Self-assembled core-shell nanoparticles with embedded internal standards for SERS quantitative detection and identification of nicotine released from snus products.

Surface enhanced Raman spectroscopy (SERS) is a unique analytical technique with excellent performance in terms of sensitivity, non-destructive detection and resolution. However, due to the randomness and poor repeatability of hot spot distribution, SERS quantitative analysis is still challenging. Meanwhile, snus is a type of tobacco product that can release nicotine and other components in the mouth without burning, and the rapid detection technique based on SERS can reliably evaluate the amount of nicotine released from snus, which is of great significance for understanding its characteristics and regulating its components. Herein, the strategy was proposed to solve the feasibility of SERS quantitative detection based on self-assembled core-shell nanoparticles with embedded internal standards (EIS) due to EIS signal can effectively correct SERS signal fluctuations caused by different aggregation states and measurement conditions, thus allowing reliable quantitative SERS analysis of targets with different surface affinity. By means of process control, after the Au nanoparticles (Au NPs) were modified with 4-Mercaptobenzonitrile (4-MBN) as internal standard molecules, Ag shell with a certain thickness was grown on the surface of the AuNP@4-MBN, and then the Au@4-MBN@Ag NPs were used to regulate and control the assembly of liquid-liquid interface. The high-density nano-arrays assembled at the liquid-liquid interface ensure high reproducibility as SERS substrates, and which could be used for SERS detection of nicotine released from snus products. In addition, time-mapping research shows that this method can also be used to dynamically monitor the release of nicotine. Moreover, such destruction-free evaluation of the release of nicotine from snus products opens up new perspectives for further research about the impact of nicotinoids-related health programs.

Au nanorod assembly for sensitive SERS detection of airway inflammatory factors in sputum.

In this paper, we demonstrate a surface-enhanced Raman spectroscopy (SERS) biosensor based on the self-assembly of gold nanorods (AuNRs) for the specific detection of airway inflammatory factors in diluted sputum. The AuNR surface was modified with an antibody that was able to specifically recognize an airway inflammatory factor, interleukin-5 (IL-5), so that a end-to-end self-assembly system could be obtained, resulting in an order of magnitude amplification of the Raman signal and greatly improved sensitivity. Meanwhile, the outer layer of the biosensor was coated with silicon dioxide, which improved the stability of the system and facilitated its future applications. When the detected concentration was in the range of 0.1-50 pg/mL, the SERS signal generated by the sensor showed a good linear relationship with the IL-5 concentration. Moreover, it had satisfactory performance in diluted sputum and clinical subjects with asthma, which could achieve sensitive detection of the airway inflammatory factor IL-5. Overall, the developed biosensor based on the SERS effect exhibited the advantages of rapid and sensitive detecting performance, which is suitable for monitoring airway inflammatory factors in sputum.

Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.

BACKGROUND/PURPOSE OF THE STUDY: There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH. METHODS: Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module. RESULTS: Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70-0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48-0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51-0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups. CONCLUSIONS: Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers ( http://www.pan-chess.cn/calculator/RAMAN_score ).

Microneedle-based devices for point-of-care infectious disease diagnostics.

Recent infectious disease outbreaks, such as COVID-19 and Ebola, have highlighted the need for rapid and accurate diagnosis to initiate treatment and curb transmission. Successful diagnostic strategies critically depend on the efficiency of biological sampling and timely analysis. However, current diagnostic techniques are invasive/intrusive and present a severe bottleneck by requiring specialist equipment and trained personnel. Moreover, centralised test facilities are poorly accessible and the requirement to travel may increase disease transmission. Self-administrable, point-of-care (PoC) microneedle diagnostic devices could provide a viable solution to these problems. These miniature needle arrays can detect biomarkers in/from the skin in a minimally invasive manner to provide (near-) real-time diagnosis. Few microneedle devices have been developed specifically for infectious disease diagnosis, though similar technologies are well established in other fields and generally adaptable for infectious disease diagnosis. These include microneedles for biofluid extraction, microneedle sensors and analyte-capturing microneedles, or combinations thereof. Analyte sampling/detection from both blood and dermal interstitial fluid is possible. These technologies are in their early stages of development for infectious disease diagnostics, and there is a vast scope for further development. In this review, we discuss the utility and future outlook of these microneedle technologies in infectious disease diagnosis.

Current state of diagnostic, screening and surveillance testing methods for COVID-19 from an analytical chemistry point of view.

Since December 2019, we have been in the battlefield with a new threat to the humanity known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this review, we describe the four main methods used for diagnosis, screening and/or surveillance of SARS-CoV-2: Real-time reverse transcription polymerase chain reaction (RT-PCR); chest computed tomography (CT); and different complementary alternatives developed in order to obtain rapid results, antigen and antibody detection. All of them compare the highlighting advantages and disadvantages from an analytical point of view. The gold standard method in terms of sensitivity and specificity is the RT-PCR. The different modifications propose to make it more rapid and applicable at point of care (POC) are also presented and discussed. CT images are limited to central hospitals. However, being combined with RT-PCR is the most robust and accurate way to confirm COVID-19 infection. Antibody tests, although unable to provide reliable results on the status of the infection, are suitable for carrying out maximum screening of the population in order to know the immune capacity. More recently, antigen tests, less sensitive than RT-PCR, have been authorized to determine in a quicker way whether the patient is infected at the time of analysis and without the need of specific instruments.

Cubic Silver Nanoparticles Fixed on TiO2 Nanotubes as Simple and Efficient Substrates for Surface Enhanced Raman Scattering.

In this work we show that ordered freestanding titanium oxide nanotubes (TiO2 NT) may be used as substrates for the simple and efficient immobilization of anisotropic plasmonic nanoparticles. This is important because anisotropic plasmonic nanostructures usually give greater spectral enhancement than spherical nanoparticles. The size of the pores in a layer of titanium oxide nanotubes can be easily fitted to the size of many silver plasmonic nanoparticles highly active in SERS (surface-enhanced Raman scattering) spectroscopy (for example, silver nanocubes with an edge length of ca. 45 nm), and hence, the plasmonic nanoparticles deposited can be strongly anchored in such a titanium oxide substrate. The tubular morphology of the TiO2 substrate used allows a specific arrangement of the silver plasmonic nanoparticles that may create many so-called SERS hot spots. The SERS activity of a layer of cubic Ag nanoparticles (AgCNPs) deposited on a tubular TiO2 substrate (AgCNPs@TiO2 NT) is about eight times higher than that of the standard electrochemically nanostructured surface of a silver electrode (produced by oxidation reduction cycling). Furthermore, a super hydrophilic character of the TiO2 nanotubes surface allows for a uniform distribution of AgCNPs, which are deposited from an aqueous suspension. The new AgCNPs@TiO2 NT hybrid layer ensures a good reproducibility of SERS measurements and exhibits a higher temporal stability of the achievable total SERS enhancement factor-one that is far better than standard SERS silver substrates. To characterize the morphology and chemical composition of such evidently improved SERS platforms thus received, we applied microscopic techniques (SEM, and scanning transmission electron microscopy (STEM)) and surface analytical techniques (Auger electron spectroscopy (AES) and X-ray photoelectron spectroscopy (XPS)).