These days, cancer is thought to be more than just one illness, with several complex subtypes that require different screening approaches. These subtypes can be distinguished by the distinct markings left by metabolites, proteins, miRNA, and DNA. Personalized illness management may be possible if cancer is categorized according to its biomarkers. In order to stop cancer from spreading and posing a significant risk to patient survival, early detection and prompt treatment are essential. Traditional cancer screening techniques are tedious, time-consuming, and require expert personnel for analysis. This has led scientists to reevaluate screening methodologies and make use of emerging technologies to achieve better results. Using time and money saving techniques, these methodologies integrate the procedures from sample preparation to detection in small devices with high accuracy and sensitivity. With its proven potential for biomedical use, surface-enhanced Raman scattering (SERS) has been widely used in biosensing applications, particularly in biomarker identification. Consideration was given especially to the potential of SERS as a portable clinical diagnostic tool. The approaches to SERS-based sensing technologies for both invasive and non-invasive samples are reviewed in this article, along with sample preparation techniques and obstacles. Aside from these significant constraints in the detection approach and techniques, the review also takes into account the complexity of biological fluids, the availability of biomarkers, and their sensitivity and selectivity, which are generally lowered. Massive ways to maintain sensing capabilities in clinical samples are being developed recently to get over this restriction. SERS is known to be a reliable diagnostic method for treatment judgments. Nonetheless, there is still room for advancement in terms of portability, creation of diagnostic apps, and interdisciplinary AI-based applications. Therefore, we will outline the current state of technological maturity for SERS-based cancer biomarker detection in this article. The review will meet the demand for reviewing various sample types (invasive and non-invasive) of cancer biomarkers and their detection using SERS. It will also shed light on the growing body of research on portable methods for clinical application and quick cancer detection. 

SERS sensing for cancer biomarker: Approaches and directions

Conventional methodology for accurate identification of breast tumor subtypes is complex and time-consuming. Label-free SERS (surface-enhanced Raman scattering) enables rapid detection of biomolecules and provides intrinsic fingerprint information of analytes. Exploring reliable SERS bioprobes with high sensitivity and repeatability is essential for efficient typing of cancer cells. In this study, a novel core-shell noble metal-semiconductor Cu2O@Ag heterostructure with uniform morphology and outstanding SERS activity for label-free detection has been proposed. The Cu2O@Ag SERS substrates exhibit exceptional sensitivity to detect 4 nitrophenthiol (4NTP) at extreme low concentration (10−15 M), and the SERS spectra demonstrate excellent selectivity and repeatability (RSD ≈ 8.84%). The enhancement mechanism is attributed to the synergistic enhancement of photoinduced charge transfer and boosted surface plasmon resonance effect, confirmed by reduced fluorescence lifetime of methylene blue on SERS substrates and enhanced electromagnetic field intensity via finite-difference time-domain computational simulation. The ultrahigh sensitivity and good signal stability endow Cu2O@Ag SERS bioprobe potential for accurate detection of four breast cancer cells subtypes. The rich fingerprint information from SERS spectra is processed using linear differentiation analysis, revealing a high accuracy of 93.3% for subtypes classification. These results demonstrate an excellent SERS bioprobe with high sensitivity and repeatability for precision diagnosis has been developed. 

Synergistic enhancement of ultrahigh SERS activity via Cu2O@Ag Core-Shell structure for accurate label-free identification of breast tumor subtypes

Chinese hamster ovary (CHO) cells are widely utilized in the production of antibody drugs. To ensure the production of large quantities of antibodies that meet the required specifications, it is crucial to monitor and control the levels of metabolites comprehensively during CHO cell culture. In recent years, continuous analysis methods employing on-line/in-line techniques using Raman spectroscopy have attracted attention. While these analytical methods can nondestructively monitor culture data, constructing a highly accurate measurement model for numerous components is time-consuming, making it challenging to implement in the rapid research and development of pharmaceutical manufacturing processes. In this study, we developed a comprehensive, simple, and automated method for constructing a Raman model of various components measured by LC-MS and other techniques using machine learning with Python. Preprocessing and spectral-range optimization of data for model construction (partial least square (PLS) regression) were automated and accelerated using Bayes optimization. Subsequently, models were constructed for each component using various model construction techniques, including linear regression, ridge regression, XGBoost, and neural network. This enabled the model accuracy to be improved compared with PLS regression. This automated approach allows continuous monitoring of various parameters for over 100 components, facilitating process optimization and process monitoring of CHO cells. 

Comprehensive modeling of cell culture profile using Raman spectroscopy and machine learning

Flexible surface-enhanced Raman scattering (SERS) substrates are very promising to meet the needs for real-time and on-field detection in practical applications. However, high-performance flexible SERS substrates often suffer from complexity and high cost in fabrication, limiting their widespread applications. Herein, we developed a facile method to fabricate a flexible multicavity SERS substrate composed of a silver nanoparticle (AgNP)-decorated aluminum hydrous oxide nanoflake array (NFA) grown on a polydimethylsiloxane (PDMS) membrane. Strong plasmon couplings promoted by multiple nanocavities afford high-density hotspots within such a flexible AgNPs@NFA/PDMS film, boosting high SERS sensitivity with an enhancement factor (EF) of ∼1.54 × 109, and a limit of detection (LOD) of ∼7.4 × 10–13 M for rhodamine 6G (R6G) molecules. Furthermore, benefiting from the high sensitivity, high mechanical stability, and transparency of this substrate, in situ SERS detections of trace thiram and crystal violet (CV) molecules on the surface of cherry tomatoes and fish have been realized, with LODs much lower than the maximum allowable limit in food, demonstrating the great potential of such a flexible substrate in food safety monitoring. More importantly, the preparation processes are very simple and environmentally friendly, and the techniques involved are completely compatible with well-established silicon device technologies. Therefore, large-area fabrication with low cost can be readily realized, enabling the extensive applications of SERS sensors in daily life. 

