Label-Free Surface-Enhanced Raman Spectroscopy Biosensor for On-Site Breast Cancer Detection Using Human Tears

Transformative biomedical devices to overcome biomatrix effects

The emergence of high-performance biomedical devices and sensing technologies highlights the technological advancements in the field. Recently during COVID-19 pandemic, biosensors played an important role in medical diagnostics and disease monitoring. In the past few decades, biosensors have made impressive advances in terms of sensing capability, methodology, and applications, and modern biosensors show higher performance and functionality compared to traditional biosensing platforms. Currently, various biomedical devices are already in the market or on the verge of commercialization, such as disposable paper-based devices, lab-on-a-chip devices, wearable sensors, and artificial intelligence-assisted systems, all contributing to the evolution of digital health. Despite the promising features of detection methods for developing practical biosensors, there are substantial barriers to the commercialization of biomedical devices. An important challenge is the matrix effect in the detection of clinical samples. Although achieving low limit of detection values under controlled laboratory conditions is feasible, maintaining performance in real clinical samples is difficult. Matrix molecules present in these samples can interact with analytes, potentially affecting sensitivity, specificity, and sensor response. Approaches to reduce nonspecific adsorption and cross-reactivity are imperative for improving sensor performance. The detection of diagnostic biomarkers in complex biological matrices often requires laborious sample preparation, which may affect accuracy and precision. In this review, we highlight the recent efforts to detect analytes in real samples, both invasively and noninvasively, and underline technological advancements that mitigate the biomatrix effects. We also discuss commercially available biosensors and technologies promising commercial success, highlighting their potential effect on healthcare and diagnostics.

From population-based to personalized laboratory medicine: continuous monitoring of individual laboratory data with wearable biosensors

Monitoring individuals' laboratory data is essential for assessing their health status, evaluating the effectiveness of treatments, predicting disease prognosis and detecting subclinical conditions. Currently, monitoring is performed intermittently, measuring serum, plasma, whole blood, urine and occasionally other body fluids at predefined time intervals. The ideal monitoring approach entails continuous measurement of concentration and activity of biomolecules in all body fluids, including solid tissues. This can be achieved through the use of biosensors strategically placed at various locations on the human body where measurements are required for monitoring. High-tech wearable biosensors provide an ideal, noninvasive, and esthetically pleasing solution for monitoring individuals' laboratory data. However, despite significant advances in wearable biosensor technology, the measurement capacities and the number of different analytes that are continuously monitored in patients are not yet at the desired level. In this review, we conducted a literature search and examined: (i) an overview of the background of monitoring for personalized laboratory medicine, (ii) the body fluids and analytes used for monitoring individuals, (iii) the different types of biosensors and methods used for measuring the concentration and activity of biomolecules, and (iv) the statistical algorithms used for personalized data analysis and interpretation in monitoring and evaluation.

Cervical cancer screening by biomarker-free Serum-SERS technique: A three-principal-substrate approach

The complexity of serum constituents and the lack of knowledge on biomarkers bring obstacles for reliable cancer screening with the limited information obtained by surface-enhanced Raman scattering (SERS) on a single substrate. We report here an extremely high-accurate approach for cancer screening by biomarker-free serum-SERS technique which employs three principal SERS substrates that are of specially designed surface properties. With three substrates rich information on serum can be obtained from SERS spectra, of which quadratic discriminant analysis (QDA) yields excellent classifications of serum samples independently. Diagnosis with an accuracy of ∼100% can be achieved based on the three independent QDA classifications, by the unanimity principle. In this study, a screening accuracy of 100% was achieved by this approach for 122 diagnosed out of the 194 serum samples. The mechanism for the extremely high accuracy of this approach was also theoretically investigated. The three-principal-substrate approach opens up a path towards highly accurate biomarker-free cancer screening by serum-SERS technique.

Rapid detection of drug abuse via tear analysis using surface enhanced Raman spectroscopy and machine learning

With the growing global challenge of drug abuse, there is an urgent need for rapid, accurate, and cost-effective drug detection methods. This study introduces an innovative approach to drug abuse screening by quickly detecting ephedrine (EPH) in tears using drop coating deposition-surface enhanced Raman spectroscopy (DCD-SERS) combined with machine learning (ML). Using ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), the average concentration of EPH in tear fluid of Sprague-Dawley (SD) rats, measured over 3 h post-injection, was 1235 ng/mL. DCD-SERS effectively identified EPH in tear samples, with distinct Raman peaks observed at 1001 cm-1 and 1242 cm-1. To enable rapid analysis of complex SERS data, three ML algorithms-linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), and random forest (RF)-were employed. These algorithms achieved over 90% accuracy in distinguishing between EPH-injected and non-injected SD rats, with area under the ROC curve (AUC) values ranging from 0.9821 to 0.9911. This approach offers significant potential for law enforcement by being easily accessible, non-invasive and ethically appropriate for examinees, while being rapid, accurate, and affordable for examiners.

Subtype-Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics

This study explores the integration of Raman spectroscopy (RS) with machine learning for the early detection and subtyping of breast cancer using blood plasma samples. We performed detailed spectral analyses, identifying significant spectral patterns associated with cancer biomarkers. Our findings demonstrate the potential for classifying the four major subtypes of breast cancer at stage Ia with an average sensitivity and specificity of 90% and 95%, respectively, and a cross-validated macro-averaged area under the curve (AUC) of 0.98. This research highlights efforts to integrate vibrational spectroscopy with machine learning, enhancing cancer diagnostics through a non-invasive, personalised approach for early detection and monitoring disease progression. This study is the first of its kind to utilise RS and machine learning to classify the four major breast cancer subtypes at stage Ia.

A flexible plasmonic substrate for sensitive surface-enhanced Raman scattering-based detection of fentanyl

In this work, we demonstrate a straightforward and versatile approach for fabricating flexible SERS substrates for highly sensitive fentanyl detection. Our design strategy integrates the synthesis of a yolk-shell structured plasmonic nanomaterial with a flexible cellulose substrate. The resulting SERS platform demonstrates excellent sensing capabilities, achieving a fentanyl detection limit as low as 4.89 ng mL-1.

