The symptoms of esophageal hypersensitivity and refractory gastroesophageal reflux disease (rGERD) become aggressive clinical targets for treatment even when patients have the most effective acid suppression therapy. Science now supports antidepressant pain modulators such as tricyclic antidepressants (TCAs) and selective serotonin reuptake inhibitors (SSRIs) as potential therapeutic medications because they control esophageal nociceptive signals and central pain signals within the brain. This extensive review examines the functional roles which these treatments perform when treating esophageal hypersensitivity along with rGERD and conducts an assessment of their effectiveness and security data with clinical implications. The review analyzed findings from randomized controlled trials and meta-analyses and observational studies which were published in prestigious journals. The evaluations based on statistics demonstrated how antidepressants as pain modulators perform against standard GERD therapies while assessing symptom control along with life quality benefits and potential side effects in patients. The review examines visceral pain modulation neurophysiology to demonstrate potential treatment approaches for individual patients. Studies reveal that TCA medications together with SSRI medications successfully decrease esophageal pain experiences from central nervous system and peripheral nervous system mechanisms while offering better treatment outcomes to PPI non-responsive patient populations. Clinical research involving low-dose antidepressants showed both statistically relevant improvements in heartburn severity scores as well as pain intensity measurements from the chest area for treatment participants. More research needs to address safety questions and ideal medications doses along with long-term safety matters. The research demonstrates how antidepressant pain modulators serve as promising complementary therapies for treating esophageal hypersensitivity and rGERD while recommending new treatment approaches. Future research needs to improve selection criteria for patients while discovering the most effective treatment plans and increasing evidence-based applications for better clinical results. These research outcomes enable the advancement of comprehension regarding neurogastroenterology critical connection with psychopharmacology thus producing new multidisciplinary treatment models.

Traumatic Brain Injury (TBI) is widely recognized for its profound impact on brain function, but its consequences often extend beyond the brain to affect various extracranial systems. This narrative review explores the extracranial effects of TBI, focusing on the cardiovascular, respiratory, endocrine, and musculoskeletal systems, as well as immune response and metabolic changes. The review synthesizes current literature on how TBI-induced pathophysiological changes extend throughout the body and influence long-term outcomes, including physical and mental health. The interplay between intracranial injury and extracranial effects is critical for understanding the full scope of TBI’s impact. Future research should emphasize the development of comprehensive treatment protocols that address both intracranial and extracranial effects to improve outcomes for TBI patients.

Obsessive–Compulsive Disorder (OCD) is a heterogeneous psychiatric condition marked by intrusive obsessions and ritualistic compulsions that significantly impair functioning and quality of life. Advances in neuroimaging have substantially clarified its complex neurobiological basis. Structural findings frequently demonstrate morphological alterations in the orbitofrontal cortex, anterior cingulate cortex, and striatum, linked to both gray and white matter disruptions. Functional neuroimaging studies highlight hyperactivity within cortico-striato-thalamo-cortical (CSTC) loops and aberrant connectivity involving the parietal cortex, limbic structures, and cerebellum. Task-based paradigms underscore that different symptom dimensions (e.g., contamination fears, checking, hoarding) activate partially distinct yet overlapping cortical–subcortical networks. Integrating these structural and functional perspectives supports a connectome-based framework, in which OCD emerges from dysregulated interactions among diverse brain systems involved in cognitive control, emotional regulation, and habit learning. Emerging biomarkers—such as caudate volume, anterior cingulate metabolites, and orbitofrontal connectivity—show promise for predicting response to pharmacotherapy and cognitive-behavioral therapy. Future investigations may expand on these findings through larger longitudinal cohorts, inclusion of pediatric populations, and implementation of multi-omic approaches that integrate genetic, epigenetic, and neuroimaging data. This synthesis of current evidence underscores the potential for refined diagnostic stratification, personalized therapeutic interventions, and enhanced monitoring of treatment efficacy.

In Uzbekistan, the sphere of ART is relatively new and the problems of pregnancy after ART procedures remain both a socio-psychological and economic problem. The article discusses the effect of melatonin use on the probability of pregnancy as the first clinical trials in this country. A comparative analysis of the results between groups of women who used melatonin and a control group is made. The physiologic mechanisms of melatonin action, its effect on reproductive health and the prospects for its use in clinical practice are highlighted. Clinical trials using melatonin in the field of assisted reproductive technologies (ART) are conducted for the first time in Uzbekistan. This is an innovative approach aimed at studying the effectiveness and safety of this drug in improving reproductive indicators in patients.

