We propose an integrated approach based on uniform quantization over a small number of levels for the evaluation and characterization of complexity of a process. This approach integrates information-domain analysis based on entropy rate, local nonlinear prediction, and pattern classification based on symbolic analysis. Normalized and non-normalized indexes quantifying complexity over short data sequences (∼300 samples) are derived. This approach provides a rule for deciding the optimal length of the patterns that may be worth considering and some suggestions about possible strategies to group patterns into a smaller number of families. The approach is applied to 24h Holter recordings of heart period variability derived from 12 normal (NO) subjects and 13 heart failure (HF) patients. We found that: (i) in NO subjects the normalized indexes suggest a larger complexity during the nighttime than during the daytime; (ii) this difference may be lost if non-normalized indexes are utilized; (iii) the circadian pattern in the normalized indexes is lost in HF patients; (iv) in HF patients the loss of the day-night variation in the normalized indexes is related to a tendency of complexity to increase during the daytime and to decrease during the nighttime; (v) the most likely length L of the most informative patterns ranges from 2 to 4; (vi) in NO subjects classification of patterns with L=3 indicates that stable patterns (i.e., those with no variations) are more present during the daytime, while highly variable patterns (i.e., those with two unlike variations) are more frequent during the nighttime; (vii) during the daytime in HF patients, the percentage of highly variable patterns increases with respect to NO subjects, while during the nighttime, the percentage of patterns with one or two like variations decreases.

MicroRNAs (miRNAs) are emerging as key regulators of complex biological processes in several cardiovascular diseases, including atrial fibrillation (AF). Reverse transcription-quantitative polymerase chain reaction is a powerful technique to quantitatively assess miRNA expression profile, but reliable results depend on proper data normalization by suitable reference genes. Despite the increasing number of studies assessing miRNAs in cardiac disease, no consensus on the best reference genes has been reached. This work aims to assess reference genes stability in human cardiac tissue with a focus on AF investigation. We evaluated the stability of five reference genes (U6, SNORD48, SNORD44, miR-16, and 5S) in atrial tissue samples from eighteen cardiac-surgery patients in sinus rhythm and AF. Stability was quantified by combining BestKeeper, delta-C_q, GeNorm, and NormFinder statistical tools. All methods assessed SNORD48 as the best and U6 as the worst reference gene. Applications of different normalization strategies significantly impacted miRNA expression profiles in the study population. Our results point out the necessity of a consensus on data normalization in AF studies to avoid the emergence of divergent biological conclusions.

Background—: Atrial dilatation and atrial standstill are etiologically heterogeneous phenotypes with poorly defined nosology. In 1983, we described 8-years follow-up of atrial dilatation with standstill evolution in 8 patients from 3 families. We later identified 5 additional patients with identical phenotypes: 1 member of the largest original family and 4 unrelated to the 3 original families. All families are from the same geographic area in Northeast Italy. Methods and Results—: We followed up the 13 patients for up to 37 years, extended the clinical investigation and monitoring to living relatives, and investigated the genetic basis of the disease. The disease was characterized by: (1) clinical onset in adulthood; (2) biatrial dilatation up to giant size; (3) early supraventricular arrhythmias with progressive loss of atrial electric activity to atrial standstill; (4) thromboembolic complications; and (5) stable, normal left ventricular function and New York Heart Association functional class during the long-term course of the disease. By linkage analysis, we mapped a locus at 1p36.22 containing the Natriuretic Peptide Precursor A gene. By sequencing Natriuretic Peptide Precursor A , we identified a homozygous missense mutation (p.Arg150Gln) in all living affected individuals of the 6 families. All patients showed low serum levels of atrial natriuretic peptide. Heterozygous mutation carriers were healthy and demonstrated normal levels of atrial natriuretic peptide. Conclusions—: Autosomal recessive atrial dilated cardiomyopathy is a rare disease associated with homozygous mutation of the Natriuretic Peptide Precursor A gene and characterized by extreme atrial dilatation with standstill evolution, thromboembolic risk, preserved left ventricular function, and severely decreased levels of atrial natriuretic peptide.