Natural History of Subclinical Atrial Fibrillation Detected by Implanted Loop Recorders.

BACKGROUND: As new heart rhythm monitoring technologies emerge, subclinical atrial fibrillation (AF) signifies a future challenge to health care systems. The pathological characteristics of this condition are largely unknown. OBJECTIVES: This study sought to characterize the natural history of subclinical AF in at-risk patients from the general population. METHODS: The authors studied 590 individuals ≥70 years of age with ≥1 of hypertension, diabetes, previous stroke, or heart failure, without history of AF, undergoing long-term implantable loop recorder monitoring as part of the LOOP (Atrial Fibrillation Detected by Continuous ECG Monitoring Using Implantable Loop Recorder to Prevent Stroke in High-risk Individuals) study. Baseline assessments included N-terminal pro-B-type natriuretic peptide (NT-proBNP). All day-to-day heart rhythm and symptom data were extracted from the device. Endpoints included AF burden, AF progression, symptom reports, and heart rate during AF. RESULTS: A total of 685,445 monitoring days were available for analysis. Adjudicated AF episodes lasting ≥6 min were detected in 205 participants (35%). The AF burden was median 0.13% (interquartile range: 0.03% to 1.05%) of the monitoring time and changed by a factor of 1.31 (95% CI: 1.02 to 1.68) per doubling of NT-proBNP. AF episodes were present 2.7% (interquartile range: 1.0% to 15.7%) of monitoring days after debut. Progression to 24-h episodes was seen in 33 of the AF patients (16%), whereas 46 (22%) had no AF episodes in the last 6 months of monitoring or longer. Symptoms were absent in 185 (90%) at debut, and 178 (87%) never reported AF-related symptoms during follow-up. The averaged heart rate during AF was 96 (interquartile range: 83 to 114) beats/min, 24 (interquartile range: 9 to 41) beats/min faster than daytime sinus rates. CONCLUSIONS: Although previously unknown AF was highly prevalent, the burden was low, and progression was limited. In addition, symptoms were scarce, and the heart rate was only modestly elevated. (Atrial Fibrillation Detected by Continuous ECG Monitoring Using Implantable Loop Recorder to Prevent Stroke in High-risk Individuals [LOOP]; NCT02036450).

Quality assessment of Ki67 staining using cell line proliferation index and stain intensity features.

Breast cancer is the most frequent cancer among women worldwide. Ki67 can be used as an immunohistochemical pseudo marker for cell proliferation to determine how aggressive the cancer is and thereby the treatment of the patient. No standard Ki67 staining protocol exists, resulting in inter-laboratory stain variability. Therefore, it is important to determine the quality control of a staining protocol to ensure correct diagnosis and treatment of patients. Currently, quality control is performed by the organization NordiQC that use an expert panel-based qualitative assessment system. However, no objective method exists to determine the quality of a staining protocol. In this study, we propose an algorithm, to objectively assess staining quality from segmented cell nuclei structures extracted from cell lines. The cell nuclei were classified into either Ki67 positive or negative to determine the Ki67 proliferation index within the cell lines. A Ki67 stain quality model based on ordinal logistic regression was developed to determine the quality of a staining protocol from features extracted from the segmented cell nuclei in the cell lines. The algorithm was able to segment and classify Ki67 positive cell nuclei with a sensitivity and positive predictive value (PPV) of 0.90 and 0.94 and Ki67 negative cell nuclei with a sensitivity and PPV of 0.78 and 0.78. The mean difference between a manual and automatic Ki67 proliferation index was -0.003 with a standard deviation of 0.056. The ordinal logistic regression model found that the stain intensity for both the Ki67 positive and Ki67 negative cell nuclei were statistically significant as parameters determining the stain quality from the cell line cores. The framework shows great promise for using cell nuclei information from cell lines to predict the staining quality of staining protocols. © 2018 International Society for Advancement of Cytometry.

Accurate Measurement of Airway Morphology on Chest CT Images.

In recent years, the ability to accurately measuring and analyzing the morphology of small pulmonary structures on chest CT images, such as airways, is becoming of great interest in the scientific community. As an example, in COPD the smaller conducting airways are the primary site of increased resistance in COPD, while small changes in airway segments can identify early stages of bronchiectasis. To date, different methods have been proposed to measure airway wall thickness and airway lumen, but traditional algorithms are often limited due to resolution and artifacts in the CT image. In this work, we propose a Convolutional Neural Regressor (CNR) to perform cross-sectional measurements of airways, considering wall thickness and airway lumen at once. To train the networks, we developed a generative synthetic model of airways that we refined using a Simulated and Unsupervised Generative Adversarial Network (SimGAN). We evaluated the proposed method by first computing the relative error on a dataset of synthetic images refined with SimGAN, in comparison with other methods. Then, due to the high complexity to create an in-vivo ground-truth, we performed a validation on an airway phantom constructed to have airways of different sizes. Finally, we carried out an indirect validation analyzing the correlation between the percentage of the predicted forced expiratory volume in one second (FEV1%) and the value of the Pi10 parameter. As shown by the results, the proposed approach paves the way for the use of CNNs to precisely and accurately measure small lung airways with high accuracy.