Citations

Background: Disparities exist in the representation of genders in cardiovascular clinical trials. Atrial fibrillation (AF) is associated with significant morbidity and mortality; however, understanding regarding the representation of women in AF-related clinical trials remains limited. Therefore, this systematic review sought to evaluate the representation of women in AF-related clinical trials. Methods: We conducted a systematic review of clinical trials using the PubMed, Scopus, and EMBASE databases from 1996 to January 1st, 2024, focusing on AF-related lifestyle interventions, pharmacological treatments, catheter ablation, and device therapies for AF. Data extraction and analysis encompassed trial characteristics, participant demographics, and funding sources. The primary outcome was the prevalence of female enrollees, quantified through participation-to-prevalence ratios (PPRs). This was estimated overall and stratified by funding source, intervention type, and enrollment region. Results: Of the 103 clinical trials involving 218,322 participants (39.5% female), the PPR ranged from 0.00 to 1.73, with an average PPR of 1.03. Meanwhile, 43% of the trials exhibited female under-representation (PPR, <0.8). University-funded trials showed higher female enrollment (mean PPR, 0.951) compared to industry/government-funded trials (mean PPR, 0.800). No differences were observed in the representation of women when comparing enrollment regions or intervention types. Conclusions: Despite advancements in AF management, gender disparities persist in AF-related clinical trial representation, particularly in industry/government-funded studies compared to university-funded trials. Thus, addressing implicit biases and enforcing sex equality guidelines are critical steps toward more inclusive cardiovascular research.

Abstract In epilepsy patients lacking magnetic resonance imaging‐visible lesions such as focal cortical dysplasia (FCD), surgical resections target regions with abnormal electroencephalographic (EEG) activity. However, histopathological identification of subtle cortical architectural abnormalities in these specimens remains challenging. We investigated whether artificial intelligence (AI)‐based morphometric and spatial analysis of NeuN‐stained cortical sections could detect neuronal architectural disorganization in epilepsy resections, including regions without definitive histologic dysplasia. Whole slide images were generated from 83 FCD regions and 19 neurologically normal autopsy controls. Regions of interest were annotated in QuPath as FCD, FCD‐adjacent, FCD‐distant, apparently normal (abnormal EEG without histologic dysplasia), and true normal (autopsy controls). NeuN‐positive neurons were detected using QuPath (90% sensitivity, 12% false positive rate). Spatial and morphometric features—including neuronal clustering, distribution inhomogeneity, and nuclear morphology—were extracted and used to train a multinomial, Least Absolute Shrinkage and Selection Operator‐regularized regression classifier. The classifier achieved 70.6% accuracy in subregion classification and 88.2% accuracy in overall specimen diagnosis. Notably, regions with abnormal EEG but lacking histologic dysplasia exhibited quantifiable architectural disorganization similar to those seen in areas adjacent to FCD and distinct from control tissue. AI‐driven analysis of neuronal morphology and spatial distribution reveals subtle cortical disorganization in epilepsy resections, including in histologically ambiguous regions. Further investigation is warranted to determine if this methodology can enhance the diagnostic yield of neuropathological evaluation and support more precise surgical targeting in epilepsy.

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Abstract Background High-sensitivity cardiac troponin (hs-cTn) assays are recommended for the diagnosis of acute myocardial infarction. Here, we characterize the analytical performance of a next-generation hs-cTn assay, Elecsys® Troponin T hs Gen 6 (Roche Diagnostics International). Methods Surplus lithium-heparin plasma or serum samples from patients or healthy volunteers were run on Cobas® e 801, e 402, and Pro analyzers. Limits of blank (LoB), limits of detection (LoD), and limits of quantitation (LoQ) were determined according to CLSI EP17-A2, with target values of 1.0 and 1.5 ng/L for LoB/LoD and 3.0 ng/L (10% CV) and 1.5 ng/L (20% CV) for LoQ, respectively. Precision was measured, per CLSI EP17-A2, using 3 QC samples (approximately 4, 30, and 220 ng/L), 12 native samples, and 3 reagent lots. Linearity, per CLSI EP06-Ed2, was determined by diluting samples with cardiac troponin T (cTnT) concentration above the measuring range with a low/blank sample. Interference (per Glick) with endogenous and assay components at 5 cTnT concentrations was assessed. Results Measured values for LoB, LoD, and LoQ at 10% and 20% CV were 0.1 to 0.7 ng/L, 0.3 to 1.4 ng/L, 1.0 to 2.9 ng/L, and 0.4 to 1.2 ng/L, respectively. Repeatability CVs were 1.0 to 5.8% for mean cTnT concentrations of 2.6 to 9230 ng/L in lithium-heparin plasma. High precision was shown across lots, and linearity was observed across the measuring range (1.5 to 9500 ng/L, all Pearson's r = 1.00). No interferences were observed, specified up to ≤1000 mg/dL hemoglobin, ≤50 mg/dL [≤855 µmol/L] icterus/bilirubin, and ≤1200 ng/mL biotin. Conclusions The analytical performance characterization of the assay demonstrated high sensitivity, high precision at the low end and across the measuring range, and resistance to interference.

Background: Sinonasal malignancies (SNM) are rare cancers associated with poor prognosis and impaired quality of life (QOL). The 22-question Sino-Nasal Outcome Test (SNOT-22) has been used to assess QOL in patients with SNM. Objective: To determine the minimal clinically important difference (MCID) for the SNOT-22 in patients with SNM. Methods: About 264 patients with SNM were drawn from a prospective, longitudinal, multi-institutional study. SNOT-22 scores were collected at pre-treatment baseline and follow-up through 5 years post-treatment. Cronbach’s alpha was used to assess the internal consistency of the SNOT-22. Distribution-based approaches were used to estimate the MCID. Multivariable regression identified factors associated with SNOT-22 improvement exceeding the determined MCID. Results: Cronbach’s alpha was >.85. The estimated MCIDs were: 1.44 (Cohen’s effect size), 1.32 (standard error of the mean), 10.45 (1/2 of baseline standard deviation), and 3.67 (minimum detectable change). The mean MCID across methods was 4.22. Multivariable regression demonstrated higher odds of clinically meaningful improvement from baseline in patients with early-stage disease (odds ratio (OR) 3.510, P = .035), no neck irradiation (OR 11.050, P = .014), and neoadjuvant therapy (OR 16.667, P = .047). Conclusions: The SNOT-22 MCID was estimated as 4.22 in a large multi-center cohort of patients with SNM, suggesting that relatively small QOL changes are clinically relevant. When applying the MCID to the current cohort, advanced stage, treatment of neck disease, and not requiring neoadjuvant therapy were predictive of worse QOL.