Discriminatory Detection of ssDNA by Surface-Enhanced Raman Spectroscopy (SERS) and Tree-Based Support Vector Machine (Tr-SVM)

Recent Developments in Slippery Liquid-Infused Porous Surface Coatings for Biomedical Applications

Slippery liquid-infused porous surface (SLIPS), inspired by the Nepenthes pitcher plant, exhibits excellent performances as it has a smooth surface and extremely low contact angle hysteresis. Biomimetic SLIPS attracts considerable attention from the researchers for different applications in self-cleaning, anti-icing, anticorrosion, antibacteria, antithrombotic, and other fields. Hence, SLIPS has shown promise for applications across both the biomedical and industrial fields. However, the manufacturing of SLIPS with strong bonding ability to different substrates and powerful liquid locking performance remains highly challenging. In this review, a comprehensive overview of research on SLIPS for medical applications is conducted, and the design parameters and common fabrication methods of such surfaces are summarized. The discussion extends to the mechanisms of interaction between microbes, cells, proteins, and the liquid layer, highlighting the typical antifouling applications of SLIPS. Furthermore, it identifies the potential of utilizing the controllable factors provided by SLIPS to develop innovative materials and devices aimed at enhancing human health.

Novel Anti-CRISPR-Assisted CRISPR Biosensor for Exclusive Detection of Single-Stranded DNA (ssDNA)

Nucleic acid analysis plays an important role in disease diagnosis and treatment. The discovery of CRISPR technology has provided novel and versatile approaches to the detection of nucleic acids. However, the most widely used CRISPR-Cas12a detection platforms lack the capability to distinguish single-stranded DNA (ssDNA) from double-stranded DNA (dsDNA). To overcome this limitation, we first employed an anti-CRISPR protein (AcrVA1) to develop a novel CRISPR biosensor to detect ssDNA exclusively. In this sensing strategy, AcrVA1 cut CRISPR guide RNA (crRNA) to inhibit the cleavage activity of the CRISPR-Cas12a system. Only ssDNA has the ability to recruit the cleaved crRNA fragment to recover the detection ability of the CRISPR-Cas12 biosensor, but dsDNA cannot accomplish this. By measuring the recovered cleavage activity of the CRISPR-Cas12a biosensor, our developed AcrVA1-assisted CRISPR biosensor is capable of distinguishing ssDNA from dsDNA, providing a simple and reliable method for the detection of ssDNA. Furthermore, we demonstrated our developed AcrVA1-assisted CRISPR biosensor to monitor the enzymatic activity of helicase and screen its inhibitors.

Plasmonic substrates for biochemical applications of surface-enhanced Raman spectroscopy

Due to its great practical importance, the detection and determination of many biomolecules in body fluids and other samples is carried out in a large number of laboratories around the world. One of the most promising analytical techniques now being widely introduced into medical analysis is surface-enhanced Raman scattering (SERS) spectroscopy. SERS is one of the most sensitive analytical methods, and in some cases, a good quality SERS spectrum dominated by the contribution of even a single molecule can be obtained. Highly sensitive SERS measurements can only be carried out on substrates generating a very high SERS enhancement factor and a low Raman spectral background, and so using of right nanomaterials is a key element in the success of SERS biochemical analysis. In this review article, we present progress that has been made in the preparation of nanomaterials used in SERS spectroscopy for detecting various kinds of biomolecules. We describe four groups of nanomaterials used in such measurements: nanoparticles of plasmonic metals and deposits of plasmonic nanoparticles on macroscopic substrates, nanocomposites containing plasmonic and non-plasmonic parts, nanostructured macroscopic plasmonic metals, and nanostructured macroscopic non-plasmonic materials covered by plasmonic films. We also describe selected SERS biochemical analyses that utilize the nanomaterials presented. We hope that this review will be useful for researchers starting work in this fascinating field of ​​science and technology.