Through Artificial Intelligence (AI), healthcare has brought revolutionary changes to workforce administration by directing talent delegations and reforming operations with IT integration. Healthcare organizations struggle with staff shortages alongside rising operational costs while seeking high-quality patient care which makes AI-driven workforce solutions data-based when addressing these problems. This investigation reveals the ways AI technology brings improved scheduling capabilities along with better talent hiring methods and performance evaluation systems and employee maintenance procedures and implements their integration with health IT infrastructure. This research consists of both systemized review of academic studies and real-world examples and statistical data which reveals how AI automation reduces administrative obstacles while lowering staff issues and generating operational improvements. AI technological capabilities with predictive analytics and machine learning allow for flexible workforce planning and real-time performance tracking together with data-based decision making to create superior business strategies for talent optimization. Artificial intelligence enhances IT integrations which creates better interoperability between Electronic Health Records (EHR) systems as well as workforce management systems thereby optimizing human resource functions while cutting down on processing time. Research evidence demonstrates that AI implementations deliver significant operational improvements which produce enhanced staff performance along with diminished labor expenses and contented employees. AI implementation for healthcare workforce management encounters obstacles because healthcare professionals question its ethics while workforce members avoid adopting changes and the field exhibits technological differences. Future studies need to tackle the present challenges by studying AI governance programs with emphasis on staff flexibility to AI technology integration. The research presents a tactical guide which healthcare institutions can use to optimize workforce management through AI deployment in order to build sustainable operations in transforming digital environments.

Daltonism is a vision disorder in which the eye is unable to perceive one or more primary colors. This disorder is caused by a defect in the X chromosome. However, this is not the only cause of the disease. Color perception may be impaired due to eye or nervous diseases, traumatic brain injury, severe flu, stroke, or heart attack.

This pathology was named after the English chemist John Dalton, who also suffered from this disease, like his relatives, discovered and described the pathology in a book.

The implementation of AI-powered predictive analytics within healthcare business operations is transforming medical practices through improved operational performance and better clinical results. The research examines how algorithms from machine learning combined with deep learning methods and real-time data processing systems enable better decisions in clinical settings and resource management along with advanced patient care methods. The research employs both practical applications and scientific study of empirical evidence to evaluate the ability of predictive AI models in healthcare to decrease hospital readmissions while minimizing diagnostic errors while delivering better value for money in healthcare management. A quantitative data research design enables performance analysis of AI predictive systems used in multiple healthcare environments. Real-world examples and industry reports show that disease predictions becomes 95% more accurate through AI algorithms which leads to more than 30% decrease in hospital operational inefficiencies. The discussion addresses healthcare business AI adoption by reviewing ethical privacy issues about data security while discussing algorithmic bias effects alongside regulatory laws that affect feasibility. AI predictive analytics produces benefits for patients through customized medical planning as well as automated diagnosis handling and hospital resources optimization. This research publishes both implementation facilitators and deterrents which include price challenges together with data integration problems and data decision explainability doubts in AI systems. The research provides valuable suggestions to healthcare professionals and AI developers and public health planners about maximizing AI modeling methods for better healthcare delivery results and operational performance.

Cardiovascular diseases (CVDs) and obesity are leading causes of morbidity and mortality worldwide. Over the past several decades, while CVD-related deaths have declined in high-income countries, they have significantly increased in low- and middle-income countries, including Pakistan, which bears nearly 80% of the global burden. Obesity, a key modifiable risk factor for CVDs, has emerged as a serious public health challenge in Pakistan due to sedentary lifestyles, unhealthy diets, and lack of awareness. Despite the growing prevalence of obesity and its strong association with cardiovascular diseases, minimal attention has been given to preventive strategies in South Asia, particularly in Pakistan. Additionally, economic and political instability further exacerbates the rising trends of CVDs and obesity in the country. Practical efforts are required to enhance the understanding of risk factors such as poor diet, physical inactivity, and tobacco use while promoting obesity prevention through targeted interventions. This paper reviews the major modifiable risk factors in Pakistan, highlights available preventive services, and discusses evidence-based strategies for reducing the burden of both cardiovascular diseases and obesity at the population level.

