Category: Nevin Manimala

J Biomed Sci. 2025 May 21;32(1):50. doi: 10.1186/s12929-025-01137-7.

ABSTRACT

BACKGROUND: Type 2 diabetes is an increasingly prevalent metabolic disorder with moderate to high heritability. Glycemic indices are crucial for diagnosing and monitoring the disease. Previous genome-wide association study (GWAS) have identified several risk loci associated with type 2 diabetes, but data from the Taiwanese population remain relatively sparse and primarily focus on type 2 diabetes status rather than glycemic trait levels.

METHODS: We conducted a comprehensive genome-wide meta-analysis to explore the genetics of glycemic traits. The study incorporated a community-based cohort of 145,468 individuals and a hospital-based cohort of 35,395 individuals. The study integrated genetics, transcriptomics, biological pathway analyses, polygenic risk score calculation, and drug repurposing for type 2 diabetes.

RESULTS: This study assessed hemoglobin A1c and fasting glucose levels, validating known loci (FN3K, SPC25, MTNR1B, and FOXA2) and discovering new genes, including MAEA and PRC1. Additionally, we found that diabetes, blood lipids, and liver- and kidney-related traits share genetic foundations with glycemic traits. A higher PRS was associated with an increased risk of type 2 diabetes. Finally, eight repurposed drugs were identified with evidence to regulate blood glucose levels, offering new avenues for the management and treatment of type 2 diabetes.

CONCLUSIONS: This research illuminates the unique genetic landscape of glucose regulation in Taiwanese Han population, providing valuable insights to guide future treatment strategies for type 2 diabetes.

PMID:40399988 | DOI:10.1186/s12929-025-01137-7

BMC Nurs. 2025 May 21;24(1):571. doi: 10.1186/s12912-025-03199-2.

ABSTRACT

BACKGROUND: Digital competence is increasingly crucial for academic success and lifelong learning, especially in health education fields such as nursing. However, limited research examines the relationship between digital competence, academic motivation, and lifelong learning among nursing students.

AIM: To assess the relationship between digital competence, academic motivation, and lifelong learning among undergraduate nursing students and explore the mediating role of academic motivation in this relationship.

METHODS: A descriptive comparative cross-sectional study was conducted, guided by the STROBE guidelines. Using systematic random sampling, 500 undergraduate nursing students were selected from Mansoura University, Egypt. Data were collected from July to August 2024 using three validated scales: the Students’ Digital Competence Scale, the Lifelong Learning Scale, and the Academic Motivation Scale. Descriptive statistics, Pearson correlation, and multiple regression analysis were used to analyze the data.

RESULTS: The results showed a strong positive correlation between digital competence and academic motivation (r = 0.53, p < 0.001), as well as between digital competence and lifelong learning (r = 0.61, p < 0.001). Students with higher digital competence scores also had significantly higher academic motivation (4.21 ± 0.45) and lifelong learning tendencies (4.37 ± 0.48). Multiple regression analysis confirmed that digital competence significantly predicted both academic motivation (β = 0.38, p < 0.001) and lifelong learning (β = 0.44, p < 0.001).

CONCLUSION: Digital competence significantly enhances academic motivation and promotes lifelong learning among nursing students. The findings emphasize the need for nursing curricula to integrate digital competence training to improve educational outcomes and prepare students for future challenges in healthcare.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:40399954 | DOI:10.1186/s12912-025-03199-2

BMC Oral Health. 2025 May 21;25(1):745. doi: 10.1186/s12903-025-06088-x.

ABSTRACT

BACKGROUND: Previous studies have assessed the upper airway using various examination methods, such as cephalometric imaging and magnetic resonance imaging (MRI). However, there is a significant gap in the research regarding the relationship between these different imaging modalities. This study compares airway assessments using kinetic MRI and cephalometric scans, examining their correlation with three dimensional (3D) MRI data.

MATERIALS AND METHODS: Kinetic MRI, cephalometric scans, and 3D MRI of forty-seven participants were used in the present study. Airway areas and widths were measured at the retropalatal, retroglossal, and hypopharyngeal levels in both kinetic MRI and cephalometric scans. Airway volumes were calculated from 3D MRI data. Statistical analyses, including the Wilcoxon Signed Rank test, Spearman correlation, and multiple linear regression, were performed to evaluate the data and identify significant differences, correlations, and prediction models, respectively.