Chemical-Chemical Redox Cycle Signal Amplification Strategy Combined with Dual Ratiometric Immunoassay for Surface-Enhanced Raman Spectroscopic Detection of Cardiac Troponin I

Improving the sensitivity and reproducibility of surface-enhanced Raman spectroscopy (SERS) methods for the detection of bioactive molecules is crucial in biological process research and clinical diagnosis. Herein, we designed a novel SERS platform for cardiac troponin I (cTnI) detection by a chemical-chemical redox cycle signal amplification strategy combined with a dual ratiometric immunoassay. First, ascorbic acid (AA) was generated by enzyme-assisted immunoreaction with a cTnI-anchored sandwich structure. Then, oxidized 4-mercaptophenol (ox4-MP) was reacted with AA to produce 4-mercaptophenol (4-MP). Quantitative analysis of cTnI was realized by a Raman signal switch between ox4-MP and 4-MP. Specifically, AA could be regenerated by reductant (tris(2-carboxyethyl) phosphine, TCEP), which in turn produced more signal indicator 4-MP, causing significant signal amplification for cTnI analysis by SERS immunosensing. Moreover, a dual ratiometric-type SERS method was established with the intensity ratio I1077/I822 and I633/I822, which improved the reproducibility of the cTnI assay. The excellent performance of the chemical-chemical redox cycle strategy and ratio-type SERS assay endows the method with high sensitivity and reproducibility. The linear ranges of cTnI were 0.001 to 50.0 ng mL-1 with detection limits of 0.33 pg mL-1 (upon I1077/I822) and 0.31 pg mL-1 (upon I635/I822), respectively. The amount of cTnI in human serum samples yielded recoveries from 89.0 to 114%. This SERS method has remarkable analytical performance, providing an effective approach for the early diagnosis of cardiovascular diseases, and has great latent capacity in the sensitive detection of bioactive molecules.

Correction for Extrinsic Background in Raman Hyperspectral Images

Raman hyperspectral microscopy is a valuable tool in biological and biomedical imaging. Because Raman scattering is often weak in comparison to other phenomena, prevalent spectral fluctuations and contaminations have brought advancements in analytical and chemometric methods for Raman spectra. These chemometric advances have been key contributors to the applicability of Raman imaging to biological systems. As studies increase in scale, spectral contamination from extrinsic background, intensity from sources such as the optical components that are extrinsic to the sample of interest, has become an emerging issue. Although existing baseline correction schemes often reduce intrinsic background such as autofluorescence originating from the sample of interest, extrinsic background is not explicitly considered, and these methods often fail to reduce its effects. Here, we show that extrinsic background can significantly affect a classification model using Raman images, yielding misleadingly high accuracies in the distinction of benign and malignant samples of follicular thyroid cell lines. To mitigate its effects, we develop extrinsic background correction (EBC) and demonstrate its use in combination with existing methods on Raman hyperspectral images. EBC isolates regions containing the smallest amounts of sample materials that retain extrinsic contributions that are specific to the device or environment. We perform classification both with and without the use of EBC, and we find that EBC retains biological characteristics in the spectra while significantly reducing extrinsic background. As the methodology used in EBC is not specific to Raman spectra, correction of extrinsic effects in other types of hyperspectral and grayscale images is also possible.

Multi-scale sequential feature selection for disease classification using Raman spectroscopy data

Raman spectroscopy (RS) optical technology promises non-destructive and fast application in medical disease diagnosis in a single step. However, achieving clinically relevant performance levels remains challenging due to the inability to search for significant Raman signals at different scales. Here we propose a multi-scale sequential feature selection method that can capture global sequential features and local peak features for disease classification using RS data. Specifically, we utilize the Long short-term memory network (LSTM) module to extract global sequential features in the Raman spectra, as it can capture long-term dependencies present in the Raman spectral sequences. Meanwhile, the attention mechanism is employed to select local peak features that were ignored before and are the key to distinguishing different diseases. Experimental results on three public and in-house datasets demonstrate the superiority of our model compared with state-of-the-art methods for RS classification. In particular, our model achieves an accuracy of 97.9 ± 0.2% on the COVID-19 dataset, 76.3 ± 0.4% on the H-IV dataset, and 96.8 ± 1.9% on the H-V dataset.

