Background: Renal cell carcinoma (RCC) accounts for 85–90% of all primary renal malignancies in adults and 1–3% of all cancers worldwide, with incidence rising steadily year by year. Clear cell (ccRCC), papillary (pRCC), and chromophobe (chRCC) subtypes differ significantly in genetic alterations, biological behavior, and treatment response, making preoperative subtype identification clinically important. Conventional qualitative CT assessment is limited, and contrast-enhanced CT-based radiomics may represent a novel diagnostic approach. Aim: To develop a model for preoperative differentiation of ccRCC from non-ccRCC subtypes using contrast-enhanced CT-based radiomics, and to evaluate its diagnostic performance through internal and external validation. Materials and Methods: A retrospective study was conducted on 121 patients with histopathologically confirmed RCC treated at the Urology–Andrology Center of the First Central Hospital of Mongolia between 2021 and 2024. Patients were categorized into ccRCC (n=84) and non-ccRCC (n=37) groups. Tumor segmentation was performed on corticomedullary phase CT images using 3D Slicer software, and a total of 122 radiomic features were extracted, including first-order uniformity, gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), shape flatness, and GLCM maximum probability. Diagnostic performance was assessed by ROC analysis, and model generalizability was evaluated through external validation (n=73). Results: GLRLM demonstrated a statistically significant difference between the ccRCC (mean 0.1230) and non-ccRCC (mean 0.0170) groups (p<0.001). ROC analysis yielded an optimal cut-off value of 0.1493 for GLRLM, with an internal validation AUC of 0.9103, sensitivity of 83.3%, and specificity of 92.3% (p<0.01). External validation (n=73) achieved an AUC of 0.860 and an accuracy of 86.0%, confirming the model’s generalizability. First-order uniformity (AUC=0.880) and variance (AUC=0.862) showed comparable discriminative performance to GLRLM, whereas flatness (AUC=0.584) and GLCM Imc2 (AUC=0.621) demonstrated relatively poor discriminative ability. Confusion matrix analysis yielded an accuracy of 86.0% in both internal and external validation cohorts. Conclusion Non-invasive CT-based radiomics demonstrates the ability to accurately differentiate RCC subtypes preoperatively with high precision (AUC=0.92), as confirmed by both internal and external validation.

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.

Abstract Secondary metabolites present in the plants are predominantly responsible for treating various ailments. Secondary metabolites are also called as plant constituents or natural compounds which exert significant pharmacological and toxicological effects in humankind. The chemical compounds present in the plant sources are categorized as primary and secondary metabolites based on the chemical structure and biosynthetic derivation. These substances are called plants secondary metabolites. One of the most important of secondary metabolites is Tannins. Tannins are phenolic compounds, secondary metabolites of plants widespread in terrestrial and aquatic natural environments. As they can be found in plenty of plants and herbs, they have been traditionally used for medicinal and other purposes, such as the leather industry. This fact is explained by the fact that they exert plenty of different biological activities and, thus, they entail a great potential to be used in the food, nutraceutical, and pharmaceutical industry.
Among the biological activities of tannins, the most relevant ones are antioxidant, anti-inflammatory, anti-diabetic, cardioprotective, healing and antimicrobial (antiviral and antibacterial)
Consequently, this review article is directed towards the description of the biological activities exerted by tannins as they could be further extracted from by-products of the agri-food industry to produce high-added-value products.