Biology subjects

Baglaeva, I.

Publications and source records attributed to Baglaeva, I..

2 recordsLinked to original sources

bioRxiv2026

Obesity-induced changes in ultrastructure and calcium release of female rat cardiomyocytes are partially reversed by aerobic exercise

Obesity is associated with an elevated risk of pathological cardiac hypertrophy, whereas exercise confers cardioprotective effects; however, the cellular mechanisms underlying these opposing influences remain incompletely defined, particularly in females. We investigated how obesity and exercise affect cardiomyocyte ultrastructure, Ca{superscript 2} release, and contractility in female Zucker Diabetic Fatty rats and their lean littermates. Animals were assigned at 12 weeks to sedentary or aerobic exercise-trained groups and maintained on a standard diet. By 18 weeks, obese rats exhibited increased body mass and myocardial hypertrophy in the absence of diabetes. Sedentary obese animals showed a reduced fraction of compact dyads and diminished stimulated and caffeine-induced Ca{superscript 2} release, while contractility remained preserved. In lean rats, exercise increased dyad density but reduced Ca{superscript 2} release, whereas in obese rats, exercise enhanced both dyad compactness and Ca{superscript 2} release. Across all groups, global cardiomyocyte ultrastructure and contractile function were similar. Type III ANOVA revealed a significant obesity x exercise interaction for dyadic structure and Ca{superscript 2} release. These findings demonstrate that obesity itself, independent of diabetes, triggers early dyadic remodeling and altered Ca{superscript 2} handling in female myocardium before detectable impairment of global cardiomyocyte structure or contractile function. Furthermore, exercise exerts beneficial effects on dyadic ultrastructure and Ca{superscript 2} signaling in obese animals. New & NoteworthyUsing a female rat model of obesity without diabetes, we demonstrate that obesity induces early remodeling of the dyadic system and impairs Ca{superscript 2} release in cardiac myocytes. We further show that the effects of aerobic exercise on dyadic structure and function are obesity-dependent, improving both dyad organization and Ca{superscript 2} signaling. These findings identify the dyadic microdomain as a vulnerable cellular site in obesity and a potential target for exercise-induced recovery.

physiology↗

bioRxiv2022

Analysis of noisy transient signals based on Gaussian process regression

Dynamic systems such as cells or tissues generate, either spontaneously or in response to stimuli, transient signals that carry information about the system. Characterization of recorded transients is often hampered by a low signal-to-noise ratio (SNR). Reduction of the noise by filtering has limited use due to partial signal distortion. Occasionally, transients can be approximated by a mathematical function, but such a function may not hold correctly if recording conditions change. We introduce here the model-independent approximation method for general noisy transient signals based on the Gaussian process regression (GPR). The method was implemented in the software TransientAnalyzer, which detects transients in a record, finds their best approximation by the Gaussian process, constructs a surrogate spline function, and estimates specified signal parameters. The method and software were tested on a cellular model of the calcium concentration transient corrupted by various SNR levels and recorded at a low sampling frequency. Statistical analysis of the model data sets provided the error of estimation <7.5% and the coefficient of variation of estimates <17% for peak SNR=5. The performance of GPR on signals of diverse experimental origin was even better than fitting by a function. The software and its description are available on GitHub. Statement of SignificanceTransient signals convey information on the state and function of the studied system. However, the estimation of their characteristic parameters is complicated by the noise present in the recordings. Methods used for noise reduction have various disadvantages, such as distortion of the time course by filtering, the difficult superposition of many transients for accurate averaging, or a lack of a model for data fitting. In this work, we present a general method for the automatic analysis of noisy transient signals based on Gaussian process regression and its implementation in Python. The method can analyze recorded transients reliably at peak SNR [≥] 2 with a precision equivalent to the model-fitting methods.

biophysics↗