Magnetic Fe3O4@COF@Ag SERS substrate combined with machine learning algorithms for detection of three quinolone antibiotics: Ciprofloxacin, norfloxacin and levofloxacin

Quinolone antibiotics have good antibacterial properties and are commonly used antibiotics in the dairy industry. Currently, the problem of excessive antibiotics in dairy products is very serious. As an ultra-sensitive detection technology, Surface-Enhanced Raman Scattering (SERS) was applied to the detection of quinolone antibiotics in this work. In order to classify and quantify three antibiotics (Ciprofloxacin, Norfloxacin, Levofloxacin) with highly similar molecular structures, a combination of magnetic COF-based SERS substrate and machine learning algorithms (PCA-k-NN, PCA-SVM, PCA-Decision Tree) was used. The classification accuracy of the spectral dataset could reach 100% and the results of LOD calculation were: CIP: 5.61 × 10^-9M, LEV: 1.44 × 10^-8M, NFX: 1.56 × 10^-8M. This provides a new method for the detection of antibiotics in dairy products.

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis

Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.

Selectivity of natural isoquinoline alkaloid assembler in programming poly(dA) into parallel duplex by polyvalent synergy

Ligand-induced assembly of disordered DNAs attracts much attention due to its potential action in transcription regulation and molecular switches-based sensors. Among natural isoquinoline alkaloids (NIAs), we screened out nitidine (NIT) as polyvalent-binding assembler to program poly(dA) into a parallel duplex assembly at neutral pH. The molecule planarity of NIAs was believed to be a determinant factor in programming the parallel poly(dA) assembly. Poly(dA) with more than six adenines can initiate the synergistic binding of NIT to generate the parallel assembly. It is expected that one A-A pair in duplex can bind one NIT molecule provided that poly(dA) is long enough, suggesting the pivotal role of the polyvalent synergy of NIT in programming the parallel poly(dA) assembly. A gold nanoparticles-based colorimetric method was also developed to screen NIT out of NIAs having the potential to construct the poly(dA) assembly. Our work will inspire more interest in developing polyadenine-based switches and sensors by concentrating NIT within the polyadenine parallel assembly.

Activity-Based Self-Enriched SERS Sensor for Blood Metabolite Monitoring

The monitoring of metabolites in biofluids provides critical clues for disease diagnosis and evaluation. Yet, the quantitative detection of metabolites remains challenging for surface-enhanced Raman spectroscopy (SERS) due to poor reproducibility in preparation and manipulation of SERS nanoprobes. Herein, we develop an activity-based, slippery liquid-infused porous surface SERS (abSLIPSERS) sensor for facile quantification of metabolites with unmodified naked metal nanoparticles (NPs) by integrating biocatalysis-boronate oxidation cascades with SLIPS-driven self-concentration and delivering. Upon mixing the target metabolite with a specific oxidase, a H₂O₂-sensitive phenylboronate probe, and the naked Au NPs, H₂O₂ produced from the biocatalytic reaction oxidizes the phenylboronate probe to phenol, resulting in a ratiometric SERS response. Meanwhile, the SLIPS enables the complete enrichment of molecules and NPs within an evaporating liquid droplet, delivering the probes to the SERS-active sites for Raman amplification. Compared with conventional SERS biosensors, abSLIPSERS avoids multistep synthesis and biofunctionalization of nanoprobes, which significantly simplifies the detection workflow and improves the reproducibility. The abSLIPSERS sensor also shows tunable dynamic range beyond 4 orders of magnitude and allows quantifying any other metabolites with specific enzymes. We demonstrate abSLIPSERS sensing of lactate, glucose, and choline in human serum for exploring energy metabolism in lung cancer. This study opens up a new opportunity for future point-of-care testing of circulating metabolites by SERS and will help to facilitate the translation of SERS bioanalysis to clinical settings.

