Ocular diseases require localized administration of drugs to the tissues around the ocular cavity. The existing ocular drug delivery systems are fairly primitive and inefficient. However, the design of ocular system is undergoing gradual transition from an empirical to rational basis. In the recent years, there has been explosion of interest in the polymer based delivery devices. Utilization of the principles of controlled release as embodied by ocular inserts offers an attractive approach to the problem of prolonging pre-corneal drug residence times. In the present update, the authors discuss the basic concept of ocular inserts as drug delivery system and examine the few inserts, which are available in the market or are being developed by pharmaceutical companies for drug delivery. The article discusses soluble ocular drug insert (SODI), Ocusert, Collagen Shields, Ocufit, Minidisc and new ophthalmic delivery system (NODS) with special attention to biological/clinical performances, and potential for future applications and developments.

An algorithm framework based on CycleGAN and an upgraded dual‐path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model’s ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false‐positive rate, false‐negative rate, recall, precision, and F1 score. Several experiments are carried out over the selected parameters, such as making comparisons between benign and malignant classification accuracy under different normalization methods, comparison of accuracy of image level and patient level using different CNN models, correlating the correctness of DPN68‐A network with different deep learning models and other classification algorithms at all magnifications. The results thus obtained have proved that the proposed model DPN68‐A network can effectively classify the benign and malignant breast cancer pathological images at various magnifications. The proposed model also is able to better assist the pathologists in diagnosing the patients by synthesizing the images of different magnifications in the clinical stage.

Cancer remains a formidable global health challenge, necessitating innovative therapeutic strategies. Traditional small-molecule inhibitors often face limitations in selectively targeting disease associated proteins, leading to side effects and incomplete therapeutic responses. Proteolysis targeting chimeras (PROTACs) have emerged as a promising approach to address these challenges. Unlike traditional inhibitors, PROTACs leverage the cellular ubiquitin-proteasome system to selectively degrade disease-associated proteins. In this review, we discuss PROTACs as a targeted approach for cancer management, highlighting key findings, limitations, and future perspectives. For this, the authors have critically reviewed literature obtained from prime sources comprising Google Scholar, Web of Science, PubMed, and Publons. Additional relevant articles were retrieved from the reference sections of selected papers. Preclinical studies and early-phase clinical trials have demonstrated the efficacy and potential of PROTACs in cancer management. Additionally, the potential of PROTACs in overcoming therapy resistance, tackling tumor heterogeneity, and engaging multiple pathways is explored. As research advances, addressing challenges and refining PROTAC technology will pave the way for their integration into the next generation of cancer therapeutics, marking a transformative era in precision medicine.

Artificial intelligence (AI) has revolutionized the discovery and development of new drugs in biomedicine. By using advanced algorithms and computational methods, AI optimizes treatment plans, accelerates the drug development process, and improves patient outcomes. AI algorithms integrate multi-omics data sets, decipher molecular connections, and identify therapeutic targets and biomarkers. High-throughput screening, predictive modeling, and AI-powered virtual screening platforms are revolutionizing the drug development pipeline. Machine learning and deep learning models enable drug-target interactions prediction, pharmacological evaluation, and experimental validation. Structure-based drug design methodologies accelerate the discovery of new therapies. AI-driven technologies enable personalized treatment plans for patients, taking into account their unique traits and disease profiles. Pharmacogenomics, when combined with predictive analytics, improves drug selection, dosage adjustment, and treatment response prediction, enhancing therapeutic efficacy and reducing side effects.