RESULTS: Significant differences were found between kinetic MRI and cephalometric scans. Cephalometric data showed larger airway areas and widths compared to kinetic MRI measurements. Although both cephalometric and kinetic MRI showed a correlation with 3D MRI, kinetic MRI demonstrated stronger correlations with 3D MRI airway volumes than cephalometric scans. According to our linear regression model equations, RPA-Max (maximum retropalatal airway area) and RPA (retropalatal airway area) can elucidate variations in RPV (retropalatal airway volume). RGA-Med (median retroglossal airway area) and RGA-Min (minimum retroglossal airway area) can explain variations in RGV (retroglossal airway volume). HPA (hypopharyngeal airway area) and ULHPAW-Max (maximum upper limit hypopharyngeal airway width) account for variations in HPV (hypopharyngeal airway volume). Additionally, TA-Max (maximum total airway area) can account for variations in TPV (total pharyngeal airway volume).

CONCLUSION: Both cephalometric data and kinetic MRI data showed correlations with 3D MRI data. The shared posture of kinetic MRI and 3D MRI led to stronger correlations between these two modalities. Although cephalometric data had fewer correlations with 3D MRI and predictors for 3D airway volume, they were still significant. Our study highlights the complementary nature of kinetic MRI and cephalometric imaging, as both provide valuable information for airway assessment and exhibit significant correlations with 3D MRI data.

PMID:40399950 | DOI:10.1186/s12903-025-06088-x

Biomed Eng Online. 2025 May 21;24(1):63. doi: 10.1186/s12938-025-01374-9.

ABSTRACT

OBJECTIVE: The purpose of this study is to investigate the influence of nano-hydroxyapatite (nano-HA) filling on the restoration of masticatory function and the modulation of inflammatory factors within gingival sulcular fluid in patients suffering from periapical inflammation.

METHODS: 98 patients with periapical inflammation were selected and divided into a control group and a nano group using the red blue ball method, with 49 cases in each group. The control group was treated with conventional root canal therapy only, and the nano group underwent endodontic treatment with the nano-HA filling method. Gingival fluid samples were collected from all patients at enrollment, 1 week, and 3 months postoperatively to analyze the levels of interleukin- 1β (IL- 1β) and tumor necrosis factor-α (TNF-α). At the time of enrollment and 3 months after surgery, patients were monitored for bite force, masticatory efficiency, and clinical efficacy.

RESULTS: In this study, compared to the control group, the experimental group treated with nano-HA filling showed significantly better improvement in bite force and masticatory efficiency, with the difference being statistically significant (P < 0.01). Moreover, the experimental group significantly inhibited the expression of inflammatory factors such as IL- 1β and TNF-α, with a continuous decrease in their levels over time. In terms of filling effect, healing rate, and total efficacy rate, the experimental group also achieved superior results compared to the control group, with the difference being statistically significant (P < 0.05). There was a negative correlation between the application of nano-HA fillers and the gingival sulcus inflammatory factor at 1 week postoperatively and 3 months postoperatively.

CONCLUSION: In comparison with conventional restorative materials, nano-HA restorative has been shown to possess several notable advantages. These include the promotion of recovery of masticatory function, the regulation of inflammatory factor expression in gingival sulcular fluid, and the enhancement of clinical efficacy and filling effect. This study provides a theoretical basis for the clinical promotion of the use of nano-HA restorative materials in the treatment of periapical inflammation.

PMID:40399943 | DOI:10.1186/s12938-025-01374-9

Genome Biol. 2025 May 21;26(1):135. doi: 10.1186/s13059-025-03576-9.

ABSTRACT

Technological advances have enabled us to profile multiple omics layers with spatial information, significantly enhancing spatial domain detection and advancing a variety of biomedical research fields. Despite these advancements, there is a notable lack of effective methods for modeling spatial multi-omics data. We introduce SMOPCA, a Spatial Multi-Omics Principal Component Analysis method designed to perform joint dimension reduction on multimodal data while preserving spatial dependencies. Extensive experiments reveal that SMOPCA outperforms existing single-modal and multimodal dimension reduction and clustering methods, across both single-cell and spatial multi-omics datasets derived from diverse technologies and tissue structures.

PMID:40399936 | DOI:10.1186/s13059-025-03576-9

BMC Oral Health. 2025 May 21;25(1):755. doi: 10.1186/s12903-025-06075-2.

ABSTRACT

INTRO: The aim of this study was to evaluate the incidence, severity, duration of oral manifestations in individuals with Coronavirus Disease 2019 (COVID-19) and the association of these manifestations with the severity of COVID-19 and the patient’s oral hygiene.

METHODS: This study included 820 patients with confirmed COVID-19. A questionnaire form including oral hygiene habits, the severity of Covid-19, the presence, severity and durations of oral manifestations was prepared, and a web-based survey was performed using Google-forms. Obtained data was analysed with Pearson chi-square and Fisher’s exact tests with statistical significance set at P < 0.05.

