Search

Objectives: Alzheimer's disease (AD) presents a major global health issue of significant socio-economic impact. Pharmacological treatments for AD have limited efficacy, prompting the exploration of alternative therapies, such as repetitive transcranial magnetic stimulation (rTMS), a promising non-invasive technique to enhance cognitive function in AD patients. Our systematic review and meta-analysis aim to evaluate the efficacy of rTMS in relation to cognitive function in AD patients, identify optimal rTMS stimulation parameters, and understand the underlying neural mechanisms., Methods: We conducted a comprehensive literature search in PubMed using predefined search terms to identify original research articles investigating the effects of rTMS on cognitive function in AD patients. We selected only randomized controlled trials (RCTs) with sufficient quantitative data for comparing active rTMS to the sham-coil treatment, and then performed a random effects meta-analysis using standardized mean differences (SMDs) to synthesize the effects across studies., Results: The systematic review included 22 studies, among which 14 RCTs met our criteria for meta-analysis. High-frequency rTMS, particularly targeting the dorsolateral prefrontal cortex (DLPFC), evoked significant cognitive improvements in AD patients, with a moderate positive effect size of rTMS on cognitive function (Hedges' g=0.580, 95% CI [0.268, 0.892], p<0.001), albeit with substantial heterogeneity (I ² =59%). Funnel plot asymmetry and Egger's test suggested a potential publication bias, but fail-safe N analysis indicated a robust finding. Moreover, anhedonia-apathy symptoms and motor-cognitive exercises mediated the efficacy of tTMS in ameliorating cognitive functioning across several studies., Conclusion: rTMS demonstrates moderate efficacy in improving cognitive function in AD-patients, most distinctly with high-frequency rTMS stimulation protocols targeting the DLPFC area. The meta-analysis support rTMS as a viable therapeutic intervention for cognitive enhancement in AD. Future promising research should focus on personalized treatment strategies targeting mediating factors, baseline connectivity patterns, and TMS-induced neuroplasticity in AD.

Background: To estimate quality of life (QoL) in patients with paroxysmal atrial fibrillation (AF) using the SF-36 Health Status Survey., Materials and Methods: In a single-center study involving 6,630 patients, we defined a group of 97 patients having an incidental finding of atrial fibrillation (AF). The control group included 99 patients from the same primary cohort, but without paroxysmal AF. The two study groups matched closely in anthropometric parameters and comorbidity. All patients underwent standard laboratory and instrumental research methods. In the primary visit, at the time of AF detection, we evaluated the patients QoL using the classical SF-36 Health Status Survey. At the second visit (6±0.5 months follow-up) and third visit (12±0.5 months follow-up), we re-evaluated the QoL using the SF-36 Health Status Survey., Results: The absolute majority (95/97; 98%) of patients of the main group had a special variant of extrasystoles, namely the early atrial "P on T" type (versus 4.0% incidence in the control group) [OR 846 (382;187,000)]. The main group showed a significantly greater frequency of supraventricular extrasystoles. At the 1 st visit, there was no group differences in QoL scores between the main and control groups (p>0.05). However, at 6 and 12 months follow-up, metrics of physical and mental health differed significantly between groups stratified by low and high QoL (p<0.05). The asymptomatic patients with paroxysmal AF and high compliance in oral anticoagulant therapy showed higher physical activity and social functioning., Conclusions: Paroxysmal AF in asymptomatic patients is a predictor for declining QoL during 12 months follow-up in patients with cardiovascular pathology. The paroxysmal AF patients who had high compliance of oral anticoagulant therapy proved to have improved physical activity and social functioning.

