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CONSERVATIVE MANAGEMENT OF KNEE OSTEOARTHRITIS: A SYSTEMATIC REVIEW OF CONTEMPORARY APPROACHES
No full textIntroduction: Conservative treatment of knee osteoarthritis includes all non-surgical methods aimed at relieving symptoms and improving joint function. It involves pharmacotherapy, including nonsteroidal anti-inflammatory drugs (NSAIDs) and analgesics, as well as intra-articular injections such as corticosteroids, hyaluronic acid, or platelet-rich plasma. An important component of therapy is strengthening exercises for the thigh muscles, physical therapy, and the use of braces and insoles to offload the joint. Additionally, lifestyle modifications, including regular physical activity, help control symptoms and slow disease progression.
Aim of the study: The aim of our study is to review the available literature on contemporary conservative treatment methods for gonarthrosis and to summarize current knowledge. We presented the mechanisms of action, clinical efficacy, and safety profile of various non-surgical interventions, including intra-articular injections, physical therapy, and exercise-based approaches.
Methods and materials: We reviewed the literature available in the PubMed database using the following keywords: “Gonarthrosis”; “Knee osteoarthritis”; “Non-surgical treatment of knee osteoarthritis”; “Exercise therapy”; “NSAIDs”; “Duloxetine”; “Opioids”; ‘Platelet-rich plasma’; “Corticosteroid”; “ Hyaluronic acid”, “Intra-articular injection”.
Conclusion: Comprehensive treatment of knee osteoarthritis is based on non-pharmacological methods, which form the foundation of therapy and demonstrate effectiveness comparable to pharmacological treatment, with a more favorable safety profile. Pharmacotherapy, including NSAIDs, can effectively alleviate symptoms, but its use is limited by the risk of adverse effects, particularly in elderly patients. Intra-articular injections, especially with hyaluronic acid and platelet-rich plasma (PRP), represent an important adjunct to symptomatic treatment, with PRP showing longer-lasting effects. Individualizing therapy is crucial for optimal treatment outcomes
PATHOGENESIS OF ENDOMETRIOSIS: AN UMBRELLA REVIEW OF RECENT SYSTEMATIC REVIEWS AND META-ANALYSES
No full textEndometriosis is a chronic, estrogen-dependent inflammatory disease affecting approximately 10% of women of reproductive age and remains a major cause of pelvic pain and infertility. Despite its high prevalence and substantial clinical burden, the biological mechanisms underlying endometriosis are still incompletely understood. Although numerous systematic reviews and meta-analyses have addressed individual pathogenic domains, their findings are often fragmented, methodologically heterogeneous, and partially overlapping. This umbrella review aimed to provide a comprehensive synthesis of recent evidence on the pathogenesis of endometriosis while critically appraising methodological quality and overlap of primary studies.
An umbrella review was conducted in accordance with the PRISMA 2020 guidelines. PubMed was searched for systematic reviews and meta-analyses published between January 2019 and January 2026. Methodological quality was assessed using the AMSTAR 2 tool, and overlap of primary studies was evaluated using the corrected covered area (CCA). Evidence was synthesized narratively using a predefined domain-based framework.
Eighteen systematic reviews and meta-analyses were included, covering seven major pathogenic domains, including genetic and epigenetic susceptibility, immunological dysregulation, oxidative stress, tissue remodeling, microbiota dysbiosis, and systems-level molecular networks. Most reviews demonstrated moderate methodological quality, with limited overlap across most domains, except for microbiota-related evidence (CCA = 20%). Overall, the findings support a multifactorial, network-based model of endometriosis pathogenesis involving interactions between genetic susceptibility, immune dysfunction, hormonal signaling, and environmental modifiers. This umbrella review highlights key pathogenic domains, identifies areas of evidentiary fragility, and underscores the need for integrative, systems-level research to inform future mechanistic studies and targeted clinical interventions
GLOBAL LONGITUDINAL STRAIN AND 3D ECHOCARDIOGRAPHY FOR EARLY DETECTION OF ANTHRACYCLINE-INDUCED SUBCLINICAL CARDIOTOXICITY: NARRATIVE REVIEW
No full textIntroduction and purpose: Anthracyclines are highly effective chemotherapeutic agents but carry a significant risk of dose-dependent cardiac toxicity, often progressing silently before left-ventricular ejection fraction (LVEF) declines. The objective of this review was to evaluate the role of three-dimensional echocardiography (3D-ECHO) and global longitudinal strain (GLS) in the early detection of subclinical anthracycline-induced cardiotoxicity and to compare their diagnostic performance with conventional 2D-LVEF assessment.
