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Abstract Amid the global opioid crisis, the volume of drug trade via darknet markets has risen to an all-time high. The steady increase can be explained by the reliable operation of darknet markets, affected by community-building trust factors reducing the risks during the process of the darknet drug trade. This study was designed to explore the risk reduction efforts of the community of a selected darknet market and therefore contribute to the harm assessment of darknet markets. We performed Latent Dirichlet Allocation topic modelling on customer reviews of drug products (n = 25,107) scraped from the darknet market Dark0de Reborn in 2021. We obtained a model resulting in 4 topics (coherence score = 0.57): (1) feedback on satisfaction with the transaction; (2) report on order not received; (3) information on the quality of the product; and (4) feedback on vendor reliability. These topics identified in the customer reviews suggest that the community of the selected darknet market implemented a safer form of drug supply, reducing risks at the payment and delivery stages and the potential harms of drug use. However, the pitfalls of this form of community-initiated safer supply support the need for universally available and professional harm reduction and drug checking services. These findings, and our methodological remarks on applying text mining, can enhance future research to further examine risk and harm reduction efforts across darknet markets.

Abstract Background High-quality RNA extraction from woody plants is difficult because of the presence of polysaccharides and polyphenolics that bind or co-precipitate with the RNA. The CTAB (cetyl trimethylammonium bromide) based method is widely used for the isolation of nucleic acids from polysaccharide-rich plants. Despite the widespread use of the CTAB method, it is necessary to adapt it to particular plant species, tissues and organs. Here we described a simple and generalized method for RNA isolation from mature leaf tissues of several economically important woody (17) and herbaceous plants (2) rich in secondary metabolites. High yields were achieved from small amount (up to 50 mg) of plant material. Two main modifications were applied to the basic protocol: an increase in β-mercaptoethanol concentration (to 10%v/v) and the use of an effective DNase treatment. As opposed to similar studies, we tried to describe a more detailed protocol for isolating RNA, including the exact quantity and concentration of the reagents were used. Results Our modified CTAB method is proved to be efficient in extracting the total RNA from a broad range of woody and herbaceous species. The RNA yield was ranged from 2.37 to 91.33 µg/µl. The A260:A280 and A260:A230 absorbance ratios were measured from 1.77 to 2.13 and from 1.81 to 2.22. The RIN value (RNA Integrity Number) of the samples fell between 7.1 and 8.1, which indicated that a small degree of RNA degradation occurred during extraction. The presence of a single peak in the melt curve analyses and low standard errors of the Ct values of replicated measurements indicated the specificity of the primers to bind to the cDNA. Conclusions Our RNA isolation method, with fine-tuned and detailed instructions, can produce high quality RNA from a small amount of starting plant material that is suitable for use in downstream transcriptional analyses. The use of an increased concentration of the reducing agent β-mercaptoethanol in the extraction buffer, as well as the application of DNaseI-treatment resulted in a method suitable for a wide range of plants without the need of further optimalization, especially in Rhus typhina (Staghorn sumac), for which molecular-genetic studies have not yet been sufficiently explored.

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