RESULTS: The most commonly reported manifestations were taste dysfunction (63.4%), xerostomia (59.9%), halitosis (31.1%), dysphagia (27.8%), hypersensitive teeth (27.2%) and gingival bleeding (14.3%). The incidence of the oral manifestations was found significantly associated with severity of COVID-19 (P = 0.000 V = 0.151), presence of systemic diseases (P = 0.034, V = 0.074) and age (P = 0.023, V = 0.099). Tooth brushing decreased the incidence of aphthous like lesions of tongue during Covid-19 (p < 0.05).

CONCLUSION: Maintenance of oral hygiene was associated with a reduced incidence of aphthous-like lesions, underscoring the protective role of routine oral care. These findings highlight the need to integrate oral health assessment and hygiene education into COVID-19 management protocols, which may also be important for potential future pandemics.

PMID:40399926 | DOI:10.1186/s12903-025-06075-2

Nutr Metab (Lond). 2025 May 21;22(1):46. doi: 10.1186/s12986-025-00942-z.

ABSTRACT

BACKGROUND: The prognostic value of the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) remains unclear. This study aimed to evaluate the associations between the NHHR and all-cause and cause-specific mortality in patients with MASLD.

METHODS: Data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES III and the National Death Index (NDI). The NHHR was calculated according to the formula. The results of mortality associated with the NDI were recorded as of December 31, 2019. We used a multivariate Cox proportional hazard model and restricted cubic spline (RCS) regression to assess the associations between the NHHR and all-cause and cause-specific mortality. In addition, subgroup analyses were performed to explore the relationships between the NHHR and all-cause and cause-specific mortality.

RESULTS: This study included 3155 patients with a definite diagnosis of MASLD. A total of 1,381 (43.8%) patients with MASLD died, and 1,774 (56.2%) survived. Multivariate Cox proportional hazards model analysis showed that NHHR was not significantly associated with all-cause mortality in MASLD patients. The RCS curve showed a significant nonlinear trend between the NHHR and all-cause mortality in patients with MASLD. Subgroup analysis revealed that the NHHR was better suited to predict cardiovascular mortality in patients without advanced fibrosis.

CONCLUSIONS: Our study revealed the clinical value of the NHHR in the prediction of mortality in the MASLD population. The NHHR can be used as a biomarker for follow-up in people without advanced fibrosis.

PMID:40399925 | DOI:10.1186/s12986-025-00942-z

Cardiovasc Diabetol. 2025 May 21;24(1):220. doi: 10.1186/s12933-025-02768-8.

ABSTRACT

BACKGROUND AND OBJECTIVE: Atherogenicity indices have emerged as promising markers for cardiometabolic disorders, yet their relationship with prediabetes risk remains unclear. This study aimed to comprehensively evaluate the associations between six atherogenicity indices and prediabetes risk in a Chinese population, and explore the predictive value of these atherosclerotic parameters for prediabetes.

METHODS: This retrospective cohort study included 97,151 participants from 32 healthcare centers across China, with a median follow-up of 2.99 (2.13, 3.95) years. Six atherogenicity indices were calculated: Castelli’s Risk Index-I (CRI-I), Castelli’s Risk Index-II (CRI-II), Atherogenic Index of Plasma (AIP), Atherogenic Index (AI), Lipoprotein Combine Index (LCI), and Cholesterol Index (CHOLINDEX). To address the natural relationships between the atherogenicity indices and risk of prediabetes, we applied Cox proportional hazards regression with cubic spline functions and smooth curve fitting, using a recursive algorithm to calculate inflection points. Machine learning approach (XGBoost and Boruta methods) to address the high collinearity among indices and assess their relative importance, combined with time-dependent ROC analysis to evaluate the predictive performance at 3-, 4-, and 5-year follow-up.

RESULTS: During follow-up, 11,199 participants developed prediabetes (incidence rate: 3.71 per 100 person-years). Significant nonlinear associations were observed between all atherogenicity indices and prediabetes risk. Through Z-score standardization of atherogenicity indices and comprehensive Cox proportional hazards regression and advanced machine learning techniques, we identified AIP as the most significant predictor of prediabetes [HR = 1.057 (95% CI 1.035-1.080, P < 0.0001)], with LCI emerging as a secondary important marker [HR = 1.020 (95% CI 1.002-1.038, P = 0.0267)]. Our innovative XGBoost and Boruta analysis uniquely validated these findings, providing robust evidence of AIP and LCI’s critical role in prediabetes risk assessment. Time-dependent ROC analysis further validated these findings, with LCI and AIP demonstrating comparable discrimination, with overlapping AUC ranges of 0.5952-0.6082. Notably, the combined indices model achieved enhanced predictive performance (AUC: 0.6753) compared to individual indices, suggesting the potential benefit of using multiple atherogenicity indices for prediabetes risk prediction.