Recent advances of MOF-based SERS substrates in quantitative analysis of food contaminants: a review

Advancements in food-contaminant detection technologies can significantly improve food safety and human health. Surface-enhanced Raman spectroscopy (SERS) has become the preferred analytical method for food-safety detection owing to its numerous advantages, which include unique 'molecular fingerprinting' features, high sensitivity, rapid responses, and non-invasive characteristics. Raman-signal enhancements rely heavily on high-performance SERS substrates. In recent years, metal-organic framework (MOF)-based SERS substrates have gained attention as promising candidates for developing SERS technologies owing to their distinctive structures and functions. This review comprehensively examines recent advances in MOF-based SERS substrates, focusing on the main role of MOFs in SERS substrates as well as their typical categories and structures, construction methods, and representative applications in food-contaminant detection. First, the primary roles of MOFs in SERS substrates are briefly introduced. Next, a comprehensive overview of the typical categories and structures of MOF-based SERS substrates is discussed. Subsequently, a fundamental view of the general construction methods for MOF-based SERS substrates is presented. Next, the main applications of MOF-based SERS substrates for food-contaminant detection are summarised. Finally, challenges and perspectives, including improvements in SERS performance and stability, and the unification of SERS mechanisms, are addressed and discussed.

Applications of Raman spectroscopy in ocular biofluid detection

Ophthalmic and many systemic diseases may damage the eyes, resulting in changes in the composition and content of biomolecules in ocular biofluids such as aqueous humor and tear. Therefore, the biomolecules in biofluids are potential biomarkers to reveal pathological processes and diagnose diseases. Raman spectroscopy is a non-invasive, label-free, and cost-effective technique to provide chemical bond information of biomolecules and shows great potential in the detection of ocular biofluids. This review demonstrates the applications of Raman spectroscopy technology in detecting biochemical components in aqueous humor and tear, then summarizes the current problems encountered for clinical applications of Raman spectroscopy and looks forward to possible approaches to overcome technical bottlenecks. This work may provide a reference for wider applications of Raman spectroscopy in biofluid detection and inspire new ideas for the diagnosis of diseases using ocular biofluids.

SERS-based microdevices for use as in vitro diagnostic biosensors

Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.

Raman spectroscopy assisted tear analysis: A label free, optical approach for noninvasive disease diagnostics

In recent times, tear fluid analysis has garnered considerable attention in the field of biomarker-based diagnostics due to its noninvasive sample collection method. Tears encompass a reservoir of biomarkers that assist in diagnosing not only ocular disorders but also a diverse list of systemic diseases. This highlights the necessity for sensitive and dependable screening methods to employ tear fluid as a potential noninvasive diagnostic specimen in clinical environments. Considerable research has been conducted to investigate the potential of Raman spectroscopy-based investigations for tear analysis in various diagnostic applications. Raman Spectroscopy (RS) is a highly sensitive and label free spectroscopic technique which aids in investigating the molecular structure of samples by evaluating the vibrational frequencies of molecular bonds. Due to the distinct chemical compositions of different samples, it is possible to obtain a sample-specific spectral fingerprint. The distinctive spectral fingerprints obtained from Raman spectroscopy enable researchers to identify specific compounds or functional groups present in a sample, aiding in diverse biomedical applications. Its sensitivity to changes in molecular structure or environment provides invaluable insights into subtle alterations associated with various diseases. Thus, Raman Spectroscopy has the potential to assist in diagnosis and treatment as well as prognostic evaluation. Raman spectroscopy possesses several advantages, such as the non-destructive examination of samples, remarkable sensitivity to structural variations, minimal prerequisites for sample preparation, negligible interference from water, and the aptness for real-time investigation of tear samples. The purpose of this review is to highlight the potential of Raman spectroscopic technique in facilitating the clinical diagnosis of various ophthalmic and systemic disorders through non-invasive tear analysis. Additionally, the review delves into the advancements made in Raman spectroscopy with regards to paper-based sensing substrates and tear analysis methods integrated into contact lenses. Furthermore, the review also addresses the obstacles and future possibilities associated with implementing Raman spectroscopy as a routine diagnostic tool based on tear analysis in clinical settings.

Tear biomarkers

An extensive exploration of lacrimal fluid molecular biomarkers in understanding and diagnosing a spectrum of ocular and systemic diseases is presented. The chapter provides an overview of lacrimal fluid composition, elucidating the roles of proteins, lipids, metabolites, and nucleic acids within the tear film. Pooled versus single-tear analysis is discussed to underline the benefits and challenges associated with both approaches, offering insights into optimal strategies for tear sample analysis. Subsequently, an in-depth analysis of tear collection methods is presented, with a focus on Schirmer's test strips and microcapillary tubes methods. Alternative tear collection techniques are also explored, shedding light on their applicability and advantages. Variability factors, including age, sex, and diurnal fluctuations, are examined in the context of their impact on tear biomarker analysis. The main body of the chapter is dedicated to discussing specific biomarkers associated with ocular discomfort and a wide array of ocular diseases. From dry eye disease and thyroid-associated ophthalmopathy to keratoconus, age-related macular degeneration, diabetic retinopathy, and glaucoma, the intricate relationship between molecular biomarkers and these conditions is thoroughly dissected. Expanding beyond ocular pathologies, the chapter explores the applicability of tear biomarkers in diagnosing systemic diseases such as multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease, and cancer. This broader perspective underscores the potential of lacrimal fluid analysis in offering non-invasive diagnostic tools for conditions with far-reaching implications.

ZnO Nanocages Decorated with Au@AgAu Yolk-Shell Nanomaterials for SERS-Based Detection of Hyperuricemia

Surface-enhanced Raman scattering (SERS) is widely recognized as a highly sensitive technology for chemical detection and biological sensing. In SERS-based biomedical applications, developing highly efficient sensing platforms based on SERS plays a pivotal role in monitoring disease biomarker levels and facilitating the early detection of cancer biomarkers. Hyperuricemia, characterized by abnormally high concentrations of uric acid (UA) in the blood, was associated with a range of diseases, such as gouty arthritis, heart disease, and acute kidney injury. Recent reports have demonstrated the correlation between UA concentrations in blood and tears. In this work, we report the fabrication of SERS substrates utilizing ZnO nanocages and yolk-shell-structured plasmonic nanomaterials for the noninvasive detection of UA in tears. This innovative SERS substrate enables noninvasive and sensitive detection of UA to prevent hyperuricemia-related diseases.

