomic fingerprint of coronary blood in STEMI patients depends on the ischemic time and inflammatory state.

In this study we investigated whether the metabolomic analysis could identify a specific fingerprint of coronary blood collected during primary PCI in STEMI patients. Fifteen samples was subjected to metabolomic analysis. Subsequently, the study population was divided into two groups according to the peripheral blood neutrophil-to-lymphocyte ratio (NLR), a marker of the systemic inflammatory response. Regression analysis was then applied separately to the two NLR groups. A partial least square (PLS) regression identified the most significant involved metabolites and the PLS-class analysis revealed a significant correlation between the metabolic profile and the total ischemic time only in patients with an NLR > 5.77.