Alexander S. Krasichkov

Purpose: This study aimed to identify temperament traits alterations in bipolar disorder (BD) and explore their potential neuroimaging correlates using resting‐state functional magnetic resonance imaging. Methods: We assessed seed‐to‐voxel alterations in four large‐scale brain networks (Salience, Frontoparietal, Default Mode, and SensoriMotor) in 49 patients with BD and 49 healthy individuals according to the difference of temperamental traits (Reward Dependence, Novelty Seeking, Harm Avoidance, and Persistence). Also, we measured the relationship of temperamental traits with the severity of manic and depressive symptoms and impulsivity. Results: Lower Reward Dependence (t‐Welch’s (87.1) = −2.50; p = 0.014) in bipolar patients was associated with increased functional connectivity between Salience Network and Default Mode and FrontoParietal Networks. Higher Novelty Seeking (t‐Welch’s (87.3) = 4.37; p < 0.001) was associated with increased functional connectivity within FrontoParietal Network, whereas its functional connectivity with Visual and Dorsal Attention Networks was decreased. Higher Harm Avoidance (t‐Welch’s (82.8) = 4.85; p < 0.001) was associated with increased functional connectivity between FrontoParietal Network and basal ganglia. Lower Persistence ( U = 998; p = 0.002) was associated with decreased functional connectivity within FrontoParietal Network and with Default Mode Network. Higher persistence in bipolar patients was associated with greater severity of manic symptoms (Spearman’s rho = 0.302, p = 0.018), while lower Reward Dependence was associated with increased severity of depressive symptoms (Pearson’s r = −0.388, p = 0.003). Harm Avoidance negatively correlates with Persistence (Pearson’s r = −0.525, p < 0.001) and positively with reward dependence (Pearson’s r = −0.259, p = 0.036). We also found a negative correlation between impulsivity and Reward Dependence (Pearson’s r = −0.312, p = 0.029) and positive correlation between impulsivity and Novelty Seeking (Pearson’s r = 0.525, p < 0.001). Conclusions: The findings demonstrate a possible functional neuroimaging basis for altered temperamental traits in patients with bipolar disorder.

Aim. To study the relationship between a decrease in left ventricular (LV) ejection fraction (EF) and conventional electrocardiographic (ECG) signs associated with myocardial structure changes (pathological Q wave, ventricular arrhythmias), and relatively novel and less studied (fragmented QRS (fQRS), early ventricular repolarization (EVR)) and to evaluate their significance for identifying patients with mildly-reduced EF (mrEF).Material and methods. The study included 148 patients who were treated and examined at the Almazov National Medical Research Center. During the ECG analysis, fQRS, EVR, pathological Q wave, and ventricular arrhythmias (VAs) were assessed. Echocardiography data were analyzed. Statistical processing was carried out, including Fisher and chi-squared test, as well as correlation and ROC analysis.Results. Depending on the EF level, patients were divided into three groups: group 1 — patients with reduced EF (rEF) (50%). In the 1st group (with rEF), fQRS was registered in 16 (51,6%) patients, in the 2nd (with mrEF) — in 13 (44,8%), in the 3rd (with EF >50%) — in 16 (18,2%). Pathological Q wave was detected in the 1st group (rEF) in 20 (65%) patients, in the 2nd (mrEF) — in 10 (35%); in the 3rd (pEF) — in 15 (18%) (p50%) — in 11 (12,5%); the differences were not significant (p=0,5). The relationship of EVR, the number of PVCs and the presence of ventricular tachycardia with EF was not revealed.Conclusion. FQRS is significantly more often observed with a decrease in EF and may be a marker of an mildly-reduced EF. There were no significant correlations between EVR and EF. There was also no relationship between VAs and LV systolic dysfunction.

The article analyzes the data of elderly patients with airway cancer before and after surgery. To reduce the mortality of cancer patients in the postoperative period, it is necessary to identify markers that can predict the deterioration of the patient's condition. The study involved 83 patients, who recorded 216 parameters before and 47 parameters after surgery. The data contain not only general laboratory parameters, but also blood gas parameters, hemodynamic data and autonomic indices, metabolic parameters and cardiorespiratory stress tests at various levels of exercise. Statistical data analysis is carried out using the SPSS program and is based on the following methods: one-sided Kolmogorov-Smirnov test, Mann-Whitney test, chi-square test and the use of logistic regression to build a multivariate model. The investigated variable is the result of the operation, which can take the following values: no complications, complications after surgery, death. The result of this analysis, namely the determination of mortality markers, will determine which of the patients should not undergo surgery, and who need special medical supervision in the postoperative period.

According to the WHO, cardiovascular diseases are the leading cause of death worldwide, so an accurate and timely diagnosis of cardiovascular diseases is an important task. One of the most common and effective methods for diagnosing CVD is to record an electrocardiogram using Holter monitoring. In order to significantly reduce the time required to decrypt the recording of ECS, cardiologists need special programs for automated analysis of the electrocardiogram. This is especially important for the long-term monitoring task. The program for the automated analysis of pacemakers should perform the clustering of cardiac complexes, thus dividing the electrocardiogram into groups of individual cardiac complexes. Only reference cardiocomplexes obtained by statistical averaging from each such group are subjected to further analysis. When registering an electrocardiogram, interference of various physical origins arises, artifacts that significantly complicate the analysis of ECS, therefore, automated analysis programs must also carry out preliminary processing of the electrocardiogram.

We suggest an analytical approach to the adaptive thresholding in a shape anomaly detection problem. We find an analytical expression for the distribution of the cosine similarity score between a reference shape and an observational shape hindered by strong measurement noise that depends solely on the noise level and is independent of the particular shape analyzed. The analytical treatment is also confirmed by computer simulations and shows nearly perfect agreement. Using this analytical solution, we suggest an improved shape anomaly detection approach based on adaptive thresholding. We validate the noise robustness of our approach using typical shapes of normal and pathological electrocardiogram cycles hindered by additive white noise. We show explicitly that under high noise levels our approach considerably outperforms the conventional tactic that does not take into account variations in the noise level.