Review of the application of the most current sophisticated image processing methods for the skin cancer diagnostics purposes

This paper presents the most current and innovative solutions applying modern digital image processing methods for the purpose of skin cancer diagnostics. Skin cancer is one of the most common types of cancers. It is said that in the USA only, one in five people will develop skin cancer and this trend is constantly increasing. Implementation of new, non-invasive methods plays a crucial role in both identification and prevention of skin cancer occurrence. Early diagnosis and treatment are needed in order to decrease the number of deaths due to this disease. This paper also contains some information regarding the most common skin cancer types, mortality and epidemiological data for Poland, Europe, Canada and the USA. It also covers the most efficient and modern image recognition methods based on the artificial intelligence applied currently for diagnostics purposes. In this work, both professional, sophisticated as well as inexpensive solutions were presented. This paper is a review paper and covers the period of 2017 and 2022 when it comes to solutions and statistics. The authors decided to focus on the latest data, mostly due to the rapid technology development and increased number of new methods, which positively affects diagnosis and prognosis.

2D TiS2-Nanosheet-Coated Concave Gold Arrays with Triple-Coupled Resonances as Sensitive SERS Substrates

Herein, a hybrid substrate for surface-enhanced Raman scattering (SERS) is fabricated, which couples localized surface plasmon resonance (LSPR), charge transfer (CT) resonance, and molecular resonance. Exfoliated 2D TiS2 nanosheets with semimetallic properties accelerate the CT with the tested analytes, inducing a remarkable chemical mechanism enhancement. In addition, the LSPR effect is coupled with a concave gold array located underneath the thin TiS2 nanosheet, providing a strong electromagnetic enhancement. The concave gold array is prepared by etching silicone nanospheres assembled on larger polystyrene nanospheres, followed by depositing a gold layer. The LSPR intensity near the gold layer can be adjusted by changing the layer thickness to couple the molecular and CT resonances, in order to maximize the SERS enhancement. The best SERS performance is recorded on TiS2-nanosheet-coated plasmonic substrates, with a detectable methylene blue concentration down to 10-13 m and an enhancement factor of 2.1 × 109 and this concentration is several orders of magnitude lower than that of the TiS2 nanosheet (10-11 m) and plasmonic substrates (10-9 m). The present hybrid substrate with triple-coupled resonance further shows significant advantages in the label-free monitoring of curcumin (a widely applied drug for treating multiple cancers and inflammations) in serum and urine.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Challenges and Opportunities of Using Fluorescent Metal Nanocluster-Based Colorimetric Assays in Medicine

Development of rapid colorimetric methods based on novel optical-active metal nanomaterials has provided methods for the detection of ions, biomarkers, cancers, etc. Fluorescent metal nanoclusters (FMNCs) have gained a lot of attention due to their unique physical, chemical, and optical properties providing numerous applications from rapid and sensitive detection to cellular imaging. However, because of very small color changes, their colorimetric applications for developing rapid tests based on the naked eye or simple UV-vis absorption spectrophotometry are still limited. FMNCs with peroxidase-like activity have significant potential in a wide variety of applications, especially for point-of-care diagnostics. In this review, the effect of using various capping agents and metals for the preparation of nanoclusters in their colorimetric sensing properties is explored, and the synthesis and detection mechanisms and the recent advances in their application for ultrasensitive chemical and biological analysis regarding human health are highlighted. Finally, the challenges that remain as well as the future perspectives are briefly discussed. Overcoming these limitations will allow us to expand the nanocluster's application for colorimetric diagnostic purposes in medical practice.

Hand-held Raman spectrometer-based flexible plasmonic biosensor for label-free multiplex urinalysis

Urinalysis is an effective strategy to non-invasively evaluate human health, and surface-enhanced Raman scattering (SERS) may be a powerful technique for use in detecting analytes in urine. Herein, we report a wearable diaper sensor based on a handheld Raman spectrometer for use in the simple, label-free identification of biomolecules (urea, creatinine, and bilirubin) in urine. The raspberry-shaped Au substrate formed on the surface of an Si wafer provides plasmonic enhancement of the SERS signals, with an excellent uniformity and stability. The SERS sensor combines the advantages of flexibility, portability, and multifunctional detection and may be used in identifying multiple analytes in urine. The sensor exhibits high sensitivities in detecting urea, creatinine, and bilirubin, with respective detection limits of 4.17 × 10-3 M, 5.90 × 10-6 M, and 1.38 × 10-7 M (signal-to-noise ratio = 3). Furthermore, we used the wearable diaper sensor to monitor biomolecules at the diagnostic threshold, facilitating non-invasive diagnosis and medical monitoring of disease-related biomarkers.

Early on-site detection of strawberry anthracnose using portable Raman spectroscopy

We developed a method for the early on-site detection of strawberry anthracnose using a portable Raman system with multivariate statistical analysis algorithms. By using molecular markers based on Raman spectra, the proposed method can detect anthracnose in strawberry stems 3 days after exposure to Colletotrichum gloeosporioides. A fiber-optic probe was applied for the portable Raman system, and the acquisition time was 10 s. We found that the molecular markers were closely related to the following subjects: i) an increase in amide III and fatty acids of C. gloeosporioides invading strawberry stems (Raman bands at 1180-1310 cm-1) and ii) a decrease in metabolites in strawberry plants, such as phenolic compounds and terpenoids (Raman bands at 760, 800, and 1523 cm-1). We also found that the increased fluorescence background caused by various chromophores within the invading C. gloeosporioides could serve as a marker. A two-dimensional cluster plot obtained by principal component analysis (PCA) showed that the three groups (control, fungal infection, and pathogen) were distinguishable. The linear discriminant analysis (LDA)-based prediction algorithm could identify C. gloeosporioides infection with a posterior probability of over 40%, even when no symptoms were visible on the inoculated strawberry plants.

