Time dependent thermal lensing measurements of V–T energy transfer from highly excited NO2

The time dependent thermal lensing technique has been used to measure the vibrational relaxation of NO2 (initially excited at 21 631 cm−1) by Ar, Kr, and Xe. The energy transfer analysis was carried out in terms of 〈〈ΔE〉〉, the bulk average energy transferred per collision. This quantity was found to have a very strong dependence on vibrational energy, with a marked increase at energies greater than about 10 000 cm−1, where several electronic excited states (2B2, 2B1, and 2A2) mix with the ground state (2A1). This effect may be due to large amplitude vibrational motions associated with the coupled electronic states. Even at low energies, deactivation is faster than in other triatomic systems, probably because NO2 is an open shell molecule and electronic curve crossings provide efficient pathways for vibrational deactivation. The V–T rate constant for deactivation of NO2(010) by argon is estimated to be (5.1±1.0)×10−14 cm3 s−1. Results obtained for NO@B|2–NO2 collisions gave 〈〈ΔE〉〉 values in good agreement with literature results from fluorescence quenching experiments, indicating that V–T may be more important than V–V energy transfer in the quenching process.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/70683/2/JCPSA6-92-8-4793-1.pd

A polygenic and phenotypic risk prediction for polycystic ovary syndrome evaluated by phenomewide association studies

Context: As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated tobe unidentified in clinical practice. Objective: Utilizing polygenic risk prediction, we aim to identify the phenome-widecomorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventivetreatment.Design, Patients, and Methods: Leveraging the electronic health records (EHRs) of 124 852individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores(PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). Weevaluated its predictive capability across different ancestries and perform a PRS-based phenomewide association study (PheWAS) to assess the phenomic expression of the heightened risk ofPCOS.Results: The integrated polygenic prediction improved the average performance (pseudo-R2)for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null modelacross European, African, and multi-ancestry participants respectively. The subsequent PRSpowered PheWAS identified a high level of shared biology between PCOS and a range ofmetabolic and endocrine outcomes, especially with obesity and diabetes: "morbid obesity","type 2 diabetes", "hypercholesterolemia", "disorders of lipid metabolism", "hypertension",and "sleep apnea" reaching phenome-wide significance.Conclusions: Our study has expanded the methodological utility of PRS in patient stratificationand risk prediction, especially in a multifactorial condition like PCOS, across different geneticorigins. By utilizing the individual genome-phenome data available from the EHR, our approachalso demonstrates that polygenic prediction by PRS can provide valuable opportunities todiscover the pleiotropic phenomic network associated with PCOS pathogenesis.Abbreviations: AA, African ancestry; ANOVA, analysis of variance; BMI, body mass index; EA,European ancestry; EHR, electronic health records; eMERGE, electronic Medical Records andGenomics Network; GWAS, genome-wide association study; IBD, identity-by-descent; ICDCM, International Classification of Diseases, Clinical Modification; LD, linkage disequilibrium;MA, multi-ancestry; MAF, minor allele frequency; NIH, National Institutes of Health; PCA,principal component analysis; PheWAS, phenome-wide association study; PCOS, polycysticovary syndrome; PPRS, polygenic and phenotypic risk score; PRS, polygenic risk sc

Identification and assessment of classification criteria attributes for systemic lupus erythematosus in a regional medical record data network

Objective To assess the application and utility of algorithms designed to detect features of SLE in electronic health record (EHR) data in a multisite, urban data network.Methods Using the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN), a Clinical Data Research Network (CDRN) containing data from multiple healthcare sites, we identified patients with at least one positively identified criterion from three SLE classification criteria sets developed by the American College of Rheumatology (ACR) in 1997, the Systemic Lupus International Collaborating Clinics (SLICC) in 2012, and the European Alliance of Associations for Rheumatology and the ACR in 2019 using EHR-based algorithms. To measure the algorithms’ performance in this data setting, we first evaluated whether the number of clinical encounters for SLE was associated with a greater quantity of positively identified criteria domains using Poisson regression. We next quantified the amount of SLE criteria identified at a single healthcare institution versus all sites to assess the amount of SLE-related information gained from implementing the algorithms in a CDRN.Results Patients with three or more SLE encounters were estimated to have documented 2.77 (2.73 to 2.80) times the number of positive SLE attributes from the 2012 SLICC criteria set than patients without an SLE encounter via Poisson regression. Patients with three or more SLE-related encounters and with documented care from multiple institutions were identified with more SLICC criteria domains when data were included from all CAPriCORN sites compared with a single site (p<0.05).Conclusions The positive association observed between amount of SLE-related clinical encounters and the number of criteria domains detected suggests that the algorithms used in this study can be used to help describe SLE features in this data environment. This work also demonstrates the benefit of aggregating data across healthcare institutions for patients with fragmented care