Rapid Detection of SARS-CoV-2 RNA in Human Nasopharyngeal Specimens Using Surface-Enhanced Raman Spectroscopy and Deep Learning Algorithms

A rapid and cost-effective method to detect the infection of SARS-CoV-2 is fundamental to mitigating the current COVID-19 pandemic. Herein, a surface-enhanced Raman spectroscopy (SERS) sensor with a deep learning algorithm has been developed for the rapid detection of SARS-CoV-2 RNA in human nasopharyngeal swab (HNS) specimens. The SERS sensor was prepared using a silver nanorod array (AgNR) substrate by assembling DNA probes to capture SARS-CoV-2 RNA. The SERS spectra of HNS specimens were collected after RNA hybridization, and the corresponding SERS peaks were identified. The RNA detection range was determined to be 103-109 copies/mL in saline sodium citrate buffer. A recurrent neural network (RNN)-based deep learning model was developed to classify 40 positive and 120 negative specimens with an overall accuracy of 98.9%. For the blind test of 72 specimens, the RNN model gave a 97.2% accuracy prediction for positive specimens and a 100% accuracy for negative specimens. All the detections were performed in 25 min. These results suggest that the DNA-functionalized AgNR array SERS sensor combined with a deep learning algorithm could serve as a potential rapid point-of-care COVID-19 diagnostic platform.

SERS-Based Biosensors Combined with Machine Learning for Medical Application

Surface-enhanced Raman spectroscopy (SERS) has shown strength in non-invasive, rapid, trace analysis and has been used in many fields in medicine. Machine learning (ML) is an algorithm that can imitate human learning styles and structure existing content with the knowledge to effectively improve learning efficiency. Integrating SERS and ML can have a promising future in the medical field. In this review, we summarize the applications of SERS combined with ML in recent years, such as the recognition of biological molecules, rapid diagnosis of diseases, developing of new immunoassay techniques, and enhancing SERS capabilities in semi-quantitative measurements. Ultimately, the possible opportunities and challenges of combining SERS with ML are addressed.

Highly porous gold supraparticles as surface-enhanced Raman spectroscopy (SERS) substrates for sensitive detection of environmental contaminants

Surface-enhanced Raman spectroscopy (SERS) has great potential as an analytical technique for environmental analyses. In this study, we fabricated highly porous gold (Au) supraparticles (i.e., ∼100 μm diameter agglomerates of primary nano-sized particles) and evaluated their applicability as SERS substrates for the sensitive detection of environmental contaminants. Facile supraparticle fabrication was achieved by evaporating a droplet containing an Au and polystyrene (PS) nanoparticle mixture on a superamphiphobic nanofilament substrate. Porous Au supraparticles were obtained through the removal of the PS phase by calcination at 500 °C. The porosity of the Au supraparticles was readily adjusted by varying the volumetric ratios of Au and PS nanoparticles. Six environmental contaminants (malachite green isothiocyanate, rhodamine B, benzenethiol, atrazine, adenine, and gene segment) were successfully adsorbed to the porous Au supraparticles, and their distinct SERS spectra were obtained. The observed linear dependence of the characteristic Raman peak intensity for each environmental contaminant on its aqueous concentration reveals the quantitative SERS detection capability by porous Au supraparticles. The limit of detection (LOD) for the six environmental contaminants ranged from ∼10 nM to ∼10 μM, which depends on analyte affinity to the porous Au supraparticles and analyte intrinsic Raman cross-sections. The porous Au supraparticles enabled multiplex SERS detection and maintained comparable SERS detection sensitivity in wastewater influent. Overall, we envision that the Au supraparticles can potentially serve as practical and sensitive SERS devices for environmental analysis applications.

