Abnormalities in X chromosomes, either numerical or structural, cause X-linked disorders, such as Duchenne muscular dystrophy (DMD). Recent molecular and cytogenetic techniques can help identify DMD gene mutations. The accurate diagnosis of Duchenne is crucial, directly impacting patient treatment management, genetics, and the establishment of effective prevention strategies. This review provides an overview of X chromosomal disorders affecting Duchenne and discusses how mutations in Dystrophin domains can impact detection accuracy. Firstly, the efficiency and use of cytogenetic and molecular techniques for the genetic diagnosis of Duchenne disease have, thus, become increasingly important. Secondly, artificial intelligence (AI) will be instrumental in developing future therapies by enabling the aggregation and synthesis of extensive and heterogeneous datasets, thereby elucidating underlying molecular mechanisms. However, despite advances in diagnostic technology, understanding the role of Dystrophin in Duchenne disease remains a challenge. Therefore, this review aims to synthesize this complex information to significantly advance the understanding of DMD and how it could affect patient care.

The reusability of enzymes is a fundamental aspect of sustainable biotechnology and the development of biosensors. This study presents one of the first quantitative evaluations of DNA polymerase reusability by utilizing integrated quartz crystal microbalance (QCM) kinetics and real-time monitoring of exonuclease activity. The results showed that immobilized T7 DNA polymerase retained approximately 50% of its initial activity after three 90-min cycles and around 20% after five cycles. Significantly lower activities were observed for shorter, 45-min cycles. This indicates an unexpected time-dependent enhancement in stability for longer reaction times. The findings suggest a promising trend in enzyme stability and reusability, establishing a quantitative relationship between reaction duration and enzyme performance. This relationship offers a scalable pathway for the regeneration of biosensors and for sustainable enzymatic catalysis. Additionally, the work provides a transferable framework that can be applied to other DNA-processing enzymes, which supports long-term biosensor performance and industrial biocatalysis. The demonstrated approach offers a transferable and scalable methodology for the development of reusable polymerase-based biosensors and sustainable biocatalytic systems.