Flexible Multicavity SERS Substrate Based on Ag Nanoparticle-Decorated Aluminum Hydrous Oxide Nanoflake Array for Highly Sensitive In Situ Detection

Manganese dioxide (MnO2) is the most promising electrode material for supercapacitors (SCs) due to its low cost, non-toxic nature, high theoretical capacitance, and wide potential window. Instead, biomass-derived carbon has... 

pdf/manganese-dioxide-mno2-and-biomass-derived-carbon-based-4unzdyctns.pdf

Manganese dioxide (MnO2) and biomass-derived carbon-based electroactive composite materials for supercapacitor application

Breast cancer is the most commonly diagnosed cancer type worldwide. Overexpression of human epidermal growth factor receptor 2 (HER2) is an important subtype of breast cancer and results in an increased risk of recurrence and metastasis in patients. At present, immunohistochemistry (IHC) is used to detect the expression of HER2 in breast cancer tissues as the golden standard. However, IHC has some shortcomings, such as large subjective impact, long time consumption, expensive reagents, etc. In this paper, a combined morphological and spectroscopic diagnostic method based on label-free surface-enhanced Raman scattering (SERS) for HER2 expression in breast cancer is proposed. It can not only quantitively detect HER2 expression in breast cancer tissues by spectroscopic measurements but also give morphological images reflecting the distribution of HER2 in tissues. The results show that the consistency between this method and IHC is 95% and achieves the annotation of tumor regions on tissue sections. This method is time-consuming, quantifiable, intuitive, scalable, and easy to understand. Combined with deep learning approaches, it is expected to promote the development of clinical detection and diagnosis technology for breast cancer and other cancers.

Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering.

The outbreak of COVID-19 has spread worldwide, causing great damage to the global economy. Raman spectroscopy is expected to become a rapid and accurate method for the detection of coronavirus. A classification method of coronavirus spike proteins by Raman spectroscopy based on deep learning was implemented. A Raman spectra dataset of the spike proteins of five coronaviruses (including MERS-CoV, SARS-CoV, SARS-CoV-2, HCoVHKU1, and HCoV-OC43) was generated to establish the neural network model for classification. Even for rapidly acquired spectra with a low signal-to-noise ratio, the average accuracy exceeded 97%. An interpretive analysis of the classification results of the neural network was performed, which indicated that the differences in spectral characteristics captured by the neural network were consistent with the experimental analysis. The interpretative analysis method provided a valuable reference for identifying complex Raman spectra using deep-learning techniques. Our approach exhibited the potential to be applied in clinical practice to identify COVID-19 and other coronaviruses, and it can also be applied to other identification problems such as the identification of viruses or chemical agents, as well as in industrial areas such as oil and gas exploration. 

/pdf/classification-of-coronavirus-spike-proteins-by-deep-2anj0svz.pdf

Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis

Pre-activation strategy of α-MnO2 electrode to construct high-performance asymmetric sodium-ion supercapacitors

The coronavirus disease 2019 (COVID-19) epidemic has given a warning that it is important to explore the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in clinical specimens or environmental samples for public health strategies and future variants. The surface-enhanced Raman spectroscopy (SERS) technique was demonstrated to achieve this goal. However, the consistency of signals originating from the poor compatibility of virions with SERS hotspots remains a key scientific challenge for the practical applications of SERS. Herein, we develop a SERS platform for the ultrasensitive and rapid detection of SARS-CoV-2 antigen within 20 min by the combination of a highly consistent SERS substrate and a supervised deep learning algorithm. A V-shaped resonant cavity array (VRC) substrate was fabricated to trap SARS-CoV-2 virions in the periodic V cavity array and stimulate the integral SERS signal of the virus via a resonance coupling effect. Benefiting from the unique architecture of the VRC substrate, we were able to directly detect the SARS-CoV-2 virus with high sensitivity and high consistency. These excellent performances enabled us to identify five different kinds of SARS-CoV-2 variants and detect SARS-CoV-2 from clinical and environmental samples with high accuracies.

Rapid Detection of SARS-CoV-2 in Clinical and Environmental Samples via a Resonant Cavity SERS Platform within 20 min.

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Combined Morphological and Spectroscopic Diagnostic of HER2 Expression in Breast Cancer Tissues Based on Label-Free Surface-Enhanced Raman Scattering.

Classification of Coronavirus Spike Proteins by Deep-Learning-Based Raman Spectroscopy and its Interpretative Analysis

Pre-activation strategy of α-MnO2 electrode to construct high-performance asymmetric sodium-ion supercapacitors

Rapid Detection of SARS-CoV-2 in Clinical and Environmental Samples via a Resonant Cavity SERS Platform within 20 min.