Acceptability, Usability, and Performance of Lateral Flow Immunoassay Tests for Severe Acute Respiratory Syndrome Coronavirus 2 Antibodies: REACT-2 Study of Self-Testing in Nonhealthcare Key Workers.

BACKGROUND: Seroprevalence studies are essential to understand the epidemiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Various technologies, including laboratory assays and point-of-care self-tests, are available for antibody testing. The interpretation of seroprevalence studies requires comparative data on the performance of antibody tests. METHODS: In June 2020, current and former members of the United Kingdom police forces and fire service performed a self-test lateral flow immunoassay (LFIA), had a nurse-performed LFIA, and provided a venous blood sample for enzyme-linked immunosorbent assay (ELISA). We present the prevalence of antibodies to SARS-CoV-2 and the acceptability and usability of self-test LFIAs, and we determine the sensitivity and specificity of LFIAs compared with laboratory ELISA. RESULTS: In this cohort of 5189 current and former members of the police service and 263 members of the fire service, 7.4% (396 of 5348; 95% confidence interval [CI], 6.7-8.1) were antibody positive. Seroprevalence was 8.9% (95% CI, 6.9-11.4) in those under 40 years, 11.5% (95% CI, 8.8-15.0) in those of nonwhite ethnicity, and 7.8% (95% CI, 7.1-8.7) in those currently working. Self-test LFIA had an acceptability of 97.7% and a usability of 90.0%. There was substantial agreement between within-participant LFIA results (kappa 0.80; 95% CI, 0.77-0.83). The LFIAs had a similar performance: compared with ELISA, sensitivity was 82.1% (95% CI, 77.7-86.0) self-test and 76.4% (95% CI, 71.9-80.5) nurse-performed with specificity of 97.8% (95% CI, 97.3-98.2) and 98.5% (95% CI, 98.1-98.8), respectively. CONCLUSIONS: A greater proportion of this nonhealthcare key worker cohort showed evidence of previous infection with SARS-CoV-2 than the general population at 6.0% (95% CI, 5.8-6.1) after the first wave in England. The high acceptability and usability reported by participants and similar performance of self-test and nurse-performed LFIAs indicate that the self-test LFIA is fit for purpose for home testing in occupational and community prevalence studies.

Machine Learning Analysis of Direct Dynamics Trajectory Outcomes for Thermal Deazetization of 2,3-Diazabicyclo[2.2.1]hept-2-ene.

Experimentally, the thermal gas-phase deazetization of 2,3-diazabicyclo[2.2.1]hept-2-ene (1) results in the loss of N2 and the formation of bicyclo products 3 (exo) and 4 (endo) in a nonstatistical ratio, with preference for the exo product. Here, we report unrestricted M06-2X quasiclassical trajectories initialized from the concerted N2 ejection transition state that were able to replicate the experimental preference to form 3. We found that the 3:4 ratio results from the relative amounts of very fast (ballistic) exotype trajectories versus trajectories that lead to the 1,3-diradical intermediate 2. These quasiclassical trajectories provided a set of transition-state vibrational, velocity, momenta, and geometric features for the machine learning analysis. A selection of popular supervised classification algorithms (e.g., random forest) provided poor prediction of trajectory outcomes based on only transition-state vibrational quanta and energy features. However, these machine learning models provided more accurate predictions using atomic velocities and atomic positions, attaining ∼70% accuracy using initial conditions and between 85 and 95% accuracy at later reaction time steps. This increased accuracy allowed the feature importance analysis to reveal that, at the later-time analysis, the methylene bridge out-of-plane bending is correlated with trajectory outcomes for the formation of either the exo product or toward the diradical intermediate. Possible reasons for the struggle of machine learning algorithms to classify trajectories based on transition-state features is the heavily overlapping feature values, the finite but very large possible vibrational mode combinations, and the possibility of chaos as trajectories propagate. We examined this chaos by comparing a set of nearly identical trajectories that differed by only a very small scaling of the kinetic energies resulting from the transition-state reaction coordinate.

Direct dynamics analysis of the cationic Cp*(PMe3)Ir(CH3) methane C-H activation mechanism.

For the σ-bond metathesis reaction between methane and cationic Cp*(PMe3)IrIII(CH3), previous static DFT calculations revealed a two-step oxidative addition/reductive elimination mechanism with an intervening IrV-H intermediate. We recently reported quasiclassical direct molecular dynamics simulations where starting from the vibrationally-averaged oxidative addition transition state a minor, but significant, amount of trajectories bypassed the IrV-H intermediate in a dynamical one-step mechanism. These trajectories also revealed that after the reductive coupling transition state is passed methane always dissociates and the C-H σ-complex is skipped. Here we report direct dynamics simulations using a microcanonical temperature sampling method with trajectories initialized and propagated using the Gaussian program. Sets of 30 trajectories were examined with average time steps ranging from 1.5 to 0.25 femtoseconds (fs). These results showed that a step size of ∼1.5 fs is too large and likely overestimates the amount of trajectories skipping the IrV-H intermediate. Trajectories with 0.75 and 0.25 fs time steps gave qualitatively similar profiles compared to our previous results using our program DynSuite with a 1 fs time step. Reverse trajectories with an average time step of 0.25 fs showed complete skipping of the methane σ complex. 30 trajectories were also propagated using a SMD continuum dichloromethane solvent model. These trajectories showed very similar behavior to gas-phase trajectories.