Since the revolutionary breakthrough in the successful isolation and characterization of graphene in 2004, the field of two-dimensional materials has undergone a major transformation. This pivotal advancement not only confirmed the unique ability of atomically thin layers to confine charge carriers within a two-dimensional plane but also sparked a wave of research into transition metal dichalcogenides and Xene derivatives such as silicene, boronene, and germanene. These materials exhibit exceptional biophysical properties, electrical performance, mechanical flexibility, and optical responsiveness, providing a novel platform for developing bioelectronic devices. Current research centers on functional device innovation: electrodes, transistors, and p-n junctions based on two-dimensional materials are advancing biosensor upgrades for seamless skin interaction; emerging neural interface systems can collaborate with brain and ocular tissues; and smart tattoo sensors demonstrate self-powered detection potential. These breakthrough technologies collectively point toward the convergent development of wearable devices, implantable systems, and bioelectronic interfaces, laying the foundation for next-generation intelligent sensing technologies.

AI-based large language models (LLMs) have gradually made their way into various fields, transforming industries and changing the way we solve problems. LLMs have great potential in healthcare, where they can share the burden of data management, retrieval, and decision-making. The objective of this paper is to analyze the pivotal role of LLMs in healthcare by underscoring its current applications in healthcare, its advantages and limitations, its real-world implementations of LLMs, along with expectations for the future. LLMs can be of service to the physicians by aiding diagnosis and management and device personalized therapeutic plans for the patients. Currently, LLMs are serving a purpose in healthcare by facilitating patient communication and education, medical documentation, and dissecting medical literature although each come its own challenges. Its advanced efficiency, accessibility, patient centric care, and predictive power is what makes it a powerful tool in healthcare. However, certain concerns such as data privacy and security, bias, regulatory issues, and technical challenges greatly limits its integration into the healthcare system. There can be further research done on exploring advanced training techniques as well as on appropriate regulatory models. Despite these limitations, it has the potential to revolutionize healthcare delivery.

Kinase inhibitors are essential in targeted cancer therapy, yet resistance often emerges through secondary mutations, activation of compensatory signaling pathways, or drug-efflux mechanisms. Artificial intelligence (AI) provides a workflow-based strategy rather than a list of unrelated tools for predicting and addressing kinase-inhibitor resistance. In this review, AI methodologies are systematically classified into machine-learning frameworks, molecular-modeling tools, bioinformatics databases, network-biology resources, and explainability platforms, offering a structured perspective on their interdependence within resistance prediction workflows. Deep learning models demonstrate superior predictive performance compared to traditional approaches, while explainable-AI (XAI) techniques such as SHAP and LIME enhance interpretability and clinical trust. Integration of multi-omics data including genomic, proteomic, and transcriptomic profiles further strengthens model robustness and clinical relevance. AI-driven in silico simulations of kinase drug interactions are also facilitating the design of next-generation inhibitors. By emphasizing workflow integration and methodological taxonomy, this review highlights how AI can revolutionize resistance prediction and rational drug development, paving the way toward precision medicine in kinase-targeted therapies.

The isoproterenol-induced myocardial infarction model is a well-established experimental approach for studying cardiac injury and testing potential protective treatments. By overstimulating beta-adrenergic receptors, this model closely reproduces key features of human heart attacks, including oxidative damage, calcium imbalance, inflammatory responses, and cell death. Characteristic changes in heart electrical activity (such as ST-segment elevation) and tissue alterations (including scarring and immune cell accumulation) further validate its relevance for cardiovascular research. Physical assessments include the heart-to-body weight ratio and electrocardiogram abnormalities. Blood tests detect elevated levels of specific proteins that indicate heart muscle injury, such as cardiac troponins and creatine kinase-MB. Measurements of antioxidant enzymes and oxidative damage markers help assess stress levels in heart tissue. Heart function is evaluated through blood pressure monitoring and ventricular pressure measurements. Inflammatory molecules in the bloodstream reveal the body's response to heart damage. Microscopic examination of heart tissue shows structural changes, including cell death, scar formation, and immune cell invasion. By combining these different types of measurements, researchers can thoroughly analyze heart damage in this experimental model. This comprehensive approach supports the discovery of new heart-protective treatments and improves our understanding of heart attack mechanisms, benefiting both laboratory studies and patient care.

To address the diagnostic challenge posed by overlapping features, we created a deep learning (DL) model for non-invasive differentiation of lung cancer (LC) from tuberculosis using clinical and CT data. A total of 229 patients at the Affiliated Cancer Hospital of Harbin Medical University had their clinical and CT data that were retrospectively gathered. In order to get areas of interest (ROIs), lung window CT images were manually segmented. Extracted features included clinical variables, CT semantic descriptors, radiomic profiles, and 2D/3D DL features. Logistic regression models were employed to assess the diagnostic performance of each set of features. The model incorporating 3D-DLF extracted via a 3D-ResNet network demonstrated the best performance. In the training cohort (sensitivity = 0.976, specificity = 0.961) and the test cohort (sensitivity = 0.789, specificity = 0.935), it obtained an Area Under the Curve (AUC) of 0.992 (95% Confidence Interval (CI): 0.984–1) and a AUC of 0.963 (95% CI: 0.929–0.998). Performance in the external validation cohort yielded an AUC of 0.709 (95% CI: 0.542–0.876; sensitivity = 0.875, specificity = 0.286). The 3D-ResNet model outperformed those using clinical, semantic, and conventional radiomic features, highlighting DL's potential to enhance computer-aided differentiation of LC and tuberculosis.