Identification of late-life depression and mild cognitive impairment via serum surface-enhanced Raman spectroscopy and multivariate statistical analysis

Urine-based SERS and multivariate statistical analysis for identification of non-muscle-invasive bladder cancer and muscle-invasive bladder cancer

Bladder cancer (BC) is an epidemiological urologic malignancy that continues to increase each year. Early diagnosis and prognosis monitoring is always significant in clinical practice, especially in distinguishing non-muscle-invasive bladder cancer (NMIBC) from muscle-invasive bladder cancer (MIBC), due to the various depths of tumor invasion related to different therapeutic schedules and recurrence rates. Common diagnostic approaches are too invasive or generally inefficient in accuracy and specificity. In this work, a totally non-invasive and cost-effective method is established by investigating urine samples using surface-enhanced Raman spectroscopy (SERS) and multivariate statistical analysis. The comparison of urine SERS spectra shows the intensities of characteristic peaks for DNA/RNA, hypoxanthine, albumin, D-( +)-galactosamine, fatty acids, and some amino acids are distinguishable in BC occurrence and invasion progression. A PLS-LDA-based two-step binary classification scheme is performed on urine SERS spectra and the diagnostic accuracies were 97.7% and 96.3% for healthy individuals versus BC patients and NMIBC versus MIBC patients, respectively. Moreover, the impact of urine SERS spectral lengths in reaching high-precision recognition of BC is investigated. The results show that the Raman peaks at 803, 893, 1139, 1375, and 1466 cm-1 play an essential role in correctly categorizing healthy control, NMIBC, and MIBC patients, and SERS spectra ranges from 400 to 1600 cm-1 are enough for this identification task. These findings provide a sensitive, label-free, rapid, and totally non-invasive way for assessment of invasion depth of BC to its early diagnosis and prognosis monitoring, as well as valuable insights for selecting reasonable spectral range to enhance the measurement efficiency especially in large-scale sample datasets.

Multi-branch attention Raman network and surface-enhanced Raman spectroscopy for the classification of neurological disorders

Surface-enhanced Raman spectroscopy (SERS), a rapid, low-cost, non-invasive, ultrasensitive, and label-free technique, has been widely used in-situ and ex-situ biomedical diagnostics questions. However, analyzing and interpreting the untargeted spectral data remains challenging due to the difficulty of designing an optimal data pre-processing and modelling procedure. In this paper, we propose a Multi-branch Attention Raman Network (MBA-RamanNet) with a multi-branch attention module, including the convolutional block attention module (CBAM) branch, deep convolution module (DCM) branch, and branch weights, to extract more global and local information of characteristic Raman peaks which are more distinctive for classification tasks. CBAM, including channel and spatial aspects, is adopted to enhance the distinctive global information on Raman peaks. DCM is used to supplement local information of Raman peaks. Autonomously trained branch weights are applied to fuse the features of each branch, thereby optimizing the global and local information of the characteristic Raman peaks for identifying diseases. Extensive experiments are performed for two different neurological disorders classification tasks via untargeted serum SERS data. The results demonstrate that MBA-RamanNet outperforms commonly used CNN methods with an accuracy of 88.24% for the classification of healthy controls, mild cognitive impairment, Alzheimer's disease, and Non-Alzheimer's dementia; an accuracy of 90% for the classification of healthy controls, elderly depression, and elderly anxiety.

Advancing Brain Research through Surface-Enhanced Raman Spectroscopy (SERS): Current Applications and Future Prospects

Surface-enhanced Raman spectroscopy (SERS) has recently emerged as a potent analytical technique with significant potential in the field of brain research. This review explores the applications and innovations of SERS in understanding the pathophysiological basis and diagnosis of brain disorders. SERS holds significant advantages over conventional Raman spectroscopy, particularly in terms of sensitivity and stability. The integration of label-free SERS presents promising opportunities for the rapid, reliable, and non-invasive diagnosis of brain-associated diseases, particularly when combined with advanced computational methods such as machine learning. SERS has potential to deepen our understanding of brain diseases, enhancing diagnosis, monitoring, and therapeutic interventions. Such advancements could significantly enhance the accuracy of clinical diagnosis and further our understanding of brain-related processes and diseases. This review assesses the utility of SERS in diagnosing and understanding the pathophysiological basis of brain disorders such as Alzheimer's and Parkinson's diseases, stroke, and brain cancer. Recent technological advances in SERS instrumentation and techniques are discussed, including innovations in nanoparticle design, substrate materials, and imaging technologies. We also explore prospects and emerging trends, offering insights into new technologies, while also addressing various challenges and limitations associated with SERS in brain research.