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Association between idiopathic scoliosis and facial asymmetry: a gender-balanced case–control study
No full textAbstract This study aimed to investigate the association between idiopathic scoliosis (IS) and facial asymmetry in a gender-balanced case-control design. Based on power analysis, 100 participants were recruited (50 IS patients: 25 male, 25 female; 50 controls: 25 male, 25 female). Facial asymmetry was evaluated through clinical examination and frontal cephalometric analysis. Statistical analysis was performed using chi-square tests and logistic regression, with post-hoc power calculation. Facial asymmetry was present in 41 (82%) of IS patients compared to 18 (36%) of controls (p < 0.001). The achieved power was 99% for detecting this difference. Among IS patients, 21 males (84%) and 20 females (80%) exhibited facial asymmetry. In controls, facial asymmetry was observed in 10 males (40%) and 8 females (32%). The odds ratio for facial asymmetry in IS patients was 7.64 (95% CI 3.02–19.32) compared to controls. Idiopathic scoliosis is significantly associated with facial asymmetry, with no substantial gender difference in this association. The high statistical power confirms the reliability of these findings
A multimodal retinal aging clock for biological age prediction and systemic health assessment via OCT and fundus imaging
No full textAbstract Herein we developed age clocks that predict biological age from fundus photography and optical coherence tomography. We evaluated our multimodal models’ clinical relevance by examining their associations between predicted biological age and the Charlson Comorbidity Index (CCI). Study 1 assessed how models trained on normal eyes generalize to diseased eyes, and Study 2 tested whether incorporating disease labels improves performance and systemic associations. Models were fine-tuned to the imaging dataset to predict biological age. Linear regressors were trained on chronological and biological features to infer CCI. Gradient-weighted regression activation mapping also generated heatmaps to identify the model’s region of focus. Prediction performance improved when trained on both normal and diseased eyes. Predicted biological age showed significantly stronger correlations with CCI than chronological age across both studies, supporting our algorithm’s association with this validated measure of mortality. Thus, our algorithm may provide insight into systemic health burdens beyond that of traditional risk assessments
R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data
No full textAbstract Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often constrained by limited availability of labeled corpora and the high computational demands of transformer-based approaches. To address these challenges, we propose a Residual Graph Attention Network (R-GAT) that integrates multi-head attention with residual connections to capture semantic and relational dependencies in biomedical texts. Evaluated on a curated dataset of 1,875 PubMed abstracts spanning thyroid, colon, lung, and generic cancer topics, R-GAT achieves stable and competitive performance (macro-F1: 0.96 ± 0.01), comparable to transformer-based models such as BioBERT and BioClinicalBERT and strong classical baselines like Logistic Regression, while requiring significantly fewer computational resources. Ablation studies confirm the importance of attention and residual connections in ensuring robustness under limited-data conditions. To support reproducibility and facilitate future research, we also release the curated dataset. Together, these contributions demonstrate the value of lightweight graph-based architectures as reliable and resource-efficient alternatives to computationally intensive transformers in biomedical NLP
Comparative analysis of ovine and human aortic valve tissue for bioprosthetic valve development using relaxation tests and numerical simulation
No full textAbstract As the demand for aortic valve prostheses grows, optimizing their mechanical performance and durability is essential. While mechanical valves offer longevity, their need for lifelong anticoagulation limits their use, making bioprosthetic valves a preferred alternative. However, bioprosthetic valves made from bovine pericardium face durability challenges due to structural degradation. Given that valve functionality is heavily influenced by the collagen architecture and mechanical properties of the tissue, selecting an optimal replacement is essential. This study evaluates treated ovine aortic valves as an alternative material, comparing their mechanical behavior to native human valves. Tensile tests showed an elastic modulus of 20.17 MPa for treated ovine leaflets, while human leaflets ranged from 6.15 MPa to 28.10 MPa. Stress relaxation tests indicated a 41% stress reduction in treated ovine valves compared to 21% in human valves after 300 s, suggesting greater viscoelasticity. Finite element analysis revealed lower peak systolic stress in treated ovine valves (0.36 MPa vs. 0.72 MPa in human valves), with stress distributions aligning with clinically observed degradation sites. These findings highlight ovine tissue’s potential for improved durability and flexibility, making it a strong candidate for next-generation bioprosthetic heart valves
