International Journal of Medical Science and Public Health Research

Development of A Personalized Treatment Algorithm for Pediatric Atopic Dermatitis Based on Molecular Allergy Diagnostics

Rasikova Gulmira Rustamovna — 2026-04-14

Background. Atopic dermatitis is one of the most common chronic inflammatory skin diseases in children, characterized by early onset, recurrent course, and significant impact on quality of life. The heterogeneity of clinical manifestations and allergen sensitization profiles makes standard treatment approaches insufficiently effective. Recent advances in molecular allergy diagnostics, including component-resolved diagnostics, allow identification of individual allergen sensitization patterns and support personalized treatment strategies. However, the development of personalized treatment algorithms based on molecular allergy diagnostics in children with atopic dermatitis remains insufficiently studied. Aim. To develop a personalized treatment algorithm for children with atopic dermatitis based on molecular allergy diagnostics using Phadia 200 (ImmunoCAP) and MADx ALEX platforms. Materials and Methods. This prospective study will be conducted at the Republican Specialized Scientific-Practical Allergology Center between 2023 and 2026. A total of 200 children aged 3–18 years diagnosed with atopic dermatitis will be included. Clinical assessment will include dermatological examination, allergic history evaluation, and disease severity assessment using the SCORAD index. Laboratory investigations will include complete blood count, eosinophil count, and total IgE measurement. Molecular allergy diagnostics will be performed using Phadia 200 (ImmunoCAP) and MADx ALEX platforms to determine individual allergen sensitization profiles. Personalized treatment strategies will be developed based on obtained results. Statistical analysis will be performed using SPSS software, with p<0.05 considered statistically significant. Results. The study is expected to identify individual allergen sensitization profiles and their association with disease severity, frequency of exacerbations, and comorbid allergic diseases. Implementation of personalized treatment algorithms is expected to reduce disease severity, improve treatment effectiveness, and decrease recurrence frequency in children with atopic dermatitis. Conclusion. Molecular allergy diagnostics plays an important role in developing personalized treatment strategies for children with atopic dermatitis. Implementation of personalized approaches may improve diagnostic accuracy, enhance treatment effectiveness, and improve quality of life in pediatric patients.

Clinical Significance of Kallikrein As A Diagnostic and Prognostic Biomarker in Cardiorenal Syndrome Associated with Chronic Heart Failure

Abdigaffar Gadaev — 2026-04-07

Background. Chronic heart failure (CHF) remains one of the leading challenges in modern healthcare systems worldwide and is frequently accompanied by impaired renal function. The progressive and interrelated deterioration of cardiac and renal function leads to the development of cardiorenal syndrome (CRS), which significantly worsens patient prognosis. In recent years, alongside neurohumoral mechanisms, the kallikrein–kinin system has been increasingly recognized as an important contributor to CRS pathogenesis; however, its diagnostic value has not yet been sufficiently elucidated. Objective. To evaluate the diagnostic and potential prognostic significance of kallikrein levels in the early stages of cardiorenal syndrome developing in patients with chronic heart failure, and to determine their relationship with NT-proBNP, aldosterone, and renal function parameters. Methods. This prospective observational study included 115 patients with chronic heart failure classified as New York Heart Association (NYHA) functional classes II–III. Patients were divided into two groups according to functional class. Serum levels of kallikrein, NT-proBNP, aldosterone, cystatin C, and creatinine were measured. Glomerular filtration rate (GFR) was calculated using the CKD-EPI equation. Statistical analysis was performed using Student’s t-test and Pearson correlation analysis. Results. Kallikrein levels were significantly lower in patients with NYHA class III compared to class II (535.86±12.37 vs. 778.79±17.8 ng/mL; p<0.001). In contrast, NT-proBNP levels were significantly higher (738.6±45.8 vs. 587.3±59.9 pg/mL; p<0.05). Kallikrein demonstrated a negative correlation with NT-proBNP (r = -0.51; p<0.001) and aldosterone (r = -0.48; p<0.001), while showing a positive correlation with GFR calculated based on cystatin C (r = 0.66; p<0.001). Conclusion. Decreased kallikrein levels are associated with increased severity of cardiorenal syndrome and exhibit inverse relationships with NT-proBNP and aldosterone, while correlating positively with renal function parameters. The combined assessment of these biomarkers may have potential clinical value for the early diagnosis and prognostic stratification of cardiorenal syndrome.

The Relationship Between Physical Activity Levels and Immune Function

Tuhtaboev Usmonjon Bahodirjonovich — 2026-04-07

The relevance of this study stems from the need to identify the relationship between the level of physical activity and the state of the immune system, including the incidence of infectious diseases and the rate of recovery. The study also examines myokines released in response to physical exercise that affect the immune system.

To assess the relationship between physical activity and immune system activity, a survey was conducted among 103 people. The results showed an ambiguous relationship between the level of physical activity and the rate of recovery; to clarify the results, a study with a larger sample size is required. Additionally, an analysis of empirical studies available in the PubMed database was conducted.

Analysis of Multi-Tracer Adaptability in Machine Intelligence Models for Positron Emission Tomography Bias Adjustment

Dr. Aisha Rahman — 2026-04-01

Positron Emission Tomography (PET) imaging plays a critical role in functional and molecular diagnostics; however, its quantitative accuracy is significantly influenced by attenuation-related biases. Traditional correction techniques rely heavily on structural imaging modalities such as computed tomography (CT) or magnetic resonance imaging (MRI), which introduce limitations in multi-tracer adaptability due to modality-specific inconsistencies and tracer-dependent variations. Recent advances in machine intelligence, particularly deep learning-based models, have enabled data-driven attenuation correction methods that demonstrate improved generalization capabilities across imaging conditions. Nevertheless, the ability of these models to maintain robustness across diverse radiotracers remains an unresolved challenge.

This study presents a comprehensive analysis of multi-tracer adaptability in machine intelligence models designed for PET bias adjustment. It examines how variations in tracer distribution, photon attenuation properties, and biological uptake patterns affect the generalization capacity of learning-based correction systems. By synthesizing existing frameworks—including convolutional neural networks, adversarial architectures, and joint reconstruction models—the research evaluates their effectiveness in handling heterogeneous tracer datasets.

The proposed analytical framework integrates spectral and structural feature learning with domain adaptation mechanisms to enhance cross-tracer generalizability. Emphasis is placed on understanding how training strategies, including multi-site normalization (Onofrey, 2019) and adversarial learning (Arabi et al., 2019), contribute to model robustness. Furthermore, the study explores the role of joint activity–attenuation reconstruction (Rezaei, 2012; Rezaei et al., 2018) and synthetic CT generation (Dong, 2019) in reducing tracer-specific biases.

Findings indicate that while deep learning approaches significantly outperform traditional methods in single-tracer scenarios, their performance degrades when exposed to unseen tracer distributions unless explicit generalization strategies are incorporated. The integration of multi-tracer datasets and hybrid modeling approaches emerges as a key factor in achieving reliable bias correction.

This research contributes to the advancement of PET imaging by providing a critical evaluation of machine intelligence adaptability, identifying limitations in current methodologies, and proposing directions for developing tracer-agnostic correction frameworks. The results hold significant implications for improving clinical reliability and expanding the applicability of PET imaging across diverse diagnostic contexts.