Citations

ObjectiveThe ADVOCATE trial demonstrated that treatment of active granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) with avacopan was noninferior in achieving remission at week 26 and superior for sustained remission at week 52 compared with a prednisone taper. This analysis of ADVOCATE evaluated the efficacy and safety of avacopan in patients with ear, nose, throat (ENT), or lung manifestations.MethodsThis post hoc analysis included patients enrolled in ADVOCATE with ENT or lung manifestations at baseline. Outcomes included remission at weeks 26 and 52, respiratory tract manifestations over time, relapse rate, glucocorticoid‐related toxicity, health‐related quality of life (HRQoL), and safety.ResultsADVOCATE included 144 patients with ENT manifestations (avacopan: n = 75, prednisone taper: n = 69) and 142 with lung manifestations (avacopan: n = 71, prednisone taper: n = 71). Among patients with ENT manifestations, remission was achieved at week 26 by 72.0% (avacopan group) and 71.0% (prednisone taper group) and was sustained at week 52 by 62.7% (avacopan) and 53.6% (prednisone taper). Among patients with lung manifestations, remission was achieved at week 26 by 73.2% (avacopan) and 66.2% (prednisone taper) and sustained at week 52 by 67.6% (avacopan) and 53.5% (prednisone taper). The relapse rate, glucocorticoid toxicity, and measures of HRQoL favored avacopan for both respiratory tract manifestation groups. Safety was comparable across treatment groups.ConclusionPatients with GPA or MPA with ENT or lung manifestations demonstrated similar responses to avacopan, reflecting results reported in the overall ADVOCATE trial population and supporting the administration of avacopan in these subgroups.

Abstract Electroconvulsive therapy (ECT) is an effective treatment for severe depression, especially in treatment-resistant cases. However, its potential cognitive side effects necessitate careful dosing to balance therapeutic benefits and cognitive stability. Recent advances in electric field (E-field) modeling offer promising avenues to optimize ECT dosing. This review synthesizes current knowledge on E-field modeling in ECT and explores its clinical applications. It examines the variability in E-field strengths and distributions induced by ECT and their impact on clinical outcomes. Additionally, the relationship between E-field strengths, neuroplasticity, and therapeutic efficacy is discussed. Translational studies of E-field-informed ECT are highlighted, emphasizing individualized optimal amplitude dosing and potential clinical applications. This review provides useful insights into how E-field modeling can improve the effectiveness of ECT while minimizing adverse effects, helping guide future research and clinical practice.

BACKGROUND: Power-controlled radiofrequency ablation with irrigated-tip catheters has been the norm for ventricular ablation for almost 2 decades. New catheter technology has recently integrated more accurate tissue temperature sensing enabling temperature-controlled irrigated ablation. We aimed to investigate the in vivo ablation parameters and lesion formation characteristics in ventricular myocardium using a novel temperature-controlled radiofrequency catheter. METHODS: Twenty canines were divided into 3 groups: 4 noninfarcted, acute (phase I); 8 noninfarcted, chronic (phase II); and 8 infarcted, chronic (phase III). Lesions were delivered with a temperature-controlled radiofrequency system utilizing a chemical vapor deposit diamond for efficient thermal diffusivity. In phase I, 17 ablation settings were tested (temperature set points, 50/60/70 °C; ablation duration, 15/30/60/90/120 s; and power limit, 30/50 W). Four and one of these sets of parameters were further tested in phases II and III, respectively. Lesions were assessed by ex vivo contrast-enhanced magnetic resonance imaging and gross pathology 5 weeks after ablation in phases II/III. RESULTS: Across all phases, 111 ablation lesions were delivered. Ablation with the power limit of 50 W, the temperature set point of 60 °C, and the duration of 60 s produced significantly larger and deeper lesions (mean, 569.2 mm 3 ; mean maximal depth, 9.8 mm) compared with 50 W/60 °C/30 s (mean, 340.4 mm 3 ; mean maximal depth, 8.3 mm) and 50 W/50 °C/60 s (mean, 227 mm 3 ; mean maximal depth, 6.9 mm), with P <0.05 for all pairwise comparisons. Ablation of infarcted myocardium in phase III (50 W/60 °C/30 s) resulted in smaller impedance and bipolar electrogram amplitude changes and lesion size compared with ablation in normal myocardium with the same settings. No steam pop, myocardial perforation, or char formation was observed in any of the 111 ablations across all phases. CONCLUSIONS: In vivo radiofrequency ablation in a canine model with a diamond-tip temperature-controlled catheter using a temperature set point of 60 °C and a power limit of 50 W created large lesions without steam pop risk in both normal and infarcted ventricular myocardia.

Abstract Advances in genomic medicine have accelerated the identification of mutations in disease- associated genes, but the pathogenicity of many mutations remains unknown, hindering their use in diagnostics and clinical decision-making. Predictive AI models have been generated to combat this issue, but current tools display low accuracy when tested against functionally validated datasets. We show that integrating detailed conformational data extracted from molecular dynamics simulations (MDS) into advanced AI-based models can increase their predictive power. We carried out an exhaustive mutational analysis of the disease gene PMM2 and subjected structural models of each variant to MDS. AI models trained on this dataset outperformed existing tools when predicting the known pathogenicity of mutations. Our best performing model, a neuronal networks model, was also able to predict the pathogenicity of several PMM2 mutations currently considered of unknown significance. We believe this new model will help alleviate the burden of unknown variants in genomic medicine.

Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGS BMI ) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R 2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R 2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGS BMI –covariate interaction effects, modifying PGS BMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R 2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R 2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGS BMI individuals have highest R 2 and increase in PGS effect. Using quantile regression, we show the effect of PGS BMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R 2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGS BMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R 2 (mean 23%) across datasets. Finally, creating PGS BMI directly from GxAge genome-wide association studies effects increased relative R 2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGS BMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

Rheumatoid arthritis (RA) is an autoimmune disease with an unknown etiology. While certain genes provide strong susceptibility factors, the role of environmental factors is becoming increasingly recognized. Among genetic factors, human leukocyte antigen (HLA) genes, encoded within the major histocompatibility complex (MHC), have been linked to predisposition to RA, while among environmental factors, smoking, infections and diet are the major contributors. Genetic and environmental factors impact microbial composition in the host. Based on the dysbiosis observed in the gut and lung microbiome, a mucosal origin of RA has been suggested. However, proving whether genes or microbes provide a stronger risk factor has been difficult. Studies from RA patients and various mouse models, specifically humanized mice expressing HLA class II genes, have been instrumental in defining the role of environmental factors such as smoking and endogenous small intestinal microbes in modulating arthritis severity. The consensus based on most studies support an interaction between host genetic and environmental factors in the onset and severity of disease. However, until now, no microbial markers for disease prognosis or treatment efficacy have been available. Here, the role of gut microbes as markers of disease severity, and the potential for using endogenous commensals for modulating immune responses to suppress inflammation in the context of genetic factors, are discussed.