Amplified fluorogenic immunoassay for early diagnosis and monitoring of Alzheimer's disease from tear fluid

Accurate diagnosis of Alzheimer's disease (AD) in its earliest stage can prevent the disease and delay the symptoms. Therefore, more sensitive, non-invasive, and simple screening tools are required for the early diagnosis and monitoring of AD. Here, we design a self-assembled nanoparticle-mediated amplified fluorogenic immunoassay (SNAFIA) consisting of magnetic and fluorophore-loaded polymeric nanoparticles. Using a discovery cohort of 21 subjects, proteomic analysis identifies adenylyl cyclase-associated protein 1 (CAP1) as a potential tear biomarker. The SNAFIA demonstrates a low detection limit (236 aM), good reliability (R2 = 0.991), and a wide analytical range (0.320-1000 fM) for CAP1 in tear fluid. Crucially, in the verification phase with 39 subjects, SNAFIA discriminates AD patients from healthy controls with 90% sensitivity and 100% specificity in under an hour. Utilizing tear fluid as a liquid biopsy, SNAFIA could potentially aid in long-term care planning, improve clinical trial efficiency, and accelerate therapeutic development for AD.

Raman Spectroscopy Spectral Fingerprints of Biomarkers of Traumatic Brain Injury

Traumatic brain injury (TBI) affects millions of people of all ages around the globe. TBI is notoriously hard to diagnose at the point of care, resulting in incorrect patient management, avoidable death and disability, long-term neurodegenerative complications, and increased costs. It is vital to develop timely, alternative diagnostics for TBI to assist triage and clinical decision-making, complementary to current techniques such as neuroimaging and cognitive assessment. These could deliver rapid, quantitative TBI detection, by obtaining information on biochemical changes from patient's biofluids. If available, this would reduce mis-triage, save healthcare providers costs (both over- and under-triage are expensive) and improve outcomes by guiding early management. Herein, we utilize Raman spectroscopy-based detection to profile a panel of 18 raw (human, animal, and synthetically derived) TBI-indicative biomarkers (N-acetyl-aspartic acid (NAA), Ganglioside, Glutathione (GSH), Neuron Specific Enolase (NSE), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase L1 (UCHL1), Cholesterol, D-Serine, Sphingomyelin, Sulfatides, Cardiolipin, Interleukin-6 (IL-6), S100B, Galactocerebroside, Beta-D-(+)-Glucose, Myo-Inositol, Interleukin-18 (IL-18), Neurofilament Light Chain (NFL)) and their aqueous solution. The subsequently derived unique spectral reference library, exploiting four excitation lasers of 514, 633, 785, and 830 nm, will aid the development of rapid, non-destructive, and label-free spectroscopy-based neuro-diagnostic technologies. These biomolecules, released during cellular damage, provide additional means of diagnosing TBI and assessing the severity of injury. The spectroscopic temporal profiles of the studied biofluid neuro-markers are classed according to their acute, sub-acute, and chronic temporal injury phases and we have further generated detailed peak assignment tables for each brain-specific biomolecule within each injury phase. The intensity ratios of significant peaks, yielding the combined unique spectroscopic barcode for each brain-injury marker, are compared to assess variance between lasers, with the smallest variance found for UCHL1 (σ2 = 0.000164) and the highest for sulfatide (σ2 = 0.158). Overall, this work paves the way for defining and setting the most appropriate diagnostic time window for detection following brain injury. Further rapid and specific detection of these biomarkers, from easily accessible biofluids, would not only enable the triage of TBI, predict outcomes, indicate the progress of recovery, and save healthcare providers costs, but also cement the potential of Raman-based spectroscopy as a powerful tool for neurodiagnostics.

3D Printing for Cancer Diagnosis: What Unique Advantages Are Gained?

Cancer is a complex disease with global significance, necessitating continuous advancements in diagnostics and treatment. 3D printing technology has emerged as a revolutionary tool in cancer diagnostics, offering immense potential in detection and monitoring. Traditional diagnostic methods have limitations in providing molecular and genetic tumor information that is crucial for personalized treatment decisions. Biomarkers have become invaluable in cancer diagnostics, but their detection often requires specialized facilities and resources. 3D printing technology enables the fabrication of customized sensor arrays, enhancing the detection of multiple biomarkers specific to different types of cancer. These 3D-printed arrays offer improved sensitivity, allowing the detection of low levels of biomarkers, even in complex samples. Moreover, their specificity can be fine-tuned, reducing false-positive and false-negative results. The streamlined and cost-effective fabrication process of 3D printing makes these sensor arrays accessible, potentially improving cancer diagnostics on a global scale. By harnessing 3D printing, researchers and clinicians can enhance early detection, monitor treatment response, and improve patient outcomes. The integration of 3D printing in cancer diagnostics holds significant promise for the future of personalized cancer care.

Application of biosensors in cancers, an overview

The deadliest disease in the world, cancer, kills many people every year. The early detection is the only hope for the survival of malignant cancer patients. As a result, in the preliminary stages of, the diagnosis of cancer biomarkers at the cellular level is critical for improving cancer patient survival rates. For decades, scientists have focused their efforts on the invention of biosensors. Biosensors, in addition to being employed in other practical scenarios, can essentially function as cost effective and highly efficient devices for this purpose. Traditional cancer screening procedures are expensive, time-consuming, and inconvenient for repeat screenings. Biomarker-based cancer diagnosis, on the other hand, is rising as one of the most potential tools for early detection, disease progression monitoring, and eventual cancer treatment. As Biosensor is an analytical device, it allows the selected analyte to bind to the biomolecules being studied (for example RNA, DNA, tissue, proteins, and cells). They can be divided based on the kind of biorecognition or transducer elements on the sensor. Most biosensor analyses necessitate the analyte being labeled with a specific marker. In this review article, the application of distinct variants of biosensors against cancer has been described.

Plasmonic nanomaterials-based flexible strips for the SERS detection of gouty arthritis

Flexible biochips that enable sensitive detection and simultaneous quantification of biomarkers are of great importance in the field of point-of-care testing. Recently, surface-enhanced Raman scattering (SERS)-based flexible biochips have attracted a great deal of research attention for disease detection due to their rapid, sensitive, and noninvasive sensing abilities. Phenomenal progress in the synthesis of structure-controlled plasmonic nanomaterials has made SERS a powerful sensing platform for disease diagnosis and trace detection. Here, we demonstrate flexible plasmonic biochips for the SERS-based detection of uric acid (UA). Flexible strips exhibited excellent sensing performance with a detection limit of around 10 μM of UA, which is lower than the average level of UA in tears. This rapid and sensitive detection method enables the noninvasive diagnosis of gouty arthritis.

