Online surveillance of novel psychoactive substances (NPS): Monitoring Reddit discussions as a predictor of increased NPS-related exposures.

BACKGROUND: Novel psychoactive substances (NPS) present continuous and growing challenges for the scientific, medical, and interventional communities as emerging substances on recreational drug markets change national and international drug landscapes. NPS account for an increasing proportion of adverse events, hospitalizations, and deaths due to increasing potency and unanticipated biological effects compared to predecessors. This study evaluated the utility of drug use forums as an early indicator or predictor of impending intoxications with potentially harmful or lethal outcomes prior to their occurrences. METHODS: Eight NPS were selected for evaluation to assess the relationship between online mentions of drugs and their involvement in toxic exposures or overdoses. Mentions on Reddit drug forum discussions were tallied and toxicology testing results from forensic investigations in the US were assessed. The selected NPS covered several subclasses and a predetermined time range (2013-2020). They included carfentanil, U-47700, eutylone, flualprazolam, N-ethylpentylone, 5F-MDMB-PICA, isotonitazene, and brorphine. RESULTS: Seven NPS (excluding 5F-MDMB-PICA) appeared in discussions on Reddit prior to their implication in poisonings or intoxications. Distinct increases and decreases in number of mentions and number of exposures were observed. For most substances (n = 5, 63%), a rise in Reddit mentions was soon followed by a corresponding rise in toxicology positivity. Peak positivity for carfentanil and flualprazolam, however, preceded peak Reddit mentions. CONCLUSIONS: This study demonstrated the utility of social media sites, such as Reddit, as a predictor for future trends in NPS-related exposures. These results provide confirmation that activity on drug use forums in the virtual world can help predict changes in exposures associated with new or re-emerging NPS in the real world. The results warrant further evaluation as a strategy for inclusion in early warning systems.

A deep learning approach to identifying immunogold particles in electron microscopy images.

Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.