Saveetha Institute of Medical & Technical Sciences

MXene-based smart contact lenses demonstrate a cutting-edge advancement in wearable ophthalmic technology, combining real-time biosensing, therapeutic capabilities, and user comfort in a single platform. These devices take the advantage of the exceptional electrical conductivity, mechanical flexibility, and biocompatibility of two-dimensional MXenes to enable noninvasive, tear-based monitoring of key physiological markers such as intraocular pressure and glucose levels. Recent developments focus on the integration of transparent MXene films into the conventional lens materials, allowing multifunctional performance including photothermal therapy, antimicrobial and anti-inflammation protection, and dehydration resistance. These innovations offer promising strategies for ocular disease management and eye protection. In addition to their multifunctionality, improvements in MXene synthesis and device engineering have enhanced the stability, transparency, and wearability of these lenses. Despite these advances, challenges remain in long-term biostability, scalable production, and integration with wireless communication systems. This review summarizes the current progress, key challenges, and future directions of MXene-based smart contact lenses, highlighting their transformative potential in next-generation digital healthcare and ophthalmic care.

Traffic accidents usually result from driver's inattention, sleepiness, and distraction, posing a substantial danger to worldwide road safety. Advances in computer vision and artificial intelligence (AI) have provided new prospects for designing real-time driver monitoring systems to reduce these dangers. In this paper, we assessed four known deep learning models, MobileNetV2, DenseNet201, NASNetMobile, and VGG19, and offer a unique Hybrid CNN-Transformer architecture reinforced with Efficient Channel Attention (ECA) for multi-class driver activity categorization. The framework defines seven important driving behaviors: Closed Eye, Open Eye, Dangerous Driving, Distracted Driving, Drinking, Yawning, and Safe Driving. Among the baseline models, DenseNet201 (99.40%) and MobileNetV2 (99.31%) achieved the highest validation accuracies. In contrast, the proposed Hybrid CNN-Transformer with ECA attained a near-perfect validation accuracy of 99.72% and further demonstrated flawless generalization with 100% accuracy on the independent test set. Confusion matrix studies further indicate a few misclassifications, verifying the model's high generalization capacity. By merging CNN-based local feature extraction, attention-driven feature refinement, and Transformer-based global context modeling, the system provides both robustness and efficiency. These findings show the practicality of using the suggested technology in real-time intelligent transportation applications, presenting a viable avenue toward reducing traffic accidents and boosting overall road safety.

The objective of this study is to assess the biomechanical efficacy of different attachment systems in facilitating the lower canine retraction using clear aligners by means of a finite element method (FEM) analysis.

A FEM was developed by utilizing intraoral scan data and CBCT data from an 18-year-old female with ideal occlusion. Four experimental groups were assessed: group 1 (unattached), group 2 (single vertical rectangular attachment), group 3 (double vertical rectangular attachments), and group 4 (optimized quarter-ellipsoid attachments). The study evaluated the stress distribution, plastic deformation and displacement of the crown and root during simulated canine retraction.

Highest bodily translation (85-90%) was observed with optimized attachments (group 4) attaining nearly equal crown and root displacement. Tipping was observed in group 1, less of bodily movement was seen (60-65%) compared to group 2, which demonstrated better physiological translation (70-75%), while group 3 further enhanced efficiency to 75-80%. Group 4 exhibited the most uniform stress distribution, and plastic deformation was minimal (0.01mm), suggesting superior force application and aligner stability.

The efficiency of canine retraction was considerably improved by optimized quarter-ellipsoid attachments, which resulted in minimal stress concentration and deformation. These findings substantiate the use of optimized attachments to improve the results of clear aligner treatment. Further clinical research is advised to verify these findings and investigate alternative attachment designs and materials to optimize aligner therapy.