Methods: A narrative review was conducted using clinical and observational studies indexed in PubMed over the last ten years. Only peer-reviewed human research evaluating anthracycline-induced cardiotoxicity was included. Studies comparing 2D-LVEF with 3D-LVEF and/or global longitudinal strain (GLS), as well as those assessing early markers of subclinical left-ventricular dysfunction, were selected. Extracted data focused on diagnostic effectiveness, time-to-detection of myocardial injury, and prognostic relevance of strain-based parameters.
Conclusion: GLS and 3D-echocardiography outperform conventional 2D-LVEF in identifying early, subclinical anthracycline-related cardiotoxicity. GLS provides the highest sensitivity for early myocardial injury, while 3D-STE enhances spatial assessment and detects dysfunction before EF decline. Routine integration of these modalities into cardio-oncology surveillance may enable earlier intervention, prevent irreversible damage, and improve long-term cardiac outcomes
COGNITIVE IMPAIRMENT, FATIGUE, AND WORKPLACE ACCOMMODATIONS IN MULTIPLE SCLEROSIS: IMPACT ON EMPLOYMENT RETENTION
No full textMultiple Sclerosis significantly impacts employment retention due to its early onset and progressive nature, necessitating a comprehensive understanding of contributing factors beyond physical disability. This narrative review explores the profound influence of cognitive impairment, fatigue, and the efficacy of workplace accommodations on maintaining employment for individuals with MS. Specifically, this review synthesizes current literature to delineate how these non-physical symptoms frequently precipitate job loss and absenteeism among people with MS (pwMS). Beyond merely physical limitations, factors such as fatigue, cognitive decline, and mental health issues are significant predictors of reduced working hours and early retirement for individuals with MS (Pokryszko-Dragan et al., 2022). These non-motor symptoms can create a complex "vicious circle" that adversely affects vocational status, often intertwining with mental health challenges that independently influence professional activities (Pokryszko‐Dragan et al., 2022). The diverse manifestations of MS, encompassing motor, sensory, and cognitive deficits, along with chronic fatigue, significantly impede work performance and overall quality of life (Pokryszko-Dragan et al., 2022; Valadkevičienė et al., 2024). While previous research has identified disease severity, fatigue, and cognitive impairments as key predictors of employment outcomes, persistent limitations in generalizability stem from sample variability, socioeconomic factors, and diverse healthcare systems (Iron et al., 2025). This narrative review aims to comprehensively address these gaps by synthesizing evidence on the multifaceted interplay between cognitive impairment, fatigue, workplace accommodations, and their collective impact on employment retention in MS. Understanding these intricate relationships is critical for developing targeted interventions and supportive strategies to enhance vocational longevity for individuals living with MS (Moccia et al., 2022; Pokryszko‐Dragan et al., 2022)
INNOVATIVE TECHNOLOGIES IN THE DIAGNOSIS AND TREATMENT OF INFERTILITY: A COMPREHENSIVE REVIEW
No full textInfertility is increasingly recognized as a condition affecting diverse populations and one that requires advanced medical and technological approaches to support effective diagnosis and treatment. In recent years, significant progress in medical sciences and digital technologies has refined the ways infertility is assessed and managed. Contemporary diagnostic methods now include high-resolution imaging, hormonal and genetic testing, microbiome evaluation, and immunological analysis. These advancements enable clinicians to identify reproductive disorders with greater precision.
Artificial intelligence (AI) and robotic systems further support clinical decision-making by improving embryo selection, predicting treatment outcomes, and standardizing laboratory procedures. Advances in assisted reproductive technologies (ART) have expanded therapeutic options for patients who previously had limited chances of achieving pregnancy.