The adaptive nature of the foam proteome produced by Mahanarva spectabilis (Hemiptera: Cercopidae) when infesting forage grasses with different levels of antibiosis-type resistance
No full textAbstract The spittlebug Mahanarva spectabilis (Distant, 1909) (Hemiptera: Cercopidae) produces a stable extracellular foam during its nymphal stage, which plays a critical role in survival and host plant interactions. In this study, we present the first comprehensive proteomic characterization of foam secreted by M. spectabilis nymphs, using a shotgun LC–MS/MS approach. We analyzed the foam produced by nymphs feeding on four forage cultivars showing different levels of antibiosis-type resistance against M. spectabilis, as follows: Cenchrus purpureus cv. Pioneiro (moderately resistant) and cv. Roxo de Botucatu (susceptible); Urochloa brizantha cv. Marandu (resistant), and Urochloa decumbens cv. Basilisk (susceptible). A total of 196 proteins were identified, including a substantial fraction of unannotated proteins with high abundance, suggesting specialized foam-specific functions. Functional annotation revealed enrichment in hydrolases, oxidoreductases, and binding proteins, highlighting potential roles in microbial regulation, stress response, and structural maintenance. Comparative analysis revealed consistent up-regulation of cytoskeletal and metabolic proteins in resistant/moderately resistant hosts, alongside repression of proteins related to carbohydrate and lipid metabolism. Multivariate and GO-based analyses confirmed host genotype-dependent modulation of foam composition. The findings demonstrate that higher levels of resistance to M. spectabilis are associated with two key strategies: (1) the suppression of metabolic pathways, likely limiting nutrient availability to the insect, and (2) the activation of defence-related proteins, such as antioxidant enzymes, which enhance the plant’s ability to cope with stress. These findings underscore the dynamic and adaptive nature of the foam proteome, reflecting both environmental and physiological constraints. Our results provide new insights into the molecular basis of foam function and its relevance for insect survival, offering promising avenues for the development of novel strategies targeting foam-mediated defence mechanisms
Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers
No full textAbstract Post-COVID-19 condition (PCC) manifests with prolonged, heterogeneous symptoms challenging both, diagnosis and therapeutic management. This three-year longitudinal study analyzed data from 93 adults (mean age of 48.9 ± 14.0, 60 female) after confirmed SARS-CoV-2 infection. Every follow-up visit included clinical, neuropsychological, and laboratory assessments, capturing multidimensional indicators of patient health. A machine learning framework was implemented to classify temporal stage of patient health status, identify visit-specific predictive markers, and manage incomplete data using both native handling in tree-based models and explicit imputation techniques. Gradient boosting methods consistently achieved the best performance across all visit comparisons, achieving F1-scores close to or above 90%. Classification performance improved with greater time intervals between visits, suggesting progressive divergence in patient phenotypes over time. For discriminating follow-up stages, inflammatory markers emerged as the most informative predictors, followed by SARS-CoV-2 antibody levels and neuropsychiatric measures for fatigue and cognitive performance. Interpretability analyses using SHAP and LIME confirmed the contribution of these features, while revealing shifts in feature relevance across years. These findings highlight the utility of machine learning in characterizing follow-up stage separability in PCC and offer clinically interpretable insights that prioritize immune and neuropsychological measures for monitoring and risk-stratified follow-up
Anticancer activity of curcumin loaded hybrid system of silver-amine functionalized silica nanoparticles
No full textAbstract Curcumin, a polyphenolic compound, has gained considerable attention in recent years as a therapeutic agent due to its ability to act at the cellular level and modulate multiple signaling pathways. However, its poor solubility, low bioavailability, and rapid metabolism limit its clinical applications for cancer treatment. The aim of the present study was to develop a novel hybrid nanocarrier system comprising curcumin-loaded silver–amine functionalized silica nanoparticles (CUR@Ag-AFS) for potential anticancer applications. Monodisperse silica nanoparticles were synthesized using a modified sol–gel method, functionalized with amine groups using 3-aminopropyltriethoxysilane (APTES), and decorated with silver ions to form a stable hybrid matrix. Curcumin extracted from Curcuma longa was effectively encapsulated within the nanocarrier, achieving a high encapsulation efficiency (77%) and drug loading capacity (25%). FTIR, XRD, SEM–EDS, TGA, DSC, and zeta potential analyses confirmed successful surface modification, uniform drug incorporation, and improved stability. In vitro release studies demonstrated sustained and pH-responsive release behaviour, with maximum cumulative release (91.6%) under acidic conditions (pH 5.5), indicating suitability for tumor-targeted delivery. Furthermore, the CUR@Ag-AFS system exhibited enhanced dose-dependent cytotoxicity against MCF-7 cancer cells compared to pure curcumin, due to the combined therapeutic actions of curcumin and silver species. These findings highlight the potential of the CUR@Ag-AFS hybrid system for sustained, pH-sensitive curcumin delivery, offering multifunctional therapeutic applications in cancer rehabilitation and disability management
