The Effectiveness of Early Rehabilitation in Limiting the Progression of Idiopathic Scoliosis.

Background: The purpose of this study was to assess specific rehabilitation methods' effectiveness in early idiopathic scoliosis (IS) development, focusing on lower limb functional inequality's role in scoliosis progression. Materials and Methods: This study comprised 812 patients aged 6-16 years at risk of developing idiopathic scoliosis (IS). The mean (SD) age was 10.66 (3.16) years. Patients were categorized into high- and medium-risk groups based on the angle of trunk rotation (ATR) size. Specific scoliosis physiotherapy was used, and the average follow-up period was 28.1 ± 14.5 months. Changes in ATR, Cobb angle, and functional length of the lower limbs pre- and post-treatment were statistically analyzed across three age groups (6-9, 10-12, and 13-16 years) and three scoliosis locations. Results: Significant effectiveness of early rehabilitation was observed in the high-risk group of children aged 6-9 years. In the medium-risk group, significant reductions in ATR were observed in both the 6-9 and 10-12 age groups across all three scoliosis locations. Additionally, there was a significant decrease in the Cobb angle in the thoracolumbar region and a significant reduction in lower limb inequality across all age groups and scoliosis locations. Conclusions: The early implementation of specific physiotherapy may enhance the efficacy of idiopathic scoliosis treatment by attenuating factors contributing to its progression.

An Effective Method of Detecting Characteristic Points of Impedance Cardiogram Verified in the Clinical Pilot Study.

Accurate and reliable determination of the characteristic points of the impedance cardiogram (ICG) is an important research problem with a growing range of applications in the cardiological diagnostics of patients with heart failure (HF). The shapes of the characteristic waves of the ICG signal and the temporal location of the characteristic points B, C, and X provide significant diagnostic information. On this basis, essential diagnostic cardiological parameters can be determined, such as, e.g., cardiac output (CO) or stroke volume (SV). Although the importance of this problem is obvious, we face many challenges, including noisy signals and the big variability in the morphology, which altogether make the accurate identification of the characteristic points quite difficult. The paper presents an effective method of ICG points identification intended for conducting experimental research in the field of impedance cardiography. Its effectiveness is confirmed in clinical pilot studies.

Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19.

The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019-May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases.

A Survey of Methods and Technologies Used for Diagnosis of Scoliosis.

The purpose of this article is to present diagnostic methods used in the diagnosis of scoliosis in the form of a brief review. This article aims to point out the advantages of select methods. This article focuses on general issues without elaborating on problems strictly related to physiotherapy and treatment methods, which may be the subject of further discussions. By outlining and categorizing each method, we summarize relevant publications that may not only help introduce other researchers to the field but also be a valuable source for studying existing methods, developing new ones or choosing evaluation strategies.

The Influence of Filters on EEG-ERP Testing: Analysis of Motor Cortex in Healthy Subjects.

The raw EEG signal is always contaminated with many different artifacts, such as muscle movements (electromyographic artifacts), eye blinking (electrooculographic artifacts) or power line disturbances. All artifacts must be removed for correct data interpretation. However, various noise reduction methods significantly influence the final shape of the EEG signal and thus its characteristic values, latency and amplitude. There are several types of filters to eliminate noise early in the processing of EEG data. However, there is no gold standard for their use. This article aims to verify and compare the influence of four various filters (FIR, IIR, FFT, NOTCH) on the latency and amplitude of the EEG signal. By presenting a comparison of selected filters, the authors intend to raise awareness among researchers as regards the effects of known filters on latency and amplitude in a selected area-the sensorimotor area.

Optimization of rs-fMRI parameters in the Seed Correlation Analysis (SCA) in DPARSF toolbox: A preliminary study.

There are a number of various methods of resting-state functional magnetic resonance imaging (rs-fMRI) analysis such as independent component analysis, multivariate autoregressive models, or seed correlation analysis however their results depend on arbitrary choice of parameters. Therefore, the aim of this work was to optimize the parameters in the seed correlation analysis using the Data Processing Assistant for Resting-State fMRI (DPARSF) toolbox for rs-fMRI data received from a Siemens Magnetom Skyra 3-Tesla scanner using a whole-brain, gradient-echo echo planar sequence with a 32-channel head coil. Different ranges of the following parameters: amplitude of low-frequency fluctuation (ALFF), Gaussian kernel at FWHM and radius of spherical ROI for 109 regions were tested for 20 healthy volunteers. The highest values of functional connectivity (FC) correlations were found for ALFF 0.01-0.08, spherical ROIs with the 8-mm radius and Gaussian kernel 8 mm at FWHM in all the studied areas that is, Auditory, Sensimotor, Visual, and Default Mode Network. The dominating influence of ALFF and smoothing on values of FC correlations was noted.