SERS-Based Biosensors Combined with Machine Learning for Medical Application

AI-Enhanced Visual-Spectral Synergy for Fast and Ultrasensitive Biodetection of Breast Cancer-Related miRNAs

In biomedical testing, artificial intelligence (AI)-enhanced analysis has gradually been applied to the diagnosis of certain diseases. This research employs AI algorithms to refine the precision of integrative detection, encompassing both visual results and fluorescence spectra from lateral flow assays (LFAs), which signal the presence of cancer-linked miRNAs. Specifically, the color shift of gold nanoparticles (GNPs) is paired with the red fluorescence from nitrogen vacancy color centers (NV-centers) in fluorescent nanodiamonds (FNDs) and is integrated into LFA strips. While GNPs amplify the fluorescence of FNDs, in turn, FNDs enhance the color intensity of GNPs. This reciprocal intensification of fluorescence and color can be synergistically augmented with AI algorithms, thereby improving the detection sensitivity for early diagnosis. Supported by the detection platform based on this strategy, the fastest detection results with a limit of detection (LOD) at the fM level and the R2 value of ∼0.9916 for miRNA can be obtained within 5 min. Meanwhile, by labeling the capture probes for miRNA-21 and miRNA-96 (both of which are early indicators of breast cancer) on separate T-lines, simultaneous detection of them can be achieved. The miRNA detection methods employed in this study may potentially be applied in the future for the early detection of breast cancer.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

Machine Learning for COVID-19 Determination Using Surface-Enhanced Raman Spectroscopy

The rapid, low cost, and efficient detection of SARS-CoV-2 virus infection, especially in clinical samples, remains a major challenge. A promising solution to this problem is the combination of a spectroscopic technique: surface-enhanced Raman spectroscopy (SERS) with advanced chemometrics based on machine learning (ML) algorithms. In the present study, we conducted SERS investigations of saliva and nasopharyngeal swabs taken from a cohort of patients (saliva: 175; nasopharyngeal swabs: 114). Obtained SERS spectra were analyzed using a range of classifiers in which random forest (RF) achieved the best results, e.g., for saliva, the precision and recall equals 94.0% and 88.9%, respectively. The results demonstrate that even with a relatively small number of clinical samples, the combination of SERS and shallow machine learning can be used to identify SARS-CoV-2 virus in clinical practice.

Surface-Enhanced Raman Spectroscopy-Based Detection of Micro-RNA Biomarkers for Biomedical Diagnosis Using a Comparative Study of Interpretable Machine Learning Algorithms

Surface-enhanced Raman spectroscopy (SERS) has wide diagnostic applications due to narrow spectral features that allow multiplex analysis. We have previously developed a multiplexed, SERS-based nanosensor for micro-RNA (miRNA) detection called the inverse molecular sentinel (iMS). Machine learning (ML) algorithms have been increasingly adopted for spectral analysis due to their ability to discover underlying patterns and relationships within large and complex data sets. However, the high dimensionality of SERS data poses a challenge for traditional ML techniques, which can be prone to overfitting and poor generalization. Non-negative matrix factorization (NMF) reduces the dimensionality of SERS data while preserving information content. In this paper, we compared the performance of ML methods including convolutional neural network (CNN), support vector regression, and extreme gradient boosting combined with and without NMF for spectral unmixing of four-way multiplexed SERS spectra from iMS assays used for miRNA detection. CNN achieved high accuracy in spectral unmixing. Incorporating NMF before CNN drastically decreased memory and training demands without sacrificing model performance on SERS spectral unmixing. Additionally, models were interpreted using gradient class activation maps and partial dependency plots to understand predictions. These models were used to analyze clinical SERS data from single-plexed iMS in RNA extracted from 17 endoscopic tissue biopsies. CNN and CNN-NMF, trained on multiplexed data, performed most accurately with RMSElabel = 0.101 and 9.68 × 10-2, respectively. We demonstrated that CNN-based ML shows great promise in spectral unmixing of multiplexed SERS spectra, and the effect of dimensionality reduction on performance and training speed.

Paper-based substrates for surface-enhanced Raman spectroscopy sensing

Surface-enhanced Raman scattering (SERS) has been recognized as one of the most sensitive analytical methods by adsorbing the target of interest onto a plasmonic surface. Growing attention has been directed towards the fabrication of various substrates to broaden SERS applications. Among these, flexible SERS substrates, particularly paper-based ones, have gained popularity due to their easy-to-use features by full contact with the sample surface. Herein, we reviewed the latest advancements in flexible SERS substrates, with a focus on paper-based substrates. Firstly, it begins by introducing various methods for preparing paper-based substrates and highlights their advantages through several illustrative examples. Subsequently, we demonstrated the booming applications of these paper-based SERS substrates in abiotic and biological matrix detection, with particular emphasis on their potential application in clinical diagnosis. Finally, the prospects and challenges of paper-based SERS substrates in broader applications are discussed.