Effect of sonication and protease inhibitors on Elisa quantification of selected proteins in bovine udder tissue homogenates
No full textAbstract The protocols for preparing ELISA samples vary in terms of the use of mechanical homogenization (MH) alone or the additional use of protease inhibitors (PI) and/or sonication (S). It is unclear whether the type of protocol can affect the final protein yield; thus, we evaluated how different preparation methods and their combination affect the alpha-casein (CSN1), lactoferrin (LTF), and alkaline phosphatase (ALP) concentrations in udder parenchyma using a sandwich ELISA, and tested whether the udder health state (coagulase-positive staphylococcal infection) affects the protocol efficacy. Samples from 22 cows (11 healthy, 11 infected) were processed using the four protocols. PI and S reduced protein concentrations, with the combination of MH + PI+S giving the lowest values for all three proteins. Unlike CSN1, ALP was the most sensitive to processing. CSN1 levels were higher in infected samples, while lactoferrin and ALP were not influenced by infection. All preparation methods affected the protein levels, irrespective of health state. The highest protein concentrations were obtained using MH alone. Our findings highlight the need for careful optimization and standardization of sample preparation to ensure reliable and reproducible ELISA results
Histatin-1 promotes the expression of markers associated with odontoblastic differentiation in the dental pulp and apical papilla
No full textAbstract Complex tooth injuries, including dental caries, require the reestablishment of tissue architecture and functionality through the regeneration of cellular populations that enable tertiary dentin formation and the reestablishment of vascular and neural components. Among these cellular populations, the odontoblasts play a critical role, as they are responsible for dentin formation and maintenance, and can differentiate from stem cells of the dental pulp and apical papilla. We previously demonstrated that Histatin-1, a salivary peptide with wound-healing properties, enhances the mineralizing activity of primary mesenchymal cells from immature permanent teeth. Here, we demonstrate that Histatin-1 upregulates odontoblastic differentiation markers, including dentin sialophosphoprotein (DSPP) and dentin matrix protein 1 (DMP1), together with odontoblast-like cellular features. Immunohistochemistry and tissue immunofluorescence revealed increased DSPP and DMP1 expression in Histatin-1-treated apical papilla explants, and to a lesser extent, dental pulp explants. These findings were corroborated in primary cultures of these tissues, which also showed upregulation of DSPP and β-catenin. Histatin-1 further promoted primary ciliogenesis and Golgi-polarization. Moreover, Histatin-1 stimulated in vitro mineralization and cell migration in a VEGFR2-dependent manner, as confirmed by pharmacological inhibition and a VEGFR2-binding-deficient mutant of Histatin-1. Collectively, these results suggest that Histatin-1 drives odontoblastic differentiation, opening new avenues for dental regenerative medicine
Enhanced cricket match prediction using kernel methods for feature extraction and back-propagation neural networks
No full textAbstract This study presents a dynamic machine learning framework for predicting the outcome of One Day International (ODI) cricket matches by analysing match progression at multiple game states. Each over is treated as a distinct match state, enabling real-time outcome prediction throughout the innings. Six key criteria are employed for classification, namely balls remaining, lead of Team A, wickets remaining, relative team strength, home advantage, and toss outcome. Feature extraction is performed using the League Championship Algorithm (LCA), which selects the most informative features from historical cricket data, followed by classification using a Back-Propagation Neural Network (BPNN). Experimental results demonstrate that the proposed model achieves an accuracy of 83%, a true positive rate of 0.81, a positive predictive value of 0.79, and an F1-score of 0.80 on the validation dataset, outperforming conventional prediction approaches by 5–10% across key performance metrics. The findings confirm the effectiveness of combining optimized feature extraction with neural network-based classification for accurate and interpretable cricket match outcome prediction
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