Ag NPs@PDMS nanoripple array films as SERS substrates for rapid in situ detection of pesticide residues

The large-area fabrication of flexible and transparent surface-enhanced Raman scattering (SERS) substrates with high performance by a facile and efficient method is still challenging. Here, we demonstrated a large-scale, flexible and transparent SERS substrate composed of PDMS nanoripple array film decorated with silver nanoparticles (Ag NPs@PDMS-NR array film) prepared by a combination of plasma treatment and magnetron sputtering. The performances of SERS substrates were characterized by rhodamine 6G (R6G) using a handheld Raman spectrometer. The optimal Ag NPs@PDMS-NR array film exhibited high SERS sensitivity, with a detection limitation of R6G reaching 8.20 × 10^-8 M as well as excellent uniformity (RSD = 6.8%) and batch-to-batch reproducibility (RSD = 2.3%). In addition, the substrate showed outstanding mechanical stability and good SERS enhancement by backside illumination, thus it was suitable for in situ SERS detection on curved surfaces. The detection limit of malachite green on apple and tomato peels was 1.19 × 10^-7 and 1.16 × 10^-7 M, respectively, and quantitative analysis of pesticide residues could be realized. These results demonstrate that the Ag NPs@PDMS-NR array film has great practical potential in rapid in situ detection of pollutants.

Discovering the digital biomarker of hepatocellular carcinoma in serum with SERS-based biosensors and intelligence vision

By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.

An adhesive SERS substrate based on a stretched silver nanowire-tape for the in situ multicomponent analysis of pesticide residues

Two essential factors in powerful surface-enhanced Raman spectroscopy analysis of trace pesticide residues are viz., high sensitivity and efficient sampling. Herein, owing to elastic properties, a stretched Ag nanowire (Ag NW)-tape under the strain of 15% formed a wrinkled structure with periodic microridges and microgrooves, where abundant nanogaps were generated by the aggregated Ag NWs. Compared with the unstretched Ag NW-tape substrate, an appreciable signal enhancement of the modified 4-mercaptobenzoic acid (4-MBA) molecules with a ratio of 2.6 was discerned from the sophisticated SERS substrate due to the electromagnetic enhancement induced by the relatively high density of "hot spots" around the Ag NW aggregates. The as-fabricated Ag NW-tape substrate performed admirably in detecting 4-MBA and demonstrated an enhancement factor of 1.16 × 10⁶. Moreover, for the in situ detection of tetramethylthiuram disulfide, thiabendazole, and their mixture, the relatively high recovery rates of over 88% were favorably realized by the Ag NW-tape substrate with superior sensitivity, distinct flexibility, and adhesiveness. This fascinating SERS substrate, dependent on the flexible and adhesive Ag NW-tape, is promising for application in SERS analysis of trace residues on various practical surfaces.

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.

A novel highly sensitive compilation-detachment fluorescence sensing strategy based on RNA-cleavage DNAzyme for MDA-MB-231 breast cancer biomarker determination

Herein, we designed a novel and highly sensitive fluorescence multicomponent detachable platform for MDA-MB-231 breast cancer cell detection as a model. The RNA cleavage DNAzyme was used as a central operator of the multicomponent probe through which compilation and induced detachment of probe was done. During the compilation step, the dsDNA-Sybr green 1 complexes on gold nanoparticles (GNP@dsDNA@SG1) were assembled. The intercalated Sybr green in the DNA structure has been used as an amplified signal generator on one site of DNAzyme and magnetic nanoparticles (MNP) act as a biological carrier and probe collector on the opposite side. The enzyme activator co-factor (MDA-MB-231 cell cytoplasmic protein) provokes the activation of the catalytic core of enzyme sequence in the DNAzyme molecule, followed by cleavage reaction in the substrate sequence and releasing GNP@ dsDNA@SG1 into the solution. The results indicate that the Sybr green emission fluorescence (520 nm) increases with the increment of MDA-MB-231 protein concentration in the linear dynamic range of 8.10 × 10^-2 to 1.95 ng ml^-1 (0.77 × 10^-3-0.019 cell ml^-1) with a detection limit (LOD) of 1/72 × 10^-2 pg ml^-1 under optimal conditions. The proposed immunosensor has great potential in developing ultrasensitive and rapid diagnostic platforms.

Overview of Optical Biosensors for Early Cancer Detection: Fundamentals, Applications and Future Perspectives

Conventional cancer detection and treatment methodologies are based on surgical, chemical and radiational processes, which are expensive, time consuming and painful. Therefore, great interest has been directed toward developing sensitive, inexpensive and rapid techniques for early cancer detection. Optical biosensors have advantages in terms of high sensitivity and being label free with a compact size. In this review paper, the state of the art of optical biosensors for early cancer detection is presented in detail. The basic idea, sensitivity analysis, advantages and limitations of the optical biosensors are discussed. This includes optical biosensors based on plasmonic waveguides, photonic crystal fibers, slot waveguides and metamaterials. Further, the traditional optical methods, such as the colorimetric technique, optical coherence tomography, surface-enhanced Raman spectroscopy and reflectometric interference spectroscopy, are addressed.

Review: Emerging Eye-Based Diagnostic Technologies for Traumatic Brain Injury

The study of ocular manifestations of neurodegenerative disorders, Oculomics, is a growing field of investigation for early diagnostics, enabling structural and chemical biomarkers to be monitored overtime to predict prognosis. Traumatic brain injury (TBI) triggers a cascade of events harmful to the brain, which can lead to neurodegeneration. TBI, termed the "silent epidemic" is becoming a leading cause of death and disability worldwide. There is currently no effective diagnostic tool for TBI, and yet, early-intervention is known to considerably shorten hospital stays, improve outcomes, fasten neurological recovery and lower mortality rates, highlighting the unmet need for techniques capable of rapid and accurate point-of-care diagnostics, implemented in the earliest stages. This review focuses on the latest advances in the main neuropathophysiological responses and the achievements and shortfalls of TBI diagnostic methods. Validated and emerging TBI-indicative biomarkers are outlined and linked to ocular neuro-disorders. Methods detecting structural and chemical ocular responses to TBI are categorised along with prospective chemical and physical sensing techniques. Particular attention is drawn to the potential of Raman spectroscopy as a non-invasive sensing of neurological molecular signatures in the ocular projections of the brain, laying the platform for the first tangible path towards alternative point-of-care diagnostic technologies for TBI.