Although technology plays a crucial role in modern infertility care, patient experiences and psychological well-being remain equally important, as treatment can be both emotionally and physically demanding. This review summarizes current knowledge on innovative technologies used in the diagnosis and treatment of infertility and highlights how the continued development of these methods enhances clinical outcomes and patient care
VIRTUAL REALITY AND EXERGAMING FOR FALL PREVENTION IN OLDER ADULTS: A REVIEW OF CLINICAL EFFICACY AND PATIENT ENGAGEMENT
No full textBackground: Falls are a primary cause of injury and loss of independence among older adults. Digital technologies like virtual reality (VR) and exergaming are increasingly used to enhance balance and mobility, yet their overall clinical efficacy requires synthesis.
Objective: This review consolidates evidence from 2015–2024 regarding the effectiveness of VR and exergaming interventions on fall prevention, specifically examining balance, functional mobility, dual-task performance, and patient engagement.
Methods: A narrative review was conducted using PubMed to identify peer-reviewed studies, including randomized controlled trials and meta-analyses. The review included interventions targeting balance or fall risk in adults aged 60 and older.
Results: The synthesized evidence indicates that both VR and exergaming yield significant improvements in balance, gait adaptability, and dual-task performance. These interventions utilize multisensory feedback and gamification to integrate cognitive and motor skills, thereby supporting motor learning and adherence. However, findings concerning actual reductions in fall incidence remain mixed due to methodological variations and limited long-term follow-up.
Conclusion: VR and exergaming serve as effective, engaging complementary tools for fall prevention. While they reliably improve functional outcomes, future research must focus on standardizing protocols and evaluating long-term efficacy to support clinical implementation
WORK PRODUCTIVITY AND COGNITIVE LOAD ACROSS MENSTRUAL CYCLE PHASES: A NARRATIVE LITERATURE REVIEW
No full textHormonal fluctuations across the menstrual cycle are increasingly recognized as meaningful contributors to variations in cognition, emotional regulation, and daily functioning. However, empirical evidence remains fragmented across disciplines, with studies differing widely in methodological rigor, outcome measures, and approaches to menstrual phase classification. This lack of integration limits the field’s ability to draw coherent conclusions about how cycle-related changes influence cognitive load and work productivity—domains of growing significance for women’s health and occupational wellbeing. This narrative review addresses this gap by synthesizing findings from nineteen empirical studies spanning neuroscience, psychology, occupational health, and digital menstrual tracking.
A structured search of PubMed, Scopus, and Web of Science was used to identify eligible studies. Data were then extracted using a standardized analytical protocol. The findings reveal domain-specific patterns of cognitive variation, with the most consistent differences observed in reaction time, attentional stability, and temporal anticipation. Cognitive-emotional processes—including rumination, stress reactivity, and negative attentional bias—showed particularly pronounced fluctuations during the luteal and premenstrual phases, especially among individuals with PMS or PMDD, and were associated with reduced efficiency, motivation, and routine management. Digital tracking tools provided additional insight into menstrual variability but demonstrated methodological limitations that affect their reliability for studying cognitive and functional outcomes.
Overall, the evidence indicates that menstrual-cycle-related changes in cognition and emotional processing interact with symptom burden to shape cognitive load and everyday productivity. Strengthening methodological rigor, incorporating validated measures, and improving menstrual health literacy may enhance both research precision and practical strategies for supporting individuals across menstrual cycle phases
HEALTHCARE COSTS ACROSS THE BODY MASS INDEX SPECTRUM: THE ROLE OF AGE AND MULTIMORBIDITY — A SYSTEMATIC REVIEW
No full textBackground: Body mass index (BMI) is widely used to classify nutritional status and assess health risk at the population level. Both low and high BMI values are associated with adverse health outcomes; however, the economic consequences of abnormal BMI across the full BMI spectrum have not been comprehensively synthesized in recent literature.
Objective: To systematically review evidence published since 2015 on healthcare costs associated with BMI categories ranging from underweight to severe obesity, with particular attention to the modifying roles of age and multimorbidity.