Introduction: Self-harm behavior is a significant global concern, with Russia among the countries with high prevalence rates. Adolescents and young adults (15-29 years old) are particularly vulnerable, with suicide being the fourth leading cause of death in this age group. Our objective was to present statistics on suicidality and non-suicidal self-harm behavior (NSSH) among adolescents in the Samara region and to identify psychosocial differences between patients hospitalized for the first time and those hospitalized repeatedly., Subjects and Methods: This study is a retrospective chart analysis of adolescents hospitalized due to suicidal ideation or attempts in 2023. Data were collected from hospital records, comprising a diverse sample of adolescents., Results: The sample included 76 adolescents, with a significant gender imbalance, as 84.2% were female. Chronic family conflicts presumably were the most influential factor, rather than family composition. Data on hereditary predisposition were subjective and presumably did not correlate with the number of hospitalizations. Fetal hypoxia was the only notable perinatal pathology. Self-harm behavior was more common in readmissions, while suicidal thoughts were present in similar proportions in both initial and repeat hospitalizations. The main reasons for self-harm behavior were the desire to gain control over life or to relieve emotional pain. Only 2.6% of cases were directly aimed at suicide., Conclusions: Identified risk factors for suicidal behavior among adolescents included female gender, an unfavorable family environment, and NSSH, which, although not directly suicidal, increased the risk of future suicidal behavior. These factors should be considered in the diagnosis and prevention of suicidal behavior.

Nutritional support is considered as one of the components of disease-modifying therapy for postpartum depressive disorder. Such nutrients include iodine, which is an important trace element in the development and functioning of the central nervous system. The brief review presents updated knowledge about the relationship of iodine deficiency with the development and severity of postpartum depressive disorders in women, based on the analysis and generalization of the results of domestic and international studies.

Rebamipide contributes to the improvement of blood supply of the GI mucosa, activates its barrier function, activates alkaline secretion of the stomach, increases proliferation and metabolism of epithelial cells of the GI tract, cleanses the mucosa from hydroxyl radicals and suppresses superoxides, produced by polymorphonuclear leukocytes and neutrophils in the presence of Helicobacter pylori, protects the GI mucosa from bacterial invasion and the damaging effects of non-steroidal anti-inflammatory drugs (NSAIDs) on the mucosa. Rebamipide, originally developed as a treatment for gastric ulcers, has attracted the attention of researchers as a potential drug for the treatment of UC due to its ability to stimulate mucus production, reduce oxidative stress, and decrease inflammation. Due to the presence of these properties, it is hypothesized that rebamipide may have a protective effect on the intestinal mucosa during prolonged inflammation, making it a promising candidate for inclusion in therapeutic strategies for ulcerative colitis. The results of this study suggest that rebamipide holds potential therapeutic benefits for the treatment of ulcerative colitis.

Background: Depression is a common mental illness, with around 280 million people suffering from depression worldwide. At present, the main way to quantify the severity of depression is through psychometric scales, which entail subjectivity on the part of both patient and clinician. In the last few years, deep (machine) learning is emerging as a more objective approach for measuring depression severity. We now investigate how neural networks might serve for the early diagnosis of depression., Subjects and Methods: We searched Medline (Pubmed) for articles published up to June 1, 2023. The search term included Depression AND Diagnostics AND Artificial Intelligence. We did not search for depression studies of machine learning other than neural networks, and selected only those papers attesting to diagnosis or screening for depression., Results: Fifty-four papers met our criteria, among which 14 using facial expression recordings, 14 using EEG, 5 using fMRI, and 5 using audio speech recording analysis, whereas 6 used multimodality approach, two were the text analysis studies, and 8 used other methods., Conclusions: Research methodologies include both audio and video recordings of clinical interviews, task performance, including their subsequent conversion into text, and resting state studies (EEG, MRI, fMRI). Convolutional neural networks (CNN), including 3D-CNN and 2D-CNN, can obtain diagnostic data from the videos of the facial area. Deep learning in relation to EEG signals is the most commonly used CNN. fMRI approaches use graph convolutional networks and 3D-CNN with voxel connectivity, whereas the text analyses use CNNs, including LSTM (long/short-term memory). Audio recordings are analyzed by a hybrid CNN and support vector machine model. Neural networks are used to analyze biomaterials, gait, polysomnography, ECG, data from wrist wearable devices, and present illness history records. Multimodality studies analyze the fusion of audio features with visual and textual features using LSTM and CNN architectures, a temporal convolutional network, or a recurrent neural network. The accuracy of different hybrid and multimodality models is 78-99%, relative to the standard clinical diagnoses.