International Journal of Medical Science and Public Health Research

Measles Outbreaks In The Post-Pandemic Period: Epidemiological Analysis Of Vaccine Refusal And Healthcare Response

Rahimova Mohinur Ravshanovna — 2025-12-18

Background: After COVID-19 pandemic, Uzbekistan has faced renewed challenges in maintaining high routine immunization coverage. After several years of relative stability, measles has re-emerged as a public health concern, with outbreaks reported across multiple regions since early 2023. The situation reflects both the lingering effects of disrupted vaccination services and a noticeable rise in vaccine refusal among adults. Understanding these trends is crucial for rebuilding the country’s progress toward measles elimination. Objective:

This study aimed to describe the epidemiological features of measles outbreaks in Uzbekistan from 2023 to 2025 and to assess the impact of vaccine refusal and the healthcare system’s response on disease dynamics. Methods: We conducted a nationwide, registry-based analysis using official data from the Republican Measles Registry of the Ministry of Health of Uzbekistan. All laboratory-confirmed cases (IgM ELISA) reported between January 2023 and November 2024 were included. Data were analyzed by age, vaccination status, and geographic region. Additional information on national immunization coverage and outbreak control measures was obtained from reports by the WHO, UNICEF, and Gavi. Results: During the study period, 20,790 measles cases were confirmed across Uzbekistan. The most significant numbers were recorded in Tashkent City (4,148 cases), Kashkadarya (3,283), and Khorezm (2,875). Adults accounted for approximately 46% of all cases, with the highest proportion observed among individuals aged 20–39 years. About 41 % of all patients were unvaccinated, and around one in ten reported active refusal of vaccination, primarily due to misinformation or distrust of medical advice. The Ministry of Health responded with nationwide catch-up campaigns, mobile immunization units, and targeted communication in high-risk districts. By mid-2024, first-dose coverage among children under five had again reached above 95 %, though adult immunity gaps persisted. Conclusion: The recent measles outbreaks in Uzbekistan underscore the rapid erosion of immunity gaps when vaccination programs are disrupted and public confidence wanes. Although emergency immunization measures helped to curb transmission, strengthening public trust and expanding adult vaccination remain vital for preventing future outbreaks.

Comparative Analysis Of Pregnancy Rates In ART In Young Women With Uterine Fibroids After Different Approaches To Organ-Preserving Treatment

Abdurakhmanova Sitora Ibragimovna — 2025-12-19

Background. Myomatous nodes can impair embryo implantation, alter blood supply to the endometrium, cause an inflammatory response and lead to a decrease in the effectiveness of assisted reproductive technologies (ART). Material and methods. A total of 106 patients with uterine myoma and infertility were examined and divided into 3 groups: 1-group consisted of 35 patients who received drug therapy, 2-group - 32 patients who underwent uterine artery embolization, and 3-group - 39 patients after myomectomy. Conclusion. The choice of the treatment method should take into account the individual characteristics of the patient and her reproductive plans. The frequency of pregnancy after ART varies depending on the method of myoma treatment. Drug therapy - 22.8%, EMA - 12.5%, the best indicators were noted after myomectomy - 30.7%.

Multimodal Fusion of Vocal Biomarkers and Wearable Sensor Data for Ultra-Early Parkinson’s Disease Detection Using Explainable AI

Rasel Mahmud Jewel — 2025-12-10

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder for which early diagnosis significantly improves patient outcomes. Vocal impairments—such as altered pitch, articulation, rhythm, and voice clarity—emerge in preclinical stages and provide non-invasive biomarkers for early detection. In this study, we present a comprehensive evaluation of machine learning models applied to vocal biomarkers recorded in 2025, incorporating self-supervised speech representations, interpretable deep learning, and domain-adaptive architectures to enhance generalization across languages and populations. Leveraging contemporary datasets spanning multiple languages and at-home recordings, our models—ranging from traditional classifiers like Random Forest and SVM to advanced deep architectures—demonstrate high sensitivity and specificity, with cross-lingual performance exceeding 90% in several cases. We also introduce novel interpretability techniques that highlight the vocal segments most predictive of PD, enabling clinicians to understand underlying neuromuscular impairments. Our findings support the viability of deploying accessible, voice-based diagnostic tools—via smartphones or smart home devices—for early screening, thereby contributing to timely intervention strategies.