Objective: To evaluate the limits of conservative treatment compared to surgical treatment.

Methodology: Information was searched using the Pubmed database using the keywords: "developmental", "spondylolisthesis", "classification", "surgical", "treatment", "graft", "fusion", "Gaines". The search was restricted to articles in English, French and Portuguese. After selection, 05 articles were consulted for analysis and construction of the study.

Results : Surgical treatment proved to be more effective in assessing pain in studies in which the patients were children and adolescents. Conservative treatment, in the majority of studies, was not effective in terms of improving mental health and consequently improving the quality of life experienced by the patient.

Conclusion: Conservative treatment is indicated as the first choice in most cases, leaving the invasive option for residual symptoms or advanced degrees of anatomical involvement, and it is worth noting that the surgical procedure is shown in the evaluation of pain, mental health and quality of life in the studies in which the patients were children and adolescents.

Objective: To analyze the effectiveness of hemiarthroplasty compared to total arthroplasty in the treatment of hip fractures. Methodology: Systematic literature review, with a quantitative and qualitative approach to the data collected, which was structured according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA): “total hip arthroplasty” and ‘hemiarthroplasty’ with ‘AND’ and ‘OR’ combinations. Results: The five articles selected evaluated the effectiveness of hemiarthroplasty and total hip arthroplasty techniques using the WOMAC score, Harris Hip Score (HHS), SF-36 (Short Form Health Survey 36) and/or Visual analogue scale (VAS). Total arthroplasty showed better results in most studies (p<0.01). Conclusion: Total arthroplasty was considered the procedure of choice, especially for active elderly patients.

This dissertation investigates the prevalence and contributing factors of obesity among young children in Uzbekistan, aiming to address the critical issue of increasing obesity rates in this demographic. Employing a quantitative approach, the study analyzes body mass index (BMI) data alongside dietary habits, physical activity levels, and socioeconomic factors affecting children's health. The findings reveal a significant prevalence of obesity in young children, which is closely linked to poor nutrition, insufficient physical activity, and socioeconomic disparities. Notably, a substantial percentage of the population exhibited unhealthy dietary patterns and low engagement in physical exercise, underscoring the multifaceted nature of this public health challenge. The implications of this research are profound, as it highlights the urgent need for targeted interventions and policy reforms to promote healthier lifestyles and reduce obesity rates in Uzbekistan. By addressing the specific factors contributing to childhood obesity, this study underscores the importance of integrating nutrition education and physical activity programs within healthcare initiatives. Furthermore, the results contribute to the broader discourse on childhood health issues in developing countries, providing a framework for future research and intervention strategies aimed at ameliorating the obesity epidemic and fostering a healthier future generation.

Objectives: to evaluate the results obtained from the arthroplasty, arthrodesis and trapeziectomy with tendon interposition techniques used to treat rhizarthrosis.

Methodology: This is a systematic review with meta-analysis carried out by searching the electronic databases PubMed/MEDLINE and Cochrane Library without language restriction for publications up to June 2024 to analyze the surgical treatment of arthritis of the first carpometacarpal joint.

Results: 289 patients were included, of whom 63 underwent trapeziectomy with tendon interposition, 70 underwent arthrodesis and 156 underwent arthroplasty. Arthroplasty showed good long-term results when compared to the most commonly used techniques for correcting rhizarthrosis. Trapeziectomy showed no significant improvement in strength or functionality when compared to arthrodesis, and was also more associated with cases of joint reduction failure and consequent re-intervention.

Conclusion: No surgical technique is superior to another in terms of pain, physical function and the patient's overall assessment.

Kartagener's syndrome (KS) is a rare hereditary disease characterized by a triad of symptoms: primary ciliary dyskinesia, situs inversus, and chronic respiratory infections. This article presents a family case of KS in children, emphasizing the clinical features, diagnostic challenges, and management strategies. The study analyzes the genetic aspects, pathophysiology, and progression of the disease in affected siblings. Special attention is given to respiratory complications, recurrent infections, and the impact on the quality of life. Early diagnosis and comprehensive therapeutic approaches, including airway clearance techniques and antibiotic prophylaxis, are crucial for improving long-term outcomes in children with KS.