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements' presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.

Nanomaterials meet surface-enhanced Raman scattering towards enhanced clinical diagnosis: a review

Surface-enhanced Raman scattering (SERS) is a very promising tool for the direct detection of biomarkers for the diagnosis of i.e., cancer and pathogens. Yet, current SERS strategies are hampered by non-specific interactions with co-existing substances in the biological matrices and the difficulties of obtaining molecular fingerprint information from the complex vibrational spectrum. Raman signal enhancement is necessary, along with convenient surface modification and machine-based learning to address the former issues. This review aims to describe recent advances and prospects in SERS-based approaches for cancer and pathogens diagnosis. First, direct SERS strategies for key biomarker sensing, including the use of substrates such as plasmonic, semiconductor structures, and 3D order nanostructures for signal enhancement will be discussed. Secondly, we will illustrate recent advances for indirect diagnosis using active nanomaterials, Raman reporters, and specific capture elements as SERS tags. Thirdly, critical challenges for translating the potential of the SERS sensing techniques into clinical applications via machine learning and portable instrumentation will be described. The unique nature and integrated sensing capabilities of SERS provide great promise for early cancer diagnosis or fast pathogens detection, reducing sanitary costs but most importantly allowing disease prevention and decreasing mortality rates.

Prospects of microbial-engineering for the production of graphene and its derivatives: Application to design nanosystms for cancer theranostics

Cancer is known as one of the leading causes of morbidity and fatality, currently faced by our society. The prevalence of cancer related dieses is rapidly increasing around the world. To reduce the mortality rates, early diagnosis and subsequent treatment of cancer in timely manner is quite essential. Advancements have been made to achieve effective theranostics strategies to tackle cancerous dieses, yet very challenging to overcome this issue. Recently, advances made in the field of nanotechnology have shown tremendous potential for cancer theranostics. Different types of nanomaterials have been successfully employed to develop sophisticated diagnosis and therapy techniques. In this context, graphene and its derivatives e.g. graphene oxide (GO) and reduced graphene oxide (RGO) have been investigated as promising candidates to design graphene-based nanosystems for the diagnosis and therapeutic purpose. Further, to synthesize graphene and its derivatives different types of physicochemical methods are being adopted. However, each method has its own advantage and disadvantages. In this reference, among diverse biological methods, microbial technique can be one of the most promising and eco-friendly approach for the preparation of graphene and its derivatives, particularly GO and RGO. In this review, we summarize studies performed on the preparation of graphene and its derivatives following microbial routes meanwhile focus has been made on the preparation method and the possible mechanism involved therein. Thereafter, we have discussed applications of graphene and its derivatives to developed advanced nanosystem that can be imperative for the cancer theranostics. Results of recent studies exploring applications graphene based nanosystem for the preparation of different types of biosensors for early diagnosis; advanced therapeutic approaches by designing drug delivery nanosystems along with multifunctionality (e.g cancer imaging, drug delivery, photodynamic and photo thermal therapy) in cancer theranostics have been discussed. Particularly, emphasis has been given on the preparation techniques of graphene based nanosystems, being employed in designing of biosensing platforms, drug delivery and multifunctional nanosystems. Moreover, issues have been discussed on the preparation of graphene and its derivatives following microbial technique and the implementation of graphene based nanosystems in cancer theranostics.

Recent Progress on Solid Substrates for Surface-Enhanced Raman Spectroscopy Analysis

Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy technique with distinguished features of non-destructivity, ultra-sensitivity, rapidity, and fingerprint characteristics for analysis and sensors. The SERS signals are mainly dependent on the engineering of high-quality substrates. Recently, solid SERS substrates with diverse forms have been attracting increasing attention due to their promising features, including dense hot spot, high stability, controllable morphology, and convenient portability. Here, we comprehensively review the recent advances made in the field of solid SERS substrates, including their common fabrication methods, basic categories, main features, and representative applications, respectively. Firstly, the main categories of solid SERS substrates, mainly including membrane substrate, self-assembled substrate, chip substrate, magnetic solid substrate, and other solid substrate, are introduced in detail, as well as corresponding construction strategies and main features. Secondly, the typical applications of solid SERS substrates in bio-analysis, food safety analysis, environment analysis, and other analyses are briefly reviewed. Finally, the challenges and perspectives of solid SERS substrates, including analytical performance improvement and largescale production level enhancement, are proposed.

Label-Free Surface Enhanced Raman Spectroscopy for Cancer Detection

Blood is a vital reservoir housing numerous disease-related metabolites and cellular components. Thus, it is also of interest for cancer diagnosis. Surface-enhanced Raman spectroscopy (SERS) is widely used for molecular detection due to its very high sensitivity and multiplexing properties. Its real potential for cancer diagnosis is not yet clear. In this study, using silver nanoparticles (AgNPs) as substrates, a number of experimental parameters and scenarios were tested to disclose the potential for this technique for cancer diagnosis. The discrimination of serum samples from cancer patients, healthy individuals and patients with chronic diseases was successfully demonstrated with over 90% diagnostic accuracies. Moreover, the SERS spectra of the blood serum samples obtained from cancer patients before and after tumor removal were compared. It was found that the spectral pattern for serum from cancer patients evolved into the spectral pattern observed with serum from healthy individuals after the removal of tumors. The data strongly suggests that the technique has a tremendous potential for cancer detection and screening bringing the possibility of early detection onto the table.

