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

Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach

IntroductionImmune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors.MethodsWe conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs.ResultsLogistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43).DiscussionOur machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA

Evaluation of structured data from electronic health records to identify clinical classification criteria attributes for systemic lupus erythematosus

Objective Our objective was to develop algorithms to identify lupus clinical classification criteria attributes using structured data found in the electronic health record (EHR) and determine whether they could be used to describe a cohort of people with lupus and discriminate them from a defined healthy control cohort.Methods We created gold standard lupus and healthy patient cohorts that were fully adjudicated for the American College of Rheumatology (ACR), Systemic Lupus International Collaborating Clinics (SLICC) and European League Against Rheumatism/ACR (EULAR/ACR) classification criteria and had matched EHR data. We implemented rule-based algorithms using structured data within the EHR system for each attribute of the three classification criteria. Individual criteria attribute and classification criteria algorithms as a whole were assessed over our combined cohorts and the overall performance of the algorithms was measured through sensitivity and specificity.Results Individual classification criteria attributes had a wide range of sensitivities, 7% (oral ulcers) to 97% (haematological disorders) and specificities, 56% (haematological disorders) to 98% (photosensitivity), but all could be identified in EHR data. In general, algorithms based on laboratory results performed better than those primarily based on diagnosis codes. All three classification criteria systems effectively distinguished members of our case and control cohorts, but the SLICC criteria-based algorithm had the highest overall performance (76% sensitivity, 99% specificity).Conclusions It is possible to characterise disease manifestations in people with lupus using classification criteria-based algorithms that assess structured EHR data. These algorithms may reduce chart review burden and are a foundation for identifying subpopulations of patients with lupus based on disease presentation to support precision medicine applications

Genome Analysis of F. nucleatum sub spp vincentii and Its Comparison With the Genome of F. nucleatum ATCC 25586

We present the draft genome sequence and its analysis for Fusobacterium nucleatum sub spp. vincentii (FNV), and compare that genome with F. nucleatum ATCC 25586 (FN). A total of 441 FNV open reading frames (ORFs) with no orthologs in FN have been identified. Of these, 118 ORFs have no known function and are unique to FNV, whereas 323 ORFs have functional orthologs in other organisms. In addition to the excretion of butyrate, H(2)S and ammonia-like FN, FNV has the additional capability to excrete lactate and aminobutyrate. Unlike FN, FNV is likely to incorporate galactopyranose, galacturonate, and sialic acid into its O-antigen. It appears to transport ferrous iron by an anaerobic ferrous transporter. Genes for eukaryotic type serine/threonine kinase and phosphatase, transpeptidase E-transglycosylase Pbp1A are found in FNV but not in FN. Unique ABC transporters, cryptic phages, and three types of restriction-modification systems have been identified in FNV. ORFs for ethanolamine utilization, thermostable carboxypeptidase, γ glutamyl-transpeptidase, and deblocking aminopeptidases are absent from FNV. FNV, like FN, lacks the classical catalase-peroxidase system, but thioredoxin/glutaredoxin enzymes might alleviate oxidative stress. Genes for resistance to antibiotics such as acriflavin, bacitracin, bleomycin, daunorubicin, florfenicol, and other general multidrug resistance are present. These capabilities allow Fusobacteria to survive in a mixed culture in the mouth