Magnesium (Mg) and its alloys are promising candidates for next-generation biodegradable implants owing to their biocompatibility, suitable mechanical properties, and ability to gradually degrade in vivo, eliminating the need for secondary removal surgery. However, their rapid corrosion in physiological environments remains a challenge. Wire Electrical Discharge Machining (WEDM) is increasingly considered for manufacturing complex Mg-based medical devices, yet its influence on surface integrity and degradation behaviour requires careful evaluation. In the present study, the in vitro corrosion performance of polished (Control), and polished/WEDM-processed AZ91 Mg alloy was systematically investigated. Specimens were immersed in simulated body fluid (SBF) for up to 168 h, following ASTM G31-72, and characterised using SEM/EDX, XRD, and electrochemical impedance spectroscopy (EIS). WEDM processing produced a re-solidified layer with micro-cracks, selective evaporation of Al and Zn, while polished samples retained alpha-Mg traces. A higher maximum area polarisation resistance for polished samples of 280 Omega cm & sup2; was obtained after 72 h immersion in SBF (pH 7.4) maintained at 37 +/- 0.5 degrees C. EIS revealed higher polarisation resistance for polished samples (peak 280 Omega cm & sup2; at 72 h) vs. WEDMed (70 Omega cm & sup2; at 168 h). Hydrogen evolution rate (HER) and pH monitoring indicated more rapid alkalisation for WEDMed samples, corroborating accelerated corrosion. Post-immersion analysis showed a Ca/P ratio of 1.63 on polished samples, indicating stable hydroxyapatite deposition, compared to 1.29 for WEDMed surfaces, signifying less crystalline Ca - P phases. XRD corroborated these trends, revealing well-crystallised apatite on polished surfaces and predominantly soluble magnesium salts on WEDMed alloys. While WEDM offers manufacturing precision, its surface defects compromise corrosion resistance and bioactivity, highlighting the need for post-processing treatments to optimise implant performance.
Molecular changes in the early stage of Parkinson's disease (PD) and Alzheimer disease (AD) occur before disease onset, and thus, transcriptomic profiling could be an effective approach to risk prediction in the early phases of the disease. Nonetheless, the generated datasets of gene expression are very high-dimensional usually having thousands of genes with few samples, that are not readily accommodated by traditional machine learning methods, which face the risk of overfitting and low nonlinear representational properties. Strong computational models are thus needed to obtain discriminative molecular signatures and generalise at the same time. This work suggests a quantum-machine learning (QML) framework to profile high-dimensional gene expression to enhance prediction of the early risk of both PD and AD and maintain biological insights. Most public transcriptomic datasets were preprocessed with log transformation, Z-score normalisation, and then they were subjected to the analysis of differential expression and mutually informative feature selection. The encoding of the genes of interest into a variational quantum circuit was through angle encoding, which allowed nonlinear representation to a higher-dimensional feature space. Regularised cross-entropy loss was used to train a hybrid quantum-classical architecture that used parameterized quantum layers followed by a classical classifier. Accuracy, precision, recall, F1-score and ROC-AUC were used to measure performance that was benchmarked against support vector machines, random forests and classical neural networks. This model proposed was more successful in higher ROC- AUC, F1-scores in single datasets of PD, and AD, and showed greater ability to perform in high-dimensional and small samples. The process of neuroinflammation via genes associated with neuroinflammatory pathways, mitochondrial dysfunction, and synaptic signaling were determined by feature importance analyses and pathway enrichment as per known neurodegenerative mechanisms.
