Background: Hemophilia is a congenital bleeding disorder caused by a lack of clotting factor in the blood. Hemophilia A, B, and C are caused by congenital deficiencies of clotting factors VIII, IX, and IX which are involved in thrombin generation of intrinsic coagulation pathway. Hemophilia A and B is a recessive X-linked congenital bleeding disorder but hemophilia C is typically inherited in an autosomal recessive, these are rare inherited coagulation disorders. This study was conducted based on the lack of database on the epidemiology, clinical type and severity in hemophilia in Mongolia. Aim: The aim of this study was to investigate the relationship between clotting factor levels and bleeding severity of hemophilia. Materials and Methods: A cross sectional study included a total of 30 patients with hemophilia who were registered from May to October 2025 at the Comprehensive Hemophilia Center, MNUMS. The study was approved by the Research Ethics Committee of MNUMS. Result: In this study 30 patients with hemophilia mean age was 18.4±15.7 years, and 96.7% were male. When categorized by age group, 13.4% (n=4) were under 4 years, 40% (n=12) were 5–13 years, 10% (n=3) were 14–18 years, 33.3% (n=10) were 19–44 years, and 3.3% (n=1) were over 45 years. Of the total cases, 83.3% (n=25) had Hemophilia A and 16.7% (n=5) had Hemophilia B. Of the total case, 80% of cases were classified as severe and 20% as moderate hemophilia. Additionally, 40% of cases had joint complications. Conclusion Joint bleeding was observed in 41.6% of patients with severe hemophilia and in 33.3% of those with moderate hemophilia. Target joint damage was found in 40% of the study population because 86.6% of patients were not able to receive prophylactic factor replacement therapy. Joint damage, joint deformities, reduced range of motion, and deterioration in quality of life were all adverse outcomes related to the early onset of joint bleeding.

Background: Life expectancy in Mongolia has increased by 8.3 years over the past three decades (1995–2025), reaching 72.1 years in 2025 (68.1 years for men and 76.2 years for women; National Statistics Office of Mongolia, 2025). Concurrently, the population aged ≥60 years has increased 2.65-fold since 1990, exceeding 338,000 in 2025. Particularly marked increases have been observed among those aged 60–64 years (241%) and 65–69 years (195%), reflecting the rapid pace of population ageing. These demographic changes are expected to increase the demand for age-appropriate oral healthcare, prosthodontic treatment, and oral rehabilitation services. Aim: To assess oral health status among older adults and examine its association with eating ability. Materials and Methods: An analytical cross-sectional study was conducted among 301 adults aged ≥50 years residing in the Chingeltei, Bayangol, Sukhbaatar, and Khan-Uul districts of Ulaanbaatar, Mongolia. Oral health status was assessed through clinical examination, including tooth loss and denture use, and using the eight-item Oral Health Assessment Tool (OHAT). Based on the OHAT assessment, oral health status was categorized as good or poor. Eating ability was assessed using a standardized questionnaire covering four domains: (1) difficulty biting and chewing hard-textured foods, such as whole pieces of meat and apples, using an ordinal scale; (2) ability to comfortably eat desired foods; (3) restriction of the type or amount of food consumed due to oral health problems; and (4) oral sensitivity to hot, cold, or sweet foods. Associations between categorical variables were assessed using Pearson’s chi-square (χ) test, with statistical significance set at p<0.05. Demographic variables, including age group and sex, were also considered in the analyses. Result: The mean age of the participants was 69.0±7.8 years; 74% were women and 26% were men. Overall, 98.6% had experienced tooth loss, and 23.6% were completely edentulous. The use of removable dentures increased significantly with age (χ=8.83, p=0.032) and was more common among women than men (65.9% vs. 45.9%; χ=8.45, p=0.004). According to the OHAT assessment, 21.0% of participants had poor oral health status, which was significantly associated with educational level (χ=27.69, p<0.001). Among participants with available data on the ability to comfortably eat desired foods (n=240), 27.1% reported being completely unable to do so. This outcome did not differ significantly by age group (p=0.823) or sex (p=0.405). Regarding hard-textured foods such as whole pieces of meat and apples, 50.6% reported being completely unable to bite or chew them, whereas only 11.3% reported being able to consume them without difficulty. The proportion unable to consume hard foods was substantially higher among participants with dietary restrictions than among those without restrictions (82% vs. 22%; χ=74.1, p<0.001). Oral sensitivity was significantly associated with dietary restriction (χ=11.5, p=0.022) and self-rated oral health status (χ=7.7, p=0.021), but was not significantly associated with specific food types, such as meat or citrus fruits (p>0.05). These findings suggest that oral sensitivity is associated with overall dietary restriction rather than selective avoidance of particular foods. Conclusion Oral health problems among older adults are associated with impaired biting and chewing ability and may contribute to dietary restriction. Therefore, nutritional assessment in older adults should extend beyond clinical oral examination to include functional eating ability. A multidisciplinary approach involving dental professionals, geriatricians, and dietitians may provide a more comprehensive assessment of oral health–related nutritional risks in this population.