A Separation-Sensing Platform Performing Accurate Diagnosis of Jaundice in Complex Biological Tear Fluids

The detection of biomarkers in tears has aroused great interest owing to the advantages of non-invasive and rapid collection. The combination of ultrasensitivity and label-free detection of surface-enhanced Raman spectroscopy (SERS) sensors is expected to achieve real-time diagnosis in home medical care. However, the surface of SERS sensors is susceptible to biofouling and inactivation by biological impurities in tears, resulting in rapid degradation of sensitivity, limiting the commercialization of point-of-care devices. Herein, a binary nanosphere array with dual properties is constructed as a separation-sensing platform for the diagnosis of target molecules in tears. The upper part of the structure is composed of Au nanoparticles (AuNPs) and a sputtering Au layer, which can bind the target molecules that interact with Au and provide high-strength and high-density SERS hotspots. The lower half is an inactive SiO₂ nanosphere array with periodic large pores that allows biological impurities to penetrate the lower part and be separated from the target analyte. Furthermore, this substrate was integrated into homemade tear kits, enabling simultaneous tear collection, pre-separation, and detection. Combined with the Raman spectra of tears and LDA analysis, we successfully identified patients with jaundice in clinics. This platform is expected to provide an opportunity for early disease screening based on biological fluids.

How Surface-Enhanced Raman Spectroscopy Could Contribute to Medical Diagnoses

In the last decade, there has been a rapid increase in the number of surface-enhanced Raman scattering (SERS) spectroscopy applications in medical research. In this article we review some recent, and in our opinion, most interesting and promising applications of SERS spectroscopy in medical diagnostics, including those that permit multiplexing within the range important for clinical samples. We focus on the SERS-based detection of markers of various diseases (or those whose presence significantly increases the chance of developing a given disease), and on drug monitoring. We present selected examples of the SERS detection of particular fragments of DNA or RNA, or of bacteria, viruses, and disease-related proteins. We also describe a very promising and elegant ‘lab-on-chip’ approach used to carry out practical SERS measurements via a pad whose action is similar to that of a pregnancy test. The fundamental theoretical background of SERS spectroscopy, which should allow a better understanding of the operation of the sensors described, is also briefly outlined. We hope that this review article will be useful for researchers planning to enter this fascinating field.

Raman spectroscopy: current applications in breast cancer diagnosis, challenges and future prospects

Despite significant improvements in the way breast cancer is managed and treated, it continues to persist as a leading cause of death worldwide. If detected and diagnosed early, when tumours are small and localised, there is a considerably higher chance of survival. However, current methods for detection and diagnosis lack the required sensitivity and specificity for identifying breast cancer at the asymptomatic or very early stages. Thus, there is a need to develop more rapid and reliable methods, capable of detecting disease earlier, for improved disease management and patient outcome. Raman spectroscopy is a non-destructive analytical technique that can rapidly provide highly specific information on the biochemical composition and molecular structure of samples. In cancer, it has the capacity to probe very early biochemical changes that accompany malignant transformation, even prior to the onset of morphological changes, to produce a fingerprint of disease. This review explores the application of Raman spectroscopy in breast cancer, including discussion on its capabilities in analysing both ex-vivo tissue and liquid biopsy samples, and its potential in vivo applications. The review also addresses current challenges and potential future uses of this technology in cancer research and translational clinical application.

Exosome detection via surface-enhanced Raman spectroscopy for cancer diagnosis

As nanoscale extracellular vesicles, exosomes are secreted by various cell types, and they are widely distributed in multiple biological fluids. Studies have shown that tumor-derived exosomes can carry a variety of primary tumor-specific molecules, which may represent a novel tool for the early detection of cancer. However, the clinical translation of exosomes remains a challenge due to the requirement of large quantities of samples when enriching the cancer-related exosomes in biological fluids, the insufficiency of traditional techniques for exosome subpopulations, and the complex exosome isolation of the current commercially available exosome phenotype profiling approaches. The evolving surface-enhanced Raman scattering (SERS) technology, with properties of unique optoelectronics, easy functionalization, and the particular interaction between light and nanoscale metallic materials, can achieve sensitive detection of exosomes without large quantities of samples and multiplexed phenotype profiling, providing a new mode of real-time and noninvasive analysis for cancer patients. In the present review, we mainly discussed exosome detection based on SERS, especially SERS immunoassay. The basic structure and function of exosomes were firstly introduced. Then, recent studies using the SERS technique for cancer detection were critically reviewed, which mainly included various SERS substrates, biological modification of SERS substrates, SERS-based exosome detection, and the combination of SERS and other technologies for cancer diagnosis. This review systematically discussed the essential aspects, limitations, and considerations of applying SERS technology in the detection and analysis of cancer-derived exosomes, which could provide a valuable reference for the early diagnosis of cancer through SERS technology. STATEMENT OF SIGNIFICANCE: Surface-enhanced Raman scattering (SERS) has been applied to exosomes detection to obtain better diagnostic results. In past three years, several reviews have been published in exosome detection, which were narrowly focus on methods of exosome detection. Selection and surface functionalization of the substrate and the combination detection with different methods based on SERS will provide new strategies for the detection of exosomes. This review will focus on the above aspects. This emerging detection method is constantly evolving and contributing to the early discovery of diseases in the future.

High and stable surface-enhanced Raman spectroscopy activity of h-BN nanosheet/Au1Ag3 nanoalloy hybrid membrane for melamine determination

In this work, a hexagonal boron nitride (h-BN)/AuAg nanoalloy hybrids (NAHs) was synthesized to fabricate h-BN/Au1Ag3 membrane as a solid surface-enhanced Raman spectroscopy (SERS) substrate for sensitive SERS detection of melamine. The AuAg nanoalloys were in situ grown on h-BN by chemical reduction method, and the Au/Ag molar ratio was tuned to achieve optimal SERS performance. After the SERS performance of h-BN/AuAg NAHs with different Au/Ag ratios was analyzed, h-BN/Au1Ag3 NAHs were chosen for SERS analysis. The h-BN/Au1Ag3 membrane can be obtained through simple filtration of h-BN/Au1Ag3 NAHs on chromatographic paper. As expected, the solid SERS substrates of h-BN/Au1Ag3 membrane were uniform and demonstrated good selectivity, repeatability and reproducibility for SERS detection of melamine. The results demonstrate that h-BN/Au1Ag3 membrane exhibited high SERS activity for 4-mercaptobenzoic acid (4-MBA) with limit of detection (LOD) at 1.0 ng L-1, and its analytical enhancement factor (AEF) reached 3.6 × 108. The possible enhancement mechanism, including electromagnetic mechanisms (EM) and chemical mechanisms (CM) were illustrated by finite-difference time-domain (FDTD) and density functional theory (DFT) simulations in detail, respectively. The concentration of melamine in the 0.05-5.0 mg L-1 range showed good linear relationship (R2 = 0.9940) with SERS intensity with LOD of 0.01 mg L-1. Finally, the recoveries of melamine in liquid milk samples are 87.7-105.7% with relative standard deviations (RSDs) in range of 0.6-2.6%, providing precise safety evaluation of melamine in milk samples.