Handling DNA malfunctions by unsupervised machine learning model

The cell cycle is a rich field for research, especially, the DNA damage. DNA damage, which happened naturally or as a result of environmental influences causes change in the chemical structure of DNA. The extent of DNA damage has a significant impact on the fate of the cell in later stages. In this paper, we introduced an Unsupervised Machine learning Model for DNA Damage Diagnosis and Analysis. Mainly, we employed K-means clustering unsupervised machine learning algorithms. Unsupervised algorithms commonly draw conclusions from datasets by solely utilizing input vectors, disregarding any known or labeled outcomes. The model provided deep insight about DNA damage and exposes the protein levels for proteins when work together in sub-network model to deal with DNA damage occurrence, the unsupervised artificial model explained the sub-network biological model activities in regard to the changing in their concentrations in several clusters, they have been grouped in such as (0 - no damage, 1 - low, 2 - medium, 3 - high, and 4 - excess) DNA damage clusters. The results provided a rational and persuasive explanation for numerous important phenomena, including the oscillation of the protein p53, in a clear and understandable manner. Which is encouraging since it demonstrates that the K-means clustering approach can be easily applied to many similar biological systems, which aids in better understanding the key dynamics of these systems.

Bioinspired superhydrophobic SERS substrates for machine learning assisted miRNA detection in complex biomatrix below femtomolar limit

Surface-enhanced Raman spectroscopy (SERS) is an analytical method with high potential in the field of medicine. The design of SERS substrates, based on specific morphology and/or chemical modification, allow the recognition of the presence of specific analytes with precision close to a single-molecule detection limit. However, the SERS analysis of real samples is significantly complicated by the presence of a large number of "minor" molecules that can shield the signal from the target analyte and make it impossible to determine it in practice. In this work, an advanced SERS approach was used for the detection of cancer-related miRNA-21 in blood plasma, used as a molecular model background. The approach was based on the combination of the biomimetic plasmon-active SERS substrate, its tuned surface chemistry and advanced SERS data analysis, making use of artificial machine learning. In the first step, biomimetic SERS substrates were created using a butterfly wing as a starting template. The substrates were covered by thin Au layer and covalently grafted with hydrophobic chemical moieties to introduce superhydrophobic and water-adhesive properties. The self-concentration of the analyte on the substrates was achieved by minimizing the contact area between the analyte drop and the substrate, which is facilitated by surface superhydrophobicity and additionally enhanced by drop evaporation on the flipped over substrate. Due to the presence of cancer miRNA and blood plasma background, the measured SERS spectra represent a complex of interfering peaks. Thus, their interpretation was carried out using a specially trained machine learning model. As a result, reliable and repeatable quantitative detection of miRNAs below the femtomolar level (up to 10-16 M) on the background of human blood plasma becomes possible.

A Review on Photonic Sensing Technologies: Status and Outlook

In contemporary science and technology, photonic sensors are essential. They may be made to be extremely resistant to some physical parameters while also being extremely sensitive to other physical variables. Most photonic sensors may be incorporated on chips and operate with CMOS technology, making them suitable for use as extremely sensitive, compact, and affordable sensors. Photonic sensors can detect electromagnetic (EM) wave changes and convert them into an electric signal due to the photoelectric effect. Depending on the requirements, scientists have found ways to develop photonic sensors based on several interesting platforms. In this work, we extensively review the most generally utilized photonic sensors for detecting vital environmental parameters and personal health care. These sensing systems include optical waveguides, optical fibers, plasmonics, metasurfaces, and photonic crystals. Various aspects of light are used to investigate the transmission or reflection spectra of photonic sensors. In general, resonant cavity or grating-based sensor configurations that work on wavelength interrogation methods are preferred, so these sensor types are mostly presented. We believe that this paper will provide insight into the novel types of available photonic sensors.

Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques

Surface-enhanced Raman spectroscopy/scattering (SERS) has evolved into a popular tool for applications in biology and medicine owing to its ease-of-use, non-destructive, and label-free approach. Advances in plasmonics and instrumentation have enabled the realization of SERS's full potential for the trace detection of biomolecules, disease diagnostics, and monitoring. We provide a brief review on the recent developments in the SERS technique for biosensing applications, with a particular focus on machine learning techniques used for the same. Initially, the article discusses the need for plasmonic sensors in biology and the advantage of SERS over existing techniques. In the later sections, the applications are organized as SERS-based biosensing for disease diagnosis focusing on cancer identification and respiratory diseases, including the recent SARS-CoV-2 detection. We then discuss progress in sensing microorganisms, such as bacteria, with a particular focus on plasmonic sensors for detecting biohazardous materials in view of homeland security. At the end of the article, we focus on machine learning techniques for the (a) identification, (b) classification, and (c) quantification in SERS for biology applications. The review covers the work from 2010 onwards, and the language is simplified to suit the needs of the interdisciplinary audience.