Alzheimer disease (AD) and Parkinson disease (PD) are neurodegenerative disorders that are marked by progressive neuronal dysfunction and significant molecular heterogeneity that does not permit early diagnosis and specific intervention. Gene expression profiling provides an effective method to discovery transcriptomic biomarkers, but high dimensionality, cohort variability and multiple-testing burden results tend to undermine the reproducibility. In the research, we used a combined big data analytics and statistical genetics platform to conduct robust gene expression-based biomarkers by using the publicly available transcriptomic data of brain and peripheral blood samples (in total n = 412; 238 cases and 174 controls). The differential expression analysis was performed through moderated linear modelling with false discovery rate (FDR) control of Benjamini-Hochberg error and post-processing quality control and normalisation to minimise the effects of batching. It was used to consider genes significant as FDR < 0.05 with log 2 fold change value 1 or more and confidence interval does not cross zero. This statistical filtering found 326 dysregulated genes significant enough to be enriched with pathways which are associated with neuroinflammation, synaptic signalling, mitochondrial dysfunction, and protein homeostasis. In order to optimise the candidate biomarkers, we used a machine learning pipeline with an Elastic Net constant, Random Forest ranking of importance and stability selection and then classified them with logistic regression, support vector machine, and gradient boosting models. The consistent resampling biomarker panel was a 14-gene biomarker panel. In stratified nested cross-validation, the highest performing classifier had an area under the receiver operating characteristic curve (AUROC) of 0.91 +/- 0.03, sensitivity of 0.87 and specificity of 0.85 and was also highly stable in terms of its performance in independent validation cohorts (AUROC = 0.88). A combination of the effect size, FDR signal and confidence interval reporting was more effective in enhancing the reliability of biomarkers compared to selection using p-value. These results indicate that research methods that integrate stringent statistical genetics with machine learning algorithms that can be easily interpreted have increased the strength and forecasting capacity of gene expression-based biomarkers. The suggested framework is a consistent and biologically based approach to AI-led biomarker discovery in neurodegenerative diseases, which will be used in translational and precision medicine in the future.
Neurodegenerative diseases, such as Alzheimer disease (AD) and Parkinson disease (PD), are multifactorial biological diseases with heterogeneous genetic structure, which are multifactorial polygenic diseases. Despite the many associations of susceptibility loci, which are genome-wide, mapping the genetic susceptibility into the scale services is a difficult task to carry out. In this paper, an artificial intelligence-based bioinformatics pipeline is described that combines the modelling of quantitative genetics with the capabilities of the state-ofthe-art data analytics to classify neurodegenerative diseases in a robust way. transcriptomic profiles and SNP data of genomes were going through stringent quality control, such as filtering minor allele frequency, HardyWeinberg equilibrium, and linkage disequilibrium pruning. To measure genetic predisposition, polygenic risk scores (PRS) and the features derived using quantitative trait loci (QTL) were calculated. Ensemble machine learning and deep neural network models were trained after dimensionality cut and feature selection approach had been employed. A relative analysis to traditional genetic models in terms of logistic regression showed better classification ability with the ensemble structure having a higher discrimination ability. SHAP analyses and enrichment of pathways demonstrated that many synaptic signalling, mitochondrial dysfunction, and neuroinflammatory pathway-related genes were strongly activated and inhibited by the feature attribution analysis. The results show that the combination of quantitative genetics and AI-powered analytics increase the predictive power, maintaining biological interpretation, which can be used to build scalable precision neurogenomics models to predict the early disease risks in a stratified manner.
This study examines how task–technology fit (TTF) and perceived usefulness (PU) are associated with responsible generative AI use (RGU) in higher education. Survey data from 280 university students in China were analyzed using covariance-based structural equation modeling. The model showed excellent fit, and all hypothesized paths were positive and significant: TTF → PU (β = 0.360), TTF → RGU (β = 0.318), and PU → RGU (β = 0.273). Bootstrap results indicated a partial mediation pattern in which PU was associated with the relationship between TTF and RGU. Females reported higher RGU than males, doctoral students outscored undergraduates on TTF and PU, and frequent GenAI users scored highest across all constructs. The findings extend TTF and TAM by integrating responsibility as a behavioral outcome and indicate that task-aligned, transparent GenAI practices may support more sustainable learning.