Background: Liver transplantation has become an effective treatment option worldwide for a wide range of malignant and non-malignant liver diseases. In Mongolia, the high prevalence of single and co-infections with viral hepatitis is a major contributing factor to the development of liver cirrhosis and primary liver cancer. However, data on the indications, clinical characteristics, and outcomes of liver transplantation in the Mongolian population remain limited. Aim: To evaluate the indications and clinical characteristics of patients undergoing liver transplantation and to determine their survival outcomes. Materials and Methods: This single-center, retrospective cohort study included 238 patients who underwent liver transplantation at the National Cancer Center of Mongolia between 2018 and feb 2026. Patients were categorized into two groups according to the indication for transplantation: malignant and non-malignant liver diseases. Demographic and clinical characteristics were compared between the groups. Overall survival was estimated using the Kaplan–Meier method, and differences in survival were assessed using the log-rank test. A p-value <0.05 was considered statistically significant. Result: The mean age of the liver transplant recipients was 49.0 ± 8.4 years. Relatively older male patients were more likely to undergo liver transplantation for malignant liver diseases, whereas younger female patients predominated among those transplanted for non-malignant conditions (p=0.0001). No statistically significant difference in body mass index was observed between the groups (p>0.05). MELD and Child–Pugh (CPT) scores, reflecting the severity and prognosis of liver disease, differed significantly between the two groups. However, no statistically significant difference in survival was observed between the groups at 1-, 3-, or 5- years after liver transplantation (p=0.974). Conclusion Although age, sex, and liver disease severity as assessed by MELD and Child–Pugh scores differed significantly between patients undergoing liver transplantation for malignant and non-malignant indications, no statistically significant difference was observed in survival at 1-, 3-, or 5- years following liver transplantation.

Introduction: Chronic lymphocytic leukemia (CLL) is a hematologic malignancy characterized by the clonal proliferation and accumulation of mature monoclonal B lymphocytes in the peripheral blood. CLL is relatively common in Europe and North America, with an incidence of approximately 4–6 cases per 100,000 population, whereas it is rare in South Asia, with an incidence of approximately 0.11 cases per 100,000 population. During the course of the disease, neoplastic lymphocytes may infiltrate the bone marrow, lymph nodes, liver, spleen, and, less commonly, other organs and tissues. CLL presenting with marked splenomegaly in the absence of lymphadenopathy is uncommon. Therefore, in patients with unexplained splenomegaly, the differential diagnosis should include CLL and other lymphoproliferative disorders. Case Presentation: We report a case of CLL diagnosed on the basis of clinical findings, laboratory investigations, and additional diagnostic evaluation in a patient who presented to a hematology outpatient clinic with splenomegaly. An 81-year-old man presented with a macular rash involving the thighs, lower legs, chest, and both upper extremities, accompanied by dizziness, unsteadiness, nausea, fatigue, and generalized weakness. Physical examination revealed massive splenomegaly with a smooth surface and blunt edge, palpable approximately 25 cm below the left costal margin. Laboratory investigations demonstrated hyperleukocytosis with lymphocytosis, moderate normocytic normochromic anemia, and severe thrombocytopenia. Contrast-enhanced abdominal computed tomography revealed massive splenomegaly, with the spleen measuring 27.9×20.0×7.9 cm. The patient was treated with cyclophosphamide at a dose of 250 mg/m (400 mg administered) in combination with glucocorticoid therapy. Following treatment, leukocytosis, lymphocytosis, and monocytosis improved, and the spleen decreased substantially in size, becoming palpable approximately 4 cm below the umbilicus on physical examination. Conclusion Hematologic malignancies should be considered in the differential diagnosis of patients presenting with unexplained splenomegaly. In this case, a diagnosis of chronic lymphocytic leukemia was established based on bone marrow examination and the overall clinical and laboratory findings. Following treatment with cyclophosphamide and glucocorticoids, peripheral blood abnormalities improved and splenic enlargement decreased substantially, suggesting a favorable therapeutic response.