Pathophysiology, a foundational subject in medical and health sciences education, explores the mechanisms underlying disease processes. Despite its importance, the complexity and volume of content often pose significant challenges for students, leading to varied learning outcomes. Traditional teaching methods, which adopt a uniform approach for all learners, frequently fail to address the diverse needs, backgrounds, and learning styles of students. Personalized learning, an innovative educational strategy that tailors instruction to individual learners, offers a promising solution to these challenges. By leveraging adaptive technologies, data analytics, and customized teaching methods, personalized learning can transform pathophysiology education, making it more engaging, effective, and accessible. This article examines the principles of personalized learning, its application in pathophysiology, and the potential benefits and challenges of its implementation. Through a student-centered approach, personalized learning has the potential to enhance comprehension, retention, and critical thinking skills, ultimately preparing students for the demands of clinical practice. The integration of emerging technologies, such as artificial intelligence and virtual reality, further underscores the transformative potential of personalized learning in shaping the future of medical education.

Postoperative stroke remains one of the most serious complications following coronary artery bypass grafting (CABG), contributing significantly to increased mortality, prolonged hospitalization, and long-term disability. Despite improvements in surgical techniques and perioperative care, identifying patients at risk of postoperative stroke remains a clinical challenge.

This study aims to evaluate the key clinical and surgical predictors associated with the development of ischemic stroke in patients undergoing CABG, with a focus on identifying modifiable risk factors and predictive markers.

A retrospective analysis was conducted on patients who underwent CABG over a two-year period. Variables assessed included age, comorbid conditions (hypertension, diabetes mellitus, atrial fibrillation), carotid artery disease, duration of cardiopulmonary bypass (CPB), intraoperative hemodynamic instability, and cerebral perfusion strategies. Statistical methods were employed to determine the significance of associations between these factors and stroke occurrence.

Advanced age over 70 years, pre-existing cerebrovascular disease, prolonged CPB time over 120 minutes, poorly controlled diabetes, and intraoperative hypotension were identified as independent predictors of postoperative stroke. Carotid artery stenosis and atrial fibrillation also significantly increased stroke risk. A high comorbidity index and prolonged aortic cross-clamping were associated with worse neurological outcomes. Early identification of high-risk patients was found to improve decision-making in terms of surgical planning and perioperative management.

Postoperative stroke after CABG is influenced by a combination of patient-related, surgical, and hemodynamic factors. A comprehensive risk assessment protocol incorporating both clinical and procedural markers is essential for prevention. Tailoring surgical and anesthetic strategies based on individual risk profiles may reduce the incidence and severity of neurologic complications.

Objectives: The aim of this systematic review is to evaluate the effectiveness of the use of orthoses in the treatment of pediatric flatfoot.

Methodology: A systematic review was carried out in the online databases Cochrane Library, EMBASE, CINAHL, Medline and PubMed, using the following terms: flatfoot AND pediatric AND Orthotic

Devices. There were no limitations on gender, date or language. All results up to

February 1, 2024 were included.

Results: 213 patients under the age of 18 were included in this study. The use of medial arch support insoles proved to be effective in the treatment of flat feet in children, with an improvement in ankle internal rotation angles and knee internal and external rotation.

Conclusion: The use of orthoses has shown good results and is a reproducible and reliable approach, especially in pre- school patients who have been using them for more than 12 months, with improvements in gait, alignment and coordination of the lower limbs.

Healthcare business intelligence advances through the combination of Big Data and Machine Learning (ML) technology which improves both cost reduction and service quality. Healthcare organizations employ predictive analysis together with AI-driven choices and real-time processing to minimize costs as global healthcare fees continue increasing while improving patient care efficiency. This paper investigates the transformation of resource distribution and predictive equipment maintenance and individual medical approaches through Big Data and ML models along with supervised learning and deep learning and anomaly detection algorithms. The research follows a quantitative approach to study both actual case examples and statistical models which predict hospital admissions while optimizing resource management to lower operational flaws. AI predictive analytics produces a 30% deduction in healthcare bills supported by studies with results showing also a 25% increase in medical service delivery efficiency. Real-time data integration allows medical professionals to detect diseases earlier and develop precise treatment plans for each patient which increases patient satisfaction rates. The study adds to existing AI-driven healthcare business intelligence research by delivering practical guidelines which healthcare administrators and policymakers and technology leaders can use. The paper requirement of data governance frameworks together with ethical AI implementation methods and scalable decision systems based on ML is necessary for achieving the complete benefits of Big Data in healthcare.