Multi-scale convolution neural network with residual modules for determination of drugs in human hair using surface-enhanced Raman spectroscopy with a gold nanorod film self-assembled by inverted evaporation

Detection of illegal drug users is crucial in controlling drug-related crimes, reducing drug prevalence, and protecting human lives to ensure social stability. In this study, surface-enhanced Raman spectroscopy (SERS) and deep learning networks were employed to determine methamphetamine, ketamine, and morphine in human hair. Drugs were obtained from hair through alkaline hydrolysis and liquid-liquid extraction, and gold nanorods were employed to prepare the self-assembled film as the SERS substrate by inverted evaporation. The film showed good uniformity and excellent sensitivity, with a relative standard deviation of 15.6% and a detection limit of at least 10^-10 M in the SERS detection of crystal violet. The spectra of methamphetamine, ketamine, and morphine at 0.05-1.0, 0.1-2.0, and 0.1-2.0 ng/mg were obtained, and the three drugs could be detected. Inception, a multi-scale feature extraction network, was combined with residual modules (Inception-ResNet) to develop the identification models of drugs, and the effect of spectral input form as a vector or matrix was explored. Inception-ResNet with input form of matrix outweighed other methods with 100.00%, 100.00%, and 99.23% accuracies in the training, validation, and prediction sets, respectively. In brief, SERS and Inception-ResNet with the spectra in matrix form provide an efficient and accurate determination of drugs in human hair, enabling the retrospective evaluation of drug use, and the method will be anticipated to detect excitant, poison, and toxic chemicals in human hair.

Temperature-Responsive Nanocarrier-Regulated Alternative Release of "Cargos" for a Multiplex Photoelectrochemical Bioassay of Antibiotic-Resistant Genes

A smart temperature stimuli-driven multiplex photoelectrochemical (PEC) assay was constructed for antibiotic resistance genes (ARGs) detection, where the stimuli-responsive gatekeeping by regulating the alternative release of "cargo" allowed for the simultaneous detection of multiple tetracycline resistance gene, using tetA (T_DNA1) and tetC (T_DNA2) as the model. Dual temperature-responsive nanoassemblies were embedded in the PEC bioassay as signal DNA tages: one thermoresponsive polymer (poly(N-isopropylacrylamide), PNIPAM)-capped mesoporous silica nanoparticles (MSN) with loading the "cargo" of HgO nanoparticles as signal DNA1 tags (S_DNA1-PNIPAM@MSN@HgONPs) and the other antimony tartrate (SbT)-anchored silica nanospheres as signal DNA2 tags (S_DNA2-SbT@SiO₂NSs). At 20 °C, below the lower critical solution temperature (LCST) of PNIPAM, the "gatekeeper" PNIPAM in S_DNA1-PNIPAM@MSN@HgONPs was in an ON state, igniting Hg²⁺ release through the pore of SiO₂. While at above LCST (40 °C), it was in an OFF state. Likewise, the thermo-dependent dissociation of SbT endowed the grafted S_DNA2 tags switching from the OFF (at 20 °C) to ON state (at 40 °C), igniting SbO⁺ release. The released Hg²⁺ and SbO⁺ triggered the amplified photocurrents due to the structure evolution of the photoactive layer into HgS/ZnS or Sb₂S₃/ZnS heterostructure, thus achieving sensitive detection of multiple ARGs: tetA, tetC, tetG, tetM, tetO, tetZ, tetX, and tetW. Combined with heat map analysis, rapid screening of the ARGs profiles in 12 samples could be realized. This bioassay is simple and accessible for multiple genes analysis with the detection limit down to 0.50 nM. And it was successfully applied for measuring tetracycline ARGs in real sludge samples.

Micro Learning Support Vector Machine for Pattern Classification: A High-Speed Algorithm

The support vector machine theory has been developed into a very mature system at present. The original support vector machine to solve the optimization problem is transformed into a direct calculation formula of line in this paper and the model is o(n²) time complexity. In the model of this article, weited theory, multiclassification problem and online learning have all become the direct inference, and we have applied the new model to the UCI data set. We hope that in the future, this model will be useful in real-world problems such as stock forecasting, which require nonlinear hi-speed algorithms.

