Molecular biomarkers in salivary diagnostic materials: Point-of-Care solutions — PoC-Diagnostics and-Testing

Accurate diagnosis is fundamental to effective healthcare, guiding clinical and surgical decisions and ultimately influencing treatment outcomes and prognosis. Recent advancements in nanomaterials and fabrication techniques, coupled with emerging computational approaches such as artificial intelligence (AI), machine learning, and deep learning, have revolutionized high-throughput screening and laboratory automation. AI-driven algorithms now process and analyze salivary proteomic data with remarkable accuracy, identifying patterns and biomarkers associated with diseases such as oral cancer at an early stage. This capability not only enhances diagnostic precision but also accelerates decision-making, enabling timely interventions. Despite their lower analyte content, oral fluids— particularly saliva—offer a non-invasive and accessible alternative for biomarker testing. This has led to the development and optimization of highly sensitive and amplified detection methods, including polymerase chain reaction, isothermal amplification, microfluidic, lab-on-a-chip, and biosensor technologies such as surface plasmon resonance and electrochemical sensing. These innovations have culminated in reliable Point-of-Care (PoC) solutions for molecular diagnostics, facilitating the detection and monitoring of a broad spectrum of conditions, including steroid levels, growth factors, drug and alcohol abuse, infectious diseases (such as HIV antibodies), diabetes (via salivary glucose), periodontitis, peri-implantitis, and oral cancer, with applications extending even into forensics. The precision, simplicity, and accessibility of salivary-based biomarkers and biosensors present a promising frontier in contemporary oro-dental healthcare, offering significant benefits to clinicians and surgeons. While significant progress has been made, challenges remain in standardizing salivary diagnostic techniques and ensuring their widespread clinical adoption. Addressing these challenges requires continued research into improving assay sensitivity, data integration, and cost-effectiveness. Henceforth, ongoing advancements are expected to further integrate predictive analytics into our routine clinical practice, ultimately improving patient outcomes through personalized, cost-effective, and timely care, thereby enhancing overall healthcare quality and efficiency. These are the topics to be discussed in this review.