Background: Likert-scale questionnaire data is widely used to evaluate satisfaction; however, in practical applications, both high classification accuracy and model interpretability are essential requirements. Rule-based machine learning methods provide the opportunity to satisfy both criteria simultaneously. Despite the rapid growth of research using machine learning for classification and prediction in recent years, there is a scarcity of studies employing the C5.0 classification algorithm for Likert-scale satisfaction data, which serves as the rationale for this study. Aim: To implement the C5.0 classification algorithm using a 10-fold cross-validation method to predict two levels of satisfaction (High/Medium) and to evaluate the model’s performance and interpretability. Materials and Methods: he study was conducted at the Department of Physics and Informatics, School of Bio-Medicine, MNUMS, utilizing satisfaction survey data from graduates majoring in Health Information Technology. Data consisting of four Likert-scale variables (V1–V4) were used. The C5.0 model was trained using a parametric grid search and evaluated within a 10-fold cross-validation framework using accuracy and a confusion matrix. Furthermore, IF–THEN rules were extracted from the final rule-based model, and the decision logic was visualized in the form of a flowchart. Results: The results indicated that the rule-based C5.0 model was selected as the optimal model, demonstrating stable performance throughout the cross-validation process. The confusion matrix results showed high precision in identifying the “High” satisfaction class. Among the generated rules, it became evident that variables V4 and V2 have the most significant influence on determining satisfaction levels. Conclusion Utilizing the C5.0 algorithm within a cross-validation framework enables effective and intelligible classification of satisfaction based on Likert data. It was confirmed that the rule-based structure offers distinct advantages for model interpretation and decision-support applications.

Background: Sarcopenia is a multifactorial geriatric syndrome characterized by age-related loss of muscle mass, muscle strength, and physical performance. In recent years, neurotrophic and neuromuscular biomarkers have been investigated as potential diagnostic indicators of sarcopenia; however, evidence regarding their sex-specific differences remains limited. Aim: To evaluate the sex-specific associations of serum BDNF, GDNF, and CAF22 levels with sarcopenia and muscle-related parameters among older adults. Materials and Methods: This analytical cross-sectional study included adults aged 60 years and older. Sarcopenia was defined according to the Asian Working Group for Sarcopenia (AWGS 2019) criteria. Serum BDNF, GDNF, and CAF22 levels were measured using ELISA. Group differences were analyzed using independent t-tests, correlations were assessed using Pearson correlation analysis, and diagnostic performance for sarcopenia was evaluated using receiver operating characteristic (ROC) analysis. Multivariable logistic regression analysis was also performed. Results: No statistically significant differences were observed in serum BDNF, GDNF, and CAF22 levels between sarcopenic and non-sarcopenic participants in either the male or female groups (all p>0.05). ROC analysis demonstrated AUC values ranging from 0.540 to 0.542 in males and from 0.508 to 0.530 in females for BDNF, GDNF, and CAF22. Multivariable logistic regression analysis revealed that age (OR=1.12, 95% CI: 1.04–1.21, p=0.003) and lower fat-free mass index (OR=0.78, 95% CI: 0.66–0.91, p=0.001) were significantly associated with sarcopenia, whereas BDNF, GDNF, and CAF22 showed no independent association with sarcopenia. Conclusions 1. No statistically significant sex-specific differences were observed in serum BDNF, GDNF, and CAF22 levels between sarcopenic and non-sarcopenic older adults (all p>0.05). 2. CAF22 levels were weakly negatively correlated with muscle mass and fat-free mass index in males (p<0.05), whereas BDNF levels showed weak positive correlations with handgrip strength and fat-free mass index in females (p<0.05). CAF22 levels also demonstrated a weak positive correlation with age (p<0.05). 3. ROC and multivariable logistic regression analyses indicated limited diagnostic utility of BDNF, GDNF, and CAF22 for sarcopenia (AUC=0.508–0.542), and none of these biomarkers showed an independent association with sarcopenia (all p>0.05). In contrast, older age (OR=1.12, p=0.003) and lower fat-free mass index (OR=0.78, p=0.001) were significantly associated with sarcopenia.

Background: Teaching is a profession associated with high levels of stress. Research has shown that when job demands are high while organizational support is insufficient, levels of stress and burnout increase. Aim: Prior to conducting our study aimed at determining the stress levels among university faculty members, we performed a literature review. Materials and Methods: We conducted a literature search using the PubMed and Embase databases for studies published in English without restriction on publication year, including all relevant articles available up to September 2025. The search strategy included the following keywords: “stress,” “researchers,” “university professors,” “academic researchers,” “college,” “academics,” “scholars,” “teachers,” and “professors.” Results: We searched for English-language publications in the PubMed and Embase databases without year restrictions, including studies published up to September 2025. The search was conducted using the following mesh terms: “stress,” “researchers,” “university professors,” “academic researchers,” “college,” “academics,” “scholars,” “teachers,” and “professors.” A total of 3,961 articles were identified from the database search. After screening titles, 2,944 articles were excluded. Subsequently, 58 articles were reviewed based on their abstracts, and 12 studies that were relevant to our research objectives and questionnaire were selected for analysis. Conclusion This review study demonstrated that stress levels among university and college faculty members are high. Stress is associated with multiple factors, including workload, organizational support, and performance demands, leading to negative physical and psychological consequences. Therefore, it is necessary to implement systematic institutional-level interventions to support the well-being of faculty members.

