# Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering
## 1 Introduction  
In clinical practice disease diagnosis is a critical step towards disease management and acts as an indispensable guide towards appropriate treatment and personalised therapy [1]. The initial stage of disease diagnosis, or differential diagnosis, screens possible disease candidates unambiguously correlated to empirical clinical symptoms. This crucial process involves a systematic and objective analysis and understanding of infection-driven changes in complex metabolic processes highlighted by biological markers (biomarkers) [2]. In addition to stratifying normal biological from pathogenic processes, biomarkers may provide valuable information about severity and stage of disease, drug targets and pharmacological response to therapy [3]. Thus, there is always a growing demand to identify, evaluate and validate disease biomarkers for existing and emerging infections. However, diseases usually have complex pathogenesis and pathophysiology profiles that involve multiple intertwined networks of cellular and molecular changes. To capture this biological and biochemical complexity holistically (e.g., in biofluids, tissues, etc.) indicative of healthy or disease states, intensive high-throughput omics-based analytical procedures are frequently used [4; 5]. These aim to identify and characterise multiple biomarkers comprehensively to enhance patient outcomes, disease prevention and drug discovery. In metabolomics, gas chromatography (GC) screens and separates different metabolites in complex sample mixtures, and mass spectrometry (MS) detects mass-to-charge (\(m/z\)) ratios to resolve and identify eluted metabolites [6]. Similarly, genomics, transcriptomics and proteomics techniques, such as antibody-based and liquid chromatography (LC)-MS, are effective modalities for identifying specific patterns in biomarkers and pathophysiological mechanisms within the genome, transcriptome and proteome, respectively [7; 8]; we note that LC-MS is also extensively used within metabolomics [9]. Indeed, it is obvious that multiparameter approaches are ideal for biomarker discovery, and have been shown widely to provide systematic and clearer insights to unravel complex metabolic processes and pathways underlying various diseases. Nonetheless, when individual diagnostic and prognostic markers (e.g., metabolites, nucleic acids or proteins) are identified, validated and adopted for routine clinical use, there should be a paradigm shift towards simple, rapid, molecularly specific and sensitive analytics for more efficient detection of biomarkers [10]. Additionally, technically demanding and laborious tools like GC-MS and LC-MS are not cost-effective for diagnosing diseases which lack definitive biomarkers at present, but are frequently detected through disease-specific changes in known biomolecules. Preeclampsia is one example, a pregnancy disorder which is characterised by increased levels of a small subset of molecules, _viz._, proteins, glucose, uric acid, etc., in human biofluids [11]. Similarly, therapeutic drug monitoring (TDM), which involves routine monitoring and tracking of dose dynamics and biocearance of prescribed drugs to improve therapeutic efficacy, patient compliance and minimise drug toxicity, requires facile, noninvasive and preferably portable tools with lower running costs per sample analysis [12]. Due to the highlighted need, the 21 st century has seen an upsurge in the development of simple and high-throughput laboratory analyses to meet urgent diagnostic requirements in pathology and pharmacology. Surface-enhanced Raman scattering (SERS) has gained overwhelming attention as a powerful state-of-the-art platform for disease diagnosis at the molecular level due to superior specificity and quantitative sensitivity [13]. SERS is a nanoscale vibrational spectroscopy which combines Raman spectroscopy and plasmonic NPs, to amplify the Raman signals and facilitate trace analysis of clinically useful biomolecules at ambient conditions.