CONCLUSION: This study identifies statistically significant associations between atherogenicity indices and prediabetes risk, highlighting their nonlinear relationships and combined effects. While the predictive performance of these indices is modest (AUC 0.55-0.68), these findings may contribute to improved risk stratification when incorporated into comprehensive assessment strategies.

PMID:40399916 | DOI:10.1186/s12933-025-02768-8

Arthritis Res Ther. 2025 May 21;27(1):111. doi: 10.1186/s13075-025-03576-x.

ABSTRACT

BACKGROUND: To develop a predictive model for bone destruction in patients with rheumatoid arthritis (RA), based on the characteristics of plasma metabolites and common clinical indicators.

METHODS: The cohort comprised 60 patients with RA, with baseline metabolite features identified using the liquid chromatograph-mass spectrometer system. Radiographic outcomes were assessed using the van der Heijde-modified total Sharp score (mTSS) following a one-year follow-up period to quantify bone destruction. The longitudinal association between metabolites and radiographic progression was analyzed using several machine learning algorithms, and the significance of core metabolites was calculated. A new model incorporating metabolites and clinical indicators was created to evaluate its predictive performance for radiographic progression; the model was compared with other prediction models.

RESULTS: The median increase in mTSS was 3.50. Of the 774 detected metabolites, 77 differed between patients with different outcomes. Core metabolites identified using the Gaussian Naive Bayes algorithm included mangiferic acid, O-acetyl-L-carnitine, 5,8,11-eicosatrienoic acid, and 16-methylheptadecanoic acid. A standardized bone erosion risk score (BERS) was developed based on these core metabolite features for assessing the radiographic progression outcome. Individuals with a high BERS exhibited a lower risk of rapid radiographic progression than those with a lower score (OR = 0.01, 95% CI = 0.01-0.03, P = 0.003). The “China-Japan Friendship Hospital-BERS Model” (CjBM), combining BERS with clinical features (methotrexate and C-reactive protein), produced an area under the receiver operating characteristic curve of 0.800. Moreover, compared with the reported models, the CjBM showed near statistical significance in identifying rapid radiographic progression; adding BERS can improve the discrimination of the original reported model (P_DeLong=0.035).

CONCLUSIONS: The CjBM was developed for early prediction of bone destruction in patients with RA, and the evaluation of BERS emphasizes the significance of metabolite features.

PMID:40399914 | DOI:10.1186/s13075-025-03576-x

BMC Oral Health. 2025 May 21;25(1):748. doi: 10.1186/s12903-025-06124-w.

ABSTRACT

BACKGROUND: This study evaluated the penetration, interfacial adaptation, and push-out bond strength of four bioceramic-based root canal sealers (iRoot SP, Well-Root ST, C-Root SP, and KP-Root SP).

METHODS: A total of ninety mandibular first premolar teeth were used in this study, with eighty teeth randomly divided into eight groups (n = 10). Four groups were designated for sealer penetration analysis, using each of the four sealers mentioned above mixed with 0.1% rhodamine B and applied using the single-cone technique. Horizontal root sections were prepared at 2 mm (apical), 5 mm (middle), and 8 mm (coronal) from the root apex, resulting in a total of 120 slices. Penetration was evaluated using confocal laser scanning microscopy. The other four groups were used for marginal adaptation analysis, with the same sealers applied without rhodamine B, and adaptation was assessed using scanning electron microscopy on sections prepared at the same depths. The remaining ten teeth were used to evaluate push-out bond strength, with 30 dental slices prepared from the middle third, each drilled with four 1 mm diameter holes and randomly filled with one of the four sealers; bond strength was measured using a universal testing machine.

RESULTS: There was no statistically significant difference in the depth and circumference of dentin tubule penetration between different materials (P > 0.05). However, the coronal third was significantly higher than the apical third (P < 0.001). For iRoot SP, the percentage of dentin tubule penetration circumference at the middle third was significantly higher than that at the apical third (P < 0.05). Additionally, Well-Root ST demonstrated superior adaptability for interfacial adaptation than C-Root SP at all the sites (P < 0.05). However, the adaptability of iRoot SP was superior to C-Root SP at the coronal and middle thirds (P < 0.05). Moreover, the push-out bond strength conformed to the following order: Well-Root ST > iRoot SP > KP-Root SP > C-Root SP, with notable variations (P < 0.05).

CONCLUSION: The Well-Root ST sealer demonstrated the best interface adaptation and push-out bonding strength, as well as iRoot SP showed better permeability.

PMID:40399906 | DOI:10.1186/s12903-025-06124-w