Background: Congenital syphilis is an infection caused by transplacental transmission of Treponema pallidum from mother to fetus and remains a significant cause of neonatal morbidity and mortality worldwide. Clinically, it is a multisystem disease with manifestations resembling those of TORCH infections and may also occur as a co-infection with these pathogens. Case Presentation: We report a case of a 28-year-old woman with a prior history of syphilis who had been treated and followed up, presenting with preterm delivery at 31–32 weeks of gestation during her third pregnancy. The neonate subsequently died due to complications associated with a concomitant perinatal infection. Conclusion Early screening during pregnancy, appropriate follow-up, identification of reinfection, and timely treatment remain key strategies for reducing the burden of congenital syphilis.

Background: Population health literacy constitutes a comprehensive competency to access, understand, appraise, and apply health information, serving as a foundational prerequisite for disease prevention frameworks. Within primary healthcare settings, nursing counseling plays a pivotal role in fostering healthy behaviors among citizens; however, there remains a critical need to quantitatively evaluate the correlation between these behavioral modifications and health literacy, thereby establishing an evidence-based analysis. Aim: To assess the level of health literacy and healthy lifestyle habits among citizens, and to evaluate their correlation with nursing counseling and support. Materials and Methods: A cross-sectional analytic study was conducted between August and September 2026, enrolling 145 citizens aged 18 and older who received care at the Tsetsekhairkhan Family Health Center of Songinokhairkhan district, Ulaanbaatar city and Telmen Soum Health Center in Zavkhan Province. HL was evaluated using the Mongolian version of the HLS-EU-Q16 questionnaire, and total scores were calculated. Nursing involvement was evaluated via a structured questionnaire. Data analysis was performed using SPSS 21.0, utilizing logistic regression analysis to determine Odds Ratios (OR) and p-values. Results: The mean HLS-EU-Q16 score of the participants (n=145) was 10.4±3.2. Regarding HL level distribution, 42.1% (n=61) were inadequate, 34.5% (n=50) were problematic/limited, and 23.4% (n=34) possessed sufficient health literacy. Participants perceived nursing support as highly influential (58.8%) and moderately influential (29.8%) in adopting healthy habits. Regular recipients of health counseling from nurses demonstrated a 3.79-fold higher likelihood of actively pursuing a healthy lifestyle (OR=3.79, 95% CI: 1.84–7.81, p=0.001) and a 3.05-fold higher likelihood of implementing the counseling into practice (OR=3.05, 95% CI: 1.35–6.88, p=0.0076). Conclusion Although citizens exhibit a limited capacity to independently search for and appraise health information—reflecting an overall inadequate level of HL—nurse-led counseling and support exert a direct, statistically significant positive impact on enhancing healthy behaviors among citizens.

Background: Diabetes mellitus is a chronic metabolic disorder with an increasing global prevalence. Early detection and risk assessment are essential for preventing disease-related complications. In recent years, machine learning methods have been increasingly investigated for predicting disease status using medical data and supporting clinical decision-making. Aim: This study aimed to develop a predictive model for diabetes status using the Random Forest classification algorithm based on the PIMA Indians Diabetes Database and to evaluate its classification performance. Materials and Methods: The study used the PIMA dataset, comprising 768 observations, eight predictor variables, and one target variable. Zero values in selected measurements that were physiologically implausible were treated as missing values and imputed using the median. The dataset was split into training and test sets at an 80:20 ratio. Hyperparameters of the Random Forest model were optimized using 10-fold cross-validation on the training data. Model performance was evaluated using Accuracy, Sensitivity, Specificity, Balanced Accuracy, Cohen’s Kappa, and the area under the receiver operating characteristic curve (ROC-AUC). Variable importance was also assessed. All statistical analyses and machine learning procedures were performed in the R programming environment. Results: Cross-validation identified mtry=4 as the optimal configuration, achieving an Accuracy of 78.05% and a Cohen’s Kappa of 0.5062. On the test set, the model achieved an Accuracy of 72.55%, Sensitivity of 58.49%, Specificity of 80.00%, Balanced Accuracy of 69.25%, and Cohen’s Kappa of 0.3883. The area under the ROC curve (AUC) was 0.7802, indicating that the model could distinguish between individuals with and without diabetes better than random classification. Variable importance analysis identified blood glucose level as the most influential predictor, followed by body mass index (BMI), age, and number of pregnancies. Conclusion The Random Forest algorithm demonstrated the potential to predict diabetes status using the PIMA dataset; however, its sensitivity on the test set was relatively low. Therefore, the model should be considered a potential tool for preliminary risk assessment and supporting early detection rather than a replacement for clinical diagnosis. Further research is needed to improve sensitivity, compare the model with other machine learning algorithms, and validate its performance using larger and more diverse datasets.