Methods: A systematic literature search was conducted in PubMed to identify studies published between January 2015 and March 2025 reporting healthcare costs stratified by BMI. Observational studies, cohort studies, registry-based analyses, and cost-of-illness studies were included. Due to substantial methodological heterogeneity, a narrative synthesis was performed.
Results: Across healthcare systems, a consistent non-linear association between BMI and healthcare costs was observed. Normal BMI was associated with the lowest expenditures, while both underweight and obesity were linked to increased costs. Severe obesity generated the highest costs, largely driven by chronic disease burden and hospitalization. Age and multimorbidity substantially modified the BMI–cost relationship, with underweight particularly associated with prolonged hospitalization among older adults.
Conclusions: Healthcare costs vary significantly across the BMI spectrum. Both extremes of BMI are associated with increased economic burden, underscoring the importance of age-sensitive and multimorbidity-informed prevention and care strategies
THE IMPACT OF CAFFEINE ON SURGERY
No full textCaffeine is one of the most widely consumed psychoactive substances and is commonly used by both surgical patients and healthcare professionals. While its primary effect on the central nervous system is the reduction of fatigue and enhancement of alertness, caffeine also influences cardiovascular function, skeletal muscle activity, metabolism, and neuroendocrine regulation. Owing to these multifaceted actions, caffeine may affect perioperative outcomes and complications in both beneficial and adverse ways.
This narrative review summarizes current evidence on habitual and perioperative caffeine consumption in the surgical setting, addressing its interactions with anesthetic and analgesic drugs, effects on physiological recovery and wound healing, influence on perioperative risk, and potential impact on surgeons’ alertness and procedural precision.
The available studies are limited in number, heterogeneous in design, and frequently inconclusive, which precludes clear clinical recommendations regarding habitual or perioperative caffeine consumption. Further well-designed studies are required to clarify the benefits and risks of caffeine use for both patients and surgical staff and to support the development of evidence-based perioperative guidance
DETECTION OF ATRIAL FIBRILLATION BASED ON ECG - TESTING THE EFFECTIVENESS OF A SIMPLE AI ALGORITHM IN IDENTIFYING ARRHYTHMIAS ON ECG RECORDINGS
No full textAtrial fibrillation (AF) is the most common cardiac arrhythmia and a growing cardiovascular epidemic characterized by uncoordinated atrial activation. It is a significant risk factor for ischemic stroke, heart failure, and cognitive decline. Despite its rising prevalence, driven by aging populations and lifestyle factors, early detection remains a diagnostic challenge due to the often asymptomatic and paroxysmal nature of the condition. Traditional screening methods, relying on standard electrocardiograms (ECG) and manual interpretation, are resource-intensive and limited in their ability to provide continuous monitoring.
The rapid development of digital health technologies has introduced artificial intelligence (AI) as a pivotal tool for automating arrhythmia detection. This review assesses the effectiveness of "simple" machine learning algorithms, such as Support Vector Machines (SVM), Random Forests (RF), and k-Nearest Neighbors (k-NN), in comparison to complex deep learning architectures like Convolutional Neural Networks (CNN).
The analysis indicates that simple AI models demonstrate high diagnostic accuracy (up to 99.1%), comparable to deep learning models, while requiring significantly less computational power and offering greater interpretability. These features make them highly suitable for deployment in battery-operated consumer devices, such as smartwatches and handheld ECG recorders. The integration of these efficient algorithms into telemedicine infrastructures supports a hybrid care model, facilitating large-scale screening and early diagnosis, which is essential for mitigating the burden of AF-related complications.
Methodology: This article is a review. The data presented is based on a comprehensive analysis of peer-reviewed scientific articles, systematic reviews, and clinical reports published in reputable medical journals. The review focuses on studies evaluating the performance of AI algorithms using publicly available physiological signal databases, such as the MIT-BIH Atrial Fibrillation Database and PhysioNet Challenge datasets, as well as data collected from wearable mobile devices. The literature selected for this overview covers the period from 2010 to 2025, including foundational research and the most recent developments in mobile health technologies, approved medical devices, and algorithmic efficiency. The review critically compares the utility, accuracy, and computational requirements of various machine learning approaches in the context of remote patient monitoring and clinical diagnostics