Background: Maintaining the concentration of a drug in the blood at a therapeutic level is crucial for enhancing treatment efficacy and reducing adverse effects. Conventional dosage forms are rapidly absorbed, causing a quick rise in the drug concentration in the blood, but it soon declines. As a result, frequent dosing is required in chronic or acute conditions. This can lead to drug concentrations exceeding therapeutic levels, increasing toxicity, or failing to reach the required level. To address these limitations, drug delivery systems (DDS) have been intensively developed over the past few decades. Among these systems, microencapsulation technology enables targeted delivery of the drug within the body, maintains stability, controls release, improves bioavailability, reduces side effects, and protects the active pharmaceutical ingredient from degradation or elimination, delivering it specifically to the necessary tissues. These characteristics provided the rationale for conducting this study. Aim: Select a suitable polymer for microcapsule formation, develop a production technology using the emulsion solvent evaporation method, and evaluate certain morphological characteristics of the resulting microcapsules. Materials and Methods: The microcapsules were prepared using an O/W emulsion solvent evaporation method. Dichloromethane (DCM) was used as the solvent, and the wall materials were solutions of chitosan (2% w/v) and gelatin (5% w/v), which are polymers combining biodegradability and biocompatibility. These solutions were mixed in a total of eight different ratios and emulsified into an aqueous phase containing polyvinyl alcohol (PVA, 1% w/v) using a high-speed homogenizer at 8000–10000 rpm to form the emulsion, followed by solvent evaporation. The resulting microcapsules were freeze-dried to obtain dry microcapsule forms. The morphology and surface structure of the microcapsules were characterized using light microscopy and scanning electron microscopy (SEM), while their size and distribution were analyzed using the ImageJ software. Result: To determine the optimal wall material for the microcapsules, a total of eight formulation ratios were prepared, and the emulsion stability and viscosity were evaluated. Chitosan-dominant formulations (F1, F7, F8) exhibited excessively high viscosity and a tendency for the system to thicken, which reduced the uniformity and stability of the emulsion. In contrast, formulations with low or imbalanced gelatin content (F2, F3) failed to achieve complete emulsion stabilization and showed significant phase separation. The F5 formulation demonstrated the best stability, minimal phase separation, and appropriate viscosity, making it the optimal choice for microcapsule wall preparation. The chitosan-to-gelatin ratio of 1:2 (F5 formulation) was found to be the most suitable in terms of complete microcapsule formation, spherical shape, uniform surface, and proper viscosity. Microcapsules of the F5 formulation had an average diameter of 290±30 µm, were spherical, and maintained shape stability even after freeze-drying. A microcapsule preparation technology based on the emulsion method was developed experimentally, and the most suitable conditions were established. Conclusion 1. The results of the study indicated that a chitosan-to-gelatin ratio of 1:2 was the most suitable formulation for microcapsules in terms of complete formation, spherical shape, and uniform surface characteristics. 2. When the gelatin content increased, microcapsule morphology deteriorated and aggregation tendencies were observed, whereas higher chitosan content increased the viscosity of the solution, which affected the stability of the emulsion and resulted in non-uniform microcapsule sizes. 3. The 1:2 chitosan-gelatin ratio is therefore crucial for microcapsule preparation, and this wall composition can be applied in further studies involving drug-loaded microcapsules.

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: The human head is an integrated biological structure composed of the brain, cranial bones, facial structures, neuromuscular systems, and sensory organs, all interconnected through coordinated signaling. The relationship between brain development, function, and craniofacial growth is most evident after birth, during which the head increases approximately fourfold in size before adulthood. Brain morphology and volume are associated not only with neurological disorders but also with cognitive and behavioral characteristics. For example, children with holoprosencephaly often present with varying degrees of facial anomalies, while studies have shown that adolescents with malocclusion exhibit higher levels of anxiety, depression, and psychological stress. With the increasing availability of magnetic resonance imaging (MRI), it has become an essential diagnostic tool in clinical practice. MRI is particularly advantageous in pediatric populations due to the absence of ionizing radiation and its superior soft tissue contrast. Recent advances in image processing techniques have enabled comprehensive analysis of both brain structures and craniofacial morphology from a single MRI dataset, forming the basis of this study. Aim: To investigate the relationship between brain tissue volumes and maxillary hypoplasia using MRI-based voxelbased morphometry in children with neurological conditions. Materials and Methods: This study was conducted at the School of Biomedicine, Mongolian National University of Medical Sciences, in collaboration with Chifeng University, as part of the project titled “Investigation of the relationship between craniofacial anomalies and brain disorders using MRI”, funded by the Ministry of Education and Science. Voxel-based morphometry (VBM) analysis was performed on brain MRI scans of children aged 9–11 years. Results: Among the 25 children included in the study, 17 (68%) were male. The youngest average age was observed in the maxillary hypoplasia group (8.4±0.95 years). The signal-to-noise ratio (SNR) of MRI was lowest in the non-temporal epilepsy group (41.1±1.35) and higher in the maxillary hypoplasia group (49.6±8.22). Both epilepsy and maxillary hypoplasia groups demonstrated physiological asymmetry of the cerebral hemispheres, with left parietal lobe dominance (−0.1% and −0.3 cm, respectively). Right frontal lobe dominance (+1.1%) was observed only in the epilepsy group. Conclusion Children aged 9–11 years with maxillary hypoplasia showed a reduction in both gray and white matter volumes of the brain. These findings highlight the importance of early diagnosis of neurodevelopmental disorders affecting odontogenesis and emphasize the need for a multidisciplinary team-based management approach.

Background: The aging of the world’s population will determine global health trends. According to the 2021 report of the Capital City Health Department, the average life expectancy of the Mongolian population is 71.3 years (male 67.3, female 76.7), the difference between male and female life expectancy is 9.4 years, and elderly people aged 60 and over account for 8.1% of the total population. The report also shows that 6.5% of all outpatient visits are for people aged 60-64, and 9% are for people aged 65 and over, which means that they do not receive adequate health care services. Therefore, it is important to increase the access to and quality of health care services provided to the elderly in order to improve their health and quality of life. Comorbidities that are common among the elderly are one of the pressing issues in the health sector. Aim: We aimed to study the prevalence and risk factors of multi-morbidities among older adults (aged sixty years and over) in urban and rural areas. Materials and Methods: To accomplish our aim, we conducted 156 lifestyle related questionnaires and 18 health related questionnaires among 720 older people in Ulaanbaatar city and rural areas, and created the database. Pearson correlation coefficient was used to determine the relationship between the quantitative influence of factors using single and multi-factor linear (β-coefficient) and binary logistic regression (odds ratio, CI 95%) methods, and p value less than 0.05 was considered statistically significant. The Apriori algorithm in SPSS was used to determine the relationship between multiple chronic diseases in the elderly people. Results: The prevalence of comorbidity was higher in urban areas (48.1%) and rural areas (51.9%), and ageing (urban areas OR: 2.45, 95% CI: 0.9-6.2; rural areas OR: 6.35, 95% CI: 1.47-27.4, P<0.01) was a risk factor of multi-morbidities. Multimorbidity is defined as the presence of 2 or more chronic conditions, and 3, 4, and 5 chronic conditions were co-occurred to older adults with chronic conditions, 28.7% (165). 11 common patterns of relationships in urban areas and 18 common patterns of relationships in rural areas (support (A→B)>3%, confidence (A→B)>30%, lift (A→B)>1) were determined. Conclusion Multimorbidity was different in urban and rural areas, 11 common patterns in urban areas and 18 common patterns in rural areas were determined. It has shown that the prevalence of multimorbidity was different in urban and rural areas.