Clinical Performance of a Gene-Based Machine Learning Classifier in Assessing Risk of Developing OUD in Subjects Taking Oral Opioids: A Prospective Observational Study.

OBJECTIVE: To reduce the incidence of Opioid Use Disorder (OUD), multiple guidelines recommend assessing the risk of OUD prior to prescribing oral opioids. Although subjective risk assessments are available to help classify subjects at risk for OUD, we are aware of no clinically validated objective risk assessment tools. An objective risk assessment based on genetics may help inform shared decision-making prior to prescribing short-duration oral opioids. METHODS: A multicenter, observational cohort of adults exposed to prescription oral opioids for 4-30 days was conducted to determine the performance of an OUD classifier derived from machine learning (ML). From this cohort, the demographics of the U.S. adult opioid-prescribed population were used to create a blinded, random, representative group of subjects (n=385) for analysis to accurately estimate the performance characteristics in the intended use population. Genotyping was performed via a qualitative SNP microarray on DNA extracted from buccal samples. RESULTS: In the study subjects, the classifier demonstrated 82.5% sensitivity (95% confidence intervals: 76.1%-87.8%) and 79.9% specificity (73.7-85.2%), with no statistically significant differences in clinical performance observed based on gender, age, length of follow-up from opioid exposure, race, or ethnicity. CONCLUSION: This study demonstrates an ML classifier may provide additional objective information regarding a patient's risk of developing OUD. This information may enable subjects and healthcare providers to make more informed decisions when considering the use of oral opioids.

Interference from anti-streptavidin antibody.

Immunoassays are commonly used for clinical diagnosis, although interferences have been well documented. The streptavidin-biotin interaction provides an efficient and convenient method to manipulate assay components and is currently used in several immunoassay platforms. To date, there has been no report in the literature of interference from endogenous anti-streptavidin antibodies; however, such antibodies would potentially affect multiple diagnostic platforms. We report results from a patient being treated for thyroid dysfunction who demonstrated a T-uptake result of less than 0.2 and a nonlinear thyroid stimulating hormone dilution that suggested an immunoassay interference. Protein-A sepharose pretreatment corrected the nonlinear dilution and revealed an interference trend of falsely decreased results, as measured by sandwich assay, and falsely elevated results, as measured by competitive assay. The results of streptavidin-agarose adsorption were comparable to adsorption with protein-A sepharose. To our knowledge, this is the first published description of an endogenous anti-streptavidin antibody interfering with clinical laboratory assays.

Electrochemically gated oligopeptide nanowires bridging gold electrodes: novel bio-nanoelectronic switches operating in aqueous electrolytic environments.

We present electronic structure and transport calculations that reveal that oligopeptide based molecular nanowires support unoccupied extended electronic states that span the length of the nanowire and are resistant to disorder. Electrochemical gating in aqueous electrolytes is shown to bring these extended states into resonance with the Fermi level of gold electrodes, transforming these nanowires from insulators into conductors. Thus oligopeptide nanowires are promising candidates for bionanoelectronic switches operating in the aqueous electrolytic environments native to biological systems.

The quantum interference effect transistor.

We give a detailed discussion of the quantum interference effect transistor (QuIET), a proposed device which exploits the interference between electron paths through aromatic molecules to modulate the current flow. In the off state, perfect destructive interference stemming from the molecular symmetry blocks the current, while in the on state, the current is allowed to flow by locally introducing either decoherence or elastic scattering. Details of a model calculation demonstrating the efficacy of the QuIET are presented, and various fabrication scenarios are proposed, including the possibility of using conducting polymers to connect the QuIET with multiple leads.

Controlling quantum transport through a single molecule.

We investigate multiterminal quantum transport through single monocyclic aromatic annulene molecules, and their derivatives, using the nonequilibrium Green function approach within the self-consistent Hartree-Fock approximation. We propose a new device concept, the quantum interference effect transistor, that exploits perfect destructive interference stemming from molecular symmetry and controls current flow by introducing decoherence and/or elastic scattering that break the symmetry. This approach overcomes the fundamental problems of power dissipation and environmental sensitivity that beset nanoscale device proposals.