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Fentanyl test strips (FTS) are a commonly deployed tool in drug checking, used to test for the presence of fentanyl in street drug samples prior to consumption. Previous reports indicate that in addition to fentanyl, FTS can also detect fentanyl analogs like acetyl fentanyl and butyryl fentanyl, with conflicting reports on their ability to detect fentanyl analogs like Carfentanil and furanyl fentanyl. Yet with hundreds of known fentanyl analogs, there has been no large-scale study rationalizing FTS reactivity to different fentanyl analogs.

In this study, 251 synthetic opioids—including 214 fentanyl analogs—were screened on two brands of fentanyl test strips to (1) assess the differences in the ability of two brands of fentanyl test strips to detect fentanyl-related compounds and (2) determine which moieties in fentanyl analog chemical structures are most crucial for FTS detection. Two FTS brands were assessed in this study: BTNX Rapid Response and WHPM DanceSafe.

Of 251 screened compounds assessed, 121 compounds were detectable at or below 20,000 ng/mL by both BTNX and DanceSafe FTS, 50 were not detectable by either brand, and 80 were detectable by one brand but not the other (n = 52 BTNX,n = 28 DanceSafe). A structural analysis of fentanyl analogs screened revealed that in general, bulky modifications to the phenethyl moiety inhibit detection by BTNX FTS while bulky modifications to the carbonyl moiety inhibit detection by DanceSafe FTS.

The different “blind spots” are caused by different haptens used to elicit the antibodies for these different strips. By utilizing both brands of FTS in routine drug checking, users could increase the chances of detecting fentanyl analogs in the “blind spot” of one brand.

In metagenomics, the study of environmentally associated microbial communities from their sampled DNA, one of the most fundamental computational tasks is that of determining which genomes from a reference database are present or absent in a given sample metagenome. Existing tools generally return point estimates, with no associated confidence or uncertainty associated with it. This has led to practitioners experiencing difficulty when interpreting the results from these tools, particularly for low-abundance organisms as these often reside in the “noisy tail” of incorrect predictions. Furthermore, few tools account for the fact that reference databases are often incomplete and rarely, if ever, contain exact replicas of genomes present in an environmentally derived metagenome.

We present solutions for these issues by introducing the algorithm YACHT: Yes/No Answers to Community membership via Hypothesis Testing. This approach introduces a statistical framework that accounts for sequence divergence between the reference and sample genomes, in terms of ANI, as well as incomplete sequencing depth, thus providing a hypothesis test for determining the presence or absence of a reference genome in a sample. After introducing our approach, we quantify its statistical power and how this changes with varying parameters. Subsequently, we perform extensive experiments using both simulated and real data to confirm the accuracy and scalability of this approach.

The source code implementing this approach is available via Conda and at https://github.com/KoslickiLab/YACHT. We also provide the code for reproducing experiments at https://github.com/KoslickiLab/YACHT-reproducibles.