Context The review summarizes key studies assessing epidemiology, mechanisms, and consequences of cognitive dysfunction (CD) in type 1 diabetes (DM1). Evidence Synthesis In a number of studies, the severity of CD in DM1 was affected by the age of onset and diabetes duration, the presence of proliferative retinopathy and autonomic neuropathy. Diabetes-related CD has been observed not only in adults but also in children and adolescents. Most neuroimaging studies in DM1 did not show any differences in whole brain volumes, however, they did reveal selective deficits in grey matter volume or density within the frontal, posterior and temporal cortex, subcortical grey matter. Studies of middle-aged adults with long-standing DM1 using diffusion tensor imaging have demonstrated partial lesions of white matter and decreased fractional anisotropy in posterior brain regions. The mechanisms underlying diabetes-related CD are very complex and include factors related to diabetes per se and to diabetes-related cardiovascular disease and microvascular dysfunction: chronic hyperglycemia, hypoglycemia, macro- and microvascular disease, increased expression of inflammatory cytokines. The above mechanisms may contribute to the development and progression of both vascular dementia and Alzheimer disease. Conclusions Higher rates of CD and its faster progression in DM1 can be explained by both the direct effects of altered glucose metabolism on the brain and diabetes-related cardiovascular disease. Since the presence and progression of CD significantly worsens the quality of life of diabetic patients, further multidisciplinary studies based on the recent progress in both neuroimaging and type 1 diabetes management are warranted to tackle this problem.

Heart rate variability (HRV) as a measure of autonomic function might provide prognostic information in ischemic stroke. However, numerous difficulties are associated with HRV parameters assessment and interpretation, especially in short-term ECG recordings. For better understanding of derived HRV data and to avoid methodological bias we simultaneously recorded and analyzed heart rate, blood pressure and respiratory rate. Seventy-five ischemic stroke patients underwent short-term ECG recordings. Linear and nonlinear parameters of HRV as well as beat-to-beat blood pressure and respiratory rate were assessed and compared in patients with different functional neurological outcomes at 7th and 90th days. Values of Approximate, Sample and Fuzzy Entropy were significantly lower in patients with poor early neurological outcome. Patients with poor 90-day outcome had higher percentage of high frequency spectrum and normalized high frequency power, lower normalized low frequency power and lower low frequency/high frequency ratio. Low frequency/high frequency ratio correlated negatively with scores in the National Institutes of Health Stroke Scale and modified Rankin Scale (mRS) at the 7th and mRS at the 90th days. Mean RR interval, values of blood pressure as well as blood pressure variability did not differ between groups with good and poor outcomes. Respiratory frequency was significantly correlated with the functional neurological outcome at 7th and 90th days. While HRV assessed by linear methods seems to have long-term prognostic value, complexity measures of HRV reflect the impact of the neurological state on distinct, temporary properties of heart rate dynamic. Respiratory rate during the first days of the stroke is associated with early and long-term neurological outcome and should be further investigated as a potential risk factor.

The recognition of all main features of a healthy heart rhythm (the so-called sinus rhythm) is still one of the biggest challenges in contemporary cardiology. Recently the interesting physiological...

An analysis of exhaled breath enables specialists to noninvasively monitor biochemical processes and to determine any pathological state in the human body. Breath analysis holds the greatest potential to remold and personalize diagnostics; however, it requires a multidisciplinary approach and collaboration of many specialists. Despite the fact that breath is considered to be a less complex matrix than blood, it is not commonly used as a diagnostic and prognostic tool for early detection of disordered conditions due to its problematic sampling, analysis, and storage. This review is intended to determine, standardize, and marshal experimental strategies for successful, reliable, and especially, reproducible breath analysis

Our study aimed at exploring structural and functional differences in the brain during higher cognitive processing between middle-aged hypertensive patients and controls matched for sex, age and years of education. Two groups of 20 patients took part in MRI examinations. This article reports the results of functional MRI during a Stroop color interference task and structural evaluations based on a modified Fazekas scale. No intergroup differences were found in regards to the severity of white matter lesions (Mann–Whitney U test = 150.5, P > 0.1), nor from the task performance in the scanner (t(35) = 0.2, P > 0.1). However, brain activation patterns between patients and controls varied. Hypertensive patients involved significantly more cerebral areas during the processing, regardless of the task difficulty. Differences were found in 26 diverse regions of both primary and associative cortices (with a peak voxel located in the cuneus, Z = 6.94, P < 0.05 family-wise error corrected at voxel level). Our findings provide an insight into the brain mechanisms related to essential hypertension and suggest a functional reorganization (neuroplasticity) early in the course of the disease.

Heart rate variability (hrv) is a physiological phenomenon of the variation in the length of the time interval between consecutive heartbeats. In many cases it could be an indicator of the development of pathological states. The classical approach to the analysis of hrv includes time domain methods and frequency domain methods. However, attempts are still being made to define new and more effective hrv assessment tools. Persistent homology is a novel data analysis tool developed in the recent decades that is rooted at algebraic topology. The Topological Data Analysis (TDA) approach focuses on examining the shape of the data in terms of connectedness and holes, and has recently proved to be very effective in various fields of research. In this paper we propose the use of persistent homology to the hrv analysis. We recall selected topological descriptors used in the literature and we introduce some new topological descriptors that reflect the specificity of hrv, and we discuss their relation to the standard hrv measures. In particular, we show that this novel approach provides a collection of indices that might be at least as useful as the classical parameters in differentiating between series of beat-to-beat intervals (RR-intervals) in healthy subjects and patients suffering from a stroke episode.