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Three novel sesquiterpenoid heterodimers, designated as auckcostusolides A–C (1 – 3), were isolated from Aucklandia costus leaves. The structures of compounds 1 – 3 were elucidated through comprehensive spectroscopic analysis, with their absolute configurations established using a combination of X-ray single-crystal diffraction and electronic circular dichroism (ECD) calculations. Notably, compounds 1 and 2 , despite sharing identical planar structures derived from two identical sesquiterpenoids, exhibited opposite configurations at C-11 and C-8′. This configurational difference can be attributed to distinct Diels−Alder cycloaddition processes between the sesquiterpenoid monomers. Additionally, the cytotoxic effects of compounds 1 – 3 were evaluated against colorectal cancer HCT116 cells, fibrosarcoma HT1080 cells, and hepatocellular carcinoma HepG2 cells. Compounds 1 – 3 induced cell death was characterized by endoplasmic reticulum (ER) swelling and cytoplasmic vacuolization, typical morphological changes associated with paraptosis. Mechanistic studies revealed that compounds 1 and 3 triggered paraptosis-like cell death through the accumulation of reactive oxygen species (ROS), activation of ER stress, and stimulation of the MAPK signaling pathway. [ABSTRACT FROM AUTHOR]

Objectives Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions. Materials and methods PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining. Results Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally. Discussion Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site. Conclusion PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches. [ABSTRACT FROM AUTHOR]

We sought to determine whether machine learning and natural language processing (NLP) applied to electronic medical records could improve performance of automated health-care claims-based algorithms to identify anaphylaxis events using data on 516 patients with outpatient, emergency department, or inpatient anaphylaxis diagnosis codes during 2015–2019 in 2 integrated health-care institutions in the Northwest United States. We used one site's manually reviewed gold-standard outcomes data for model development and the other's for external validation based on cross-validated area under the receiver operating characteristic curve (AUC), positive predictive value (PPV), and sensitivity. In the development site 154 (64%) of 239 potential events met adjudication criteria for anaphylaxis compared with 180 (65%) of 277 in the validation site. Logistic regression models using only structured claims data achieved a cross-validated AUC of 0.58 (95% CI: 0.54, 0.63). Machine learning improved cross-validated AUC to 0.62 (0.58, 0.66); incorporating NLP-derived covariates further increased cross-validated AUCs to 0.70 (0.66, 0.75) in development and 0.67 (0.63, 0.71) in external validation data. A classification threshold with cross-validated PPV of 79% and cross-validated sensitivity of 66% in development data had cross-validated PPV of 78% and cross-validated sensitivity of 56% in external data. Machine learning and NLP-derived data improved identification of validated anaphylaxis events. [ABSTRACT FROM AUTHOR]

Purpose: It is a priority of the US Food and Drug Administration (FDA) to monitor the safety of medications used during pregnancy. Pregnancy exposure registries and cohort studies utilizing electronic health record data are primary sources of information but are limited by small sample sizes and limited outcome assessment. TreeScan™, a statistical data mining tool, can be applied within the FDA Sentinel System to simultaneously identify multiple potential adverse neonatal and infant outcomes after maternal medication exposure. Methods: We implemented TreeScan using the Sentinel analytic tools in a cohort of linked live birth deliveries and infants nested in the IBM MarketScan® Research Database. As a case study, we compared first trimester fluoroquinolone use and cephalosporin use. We used the Bernoulli and Poisson TreeScan statistics with compatible propensity score‐based study designs for confounding control (matching and stratification) and used multiple propensity score models with various strategies for confounding control to inform best practices. We developed a hierarchical outcome tree including major congenital malformations and outcomes of gestational length and birth weight. Results: A total of 1791 fluoroquinolone‐exposed and 8739 cephalosporin‐exposed mother‐infant pairs were eligible for analysis. Both TreeScan analysis methods resulted in single alerts that were deemed to be due to uncontrolled confounding or otherwise not warranting follow‐up. Conclusions: In this implementation of TreeScan using Sentinel analytic tools, we did not observe any new safety signals for fluoroquinolone use in the first trimester. TreeScan, with tailored or high‐dimensional propensity scores for confounding control, is a valuable tool in addition to current safety surveillance methods for medications used during pregnancy. [ABSTRACT FROM AUTHOR]

A likelihood ratio test, recently developed for the detection of signals of adverse events for a drug of interest in the FDA Adverse Events Reporting System database, is extended to detect signals of adverse events simultaneously for all the drugs in a drug class. The extended likelihood ratio test methods, based on Poisson model (Ext-LRT) and zero-inflated Poisson model (Ext-ZIP-LRT), are discussed and are analytically shown, like the likelihood ratio test method, to control the type-I error and false discovery rate. Simulation studies are performed to evaluate the performance characteristics of Ext-LRT and Ext-ZIP-LRT. The proposed methods are applied to the Gadolinium drug class in FAERS database. An in-house likelihood ratio test tool, incorporating the Ext-LRT methodology, is being developed in the Food and Drug Administration. [ABSTRACT FROM AUTHOR]