Application of Surface-Enhanced Raman Spectroscopy in the Screening of Pulmonary Adenocarcinoma Nodules

This study is aimed at evaluating the feasibility of a screening method for the pulmonary adenocarcinoma nodules through surface-enhanced Raman spectroscopy (SERS). Objective. Using SERS to measure serum from pulmonary nodules and healthy subjects, intraoperative biopsy pathological diagnosis was regarded as the gold standard for labeling serum samples. To explore the application value of SERS in the differential diagnosis of pulmonary adenocarcinoma nodules, benign nodules, and healthy, we build a machine learning model. Method. We collected 116 serum samples from patients. Radiographically confirmed nodules less than 3 cm in maximum diameter in all patients, including 58 cancer (pathologic diagnosis: adenocarcinoma nodules, labeled as cancer) patients, 58 pathologic diagnoses as benign nodule (labeled as benign) patients, and 63 healthy (labeled as normal) people from the clinical laboratory of Sichuan Cancer Hospital. Gold nanorods were employed as SERS substrates. Support vector machine (SVM) was used to classify the normal, benign, and cancer sample groups, and SVM model evaluated using cross-validation. Results. The average SERS spectra of serum were significantly different between the normal group and the cancer/benign group. While the average SERS spectra of the cancer group and the benign group differed slightly, for the cancer, benign, and normal groups, SVM models can predict with 93.33% accuracy. Conclusion. This exploratory study demonstrates that the SERS technique based on nanoparticles in conjunction with SVM has great potential as a clinical auxiliary diagnosis and screening for pulmonary adenocarcinoma nodules.

Inter-coffee-ring effects boost rapid and highly reliable SERS detection of TPhT on a light-confining structure

Triphenyltin chloride (TPhT) is a widely applied toxic compound that poses a significant threat to humans and the environment. Surface-enhanced Raman spectroscopy (SERS), capable of non-destructive, rapid, and trace detection, is desirable to better evaluate its distribution and content. However, a sensitive method with simple measuring protocols which maintains excellent reproducibility remains challenging. Here, we proposed an inter-coffee-ring effect to accelerate the sampling and measuring process while maintaining highly reproducible results. Two overlapping coffee-rings are formed through sequenced drying of gold nanorod colloids and a gold nanorod TPhT mixture on a superhydrophobic light-confining structure. Both the gold nanorods and the TPhT are enriched in the overlapping region. The gold nanorods reordered in such an area under the inter-coffee-ring effect yielded vast numbers of consistent hotspots at the sub-2 nm level. Such consistency leads to excellent SERS performance under the light-confining effect induced by the nanoarray substrates. The detection limits of the probe molecule R6G reached 10⁻¹² M, and TPhT reached 10⁻⁸ M while achieving excellent stability and reproducibility, and a linear regression coefficient above 0.99 was achieved for TPhT. Crucially, the visible nature of the inter-coffee-ring overlap enabled rapid measurements, thus providing robust support for detecting environmental pollutants.

Nanoparticles reassembling in the inter coffee-ring region simply through sequenced drying of two droplets enabled ultrasensitive and highly reliable SERS detection. A rapid test protocol is realized by exciting the visible inter-coffee-ring mark.

Serum fingerprinting by slippery liquid-infused porous SERS for non-invasive lung cancer detection

Direct and label-free analysis of clinical serum samples using slippery liquid-infused porous-enhanced Raman spectroscopy (SLIPSERS) enables the rapid non-invasive identification of lung cancer.

Raman spectrum matching with contrastive representation learning

An effective contrastive representation learning method for spectra identification with a frequentist guarantee of including the correct class prediction on two Raman datasets (Mineral and Organic) and one SERS dataset (Bacteria).