Smart Contact Lens with Dual-Sensing Platform for Monitoring Intraocular Pressure and Matrix Metalloproteinase-9

Contact lenses have become a popular health-monitoring wearable device due to their direct contact with the eyes. By integrating biosensors into contact lenses, real-time and noninvasive diagnoses of various diseases can be realized. However, current contact lens sensors often require complex electronics, which may obstruct the user's vision or even damage the cornea. Moreover, most of the reported contact lens sensors can only detect one analyte. Therefore, an optical-based dual-functional smart contact lens sensor has been introduced to monitor intraocular pressure (IOP) and detect matrix metalloproteinase-9 (MMP-9), both of which are key biomarkers in many eye-related diseases such as glaucoma. Specifically, the elevated IOP is continuously monitored by applying an antiopal structure through color changes, without any complex electronics. Together with the peptide modified gold nanobowls (AuNBs) surface-enhanced Raman scattering (SERS) substrate, the quantitative analysis of MMP-9 at a low nanomolar range is achieved in real tear samples. The dual-sensing functions are thus demonstrated, providing a convenient, noninvasive, and potentially multifunctional sensing platform for monitoring health and diagnostic biomarkers in human tears.

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Raman, Infrared and Brillouin Spectroscopies of Biofluids for Medical Diagnostics and for Detection of Biomarkers

This review surveys Infrared, Raman/SERS and Brillouin spectroscopies for medical diagnostics and detection of biomarkers in biofluids, that include urine, blood, saliva and other biofluids. These optical sensing techniques are non-contact, noninvasive and relatively rapid, accurate, label-free and affordable. However, those techniques still have to overcome some challenges to be widely adopted in routine clinical diagnostics. This review summarizes and provides insights on recent advancements in research within the field of vibrational spectroscopy for medical diagnostics and its use in detection of many health conditions such as kidney injury, cancers, cardiovascular and infectious diseases. The six comprehensive tables in the review and four tables in supplementary information summarize a few dozen experimental papers in terms of such analytical parameters as limit of detection, range, diagnostic sensitivity and specificity, and other figures of merits. Critical comparison between SERS and FTIR methods of analysis reveals that on average the reported sensitivity for biomarkers in biofluids for SERS vs FTIR is about 103 to 105 times higher, since LOD SERS are lower than LOD FTIR by about this factor. High sensitivity gives SERS an edge in detection of many biomarkers present in biofluids at low concentration (nM and sub nM), which can be particularly advantageous for example in early diagnostics of cancer or viral infections.HighlightsRaman, Infrared spectroscopies use low volume of biofluidic samples, little sample preparation, fast time of analysis and relatively inexpensive instrumentation.Applications of SERS may be a bit more complicated than applications of FTIR (e.g., limited shelf life for nanoparticles and substrates, etc.), but this can be generously compensated by much higher (by several order of magnitude) sensitivity in comparison to FTIR.High sensitivity makes SERS a noninvasive analytical method of choice for detection, quantification and diagnostics of many health conditions, metabolites, and drugs, particularly in diagnostics of cancer, including diagnostics of its early stages.FTIR, particularly ATR-FTIR can be a method of choice for efficient sensing of many biomarkers, present in urine, blood and other biofluids at sufficiently high concentrations (mM and even a few µM)Brillouin scattering spectroscopy detecting visco-elastic properties of probed liquid medium, may also find application in clinical analysis of some biofluids, such as cerebrospinal fluid and urine.

Recent Advances in Engineered Noble Metal Nanomaterials as a Surface-Enhanced Raman Scattering Active Platform for Cancer Diagnostics

Recently, noble metal nanomaterials have been extensively studied in the fields of biosensing, environmental catalysis, and cancer diagnosis and treatment, due to their excellent electrical conductivity, high surface area, and individual physical and optical properties. Early research on the surface-enhanced Raman scattering (SERS) effect was focused on the cognition of the SERS phenomenon and enhancing its sensitivity for single-molecule detection. With the development of nanomaterials and nanotechnology, the advances and applications based on SERS substrates have been accelerated. Among them, noble metal nanomaterials are mainly used as SERS-active substrates to enhance SERS signals owing to their compelling surface plasmon resonance (SPR) properties. This review provides recent advances, perspectives, and challenges in SERS assays based on engineered noble metal nanomaterials for early cancer diagnosis.

Rapid and non-invasive discrimination of acute leukemia bone marrow supernatants by Raman spectroscopy and multivariate statistical analysis

A simple and non-invasive detection method for acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) was established by systematically investigating the characteristics of bone marrow supernatants from 61 AML patients, 22 ALL patients, and 5 volunteers without hematological tumors by Raman spectroscopy and orthogonal partial least squares discriminant analysis (OPLS-DA). The control group could be well distinguished from the AML and ALL groups by Raman peaks of 859, 1031, 1437, 1443, 1446, 1579, and 1603 cm^-1 and from the AML subtypes groups (AML-M2, AML-M3, AML-M4, and AML-M5) by the Raman peaks of 859, 1221, 1230, 1437, 1443, and 1603 cm^-1, indicating high sensitivity and specificity of the method. Potentially important variables of acute leukemia (AL) prognosis, such as cholesterol, high-density lipoprotein, low-density lipoprotein, adenosine deaminase, and hemoglobin, could be effectively identified by Raman peaks of 1437, 1443, and 1579 cm^-1. Therefore, Raman spectroscopy can be considered as a new non-invasive clinical tool for the detection of different types of AL and can be used to correlate biochemical parameters of AL patients with the classification and prognosis of AL.